| $H$-Consistency Bounds: Characterization and Extensions |
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0 |
| $L_2$-Uniform Stability of Randomized Learning Algorithms: Sharper Generalization Bounds and Confidence Boosting |
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2 |
| $SE(3)$ Equivariant Convolution and Transformer in Ray Space |
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3 |
| $S^3$: Increasing GPU Utilization during Generative Inference for Higher Throughput |
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2 |
| $\textbf{A}^2\textbf{CiD}^2$: Accelerating Asynchronous Communication in Decentralized Deep Learning |
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5 |
| $\texttt{TACO}$: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning |
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5 |
| $\varepsilon$-fractional core stability in Hedonic Games. |
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1 |
| $k$-Means Clustering with Distance-Based Privacy |
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3 |
| $p$-Poisson surface reconstruction in curl-free flow from point clouds |
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4 |
| $p$-value Adjustment for Monotonous, Unbiased, and Fast Clustering Comparison |
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4 |
| (Almost) Provable Error Bounds Under Distribution Shift via Disagreement Discrepancy |
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5 |
| (Amplified) Banded Matrix Factorization: A unified approach to private training |
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4 |
| (S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability |
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1 |
| 2Direction: Theoretically Faster Distributed Training with Bidirectional Communication Compression |
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5 |
| 3D Copy-Paste: Physically Plausible Object Insertion for Monocular 3D Detection |
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4 |
| 3D Indoor Instance Segmentation in an Open-World |
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4 |
| 3D molecule generation by denoising voxel grids |
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5 |
| 3D-Aware Visual Question Answering about Parts, Poses and Occlusions |
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4 |
| 3D-IntPhys: Towards More Generalized 3D-grounded Visual Intuitive Physics under Challenging Scenes |
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4 |
| 3D-LLM: Injecting the 3D World into Large Language Models |
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4 |
| 4D Panoptic Scene Graph Generation |
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3 |
| 4M: Massively Multimodal Masked Modeling |
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5 |
| A Batch-to-Online Transformation under Random-Order Model |
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2 |
| A Bayesian Approach To Analysing Training Data Attribution In Deep Learning |
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4 |
| A Bayesian Take on Gaussian Process Networks |
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5 |
| A Bounded Ability Estimation for Computerized Adaptive Testing |
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6 |
| A Causal Framework for Decomposing Spurious Variations |
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4 |
| A Closer Look at the Robustness of Contrastive Language-Image Pre-Training (CLIP) |
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2 |
| A Combinatorial Algorithm for Approximating the Optimal Transport in the Parallel and MPC Settings |
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4 |
| A Competitive Algorithm for Agnostic Active Learning |
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1 |
| A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting |
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6 |
| A Computationally Efficient Sparsified Online Newton Method |
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6 |
| A Cross-Moment Approach for Causal Effect Estimation |
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3 |
| A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks |
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5 |
| A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability |
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6 |
| A Definition of Continual Reinforcement Learning |
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1 |
| A Diffusion-Model of Joint Interactive Navigation |
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3 |
| A Dual-Stream Neural Network Explains the Functional Segregation of Dorsal and Ventral Visual Pathways in Human Brains |
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5 |
| A Dynamical System View of Langevin-Based Non-Convex Sampling |
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0 |
| A Fast and Accurate Estimator for Large Scale Linear Model via Data Averaging |
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4 |
| A Finite-Particle Convergence Rate for Stein Variational Gradient Descent |
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1 |
| A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games |
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2 |
| A Fractional Graph Laplacian Approach to Oversmoothing |
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6 |
| A Framework for Fast and Stable Representations of Multiparameter Persistent Homology Decompositions |
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6 |
| A General Framework for Equivariant Neural Networks on Reductive Lie Groups |
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5 |
| A General Framework for Robust G-Invariance in G-Equivariant Networks |
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4 |
| A General Theory of Correct, Incorrect, and Extrinsic Equivariance |
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5 |
| A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning |
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6 |
| A Guide Through the Zoo of Biased SGD |
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5 |
| A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction |
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6 |
| A Heavy-Tailed Algebra for Probabilistic Programming |
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3 |
| A Hierarchical Spatial Transformer for Massive Point Samples in Continuous Space |
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4 |
| A Hierarchical Training Paradigm for Antibody Structure-sequence Co-design |
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5 |
| A Holistic Approach to Unifying Automatic Concept Extraction and Concept Importance Estimation |
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3 |
| A Logic for Expressing Log-Precision Transformers |
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1 |
| A Long $N$-step Surrogate Stage Reward for Deep Reinforcement Learning |
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4 |
| A Measure-Theoretic Axiomatisation of Causality |
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0 |
| A Metadata-Driven Approach to Understand Graph Neural Networks |
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3 |
| A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks |
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5 |
| A Novel Approach for Effective Multi-View Clustering with Information-Theoretic Perspective |
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2 |
| A Novel Framework for Policy Mirror Descent with General Parameterization and Linear Convergence |
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3 |
| A One-Size-Fits-All Approach to Improving Randomness in Paper Assignment |
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5 |
| A Partially-Supervised Reinforcement Learning Framework for Visual Active Search |
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6 |
| A Path to Simpler Models Starts With Noise |
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4 |
| A Privacy-Friendly Approach to Data Valuation |
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5 |
| A Pseudo-Semantic Loss for Autoregressive Models with Logical Constraints |
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6 |
| A Randomized Approach to Tight Privacy Accounting |
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4 |
| A Recurrent Neural Circuit Mechanism of Temporal-scaling Equivariant Representation |
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0 |
| A Reduction-based Framework for Sequential Decision Making with Delayed Feedback |
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1 |
| A Regularized Conditional GAN for Posterior Sampling in Image Recovery Problems |
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5 |
| A Riemannian Exponential Augmented Lagrangian Method for Computing the Projection Robust Wasserstein Distance |
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6 |
| A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods |
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5 |
| A Robust Exact Algorithm for the Euclidean Bipartite Matching Problem |
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5 |
| A Robust and Opponent-Aware League Training Method for StarCraft II |
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3 |
| A Scalable Neural Network for DSIC Affine Maximizer Auction Design |
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2 |
| A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models |
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5 |
| A Simple Solution for Offline Imitation from Observations and Examples with Possibly Incomplete Trajectories |
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5 |
| A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm |
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5 |
| A Single 2D Pose with Context is Worth Hundreds for 3D Human Pose Estimation |
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3 |
| A Single-Loop Accelerated Extra-Gradient Difference Algorithm with Improved Complexity Bounds for Constrained Minimax Optimization |
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2 |
| A Smooth Binary Mechanism for Efficient Private Continual Observation |
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4 |
| A Spectral Algorithm for List-Decodable Covariance Estimation in Relative Frobenius Norm |
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1 |
| A Spectral Theory of Neural Prediction and Alignment |
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5 |
| A State Representation for Diminishing Rewards |
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4 |
| A Sublinear-Time Spectral Clustering Oracle with Improved Preprocessing Time |
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3 |
| A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence |
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4 |
| A Theoretical Analysis of Optimistic Proximal Policy Optimization in Linear Markov Decision Processes |
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1 |
| A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression |
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1 |
| A Theory of Link Prediction via Relational Weisfeiler-Leman on Knowledge Graphs |
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5 |
| A Theory of Multimodal Learning |
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0 |
| A Theory of Transfer-Based Black-Box Attacks: Explanation and Implications |
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1 |
| A Theory of Unsupervised Translation Motivated by Understanding Animal Communication |
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4 |
| A Trichotomy for Transductive Online Learning |
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1 |
| A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning |
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6 |
| A Unified Algorithm Framework for Unsupervised Discovery of Skills based on Determinantal Point Process |
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4 |
| A Unified Approach for Maximizing Continuous DR-submodular Functions |
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1 |
| A Unified Approach to Count-Based Weakly Supervised Learning |
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3 |
| A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithm |
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5 |
| A Unified Conditional Framework for Diffusion-based Image Restoration |
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4 |
| A Unified Detection Framework for Inference-Stage Backdoor Defenses |
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5 |
| A Unified Discretization Framework for Differential Equation Approach with Lyapunov Arguments for Convex Optimization |
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1 |
| A Unified Fast Gradient Clipping Framework for DP-SGD |
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5 |
| A Unified Framework for Rank-based Loss Minimization |
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❌ |
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6 |
| A Unified Framework for U-Net Design and Analysis |
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7 |
| A Unified Framework for Uniform Signal Recovery in Nonlinear Generative Compressed Sensing |
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4 |
| A Unified Generalization Analysis of Re-Weighting and Logit-Adjustment for Imbalanced Learning |
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4 |
| A Unified Model and Dimension for Interactive Estimation |
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1 |
| A Unified Solution for Privacy and Communication Efficiency in Vertical Federated Learning |
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4 |
| A Unified, Scalable Framework for Neural Population Decoding |
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5 |
| A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learning |
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5 |
| A Variational Perspective on High-Resolution ODEs |
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3 |
| A case for reframing automated medical image classification as segmentation |
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5 |
| A fast heuristic to optimize time-space tradeoff for large models |
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5 |
| A generative model of the hippocampal formation trained with theta driven local learning rules |
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2 |
| A graphon-signal analysis of graph neural networks |
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3 |
| A new perspective on building efficient and expressive 3D equivariant graph neural networks |
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6 |
| A normative theory of social conflict |
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4 |
| A polar prediction model for learning to represent visual transformations |
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3 |
| A unified framework for information-theoretic generalization bounds |
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0 |
| A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs |
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6 |
| A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference |
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6 |
| A3FL: Adversarially Adaptive Backdoor Attacks to Federated Learning |
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4 |
| AD-PT: Autonomous Driving Pre-Training with Large-scale Point Cloud Dataset |
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5 |
| AGD: an Auto-switchable Optimizer using Stepwise Gradient Difference for Preconditioning Matrix |
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❌ |
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6 |
| AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning |
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❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| AIMS: All-Inclusive Multi-Level Segmentation for Anything |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| ALGO: Synthesizing Algorithmic Programs with Generated Oracle Verifiers |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| ALIM: Adjusting Label Importance Mechanism for Noisy Partial Label Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| AMAG: Additive, Multiplicative and Adaptive Graph Neural Network For Forecasting Neuron Activity |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| AMDP: An Adaptive Detection Procedure for False Discovery Rate Control in High-Dimensional Mediation Analysis |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| AND: Adversarial Neural Degradation for Learning Blind Image Super-Resolution |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| ANPL: Towards Natural Programming with Interactive Decomposition |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| ANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulation |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| AR-Diffusion: Auto-Regressive Diffusion Model for Text Generation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| ARTIC3D: Learning Robust Articulated 3D Shapes from Noisy Web Image Collections |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| ARTree: A Deep Autoregressive Model for Phylogenetic Inference |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| ASIF: Coupled Data Turns Unimodal Models to Multimodal without Training |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| ASPEN: Breaking Operator Barriers for Efficient Parallelization of Deep Neural Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| ATMAN: Understanding Transformer Predictions Through Memory Efficient Attention Manipulation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| ATTA: Anomaly-aware Test-Time Adaptation for Out-of-Distribution Detection in Segmentation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| AUDIT: Audio Editing by Following Instructions with Latent Diffusion Models |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| AV-NeRF: Learning Neural Fields for Real-World Audio-Visual Scene Synthesis |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| AVIS: Autonomous Visual Information Seeking with Large Language Model Agent |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| AbDiffuser: full-atom generation of in-vitro functioning antibodies |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Abide by the law and follow the flow: conservation laws for gradient flows |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Accelerated On-Device Forward Neural Network Training with Module-Wise Descending Asynchronism |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Accelerated Quasi-Newton Proximal Extragradient: Faster Rate for Smooth Convex Optimization |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Accelerated Training via Incrementally Growing Neural Networks using Variance Transfer and Learning Rate Adaptation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Accelerated Zeroth-order Method for Non-Smooth Stochastic Convex Optimization Problem with Infinite Variance |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Accelerating Exploration with Unlabeled Prior Data |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Accelerating Molecular Graph Neural Networks via Knowledge Distillation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Accelerating Monte Carlo Tree Search with Probability Tree State Abstraction |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Accelerating Motion Planning via Optimal Transport |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Accelerating Reinforcement Learning with Value-Conditional State Entropy Exploration |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Accelerating Value Iteration with Anchoring |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Accessing Higher Dimensions for Unsupervised Word Translation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Accurate Interpolation for Scattered Data through Hierarchical Residual Refinement |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Achieving $\mathcal{O}(\epsilon^{-1.5})$ Complexity in Hessian/Jacobian-free Stochastic Bilevel Optimization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Achieving Cross Modal Generalization with Multimodal Unified Representation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Act As You Wish: Fine-Grained Control of Motion Diffusion Model with Hierarchical Semantic Graphs |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Active Bipartite Ranking |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Active Learning for Semantic Segmentation with Multi-class Label Query |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Active Learning-Based Species Range Estimation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Active Negative Loss Functions for Learning with Noisy Labels |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Active Observing in Continuous-time Control |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Active Reasoning in an Open-World Environment |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Active Vision Reinforcement Learning under Limited Visual Observability |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Active representation learning for general task space with applications in robotics |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Actively Testing Your Model While It Learns: Realizing Label-Efficient Learning in Practice |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Activity Grammars for Temporal Action Segmentation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| AdANNS: A Framework for Adaptive Semantic Search |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| AdaPlanner: Adaptive Planning from Feedback with Language Models |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| AdaVAE: Bayesian Structural Adaptation for Variational Autoencoders |
❌ |
✅ |
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❌ |
❌ |
❌ |
✅ |
3 |
| AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Adapting Fairness Interventions to Missing Values |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adapting Neural Link Predictors for Data-Efficient Complex Query Answering |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Adapting to Continuous Covariate Shift via Online Density Ratio Estimation |
✅ |
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✅ |
❌ |
✅ |
❌ |
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4 |
| Adaptive Algorithms for Relaxed Pareto Set Identification |
✅ |
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❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Adaptive Contextual Perception: How To Generalize To New Backgrounds and Ambiguous Objects |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Adaptive Data Analysis in a Balanced Adversarial Model |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
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1 |
| Adaptive Linear Estimating Equations |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adaptive Normalization for Non-stationary Time Series Forecasting: A Temporal Slice Perspective |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| Adaptive Online Replanning with Diffusion Models |
✅ |
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✅ |
❌ |
✅ |
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4 |
| Adaptive Principal Component Regression with Applications to Panel Data |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Adaptive Privacy Composition for Accuracy-first Mechanisms |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Adaptive SGD with Polyak stepsize and Line-search: Robust Convergence and Variance Reduction |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adaptive Selective Sampling for Online Prediction with Experts |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Adaptive Test-Time Personalization for Federated Learning |
✅ |
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✅ |
✅ |
✅ |
❌ |
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6 |
| Adaptive Topological Feature via Persistent Homology: Filtration Learning for Point Clouds |
❌ |
✅ |
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❌ |
✅ |
✅ |
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5 |
| Adaptive Uncertainty Estimation via High-Dimensional Testing on Latent Representations |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| Adaptive recurrent vision performs zero-shot computation scaling to unseen difficulty levels |
❌ |
❌ |
✅ |
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3 |
| Adaptive whitening with fast gain modulation and slow synaptic plasticity |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Add and Thin: Diffusion for Temporal Point Processes |
✅ |
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✅ |
✅ |
✅ |
❌ |
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6 |
| Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation |
❌ |
✅ |
✅ |
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❌ |
❌ |
✅ |
4 |
| Addressing Negative Transfer in Diffusion Models |
❌ |
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❌ |
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4 |
| Addressing the speed-accuracy simulation trade-off for adaptive spiking neurons |
❌ |
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❌ |
❌ |
❌ |
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3 |
| Adjustable Robust Reinforcement Learning for Online 3D Bin Packing |
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❌ |
✅ |
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3 |
| Advancing Bayesian Optimization via Learning Correlated Latent Space |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adversarial Attacks on Online Learning to Rank with Click Feedback |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adversarial Counterfactual Environment Model Learning |
✅ |
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✅ |
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❌ |
✅ |
6 |
| Adversarial Examples Are Not Real Features |
❌ |
✅ |
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❌ |
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3 |
| Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Linear Subspaces |
❌ |
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3 |
| Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness |
✅ |
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4 |
| Adversarial Learning for Feature Shift Detection and Correction |
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✅ |
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❌ |
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6 |
| Adversarial Model for Offline Reinforcement Learning |
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❌ |
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5 |
| Adversarial Resilience in Sequential Prediction via Abstention |
✅ |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Adversarial Robustness through Random Weight Sampling |
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4 |
| Adversarial Self-Training Improves Robustness and Generalization for Gradual Domain Adaptation |
❌ |
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5 |
| Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions |
✅ |
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6 |
| Adversarial Training from Mean Field Perspective |
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3 |
| Adversarially Robust Distributed Count Tracking via Partial Differential Privacy |
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❌ |
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❌ |
❌ |
❌ |
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1 |
| Adversarially Robust Learning with Uncertain Perturbation Sets |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Advice Querying under Budget Constraint for Online Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Affinity-Aware Graph Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| Agnostic Multi-Group Active Learning |
✅ |
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❌ |
❌ |
❌ |
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1 |
| Agnostically Learning Single-Index Models using Omnipredictors |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| AiluRus: A Scalable ViT Framework for Dense Prediction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Aiming towards the minimizers: fast convergence of SGD for overparametrized problems |
✅ |
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✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| AlberDICE: Addressing Out-Of-Distribution Joint Actions in Offline Multi-Agent RL via Alternating Stationary Distribution Correction Estimation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Algorithm Selection for Deep Active Learning with Imbalanced Datasets |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Algorithmic Regularization in Tensor Optimization: Towards a Lifted Approach in Matrix Sensing |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Align Your Prompts: Test-Time Prompting with Distribution Alignment for Zero-Shot Generalization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Aligning Gradient and Hessian for Neural Signed Distance Function |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Aligning Language Models with Human Preferences via a Bayesian Approach |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Aligning Synthetic Medical Images with Clinical Knowledge using Human Feedback |
✅ |
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❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Alignment with human representations supports robust few-shot learning |
❌ |
✅ |
✅ |
❌ |
✅ |
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4 |
| All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Alleviating the Semantic Gap for Generalized fMRI-to-Image Reconstruction |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Alternating Gradient Descent and Mixture-of-Experts for Integrated Multimodal Perception |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Alternating Updates for Efficient Transformers |
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✅ |
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5 |
| Alternation makes the adversary weaker in two-player games |
✅ |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| AmadeusGPT: a natural language interface for interactive animal behavioral analysis |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Ambient Diffusion: Learning Clean Distributions from Corrupted Data |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Amortized Reparametrization: Efficient and Scalable Variational Inference for Latent SDEs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| An $\varepsilon$-Best-Arm Identification Algorithm for Fixed-Confidence and Beyond |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| An Adaptive Algorithm for Learning with Unknown Distribution Drift |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| An Alternating Optimization Method for Bilevel Problems under the Polyak-Łojasiewicz Condition |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradient |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| An Efficient Dataset Condensation Plugin and Its Application to Continual Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| An Efficient Doubly-Robust Test for the Kernel Treatment Effect |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| An Efficient End-to-End Training Approach for Zero-Shot Human-AI Coordination |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| An Efficient and Robust Framework for Approximate Nearest Neighbor Search with Attribute Constraint |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| An Empirical Study Towards Prompt-Tuning for Graph Contrastive Pre-Training in Recommendations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| An Exploration-by-Optimization Approach to Best of Both Worlds in Linear Bandits |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| An Improved Relaxation for Oracle-Efficient Adversarial Contextual Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| An Inductive Bias for Tabular Deep Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| An Information Theory Perspective on Variance-Invariance-Covariance Regularization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| An Information-Theoretic Evaluation of Generative Models in Learning Multi-modal Distributions |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| An Inverse Scaling Law for CLIP Training |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| An Iterative Self-Learning Framework for Medical Domain Generalization |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| An Optimal Structured Zeroth-order Algorithm for Non-smooth Optimization |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| An Optimal and Scalable Matrix Mechanism for Noisy Marginals under Convex Loss Functions |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| An Optimization-based Approach To Node Role Discovery in Networks: Approximating Equitable Partitions |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| An active learning framework for multi-group mean estimation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| An information-theoretic quantification of the content of communication between brain regions |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Analysis of Variance of Multiple Causal Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Analyzing Generalization of Neural Networks through Loss Path Kernels |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Analyzing Vision Transformers for Image Classification in Class Embedding Space |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Anchor Data Augmentation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Anonymous and Copy-Robust Delegations for Liquid Democracy |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Any-to-Any Generation via Composable Diffusion |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Anytime Model Selection in Linear Bandits |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Anytime-Competitive Reinforcement Learning with Policy Prior |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Approximate Allocation Matching for Structural Causal Bandits with Unobserved Confounders |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Approximate inference of marginals using the IBIA framework |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Approximately Equivariant Graph Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Approximation-Generalization Trade-offs under (Approximate) Group Equivariance |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Arbitrarily Scalable Environment Generators via Neural Cellular Automata |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Are Diffusion Models Vision-And-Language Reasoners? |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Are Emergent Abilities of Large Language Models a Mirage? |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Are GATs Out of Balance? |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Are Vision Transformers More Data Hungry Than Newborn Visual Systems? |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Are aligned neural networks adversarially aligned? |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Assessor360: Multi-sequence Network for Blind Omnidirectional Image Quality Assessment |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Assumption violations in causal discovery and the robustness of score matching |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Asymmetric Certified Robustness via Feature-Convex Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Asymptotically Optimal Quantile Pure Exploration for Infinite-Armed Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Asymptotics of Bayesian Uncertainty Estimation in Random Features Regression |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Asynchronous Proportional Response Dynamics: Convergence in Markets with Adversarial Scheduling |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Asynchrony-Robust Collaborative Perception via Bird's Eye View Flow |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Attacks on Online Learners: a Teacher-Student Analysis |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Attention as Implicit Structural Inference |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Attentive Transfer Entropy to Exploit Transient Emergence of Coupling Effect |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| AttrSeg: Open-Vocabulary Semantic Segmentation via Attribute Decomposition-Aggregation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Auditing Fairness by Betting |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Auditing for Human Expertise |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Augmentation-Aware Self-Supervision for Data-Efficient GAN Training |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Augmentation-Free Dense Contrastive Knowledge Distillation for Efficient Semantic Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Augmented Memory Replay-based Continual Learning Approaches for Network Intrusion Detection |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Augmenting Language Models with Long-Term Memory |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| AutoGO: Automated Computation Graph Optimization for Neural Network Evolution |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Autodecoding Latent 3D Diffusion Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Automated Classification of Model Errors on ImageNet |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Automatic Grouping for Efficient Cooperative Multi-Agent Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Automatic Integration for Spatiotemporal Neural Point Processes |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Autonomous Capability Assessment of Sequential Decision-Making Systems in Stochastic Settings |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Auxiliary Losses for Learning Generalizable Concept-based Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| BCDiff: Bidirectional Consistent Diffusion for Instantaneous Trajectory Prediction |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| BERT Lost Patience Won't Be Robust to Adversarial Slowdown |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| BIOT: Biosignal Transformer for Cross-data Learning in the Wild |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| BIRD: Generalizable Backdoor Detection and Removal for Deep Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Back-Modality: Leveraging Modal Transformation for Data Augmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| BadTrack: A Poison-Only Backdoor Attack on Visual Object Tracking |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game Perspective |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Balanced Training for Sparse GANs |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Balancing Risk and Reward: A Batched-Bandit Strategy for Automated Phased Release |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Balancing memorization and generalization in RNNs for high performance brain-machine Interfaces |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
3 |
| Banana: Banach Fixed-Point Network for Pointcloud Segmentation with Inter-Part Equivariance |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Bandit Social Learning under Myopic Behavior |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Bandit Task Assignment with Unknown Processing Time |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| BanditPAM++: Faster $k$-medoids Clustering |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Batch Bayesian Optimization For Replicable Experimental Design |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Batchnorm Allows Unsupervised Radial Attacks |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Bayes beats Cross Validation: Efficient and Accurate Ridge Regression via Expectation Maximization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| BayesDAG: Gradient-Based Posterior Inference for Causal Discovery |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| BayesTune: Bayesian Sparse Deep Model Fine-tuning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Bayesian Active Causal Discovery with Multi-Fidelity Experiments |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Bayesian Extensive-Rank Matrix Factorization with Rotational Invariant Priors |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Bayesian Learning of Optimal Policies in Markov Decision Processes with Countably Infinite State-Space |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Bayesian Learning via Q-Exponential Process |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Bayesian Optimisation of Functions on Graphs |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Bayesian Optimization with Cost-varying Variable Subsets |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Bayesian Risk-Averse Q-Learning with Streaming Observations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Bayesian nonparametric (non-)renewal processes for analyzing neural spike train variability |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Bayesian target optimisation for high-precision holographic optogenetics |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
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5 |
| Behavior Alignment via Reward Function Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Belief Projection-Based Reinforcement Learning for Environments with Delayed Feedback |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Best Arm Identification with Fixed Budget: A Large Deviation Perspective |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Beta Diffusion |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Better Correlation and Robustness: A Distribution-Balanced Self-Supervised Learning Framework for Automatic Dialogue Evaluation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Better Private Linear Regression Through Better Private Feature Selection |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Better with Less: A Data-Active Perspective on Pre-Training Graph Neural Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Beyond Average Return in Markov Decision Processes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Beyond Confidence: Reliable Models Should Also Consider Atypicality |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits with Dynamical Similarity Analysis |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Beyond Invariance: Test-Time Label-Shift Adaptation for Addressing "Spurious" Correlations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Beyond MLE: Convex Learning for Text Generation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Beyond Myopia: Learning from Positive and Unlabeled Data through Holistic Predictive Trends |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Beyond NTK with Vanilla Gradient Descent: A Mean-Field Analysis of Neural Networks with Polynomial Width, Samples, and Time |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Beyond Normal: On the Evaluation of Mutual Information Estimators |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial Defense |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Beyond probability partitions: Calibrating neural networks with semantic aware grouping |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Bi-Level Offline Policy Optimization with Limited Exploration |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| BiMatting: Efficient Video Matting via Binarization |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| BiSLS/SPS: Auto-tune Step Sizes for Stable Bi-level Optimization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Bias in Evaluation Processes: An Optimization-Based Model |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Bicriteria Approximation Algorithms for the Submodular Cover Problem |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bicriteria Multidimensional Mechanism Design with Side Information |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Bifurcations and loss jumps in RNN training |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Bilevel Coreset Selection in Continual Learning: A New Formulation and Algorithm |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Binarized Neural Machine Translation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Binarized Spectral Compressive Imaging |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Binary Classification with Confidence Difference |
❌ |
❌ |
✅ |
✅ |
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4 |
| Binary Radiance Fields |
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3 |
| Birder: Communication-Efficient 1-bit Adaptive Optimizer for Practical Distributed DNN Training |
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6 |
| Birth of a Transformer: A Memory Viewpoint |
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3 |
| Black-Box Differential Privacy for Interactive ML |
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1 |
| Black-box Backdoor Defense via Zero-shot Image Purification |
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6 |
| Block Broyden's Methods for Solving Nonlinear Equations |
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3 |
| Block Coordinate Plug-and-Play Methods for Blind Inverse Problems |
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4 |
| Block Low-Rank Preconditioner with Shared Basis for Stochastic Optimization |
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4 |
| Block-Coordinate Methods and Restarting for Solving Extensive-Form Games |
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3 |
| Block-State Transformers |
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5 |
| Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints |
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2 |
| Blockwise Parallel Transformers for Large Context Models |
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5 |
| Blurred-Dilated Method for Adversarial Attacks |
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3 |
| Boosting Adversarial Transferability by Achieving Flat Local Maxima |
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5 |
| Boosting Learning for LDPC Codes to Improve the Error-Floor Performance |
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5 |
| Boosting Spectral Clustering on Incomplete Data via Kernel Correction and Affinity Learning |
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5 |
| Boosting Verification of Deep Reinforcement Learning via Piece-Wise Linear Decision Neural Networks |
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3 |
| Boosting with Tempered Exponential Measures |
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4 |
| Bootstrapped Training of Score-Conditioned Generator for Offline Design of Biological Sequences |
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6 |
| Bootstrapping Vision-Language Learning with Decoupled Language Pre-training |
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4 |
| Bottleneck Structure in Learned Features: Low-Dimension vs Regularity Tradeoff |
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1 |
| Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces |
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5 |
| Boundary Guided Learning-Free Semantic Control with Diffusion Models |
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5 |
| Bounded rationality in structured density estimation |
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2 |
| Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information |
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4 |
| Bounding training data reconstruction in DP-SGD |
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4 |
| Brain Diffusion for Visual Exploration: Cortical Discovery using Large Scale Generative Models |
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5 |
| Brain Dissection: fMRI-trained Networks Reveal Spatial Selectivity in the Processing of Natural Images |
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5 |
| Brain encoding models based on multimodal transformers can transfer across language and vision |
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4 |
| Brain-like Flexible Visual Inference by Harnessing Feedback Feedforward Alignment |
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4 |
| Brant: Foundation Model for Intracranial Neural Signal |
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4 |
| Breadcrumbs to the Goal: Goal-Conditioned Exploration from Human-in-the-Loop Feedback |
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4 |
| Break It Down: Evidence for Structural Compositionality in Neural Networks |
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5 |
| Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy |
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2 |
| Bridging Discrete and Backpropagation: Straight-Through and Beyond |
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6 |
| Bridging RL Theory and Practice with the Effective Horizon |
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5 |
| Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation Models |
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3 |
| Bringing regularized optimal transport to lightspeed: a splitting method adapted for GPUs |
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4 |
| Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders |
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4 |
| Budgeting Counterfactual for Offline RL |
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5 |
| Bypass Exponential Time Preprocessing: Fast Neural Network Training via Weight-Data Correlation Preprocessing |
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1 |
| Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes |
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5 |
| Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits |
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1 |
| Byzantine-Tolerant Methods for Distributed Variational Inequalities |
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5 |
| C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder |
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4 |
| CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning |
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6 |
| CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society |
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3 |
| CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models |
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6 |
| CAPro: Webly Supervised Learning with Cross-modality Aligned Prototypes |
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6 |
| CARE: Modeling Interacting Dynamics Under Temporal Environmental Variation |
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5 |
| CAST: Cross-Attention in Space and Time for Video Action Recognition |
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5 |
| CAT-Walk: Inductive Hypergraph Learning via Set Walks |
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7 |
| CBD: A Certified Backdoor Detector Based on Local Dominant Probability |
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3 |
| CEIL: Generalized Contextual Imitation Learning |
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4 |
| CELLE-2: Translating Proteins to Pictures and Back with a Bidirectional Text-to-Image Transformer |
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4 |
| CL-NeRF: Continual Learning of Neural Radiance Fields for Evolving Scene Representation |
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1 |
| CLIP-OGD: An Experimental Design for Adaptive Neyman Allocation in Sequential Experiments |
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5 |
| CLIP4HOI: Towards Adapting CLIP for Practical Zero-Shot HOI Detection |
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2 |
| CLadder: Assessing Causal Reasoning in Language Models |
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4 |
| CLeAR: Continual Learning on Algorithmic Reasoning for Human-like Intelligence |
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3 |
| CODA: Generalizing to Open and Unseen Domains with Compaction and Disambiguation |
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2 |
| CORNN: Convex optimization of recurrent neural networks for rapid inference of neural dynamics |
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4 |
| CP-SLAM: Collaborative Neural Point-based SLAM System |
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3 |
| CQM: Curriculum Reinforcement Learning with a Quantized World Model |
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3 |
| CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders |
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5 |
| CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image Steganography |
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5 |
| CS-Isolate: Extracting Hard Confident Examples by Content and Style Isolation |
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7 |
| CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions |
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4 |
| CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversion |
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4 |
| CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy Labels |
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5 |
| CWCL: Cross-Modal Transfer with Continuously Weighted Contrastive Loss |
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4 |
| CaMP: Causal Multi-policy Planning for Interactive Navigation in Multi-room Scenes |
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3 |
| Cal-DETR: Calibrated Detection Transformer |
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4 |
| Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning |
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5 |
| Calibrate and Boost Logical Expressiveness of GNN Over Multi-Relational and Temporal Graphs |
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4 |
| Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents |
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1 |
| Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability |
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4 |
| Calibrating “Cheap Signals” in Peer Review without a Prior |
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1 |
| Calibration by Distribution Matching: Trainable Kernel Calibration Metrics |
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5 |
| CamoPatch: An Evolutionary Strategy for Generating Camoflauged Adversarial Patches |
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5 |
| Can Language Models Solve Graph Problems in Natural Language? |
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3 |
| Can Language Models Teach? Teacher Explanations Improve Student Performance via Personalization |
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5 |
| Can Pre-Trained Text-to-Image Models Generate Visual Goals for Reinforcement Learning? |
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2 |
| Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data |
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2 |
| Can semi-supervised learning use all the data effectively? A lower bound perspective |
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4 |
| Canonical normalizing flows for manifold learning |
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4 |
| Cappy: Outperforming and Boosting Large Multi-Task LMs with a Small Scorer |
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4 |
| Cascading Bandits: Optimizing Recommendation Frequency in Delayed Feedback Environments |
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3 |
| Cascading Contextual Assortment Bandits |
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1 |
| Category-Extensible Out-of-Distribution Detection via Hierarchical Context Descriptions |
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5 |
| Causal Component Analysis |
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4 |
| Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness |
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3 |
| Causal Discovery from Subsampled Time Series with Proxy Variables |
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4 |
| Causal Discovery in Semi-Stationary Time Series |
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4 |
| Causal Effect Identification in Uncertain Causal Networks |
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5 |
| Causal Effect Regularization: Automated Detection and Removal of Spurious Correlations |
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3 |
| Causal Fairness for Outcome Control |
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5 |
| Causal Imitability Under Context-Specific Independence Relations |
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2 |
| Causal Interpretation of Self-Attention in Pre-Trained Transformers |
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3 |
| Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data |
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3 |
| Causal discovery from observational and interventional data across multiple environments |
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2 |
| Causal normalizing flows: from theory to practice |
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6 |
| Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing |
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6 |
| Causes and Effects of Unanticipated Numerical Deviations in Neural Network Inference Frameworks |
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4 |
| Censored Sampling of Diffusion Models Using 3 Minutes of Human Feedback |
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4 |
| Certifiably Robust Graph Contrastive Learning |
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5 |
| Certification of Distributional Individual Fairness |
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3 |
| Certified Minimax Unlearning with Generalization Rates and Deletion Capacity |
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1 |
| Certified Robustness via Dynamic Margin Maximization and Improved Lipschitz Regularization |
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2 |
| Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models |
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2 |
| Chanakya: Learning Runtime Decisions for Adaptive Real-Time Perception |
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5 |
| Change point detection and inference in multivariate non-parametric models under mixing conditions |
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5 |
| Characteristic Circuits |
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5 |
| Characterization and Learning of Causal Graphs with Small Conditioning Sets |
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4 |
| Characterization of Overfitting in Robust Multiclass Classification |
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1 |
| Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyond |
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4 |
| Characterizing Out-of-Distribution Error via Optimal Transport |
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5 |
| Characterizing the Impacts of Semi-supervised Learning for Weak Supervision |
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5 |
| Characterizing the Optimal $0-1$ Loss for Multi-class Classification with a Test-time Attacker |
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5 |
| Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach |
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3 |
| ChatGPT-Powered Hierarchical Comparisons for Image Classification |
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6 |
| Chatting Makes Perfect: Chat-based Image Retrieval |
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5 |
| Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models |
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5 |
| Cheaply Estimating Inference Efficiency Metrics for Autoregressive Transformer Models |
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5 |
| Cinematic Mindscapes: High-quality Video Reconstruction from Brain Activity |
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✅ |
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3 |
| Circuit as Set of Points |
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✅ |
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❌ |
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4 |
| Class-Conditional Conformal Prediction with Many Classes |
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4 |
| Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label Learning |
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✅ |
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❌ |
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5 |
| Classification of Heavy-tailed Features in High Dimensions: a Superstatistical Approach |
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❌ |
❌ |
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1 |
| Clifford Group Equivariant Neural Networks |
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✅ |
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✅ |
✅ |
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❌ |
5 |
| Closing the Computational-Statistical Gap in Best Arm Identification for Combinatorial Semi-bandits |
✅ |
✅ |
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2 |
| Closing the gap between the upper bound and lower bound of Adam's iteration complexity |
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❌ |
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❌ |
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3 |
| CluB: Cluster Meets BEV for LiDAR-Based 3D Object Detection |
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✅ |
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3 |
| Cluster-aware Semi-supervised Learning: Relational Knowledge Distillation Provably Learns Clustering |
❌ |
✅ |
✅ |
❌ |
✅ |
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4 |
| ClusterFomer: Clustering As A Universal Visual Learner |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Clustering the Sketch: Dynamic Compression for Embedding Tables |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| CoDA: Collaborative Novel Box Discovery and Cross-modal Alignment for Open-vocabulary 3D Object Detection |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| CoDet: Co-occurrence Guided Region-Word Alignment for Open-Vocabulary Object Detection |
❌ |
✅ |
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❌ |
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3 |
| CoDrug: Conformal Drug Property Prediction with Density Estimation under Covariate Shift |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra |
✅ |
✅ |
✅ |
❌ |
✅ |
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5 |
| CoLLAT: On Adding Fine-grained Audio Understanding to Language Models using Token-Level Locked-Language Tuning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
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3 |
| CoPriv: Network/Protocol Co-Optimization for Communication-Efficient Private Inference |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Cocktail: Mixing Multi-Modality Control for Text-Conditional Image Generation |
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✅ |
✅ |
❌ |
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❌ |
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4 |
| Cognitive Model Discovery via Disentangled RNNs |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
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5 |
| Cognitive Steering in Deep Neural Networks via Long-Range Modulatory Feedback Connections |
❌ |
✅ |
✅ |
✅ |
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❌ |
✅ |
5 |
| Coherent Soft Imitation Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
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3 |
| Collaborative Alignment of NLP Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
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2 |
| Collaborative Learning via Prediction Consensus |
✅ |
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❌ |
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❌ |
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5 |
| Collaborative Score Distillation for Consistent Visual Editing |
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❌ |
✅ |
❌ |
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❌ |
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3 |
| Collaboratively Learning Linear Models with Structured Missing Data |
✅ |
✅ |
✅ |
❌ |
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❌ |
❌ |
3 |
| Collapsed Inference for Bayesian Deep Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Color Equivariant Convolutional Networks |
❌ |
✅ |
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❌ |
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4 |
| ComSL: A Composite Speech-Language Model for End-to-End Speech-to-Text Translation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Combating Bilateral Edge Noise for Robust Link Prediction |
✅ |
✅ |
✅ |
✅ |
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❌ |
✅ |
6 |
| Combating Representation Learning Disparity with Geometric Harmonization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Combinatorial Group Testing with Selfish Agents |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Combinatorial Optimization with Policy Adaptation using Latent Space Search |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Combining Behaviors with the Successor Features Keyboard |
❌ |
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✅ |
❌ |
✅ |
❌ |
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3 |
| Common Ground in Cooperative Communication |
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❌ |
❌ |
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1 |
| CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusion |
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✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Communication-Efficient Federated Bilevel Optimization with Global and Local Lower Level Problems |
✅ |
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✅ |
✅ |
✅ |
❌ |
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5 |
| Compact Neural Volumetric Video Representations with Dynamic Codebooks |
❌ |
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❌ |
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❌ |
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4 |
| Comparing Apples to Oranges: Learning Similarity Functions for Data Produced by Different Distributions |
✅ |
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✅ |
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4 |
| Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Complex Query Answering on Eventuality Knowledge Graph with Implicit Logical Constraints |
✅ |
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✅ |
✅ |
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❌ |
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6 |
| Complex-valued Neurons Can Learn More but Slower than Real-valued Neurons via Gradient Descent |
❌ |
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❌ |
❌ |
❌ |
❌ |
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1 |
| Complexity Matters: Rethinking the Latent Space for Generative Modeling |
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✅ |
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4 |
| Complexity of Derivative-Free Policy Optimization for Structured $\mathcal{H}_\infty$ Control |
✅ |
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4 |
| Composable Coresets for Determinant Maximization: Greedy is Almost Optimal |
✅ |
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2 |
| Composing Parameter-Efficient Modules with Arithmetic Operation |
❌ |
✅ |
✅ |
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❌ |
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5 |
| Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task |
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3 |
| Compositional Foundation Models for Hierarchical Planning |
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5 |
| Compositional Generalization from First Principles |
❌ |
✅ |
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4 |
| Compositional Policy Learning in Stochastic Control Systems with Formal Guarantees |
✅ |
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❌ |
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❌ |
❌ |
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2 |
| Compositional Sculpting of Iterative Generative Processes |
✅ |
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5 |
| Compressed Video Prompt Tuning |
❌ |
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✅ |
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4 |
| Compression with Bayesian Implicit Neural Representations |
✅ |
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✅ |
❌ |
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5 |
| Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Computational Guarantees for Doubly Entropic Wasserstein Barycenters |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
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3 |
| Computing Approximate $\ell_p$ Sensitivities |
✅ |
❌ |
✅ |
❌ |
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2 |
| Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form Games |
✅ |
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✅ |
❌ |
❌ |
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3 |
| Computing Optimal Nash Equilibria in Multiplayer Games |
✅ |
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❌ |
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6 |
| Computing a human-like reaction time metric from stable recurrent vision models |
❌ |
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❌ |
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5 |
| ConDaFormer: Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding |
❌ |
❌ |
✅ |
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4 |
| ConRad: Image Constrained Radiance Fields for 3D Generation from a Single Image |
✅ |
❌ |
✅ |
❌ |
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4 |
| Concept Algebra for (Score-Based) Text-Controlled Generative Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
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2 |
| Concept Distillation: Leveraging Human-Centered Explanations for Model Improvement |
✅ |
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✅ |
❌ |
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❌ |
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4 |
| Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
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5 |
| Conditional Matrix Flows for Gaussian Graphical Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Conditional Mutual Information for Disentangled Representations in Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Conditional Score Guidance for Text-Driven Image-to-Image Translation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| Conditional independence testing under misspecified inductive biases |
✅ |
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✅ |
✅ |
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❌ |
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6 |
| Conditional score-based diffusion models for Bayesian inference in infinite dimensions |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Coneheads: Hierarchy Aware Attention |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Conformal Meta-learners for Predictive Inference of Individual Treatment Effects |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Conformal PID Control for Time Series Prediction |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Conformal Prediction Sets for Ordinal Classification |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Conformal Prediction for Time Series with Modern Hopfield Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
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6 |
| Conformal Prediction for Uncertainty-Aware Planning with Diffusion Dynamics Model |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Conformalized matrix completion |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Connected Superlevel Set in (Deep) Reinforcement Learning and its Application to Minimax Theorems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Connecting Certified and Adversarial Training |
✅ |
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✅ |
✅ |
✅ |
❌ |
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6 |
| Connecting Multi-modal Contrastive Representations |
❌ |
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✅ |
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3 |
| Connecting Pre-trained Language Model and Downstream Task via Properties of Representation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
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2 |
| Conservative Offline Policy Adaptation in Multi-Agent Games |
✅ |
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❌ |
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❌ |
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4 |
| Conservative State Value Estimation for Offline Reinforcement Learning |
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✅ |
❌ |
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❌ |
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4 |
| Consistent Aggregation of Objectives with Diverse Time Preferences Requires Non-Markovian Rewards |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Consistent Diffusion Models: Mitigating Sampling Drift by Learning to be Consistent |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| Constant Approximation for Individual Preference Stable Clustering |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Constrained Policy Optimization with Explicit Behavior Density For Offline Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Constraint-Conditioned Policy Optimization for Versatile Safe Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Constructing Non-isotropic Gaussian Diffusion Model Using Isotropic Gaussian Diffusion Model for Image Editing |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Construction of Hierarchical Neural Architecture Search Spaces based on Context-free Grammars |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| Content-based Unrestricted Adversarial Attack |
✅ |
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✅ |
❌ |
✅ |
✅ |
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5 |
| Context Shift Reduction for Offline Meta-Reinforcement Learning |
✅ |
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❌ |
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❌ |
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4 |
| Context-PIPs: Persistent Independent Particles Demands Spatial Context Features |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
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2 |
| Context-guided Embedding Adaptation for Effective Topic Modeling in Low-Resource Regimes |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| Context-lumpable stochastic bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Contextual Bandits and Imitation Learning with Preference-Based Active Queries |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Contextual Gaussian Process Bandits with Neural Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Contextual Stochastic Bilevel Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Contextually Affinitive Neighborhood Refinery for Deep Clustering |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| ContinuAR: Continuous Autoregression For Infinite-Fidelity Fusion |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Continual Learning for Instruction Following from Realtime Feedback |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Continuous Parametric Optical Flow |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Continuous-Time Functional Diffusion Processes |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Continuous-time Analysis of Anchor Acceleration |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Contrast, Attend and Diffuse to Decode High-Resolution Images from Brain Activities |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Contrastive Modules with Temporal Attention for Multi-Task Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Contrastive Moments: Unsupervised Halfspace Learning in Polynomial Time |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RL |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Contrastive Sampling Chains in Diffusion Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Contrastive Training of Complex-Valued Autoencoders for Object Discovery |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Controlling Text-to-Image Diffusion by Orthogonal Finetuning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Convergence Analysis of Sequential Federated Learning on Heterogeneous Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Convergence analysis of ODE models for accelerated first-order methods via positive semidefinite kernels |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Convergence of Actor-Critic with Multi-Layer Neural Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Convergence of Adam Under Relaxed Assumptions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Convergence of Alternating Gradient Descent for Matrix Factorization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Convergence of mean-field Langevin dynamics: time-space discretization, stochastic gradient, and variance reduction |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Convergent Bregman Plug-and-Play Image Restoration for Poisson Inverse Problems |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Convex and Non-convex Optimization Under Generalized Smoothness |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Convex-Concave Zero-Sum Markov Stackelberg Games |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Convolution Monge Mapping Normalization for learning on sleep data |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Convolutional Neural Operators for robust and accurate learning of PDEs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Convolutional State Space Models for Long-Range Spatiotemporal Modeling |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Convolutional Visual Prompt for Robust Visual Perception |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
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3 |
| Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
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6 |
| Cookie Consent Has Disparate Impact on Estimation Accuracy |
✅ |
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❌ |
❌ |
✅ |
✅ |
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5 |
| Coop: Memory is not a Commodity |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
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4 |
| Coordinating Distributed Example Orders for Provably Accelerated Training |
✅ |
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✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Core-sets for Fair and Diverse Data Summarization |
✅ |
✅ |
✅ |
❌ |
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❌ |
✅ |
4 |
| Correlation Aware Sparsified Mean Estimation Using Random Projection |
❌ |
✅ |
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❌ |
❌ |
❌ |
✅ |
3 |
| Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| CorresNeRF: Image Correspondence Priors for Neural Radiance Fields |
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❌ |
✅ |
✅ |
❌ |
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3 |
| Corruption-Robust Offline Reinforcement Learning with General Function Approximation |
✅ |
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✅ |
❌ |
❌ |
❌ |
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3 |
| CosNet: A Generalized Spectral Kernel Network |
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✅ |
❌ |
✅ |
✅ |
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4 |
| Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| Counterfactual Evaluation of Peer-Review Assignment Policies |
❌ |
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✅ |
3 |
| Counterfactual Generation with Identifiability Guarantees |
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✅ |
✅ |
✅ |
✅ |
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5 |
| Counterfactual Memorization in Neural Language Models |
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✅ |
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✅ |
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3 |
| Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation |
❌ |
✅ |
✅ |
❌ |
❌ |
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✅ |
3 |
| Counterfactually Comparing Abstaining Classifiers |
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✅ |
✅ |
❌ |
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❌ |
✅ |
3 |
| Counterfactually Fair Representation |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Counting Distinct Elements Under Person-Level Differential Privacy |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Counting Distinct Elements in the Turnstile Model with Differential Privacy under Continual Observation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Coupled Reconstruction of Cortical Surfaces by Diffeomorphic Mesh Deformation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Covariance-adaptive best arm identification |
✅ |
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❌ |
❌ |
❌ |
✅ |
2 |
| Creating Multi-Level Skill Hierarchies in Reinforcement Learning |
✅ |
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❌ |
✅ |
3 |
| Creating a Public Repository for Joining Private Data |
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❌ |
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✅ |
3 |
| Credal Marginal MAP |
✅ |
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✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Critical Initialization of Wide and Deep Neural Networks using Partial Jacobians: General Theory and Applications |
❌ |
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❌ |
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4 |
| Cross-Domain Policy Adaptation via Value-Guided Data Filtering |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Cross-Episodic Curriculum for Transformer Agents |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Cross-Scale MAE: A Tale of Multiscale Exploitation in Remote Sensing |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Cross-links Matter for Link Prediction: Rethinking the Debiased GNN from a Data Perspective |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Cross-modal Active Complementary Learning with Self-refining Correspondence |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Cross-modal Prompts: Adapting Large Pre-trained Models for Audio-Visual Downstream Tasks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| CrossGNN: Confronting Noisy Multivariate Time Series Via Cross Interaction Refinement |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
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6 |
| Crystal Structure Prediction by Joint Equivariant Diffusion |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Curriculum Learning With Infant Egocentric Videos |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Curriculum Learning for Graph Neural Networks: Which Edges Should We Learn First |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| Curvature Filtrations for Graph Generative Model Evaluation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models |
❌ |
✅ |
✅ |
✅ |
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❌ |
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5 |
| Customizable Image Synthesis with Multiple Subjects |
✅ |
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✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| CycleNet: Rethinking Cycle Consistency in Text-Guided Diffusion for Image Manipulation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| D$^2$CSG: Unsupervised Learning of Compact CSG Trees with Dual Complements and Dropouts |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| D-CIPHER: Discovery of Closed-form Partial Differential Equations |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
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4 |
| D-Separation for Causal Self-Explanation |
❌ |
✅ |
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❌ |
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❌ |
✅ |
4 |
| D4Explainer: In-distribution Explanations of Graph Neural Network via Discrete Denoising Diffusion |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| DAC-DETR: Divide the Attention Layers and Conquer |
❌ |
✅ |
✅ |
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❌ |
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4 |
| DAMEX: Dataset-aware Mixture-of-Experts for visual understanding of mixture-of-datasets |
❌ |
✅ |
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✅ |
✅ |
❌ |
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5 |
| DASpeech: Directed Acyclic Transformer for Fast and High-quality Speech-to-Speech Translation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| DAW: Exploring the Better Weighting Function for Semi-supervised Semantic Segmentation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| DDCoT: Duty-Distinct Chain-of-Thought Prompting for Multimodal Reasoning in Language Models |
❌ |
❌ |
✅ |
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4 |
| DDF-HO: Hand-Held Object Reconstruction via Conditional Directed Distance Field |
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❌ |
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5 |
| DELIFFAS: Deformable Light Fields for Fast Avatar Synthesis |
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4 |
| DELTA: Diverse Client Sampling for Fasting Federated Learning |
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5 |
| DESSERT: An Efficient Algorithm for Vector Set Search with Vector Set Queries |
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❌ |
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5 |
| DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| DIFFER:Decomposing Individual Reward for Fair Experience Replay in Multi-Agent Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization |
❌ |
✅ |
✅ |
❌ |
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4 |
| DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| DISCOVER: Making Vision Networks Interpretable via Competition and Dissection |
❌ |
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❌ |
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5 |
| DOSE: Diffusion Dropout with Adaptive Prior for Speech Enhancement |
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❌ |
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6 |
| DP-HyPO: An Adaptive Private Framework for Hyperparameter Optimization |
✅ |
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3 |
| DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning |
✅ |
✅ |
✅ |
❌ |
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5 |
| DPM-Solver-v3: Improved Diffusion ODE Solver with Empirical Model Statistics |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| DRAUC: An Instance-wise Distributionally Robust AUC Optimization Framework |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| DSR: Dynamical Surface Representation as Implicit Neural Networks for Protein |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
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3 |
| DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
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5 |
| DaTaSeg: Taming a Universal Multi-Dataset Multi-Task Segmentation Model |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Data Augmentations for Improved (Large) Language Model Generalization |
✅ |
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✅ |
✅ |
❌ |
❌ |
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4 |
| Data Market Design through Deep Learning |
❌ |
✅ |
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❌ |
✅ |
❌ |
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4 |
| Data Minimization at Inference Time |
✅ |
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✅ |
✅ |
❌ |
❌ |
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4 |
| Data Pruning via Moving-one-Sample-out |
✅ |
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✅ |
❌ |
✅ |
❌ |
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5 |
| Data Quality in Imitation Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Data Selection for Language Models via Importance Resampling |
❌ |
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✅ |
✅ |
✅ |
❌ |
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5 |
| Data-Centric Learning from Unlabeled Graphs with Diffusion Model |
✅ |
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✅ |
✅ |
❌ |
❌ |
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4 |
| Data-Dependent Bounds for Online Portfolio Selection Without Lipschitzness and Smoothness |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Data-Informed Geometric Space Selection |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Data-driven Optimal Filtering for Linear Systems with Unknown Noise Covariances |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
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2 |
| Dataset Diffusion: Diffusion-based Synthetic Data Generation for Pixel-Level Semantic Segmentation |
❌ |
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✅ |
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6 |
| DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models |
❌ |
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❌ |
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4 |
| De novo Drug Design using Reinforcement Learning with Multiple GPT Agents |
❌ |
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4 |
| DeWave: Discrete Encoding of EEG Waves for EEG to Text Translation |
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❌ |
✅ |
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4 |
| Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
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3 |
| Debiased and Denoised Entity Recognition from Distant Supervision |
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❌ |
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5 |
| Debiasing Conditional Stochastic Optimization |
✅ |
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❌ |
❌ |
❌ |
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2 |
| Debiasing Pretrained Generative Models by Uniformly Sampling Semantic Attributes |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
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5 |
| Debiasing Scores and Prompts of 2D Diffusion for View-consistent Text-to-3D Generation |
❌ |
✅ |
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❌ |
✅ |
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5 |
| Decentralized Matrix Sensing: Statistical Guarantees and Fast Convergence |
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❌ |
❌ |
❌ |
✅ |
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2 |
| Decentralized Randomly Distributed Multi-agent Multi-armed Bandit with Heterogeneous Rewards |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment |
❌ |
✅ |
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✅ |
✅ |
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6 |
| Decision Stacks: Flexible Reinforcement Learning via Modular Generative Models |
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4 |
| Decision Tree for Locally Private Estimation with Public Data |
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✅ |
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❌ |
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6 |
| Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees |
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✅ |
❌ |
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4 |
| Decompose Novel into Known: Part Concept Learning For 3D Novel Class Discovery |
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✅ |
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❌ |
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4 |
| Decompose a Task into Generalizable Subtasks in Multi-Agent Reinforcement Learning |
✅ |
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✅ |
❌ |
❌ |
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3 |
| Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses |
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4 |
| Decorate3D: Text-Driven High-Quality Texture Generation for Mesh Decoration in the Wild |
❌ |
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❌ |
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4 |
| Deductive Verification of Chain-of-Thought Reasoning |
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3 |
| Deep Contract Design via Discontinuous Networks |
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❌ |
✅ |
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3 |
| Deep Equilibrium Based Neural Operators for Steady-State PDEs |
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❌ |
✅ |
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3 |
| Deep Fractional Fourier Transform |
❌ |
✅ |
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❌ |
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2 |
| Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems |
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❌ |
✅ |
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4 |
| Deep Insights into Noisy Pseudo Labeling on Graph Data |
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4 |
| Deep Momentum Multi-Marginal Schrödinger Bridge |
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4 |
| Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model |
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✅ |
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2 |
| Deep Non-line-of-sight Imaging from Under-scanning Measurements |
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4 |
| Deep Optimal Transport: A Practical Algorithm for Photo-realistic Image Restoration |
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4 |
| Deep Patch Visual Odometry |
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5 |
| Deep Recurrent Optimal Stopping |
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✅ |
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5 |
| Deep Reinforcement Learning with Plasticity Injection |
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❌ |
✅ |
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3 |
| Deep Stochastic Processes via Functional Markov Transition Operators |
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✅ |
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❌ |
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5 |
| Deep learning with kernels through RKHM and the Perron-Frobenius operator |
❌ |
❌ |
✅ |
❌ |
✅ |
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4 |
| DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization |
✅ |
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✅ |
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5 |
| DeepPCR: Parallelizing Sequential Operations in Neural Networks |
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4 |
| DeepSimHO: Stable Pose Estimation for Hand-Object Interaction via Physics Simulation |
❌ |
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✅ |
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❌ |
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5 |
| Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks |
✅ |
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✅ |
❌ |
❌ |
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5 |
| Defending against Data-Free Model Extraction by Distributionally Robust Defensive Training |
✅ |
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✅ |
✅ |
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5 |
| Delayed Algorithms for Distributed Stochastic Weakly Convex Optimization |
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4 |
| Delegated Classification |
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5 |
| Demo2Code: From Summarizing Demonstrations to Synthesizing Code via Extended Chain-of-Thought |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Demographic Parity Constrained Minimax Optimal Regression under Linear Model |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Demystifying Oversmoothing in Attention-Based Graph Neural Networks |
❌ |
❌ |
✅ |
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❌ |
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4 |
| Demystifying Softmax Gating Function in Gaussian Mixture of Experts |
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❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All? |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Demystifying the Optimal Performance of Multi-Class Classification |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Dense and Aligned Captions (DAC) Promote Compositional Reasoning in VL Models |
❌ |
❌ |
✅ |
❌ |
✅ |
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✅ |
3 |
| Dense-Exponential Random Features: Sharp Positive Estimators of the Gaussian Kernel |
❌ |
❌ |
✅ |
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4 |
| Density of States Prediction of Crystalline Materials via Prompt-guided Multi-Modal Transformer |
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❌ |
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5 |
| Depth-discriminative Metric Learning for Monocular 3D Object Detection |
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4 |
| Derandomized novelty detection with FDR control via conformal e-values |
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5 |
| DesCo: Learning Object Recognition with Rich Language Descriptions |
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5 |
| Describe, Explain, Plan and Select: Interactive Planning with LLMs Enables Open-World Multi-Task Agents |
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3 |
| Described Object Detection: Liberating Object Detection with Flexible Expressions |
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2 |
| Design from Policies: Conservative Test-Time Adaptation for Offline Policy Optimization |
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❌ |
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6 |
| Designing Robust Transformers using Robust Kernel Density Estimation |
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5 |
| Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models |
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5 |
| Detecting hidden confounding in observational data using multiple environments |
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❌ |
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4 |
| Detection Based Part-level Articulated Object Reconstruction from Single RGBD Image |
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❌ |
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5 |
| DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generation |
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3 |
| DiViNeT: 3D Reconstruction from Disparate Views using Neural Template Regularization |
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3 |
| Diff-Foley: Synchronized Video-to-Audio Synthesis with Latent Diffusion Models |
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✅ |
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4 |
| Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models |
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❌ |
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6 |
| DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial Purification |
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5 |
| DiffComplete: Diffusion-based Generative 3D Shape Completion |
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✅ |
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❌ |
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4 |
| DiffKendall: A Novel Approach for Few-Shot Learning with Differentiable Kendall's Rank Correlation |
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3 |
| DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing |
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❌ |
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6 |
| DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models |
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3 |
| DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model |
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5 |
| DiffUTE: Universal Text Editing Diffusion Model |
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3 |
| DiffVL: Scaling Up Soft Body Manipulation using Vision-Language Driven Differentiable Physics |
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❌ |
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❌ |
✅ |
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2 |
| Differentiable Blocks World: Qualitative 3D Decomposition by Rendering Primitives |
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4 |
| Differentiable Clustering with Perturbed Spanning Forests |
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6 |
| Differentiable Neuro-Symbolic Reasoning on Large-Scale Knowledge Graphs |
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✅ |
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❌ |
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3 |
| Differentiable Random Partition Models |
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4 |
| Differentiable Registration of Images and LiDAR Point Clouds with VoxelPoint-to-Pixel Matching |
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4 |
| Differentiable Sampling of Categorical Distributions Using the CatLog-Derivative Trick |
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❌ |
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4 |
| Differentiable and Stable Long-Range Tracking of Multiple Posterior Modes |
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3 |
| Differentiable sorting for censored time-to-event data. |
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5 |
| Differentially Private Approximate Near Neighbor Counting in High Dimensions |
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1 |
| Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protection |
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❌ |
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❌ |
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5 |
| Differentially Private Image Classification by Learning Priors from Random Processes |
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❌ |
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5 |
| Differentially Private Statistical Inference through $\beta$-Divergence One Posterior Sampling |
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❌ |
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❌ |
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4 |
| DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models |
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❌ |
❌ |
✅ |
❌ |
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3 |
| Diffused Redundancy in Pre-trained Representations |
❌ |
✅ |
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❌ |
❌ |
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3 |
| Diffused Task-Agnostic Milestone Planner |
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❌ |
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❌ |
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4 |
| Diffusion Hyperfeatures: Searching Through Time and Space for Semantic Correspondence |
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✅ |
✅ |
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❌ |
❌ |
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4 |
| Diffusion Model for Graph Inverse Problems: Towards Effective Source Localization on Complex Networks |
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✅ |
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❌ |
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6 |
| Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning |
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4 |
| Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels |
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❌ |
❌ |
✅ |
5 |
| Diffusion Probabilistic Models for Structured Node Classification |
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❌ |
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5 |
| Diffusion Representation for Asymmetric Kernels via Magnetic Transform |
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❌ |
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5 |
| Diffusion Schrödinger Bridge Matching |
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❌ |
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5 |
| Diffusion Self-Guidance for Controllable Image Generation |
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❌ |
❌ |
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1 |
| Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision |
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❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability |
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❌ |
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❌ |
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5 |
| Diffusion-Based Probabilistic Uncertainty Estimation for Active Domain Adaptation |
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❌ |
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5 |
| Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object Detection |
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❌ |
❌ |
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5 |
| Diffusion-TTA: Test-time Adaptation of Discriminative Models via Generative Feedback |
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❌ |
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6 |
| DinoSR: Self-Distillation and Online Clustering for Self-supervised Speech Representation Learning |
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6 |
| Direct Diffusion Bridge using Data Consistency for Inverse Problems |
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❌ |
❌ |
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5 |
| Direct Preference Optimization: Your Language Model is Secretly a Reward Model |
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❌ |
✅ |
❌ |
❌ |
❌ |
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2 |
| Direct Preference-based Policy Optimization without Reward Modeling |
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❌ |
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6 |
| Direct Training of SNN using Local Zeroth Order Method |
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❌ |
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❌ |
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4 |
| Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data |
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❌ |
✅ |
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❌ |
❌ |
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2 |
| Direction-oriented Multi-objective Learning: Simple and Provable Stochastic Algorithms |
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❌ |
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5 |
| Directional diffusion models for graph representation learning |
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3 |
| Dis-inhibitory neuronal circuits can control the sign of synaptic plasticity |
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6 |
| DisDiff: Unsupervised Disentanglement of Diffusion Probabilistic Models |
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4 |
| Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning |
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5 |
| Discover and Align Taxonomic Context Priors for Open-world Semi-Supervised Learning |
❌ |
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❌ |
❌ |
❌ |
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3 |
| Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design |
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❌ |
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❌ |
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5 |
| Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive Learning |
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❌ |
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4 |
| Discovering Intrinsic Spatial-Temporal Logic Rules to Explain Human Actions |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Discrete-Smoothness in Online Algorithms with Predictions |
✅ |
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❌ |
❌ |
✅ |
❌ |
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3 |
| Discriminative Calibration: Check Bayesian Computation from Simulations and Flexible Classifier |
✅ |
✅ |
✅ |
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❌ |
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5 |
| Discriminative Feature Attributions: Bridging Post Hoc Explainability and Inherent Interpretability |
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❌ |
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6 |
| Disentangled Counterfactual Learning for Physical Audiovisual Commonsense Reasoning |
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5 |
| Disentangled Wasserstein Autoencoder for T-Cell Receptor Engineering |
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❌ |
✅ |
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❌ |
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4 |
| Disentanglement via Latent Quantization |
✅ |
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❌ |
❌ |
❌ |
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4 |
| Disentangling Cognitive Diagnosis with Limited Exercise Labels |
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7 |
| Disentangling Voice and Content with Self-Supervision for Speaker Recognition |
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❌ |
✅ |
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3 |
| Dissecting Chain-of-Thought: Compositionality through In-Context Filtering and Learning |
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❌ |
❌ |
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2 |
| Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Power |
❌ |
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4 |
| Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models |
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4 |
| Distributed Inference and Fine-tuning of Large Language Models Over The Internet |
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5 |
| Distributed Personalized Empirical Risk Minimization |
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3 |
| Distribution Learnability and Robustness |
❌ |
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❌ |
❌ |
❌ |
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0 |
| Distribution-Free Model-Agnostic Regression Calibration via Nonparametric Methods |
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✅ |
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❌ |
❌ |
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5 |
| Distribution-Free Statistical Dispersion Control for Societal Applications |
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3 |
| Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generation |
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5 |
| Distributional Model Equivalence for Risk-Sensitive Reinforcement Learning |
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3 |
| Distributional Pareto-Optimal Multi-Objective Reinforcement Learning |
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5 |
| Distributional Policy Evaluation: a Maximum Entropy approach to Representation Learning |
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2 |
| Distributionally Robust Bayesian Optimization with $\varphi$-divergences |
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❌ |
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3 |
| Distributionally Robust Ensemble of Lottery Tickets Towards Calibrated Sparse Network Training |
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6 |
| Distributionally Robust Linear Quadratic Control |
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4 |
| Distributionally Robust Skeleton Learning of Discrete Bayesian Networks |
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5 |
| Diverse Conventions for Human-AI Collaboration |
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5 |
| Diverse Shape Completion via Style Modulated Generative Adversarial Networks |
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❌ |
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3 |
| Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation |
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❌ |
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6 |
| Diversify Your Vision Datasets with Automatic Diffusion-based Augmentation |
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6 |
| Diversify \& Conquer: Outcome-directed Curriculum RL via Out-of-Distribution Disagreement |
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4 |
| Diversifying Spatial-Temporal Perception for Video Domain Generalization |
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❌ |
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5 |
| Divide, Evaluate, and Refine: Evaluating and Improving Text-to-Image Alignment with Iterative VQA Feedback |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
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1 |
| Django: Detecting Trojans in Object Detection Models via Gaussian Focus Calibration |
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5 |
| Do Not Marginalize Mechanisms, Rather Consolidate! |
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❌ |
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2 |
| Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning |
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3 |
| DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining |
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5 |
| DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method |
✅ |
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5 |
| Does Graph Distillation See Like Vision Dataset Counterpart? |
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7 |
| Does Invariant Graph Learning via Environment Augmentation Learn Invariance? |
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6 |
| Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models |
❌ |
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4 |
| Does Visual Pretraining Help End-to-End Reasoning? |
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4 |
| Does a sparse ReLU network training problem always admit an optimum ? |
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2 |
| Domain Adaptive Imitation Learning with Visual Observation |
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4 |
| Domain Agnostic Fourier Neural Operators |
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4 |
| Domain Re-Modulation for Few-Shot Generative Domain Adaptation |
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3 |
| Domain Watermark: Effective and Harmless Dataset Copyright Protection is Closed at Hand |
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4 |
| Don't be so Monotone: Relaxing Stochastic Line Search in Over-Parameterized Models |
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1 |
| Don’t Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learner |
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5 |
| Don’t blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy |
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4 |
| Don’t just prune by magnitude! Your mask topology is a secret weapon |
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❌ |
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5 |
| Double Auctions with Two-sided Bandit Feedback |
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❌ |
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1 |
| Double Gumbel Q-Learning |
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6 |
| Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage |
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1 |
| Double Randomized Underdamped Langevin with Dimension-Independent Convergence Guarantee |
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2 |
| Double and Single Descent in Causal Inference with an Application to High-Dimensional Synthetic Control |
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4 |
| Doubly Constrained Fair Clustering |
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5 |
| Doubly Robust Augmented Transfer for Meta-Reinforcement Learning |
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2 |
| Doubly-Robust Self-Training |
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5 |
| Dream the Impossible: Outlier Imagination with Diffusion Models |
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✅ |
✅ |
✅ |
6 |
| DreamHuman: Animatable 3D Avatars from Text |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| DreamSparse: Escaping from Plato’s Cave with 2D Diffusion Model Given Sparse Views |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| DreamWaltz: Make a Scene with Complex 3D Animatable Avatars |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Drift doesn't Matter: Dynamic Decomposition with Diffusion Reconstruction for Unstable Multivariate Time Series Anomaly Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| DropCompute: simple and more robust distributed synchronous training via compute variance reduction |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| DropPos: Pre-Training Vision Transformers by Reconstructing Dropped Positions |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Dual Mean-Teacher: An Unbiased Semi-Supervised Framework for Audio-Visual Source Localization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Dual Self-Awareness Value Decomposition Framework without Individual Global Max for Cooperative MARL |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| DynPoint: Dynamic Neural Point For View Synthesis |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Dynamic Non-monotone Submodular Maximization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Dynamic Personalized Federated Learning with Adaptive Differential Privacy |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Dynamic Pricing and Learning with Bayesian Persuasion |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Dynamic Regret of Adversarial Linear Mixture MDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Dynamic Sparsity Is Channel-Level Sparsity Learner |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Dynamic Tensor Decomposition via Neural Diffusion-Reaction Processes |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Dynamically Masked Discriminator for GANs |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Dynamics Generalisation in Reinforcement Learning via Adaptive Context-Aware Policies |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| DäRF: Boosting Radiance Fields from Sparse Input Views with Monocular Depth Adaptation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| E2PNet: Event to Point Cloud Registration with Spatio-Temporal Representation Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| EDGI: Equivariant Diffusion for Planning with Embodied Agents |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| EICIL: Joint Excitatory Inhibitory Cycle Iteration Learning for Deep Spiking Neural Networks |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
2 |
| ELDEN: Exploration via Local Dependencies |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| EMMA-X: An EM-like Multilingual Pre-training Algorithm for Cross-lingual Representation Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| ESSEN: Improving Evolution State Estimation for Temporal Networks using Von Neumann Entropy |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Easy Learning from Label Proportions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Echoes Beyond Points: Unleashing the Power of Raw Radar Data in Multi-modality Fusion |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Effective Bayesian Heteroscedastic Regression with Deep Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Effective Human-AI Teams via Learned Natural Language Rules and Onboarding |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Effective Robustness against Natural Distribution Shifts for Models with Different Training Data |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Effective Targeted Attacks for Adversarial Self-Supervised Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Effectively Learning Initiation Sets in Hierarchical Reinforcement Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Efficient Activation Function Optimization through Surrogate Modeling |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Efficient Adaptation of Large Vision Transformer via Adapter Re-Composing |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Efficient Adversarial Attacks on Online Multi-agent Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Efficient Algorithms for Generalized Linear Bandits with Heavy-tailed Rewards |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Efficient Batched Algorithm for Contextual Linear Bandits with Large Action Space via Soft Elimination |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficient Beam Tree Recursion |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Efficient Data Subset Selection to Generalize Training Across Models: Transductive and Inductive Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Efficient Diffusion Policies For Offline Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Efficient Equivariant Transfer Learning from Pretrained Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Efficient Exploration in Continuous-time Model-based Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Efficient Hyper-parameter Optimization with Cubic Regularization |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Efficient Learning of Linear Graph Neural Networks via Node Subsampling |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Efficient Low-rank Backpropagation for Vision Transformer Adaptation |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Efficient Model-Free Exploration in Low-Rank MDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficient Neural Music Generation |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Efficient Online Clustering with Moving Costs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Potential-based Exploration in Reinforcement Learning using Inverse Dynamic Bisimulation Metric |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficient Robust Bayesian Optimization for Arbitrary Uncertain inputs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Efficient Subgame Refinement for Extensive-form Games |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Efficient Symbolic Policy Learning with Differentiable Symbolic Expression |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Efficient Test-Time Adaptation for Super-Resolution with Second-Order Degradation and Reconstruction |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Efficient Testable Learning of Halfspaces with Adversarial Label Noise |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficient Training of Energy-Based Models Using Jarzynski Equality |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficiently incorporating quintuple interactions into geometric deep learning force fields |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| EgoDistill: Egocentric Head Motion Distillation for Efficient Video Understanding |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| EgoEnv: Human-centric environment representations from egocentric video |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Egocentric Planning for Scalable Embodied Task Achievement |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Elastic Decision Transformer |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Eliminating Domain Bias for Federated Learning in Representation Space |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Embedding Space Interpolation Beyond Mini-Batch, Beyond Pairs and Beyond Examples |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Embracing the chaos: analysis and diagnosis of numerical instability in variational flows |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Embroid: Unsupervised Prediction Smoothing Can Improve Few-Shot Classification |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Emergence of Shape Bias in Convolutional Neural Networks through Activation Sparsity |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Emergent Communication for Rules Reasoning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Emergent Communication in Interactive Sketch Question Answering |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Emergent Correspondence from Image Diffusion |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Emergent and Predictable Memorization in Large Language Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Empowering Convolutional Neural Nets with MetaSin Activation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Encoding Human Behavior in Information Design through Deep Learning |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
4 |
| End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Energy Discrepancies: A Score-Independent Loss for Energy-Based Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Energy Guided Diffusion for Generating Neurally Exciting Images |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Energy Transformer |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Energy-Based Cross Attention for Bayesian Context Update in Text-to-Image Diffusion Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Energy-Based Sliced Wasserstein Distance |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Energy-Efficient Scheduling with Predictions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Energy-based learning algorithms for analog computing: a comparative study |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Enhancing Adaptive History Reserving by Spiking Convolutional Block Attention Module in Recurrent Neural Networks |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Enhancing Adversarial Robustness via Score-Based Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Enhancing CLIP with CLIP: Exploring Pseudolabeling for Limited-Label Prompt Tuning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Enhancing Knowledge Transfer for Task Incremental Learning with Data-free Subnetwork |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Enhancing Minority Classes by Mixing: An Adaptative Optimal Transport Approach for Long-tailed Classification |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Enhancing Motion Deblurring in High-Speed Scenes with Spike Streams |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Enhancing Robot Program Synthesis Through Environmental Context |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
1 |
| Enhancing Sharpness-Aware Optimization Through Variance Suppression |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Enhancing User Intent Capture in Session-Based Recommendation with Attribute Patterns |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Ensemble-based Deep Reinforcement Learning for Vehicle Routing Problems under Distribution Shift |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Entropic Neural Optimal Transport via Diffusion Processes |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Entropy-based Training Methods for Scalable Neural Implicit Samplers |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Entropy-dissipation Informed Neural Network for McKean-Vlasov Type PDEs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Epidemic Learning: Boosting Decentralized Learning with Randomized Communication |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Episodic Multi-Task Learning with Heterogeneous Neural Processes |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Epistemic Neural Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Equal Opportunity of Coverage in Fair Regression |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Equivariant Adaptation of Large Pretrained Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Equivariant Flow Matching with Hybrid Probability Transport for 3D Molecule Generation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Equivariant Neural Operator Learning with Graphon Convolution |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Equivariant Single View Pose Prediction Via Induced and Restriction Representations |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Equivariant Spatio-Temporal Attentive Graph Networks to Simulate Physical Dynamics |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Equivariant flow matching |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Error Bounds for Learning with Vector-Valued Random Features |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Error Discovery By Clustering Influence Embeddings |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Errors-in-variables Fr\'echet Regression with Low-rank Covariate Approximation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Ess-InfoGAIL: Semi-supervised Imitation Learning from Imbalanced Demonstrations |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Estimating Causal Effects Identifiable from a Combination of Observations and Experiments |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Estimating Koopman operators with sketching to provably learn large scale dynamical systems |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Estimating Noise Correlations Across Continuous Conditions With Wishart Processes |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Estimating Propensity for Causality-based Recommendation without Exposure Data |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Estimating Riemannian Metric with Noise-Contaminated Intrinsic Distance |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Estimating and Controlling for Equalized Odds via Sensitive Attribute Predictors |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Estimating the Rate-Distortion Function by Wasserstein Gradient Descent |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Evaluating Cognitive Maps and Planning in Large Language Models with CogEval |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Evaluating Neuron Interpretation Methods of NLP Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Evaluating Post-hoc Explanations for Graph Neural Networks via Robustness Analysis |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Evaluating and Inducing Personality in Pre-trained Language Models |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Evaluating the Moral Beliefs Encoded in LLMs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| EvoPrompting: Language Models for Code-Level Neural Architecture Search |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Evolutionary Neural Architecture Search for Transformer in Knowledge Tracing |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Evolving Connectivity for Recurrent Spiking Neural Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Evolving Standardization for Continual Domain Generalization over Temporal Drift |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| ExPT: Synthetic Pretraining for Few-Shot Experimental Design |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming Approach |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Exact Generalization Guarantees for (Regularized) Wasserstein Distributionally Robust Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Exact Representation of Sparse Networks with Symmetric Nonnegative Embeddings |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Exact Verification of ReLU Neural Control Barrier Functions |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Exact recovery and Bregman hard clustering of node-attributed Stochastic Block Model |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Expanding Small-Scale Datasets with Guided Imagination |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Experiment Planning with Function Approximation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Experimental Designs for Heteroskedastic Variance |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Expert load matters: operating networks at high accuracy and low manual effort |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Explain Any Concept: Segment Anything Meets Concept-Based Explanation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Explainable Brain Age Prediction using coVariance Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Explainable and Efficient Randomized Voting Rules |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Explaining Predictive Uncertainty with Information Theoretic Shapley Values |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Explaining V1 Properties with a Biologically Constrained Deep Learning Architecture |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Exploiting Connections between Lipschitz Structures for Certifiably Robust Deep Equilibrium Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Exploiting Contextual Objects and Relations for 3D Visual Grounding |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Exploiting Correlated Auxiliary Feedback in Parameterized Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Exploiting hidden structures in non-convex games for convergence to Nash equilibrium |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Explore In-Context Learning for 3D Point Cloud Understanding |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Explore to Generalize in Zero-Shot RL |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Exploring Diverse In-Context Configurations for Image Captioning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Exploring Geometry of Blind Spots in Vision models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Exploring Loss Functions for Time-based Training Strategy in Spiking Neural Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Exploring Question Decomposition for Zero-Shot VQA |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Exploring and Interacting with the Set of Good Sparse Generalized Additive Models |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Exploring the Optimal Choice for Generative Processes in Diffusion Models: Ordinary vs Stochastic Differential Equations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Exponential Lower Bounds for Fictitious Play in Potential Games |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Exponentially Convergent Algorithms for Supervised Matrix Factorization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Exposing Attention Glitches with Flip-Flop Language Modeling |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Expressive Sign Equivariant Networks for Spectral Geometric Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Expressive probabilistic sampling in recurrent neural networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Expressivity-Preserving GNN Simulation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Extensible Prompts for Language Models on Zero-shot Language Style Customization |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Extracting Reward Functions from Diffusion Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Extraction and Recovery of Spatio-Temporal Structure in Latent Dynamics Alignment with Diffusion Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Extremal Domain Translation with Neural Optimal Transport |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| FABind: Fast and Accurate Protein-Ligand Binding |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| FACE: Evaluating Natural Language Generation with Fourier Analysis of Cross-Entropy |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| FAMO: Fast Adaptive Multitask Optimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| FAST: a Fused and Accurate Shrinkage Tree for Heterogeneous Treatment Effects Estimation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| FD-Align: Feature Discrimination Alignment for Fine-tuning Pre-Trained Models in Few-Shot Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| FGPrompt: Fine-grained Goal Prompting for Image-goal Navigation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| FLSL: Feature-level Self-supervised Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Face Reconstruction from Facial Templates by Learning Latent Space of a Generator Network |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| FaceComposer: A Unified Model for Versatile Facial Content Creation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| FaceDNeRF: Semantics-Driven Face Reconstruction, Prompt Editing and Relighting with Diffusion Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Facilitating Graph Neural Networks with Random Walk on Simplicial Complexes |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Facing Off World Model Backbones: RNNs, Transformers, and S4 |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Factorized Contrastive Learning: Going Beyond Multi-view Redundancy |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Failure-Aware Gaussian Process Optimization with Regret Bounds |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fair Adaptive Experiments |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fair Allocation of Indivisible Chores: Beyond Additive Costs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Fair Canonical Correlation Analysis |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Fair Graph Distillation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Fair, Polylog-Approximate Low-Cost Hierarchical Clustering |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| FairLISA: Fair User Modeling with Limited Sensitive Attributes Information |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Fairly Recommending with Social Attributes: A Flexible and Controllable Optimization Approach |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Fairness Aware Counterfactuals for Subgroups |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Fairness-guided Few-shot Prompting for Large Language Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Faith and Fate: Limits of Transformers on Compositionality |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| False Discovery Proportion control for aggregated Knockoffs |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Fantastic Robustness Measures: The Secrets of Robust Generalization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fast Approximation of Similarity Graphs with Kernel Density Estimation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Fast Asymptotically Optimal Algorithms for Non-Parametric Stochastic Bandits |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Fast Attention Over Long Sequences With Dynamic Sparse Flash Attention |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Fast Attention Requires Bounded Entries |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Fast Bellman Updates for Wasserstein Distributionally Robust MDPs |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Fast Conditional Mixing of MCMC Algorithms for Non-log-concave Distributions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fast Exact Leverage Score Sampling from Khatri-Rao Products with Applications to Tensor Decomposition |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Fast Model DeBias with Machine Unlearning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fast Optimal Locally Private Mean Estimation via Random Projections |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Fast Optimal Transport through Sliced Generalized Wasserstein Geodesics |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Fast Partitioned Learned Bloom Filter |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Fast Projected Newton-like Method for Precision Matrix Estimation under Total Positivity |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fast Rank-1 Lattice Targeted Sampling for Black-box Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow Shrink Trees |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Fast Trainable Projection for Robust Fine-tuning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Fast and Regret Optimal Best Arm Identification: Fundamental Limits and Low-Complexity Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fast and Simple Spectral Clustering in Theory and Practice |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Faster Differentially Private Convex Optimization via Second-Order Methods |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Faster Discrete Convex Function Minimization with Predictions: The M-Convex Case |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| Faster Margin Maximization Rates for Generic Optimization Methods |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Faster Query Times for Fully Dynamic $k$-Center Clustering with Outliers |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Faster Relative Entropy Coding with Greedy Rejection Coding |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Faster approximate subgraph counts with privacy |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Feature Adaptation for Sparse Linear Regression |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
4 |
| Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Feature Learning for Interpretable, Performant Decision Trees |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using Samples |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Feature Selection in the Contrastive Analysis Setting |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Feature-Learning Networks Are Consistent Across Widths At Realistic Scales |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Fed-CO$_{2}$: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Fed-FA: Theoretically Modeling Client Data Divergence for Federated Language Backdoor Defense |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| FedFed: Feature Distillation against Data Heterogeneity in Federated Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| FedGame: A Game-Theoretic Defense against Backdoor Attacks in Federated Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| FedL2P: Federated Learning to Personalize |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| FedNAR: Federated Optimization with Normalized Annealing Regularization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Federated Compositional Deep AUC Maximization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Federated Conditional Stochastic Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Federated Learning via Meta-Variational Dropout |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Federated Learning with Bilateral Curation for Partially Class-Disjoint Data |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Federated Learning with Manifold Regularization and Normalized Update Reaggregation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Federated Linear Bandits with Finite Adversarial Actions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Federated Multi-Objective Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Federated Spectral Clustering via Secure Similarity Reconstruction |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Few-shot Generation via Recalling Brain-Inspired Episodic-Semantic Memory |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| FiGURe: Simple and Efficient Unsupervised Node Representations with Filter Augmentations |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Find What You Want: Learning Demand-conditioned Object Attribute Space for Demand-driven Navigation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
3 |
| Finding Counterfactually Optimal Action Sequences in Continuous State Spaces |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Finding Local Minima Efficiently in Decentralized Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Finding Safe Zones of Markov Decision Processes Policies |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fine-Grained Cross-View Geo-Localization Using a Correlation-Aware Homography Estimator |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fine-Grained Human Feedback Gives Better Rewards for Language Model Training |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Fine-Grained Visual Prompting |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fine-Tuning Language Models with Just Forward Passes |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Fine-grained Expressivity of Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question Answering |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| FineMoGen: Fine-Grained Spatio-Temporal Motion Generation and Editing |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Finite Population Regression Adjustment and Non-asymptotic Guarantees for Treatment Effect Estimation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Finite-Time Analysis of Single-Timescale Actor-Critic |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Finite-Time Analysis of Whittle Index based Q-Learning for Restless Multi-Armed Bandits with Neural Network Function Approximation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Finite-Time Logarithmic Bayes Regret Upper Bounds |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| First Order Stochastic Optimization with Oblivious Noise |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| First- and Second-Order Bounds for Adversarial Linear Contextual Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Fitting trees to $\ell_1$-hyperbolic distances |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Fixing the NTK: From Neural Network Linearizations to Exact Convex Programs |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Flat Seeking Bayesian Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Flexible Attention-Based Multi-Policy Fusion for Efficient Deep Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Flow Factorized Representation Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Flow Matching for Scalable Simulation-Based Inference |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Flow-Attention-based Spatio-Temporal Aggregation Network for 3D Mask Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Flow-Based Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Flow: Per-instance Personalized Federated Learning |
✅ |
✅ |
✅ |
✅ |
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❌ |
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6 |
| FlowCam: Training Generalizable 3D Radiance Fields without Camera Poses via Pixel-Aligned Scene Flow |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| FlowPG: Action-constrained Policy Gradient with Normalizing Flows |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Focus Your Attention when Few-Shot Classification |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Focus on Query: Adversarial Mining Transformer for Few-Shot Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Focused Transformer: Contrastive Training for Context Scaling |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| Follow-ups Also Matter: Improving Contextual Bandits via Post-serving Contexts |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| For SALE: State-Action Representation Learning for Deep Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| ForecastPFN: Synthetically-Trained Zero-Shot Forecasting |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Formalizing locality for normative synaptic plasticity models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Formulating Discrete Probability Flow Through Optimal Transport |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Foundation Model is Efficient Multimodal Multitask Model Selector |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| FouriDown: Factoring Down-Sampling into Shuffling and Superposing |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
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6 |
| FourierHandFlow: Neural 4D Hand Representation Using Fourier Query Flow |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Fractal Landscapes in Policy Optimization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fragment-based Pretraining and Finetuning on Molecular Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Free-Bloom: Zero-Shot Text-to-Video Generator with LLM Director and LDM Animator |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Frequency Domain-Based Dataset Distillation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Frequency-Enhanced Data Augmentation for Vision-and-Language Navigation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Frequency-domain MLPs are More Effective Learners in Time Series Forecasting |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
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6 |
| From Cloze to Comprehension: Retrofitting Pre-trained Masked Language Models to Pre-trained Machine Reader |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusion |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| From Pixels to UI Actions: Learning to Follow Instructions via Graphical User Interfaces |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| From Tempered to Benign Overfitting in ReLU Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| From Trainable Negative Depth to Edge Heterophily in Graphs |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| From ViT Features to Training-free Video Object Segmentation via Streaming-data Mixture Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledge |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Full-Atom Protein Pocket Design via Iterative Refinement |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fully Dynamic $k$-Clustering in $\tilde O(k)$ Update Time |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Function Space Bayesian Pseudocoreset for Bayesian Neural Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Functional Equivalence and Path Connectivity of Reducible Hyperbolic Tangent Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Functional Renyi Differential Privacy for Generative Modeling |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Future-Dependent Value-Based Off-Policy Evaluation in POMDPs |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| GAIA: Delving into Gradient-based Attribution Abnormality for Out-of-distribution Detection |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| GALOPA: Graph Transport Learning with Optimal Plan Alignment |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| GAN You See Me? Enhanced Data Reconstruction Attacks against Split Inference |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| GAUCHE: A Library for Gaussian Processes in Chemistry |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| GEQ: Gaussian Kernel Inspired Equilibrium Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| GEX: A flexible method for approximating influence via Geometric Ensemble |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| GLIME: General, Stable and Local LIME Explanation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| GLOBER: Coherent Non-autoregressive Video Generation via GLOBal Guided Video DecodER |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| GMSF: Global Matching Scene Flow |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| GNeSF: Generalizable Neural Semantic Fields |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| GPEX, A Framework For Interpreting Artificial Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| GRAND-SLAMIN’ Interpretable Additive Modeling with Structural Constraints |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| GUST: Combinatorial Generalization by Unsupervised Grouping with Neuronal Coherence |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Gacs-Korner Common Information Variational Autoencoder |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Game Solving with Online Fine-Tuning |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Gaussian Differential Privacy on Riemannian Manifolds |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Gaussian Membership Inference Privacy |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Gaussian Mixture Solvers for Diffusion Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Gaussian Partial Information Decomposition: Bias Correction and Application to High-dimensional Data |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Gaussian Process Probes (GPP) for Uncertainty-Aware Probing |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| GenS: Generalizable Neural Surface Reconstruction from Multi-View Images |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| General Munchausen Reinforcement Learning with Tsallis Kullback-Leibler Divergence |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Generalised f-Mean Aggregation for Graph Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Generalizable Lightweight Proxy for Robust NAS against Diverse Perturbations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Generalizable One-shot 3D Neural Head Avatar |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Generalization bounds for neural ordinary differential equations and deep residual networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Generalization in the Face of Adaptivity: A Bayesian Perspective |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Generalized Belief Transport |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Generalized Information-theoretic Multi-view Clustering |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Generalized Logit Adjustment: Calibrating Fine-tuned Models by Removing Label Bias in Foundation Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Generalized Semi-Supervised Learning via Self-Supervised Feature Adaptation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
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1 |
| Generalized Weighted Path Consistency for Mastering Atari Games |
✅ |
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✅ |
❌ |
✅ |
❌ |
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5 |
| Generalized equivalences between subsampling and ridge regularization |
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❌ |
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5 |
| Generalized test utilities for long-tail performance in extreme multi-label classification |
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❌ |
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❌ |
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5 |
| Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems |
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7 |
| Generalizing Nonlinear ICA Beyond Structural Sparsity |
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✅ |
❌ |
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❌ |
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2 |
| Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
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7 |
| Generating Behaviorally Diverse Policies with Latent Diffusion Models |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
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3 |
| Generating Images with Multimodal Language Models |
❌ |
✅ |
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❌ |
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5 |
| Generative Category-level Object Pose Estimation via Diffusion Models |
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4 |
| Generative Modeling through the Semi-dual Formulation of Unbalanced Optimal Transport |
✅ |
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❌ |
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5 |
| Generative Modelling of Stochastic Actions with Arbitrary Constraints in Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
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❌ |
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3 |
| Generative Neural Fields by Mixtures of Neural Implicit Functions |
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✅ |
✅ |
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6 |
| Generator Born from Classifier |
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❌ |
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❌ |
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3 |
| Generator Identification for Linear SDEs with Additive and Multiplicative Noise |
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❌ |
❌ |
❌ |
✅ |
1 |
| GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
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3 |
| GeoPhy: Differentiable Phylogenetic Inference via Geometric Gradients of Tree Topologies |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
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6 |
| GeoTMI: Predicting Quantum Chemical Property with Easy-to-Obtain Geometry via Positional Denoising |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Geodesic Multi-Modal Mixup for Robust Fine-Tuning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Geometric Algebra Transformer |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Geometric Analysis of Matrix Sensing over Graphs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Geometric Neural Diffusion Processes |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Geometric Transformer with Interatomic Positional Encoding |
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✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Geometry-Aware Adaptation for Pretrained Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Geometry-Informed Neural Operator for Large-Scale 3D PDEs |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design |
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❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Glance and Focus: Memory Prompting for Multi-Event Video Question Answering |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Global Convergence Analysis of Local SGD for Two-layer Neural Network without Overparameterization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Global Identifiability of $\ell_1$-based Dictionary Learning via Matrix Volume Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Global Optimality in Bivariate Gradient-based DAG Learning |
✅ |
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❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Global Structure-Aware Diffusion Process for Low-light Image Enhancement |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Global-correlated 3D-decoupling Transformer for Clothed Avatar Reconstruction |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Globally injective and bijective neural operators |
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❌ |
❌ |
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0 |
| Globally solving the Gromov-Wasserstein problem for point clouds in low dimensional Euclidean spaces |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| GloptiNets: Scalable Non-Convex Optimization with Certificates |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| GlucoSynth: Generating Differentially-Private Synthetic Glucose Traces |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| GlyphControl: Glyph Conditional Control for Visual Text Generation |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
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3 |
| Goal Driven Discovery of Distributional Differences via Language Descriptions |
❌ |
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❌ |
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5 |
| Goal-Conditioned Predictive Coding for Offline Reinforcement Learning |
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❌ |
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6 |
| Goal-conditioned Offline Planning from Curious Exploration |
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✅ |
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4 |
| Going Beyond Linear Mode Connectivity: The Layerwise Linear Feature Connectivity |
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3 |
| Going beyond persistent homology using persistent homology |
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6 |
| Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism |
❌ |
✅ |
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✅ |
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6 |
| GradOrth: A Simple yet Efficient Out-of-Distribution Detection with Orthogonal Projection of Gradients |
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❌ |
✅ |
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5 |
| Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy |
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❌ |
✅ |
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2 |
| Gradient Flossing: Improving Gradient Descent through Dynamic Control of Jacobians |
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3 |
| Gradient Informed Proximal Policy Optimization |
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6 |
| Gradient-Based Feature Learning under Structured Data |
✅ |
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❌ |
❌ |
❌ |
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1 |
| Gradient-Free Kernel Stein Discrepancy |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
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4 |
| Grammar Prompting for Domain-Specific Language Generation with Large Language Models |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Granger Components Analysis: Unsupervised learning of latent temporal dependencies |
✅ |
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✅ |
❌ |
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❌ |
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4 |
| Graph Contrastive Learning with Stable and Scalable Spectral Encoding |
✅ |
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❌ |
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6 |
| Graph Convolutional Kernel Machine versus Graph Convolutional Networks |
❌ |
✅ |
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❌ |
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4 |
| Graph Denoising Diffusion for Inverse Protein Folding |
✅ |
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✅ |
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7 |
| Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling |
❌ |
✅ |
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✅ |
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❌ |
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5 |
| Graph of Circuits with GNN for Exploring the Optimal Design Space |
✅ |
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❌ |
✅ |
❌ |
❌ |
❌ |
2 |
| Graph-Structured Gaussian Processes for Transferable Graph Learning |
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6 |
| GraphAdapter: Tuning Vision-Language Models With Dual Knowledge Graph |
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4 |
| GraphMP: Graph Neural Network-based Motion Planning with Efficient Graph Search |
✅ |
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❌ |
✅ |
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❌ |
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4 |
| GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
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6 |
| Grassmann Manifold Flows for Stable Shape Generation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Greatness in Simplicity: Unified Self-Cycle Consistency for Parser-Free Virtual Try-On |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
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3 |
| Greedy Poisson Rejection Sampling |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Greedy Pruning with Group Lasso Provably Generalizes for Matrix Sensing |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Grounded Decoding: Guiding Text Generation with Grounded Models for Embodied Agents |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Grounding Neural Inference with Satisfiability Modulo Theories |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Group Fairness in Peer Review |
✅ |
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❌ |
❌ |
❌ |
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3 |
| Group Robust Classification Without Any Group Information |
✅ |
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✅ |
✅ |
❌ |
❌ |
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5 |
| Guarantees for Self-Play in Multiplayer Games via Polymatrix Decomposability |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Guide Your Agent with Adaptive Multimodal Rewards |
❌ |
✅ |
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❌ |
✅ |
❌ |
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4 |
| Guiding Large Language Models via Directional Stimulus Prompting |
❌ |
✅ |
✅ |
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❌ |
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5 |
| Guiding The Last Layer in Federated Learning with Pre-Trained Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| H-InDex: Visual Reinforcement Learning with Hand-Informed Representations for Dexterous Manipulation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| H-nobs: Achieving Certified Fairness and Robustness in Distributed Learning on Heterogeneous Datasets |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| H2RBox-v2: Incorporating Symmetry for Boosting Horizontal Box Supervised Oriented Object Detection |
❌ |
✅ |
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✅ |
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6 |
| H3T: Efficient Integration of Memory Optimization and Parallelism for Large-scale Transformer Training |
✅ |
✅ |
✅ |
❌ |
✅ |
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6 |
| HAP: Structure-Aware Masked Image Modeling for Human-Centric Perception |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| HASSOD: Hierarchical Adaptive Self-Supervised Object Detection |
❌ |
✅ |
✅ |
✅ |
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❌ |
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5 |
| HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point Clouds |
❌ |
✅ |
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✅ |
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❌ |
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5 |
| HIQL: Offline Goal-Conditioned RL with Latent States as Actions |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| Hardness of Low Rank Approximation of Entrywise Transformed Matrix Products |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Hardware Resilience Properties of Text-Guided Image Classifiers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Harnessing Hard Mixed Samples with Decoupled Regularizer |
❌ |
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❌ |
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5 |
| Harnessing the power of choices in decision tree learning |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Have it your way: Individualized Privacy Assignment for DP-SGD |
✅ |
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✅ |
❌ |
✅ |
✅ |
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5 |
| HeadSculpt: Crafting 3D Head Avatars with Text |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
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4 |
| HiBug: On Human-Interpretable Model Debug |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| HiNeRV: Video Compression with Hierarchical Encoding-based Neural Representation |
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❌ |
✅ |
❌ |
✅ |
✅ |
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4 |
| Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks |
✅ |
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✅ |
❌ |
❌ |
❌ |
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3 |
| Hierarchical Adaptive Value Estimation for Multi-modal Visual Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| Hierarchical Gaussian Mixture based Task Generative Model for Robust Meta-Learning |
✅ |
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✅ |
✅ |
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❌ |
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5 |
| Hierarchical Integration Diffusion Model for Realistic Image Deblurring |
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4 |
| Hierarchical Multi-Agent Skill Discovery |
✅ |
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❌ |
❌ |
❌ |
✅ |
3 |
| Hierarchical Open-vocabulary Universal Image Segmentation |
❌ |
❌ |
✅ |
❌ |
✅ |
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3 |
| Hierarchical Randomized Smoothing |
✅ |
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✅ |
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❌ |
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6 |
| Hierarchical Semi-Implicit Variational Inference with Application to Diffusion Model Acceleration |
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❌ |
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5 |
| Hierarchical VAEs provide a normative account of motion processing in the primate brain |
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❌ |
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5 |
| Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection |
❌ |
✅ |
✅ |
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5 |
| Hierarchical clustering with dot products recovers hidden tree structure |
✅ |
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❌ |
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5 |
| Hierarchically Gated Recurrent Neural Network for Sequence Modeling |
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❌ |
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6 |
| High Precision Causal Model Evaluation with Conditional Randomization |
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❌ |
❌ |
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1 |
| High dimensional, tabular deep learning with an auxiliary knowledge graph |
✅ |
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❌ |
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6 |
| High-Fidelity Audio Compression with Improved RVQGAN |
❌ |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| High-dimensional Asymptotics of Denoising Autoencoders |
❌ |
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✅ |
❌ |
❌ |
❌ |
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3 |
| High-dimensional Contextual Bandit Problem without Sparsity |
✅ |
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❌ |
❌ |
❌ |
❌ |
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2 |
| Higher-Order Uncoupled Dynamics Do Not Lead to Nash Equilibrium - Except When They Do |
❌ |
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❌ |
❌ |
❌ |
❌ |
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1 |
| History Filtering in Imperfect Information Games: Algorithms and Complexity |
✅ |
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❌ |
❌ |
❌ |
❌ |
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2 |
| Holistic Transfer: Towards Non-Disruptive Fine-Tuning with Partial Target Data |
❌ |
❌ |
✅ |
❌ |
✅ |
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3 |
| Homotopy-based training of NeuralODEs for accurate dynamics discovery |
✅ |
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❌ |
❌ |
❌ |
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4 |
| Honesty Is the Best Policy: Defining and Mitigating AI Deception |
✅ |
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4 |
| Horospherical Decision Boundaries for Large Margin Classification in Hyperbolic Space |
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❌ |
✅ |
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✅ |
❌ |
✅ |
4 |
| HotBEV: Hardware-oriented Transformer-based Multi-View 3D Detector for BEV Perception |
❌ |
✅ |
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❌ |
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5 |
| How Does Adaptive Optimization Impact Local Neural Network Geometry? |
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❌ |
✅ |
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❌ |
❌ |
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2 |
| How Re-sampling Helps for Long-Tail Learning? |
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6 |
| How a Student becomes a Teacher: learning and forgetting through Spectral methods |
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4 |
| How do Minimum-Norm Shallow Denoisers Look in Function Space? |
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2 |
| How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model |
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3 |
| How many samples are needed to leverage smoothness? |
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2 |
| How to Fine-tune the Model: Unified Model Shift and Model Bias Policy Optimization |
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5 |
| How to Scale Your EMA |
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❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| How to Select Which Active Learning Strategy is Best Suited for Your Specific Problem and Budget |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| How to Turn Your Knowledge Graph Embeddings into Generative Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| How2comm: Communication-Efficient and Collaboration-Pragmatic Multi-Agent Perception |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| HubRouter: Learning Global Routing via Hub Generation and Pin-hub Connection |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Human spatiotemporal pattern learning as probabilistic program synthesis |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Human-Aligned Calibration for AI-Assisted Decision Making |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Human-Guided Complexity-Controlled Abstractions |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Human-in-the-Loop Optimization for Deep Stimulus Encoding in Visual Prostheses |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Human-like Few-Shot Learning via Bayesian Reasoning over Natural Language |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| HyP-NeRF: Learning Improved NeRF Priors using a HyperNetwork |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| HyTrel: Hypergraph-enhanced Tabular Data Representation Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Hybrid Policy Optimization from Imperfect Demonstrations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Hybrid Search for Efficient Planning with Completeness Guarantees |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Hyper-HMM: aligning human brains and semantic features in a common latent event space |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Hyperbolic Space with Hierarchical Margin Boosts Fine-Grained Learning from Coarse Labels |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Hyperbolic VAE via Latent Gaussian Distributions |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Hypervolume Maximization: A Geometric View of Pareto Set Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Hypothesis Selection with Memory Constraints |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| IBA: Towards Irreversible Backdoor Attacks in Federated Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| IDEA: An Invariant Perspective for Efficient Domain Adaptive Image Retrieval |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| IDRNet: Intervention-Driven Relation Network for Semantic Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| IEBins: Iterative Elastic Bins for Monocular Depth Estimation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AI |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| IPMix: Label-Preserving Data Augmentation Method for Training Robust Classifiers |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| ISP: Multi-Layered Garment Draping with Implicit Sewing Patterns |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Idempotent Learned Image Compression with Right-Inverse |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Identifiability Guarantees for Causal Disentanglement from Soft Interventions |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Identifiable Contrastive Learning with Automatic Feature Importance Discovery |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Identification of Nonlinear Latent Hierarchical Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Ignorance is Bliss: Robust Control via Information Gating |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Im-Promptu: In-Context Composition from Image Prompts |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Image Captioners Are Scalable Vision Learners Too |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| ImageBrush: Learning Visual In-Context Instructions for Exemplar-Based Image Manipulation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Imagine That! Abstract-to-Intricate Text-to-Image Synthesis with Scene Graph Hallucination Diffusion |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Imbalanced Mixed Linear Regression |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Imitation Learning from Imperfection: Theoretical Justifications and Algorithms |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Imitation Learning from Vague Feedback |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Implicit Bias of (Stochastic) Gradient Descent for Rank-1 Linear Neural Network |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Implicit Bias of Gradient Descent for Two-layer ReLU and Leaky ReLU Networks on Nearly-orthogonal Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Implicit Contrastive Representation Learning with Guided Stop-gradient |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Implicit Convolutional Kernels for Steerable CNNs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Implicit Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Implicit Manifold Gaussian Process Regression |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Implicit Regularization in Over-Parameterized Support Vector Machine |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Implicit Transfer Operator Learning: Multiple Time-Resolution Models for Molecular Dynamics |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Implicit Variational Inference for High-Dimensional Posteriors |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Implicit variance regularization in non-contrastive SSL |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Importance-aware Co-teaching for Offline Model-based Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Improved Best-of-Both-Worlds Guarantees for Multi-Armed Bandits: FTRL with General Regularizers and Multiple Optimal Arms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Improved Convergence in High Probability of Clipped Gradient Methods with Heavy Tailed Noise |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved Frequency Estimation Algorithms with and without Predictions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improvements on Uncertainty Quantification for Node Classification via Distance Based Regularization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Improving *day-ahead* Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Improving Adversarial Robustness via Information Bottleneck Distillation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Improving Adversarial Transferability via Intermediate-level Perturbation Decay |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Improving CLIP Training with Language Rewrites |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Improving Compositional Generalization using Iterated Learning and Simplicial Embeddings |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Improving Diffusion-Based Image Synthesis with Context Prediction |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Improving Graph Matching with Positional Reconstruction Encoder-Decoder Network |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Improving Language Plasticity via Pretraining with Active Forgetting |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Improving Robustness with Adaptive Weight Decay |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Improving Self-supervised Molecular Representation Learning using Persistent Homology |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Improving neural network representations using human similarity judgments |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Improving the Knowledge Gradient Algorithm |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| In Defense of Softmax Parametrization for Calibrated and Consistent Learning to Defer |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| In-Context Impersonation Reveals Large Language Models' Strengths and Biases |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| In-Context Learning Unlocked for Diffusion Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Incentives in Private Collaborative Machine Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Incentivized Communication for Federated Bandits |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Incentivizing Honesty among Competitors in Collaborative Learning and Optimization |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Incomplete Multimodality-Diffused Emotion Recognition |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Inconsistency, Instability, and Generalization Gap of Deep Neural Network Training |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Individual Arbitrariness and Group Fairness |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Individualized Dosing Dynamics via Neural Eigen Decomposition |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Inference-Time Intervention: Eliciting Truthful Answers from a Language Model |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Inferring Hybrid Neural Fluid Fields from Videos |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Inferring the Future by Imagining the Past |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| InfoCD: A Contrastive Chamfer Distance Loss for Point Cloud Completion |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| InfoPrompt: Information-Theoretic Soft Prompt Tuning for Natural Language Understanding |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Information Design in Multi-Agent Reinforcement Learning |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Information Geometry of the Retinal Representation Manifold |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Information Maximization Perspective of Orthogonal Matching Pursuit with Applications to Explainable AI |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Information Maximizing Curriculum: A Curriculum-Based Approach for Learning Versatile Skills |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Information Theoretic Lower Bounds for Information Theoretic Upper Bounds |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Information-guided Planning: An Online Approach for Partially Observable Problems |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Initialization-Dependent Sample Complexity of Linear Predictors and Neural Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Injecting Multimodal Information into Rigid Protein Docking via Bi-level Optimization |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Inner Product-based Neural Network Similarity |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Inner-Outer Aware Reconstruction Model for Monocular 3D Scene Reconstruction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| InsActor: Instruction-driven Physics-based Characters |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Inserting Anybody in Diffusion Models via Celeb Basis |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| InstanT: Semi-supervised Learning with Instance-dependent Thresholds |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Instructing Goal-Conditioned Reinforcement Learning Agents with Temporal Logic Objectives |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Interaction Measures, Partition Lattices and Kernel Tests for High-Order Interactions |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Interactive Multi-fidelity Learning for Cost-effective Adaptation of Language Model with Sparse Human Supervision |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Interpretability at Scale: Identifying Causal Mechanisms in Alpaca |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
5 |
| Interpretable Graph Networks Formulate Universal Algebra Conjectures |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Interpretable Prototype-based Graph Information Bottleneck |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Interpreting Unsupervised Anomaly Detection in Security via Rule Extraction |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Intervention Generalization: A View from Factor Graph Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Intra-Modal Proxy Learning for Zero-Shot Visual Categorization with CLIP |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Intriguing Properties of Quantization at Scale |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Intrinsic Dimension Estimation for Robust Detection of AI-Generated Texts |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Invariant Anomaly Detection under Distribution Shifts: A Causal Perspective |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Invariant Learning via Probability of Sufficient and Necessary Causes |
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5 |
| Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation |
❌ |
✅ |
✅ |
✅ |
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❌ |
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5 |
| Inverse Preference Learning: Preference-based RL without a Reward Function |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Inverse Reinforcement Learning with the Average Reward Criterion |
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❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Investigating how ReLU-networks encode symmetries |
❌ |
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✅ |
❌ |
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❌ |
❌ |
3 |
| Is Distance Matrix Enough for Geometric Deep Learning? |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning |
✅ |
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❌ |
✅ |
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4 |
| Is Learning in Games Good for the Learners? |
✅ |
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❌ |
❌ |
1 |
| Is RLHF More Difficult than Standard RL? A Theoretical Perspective |
✅ |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Is This Loss Informative? Faster Text-to-Image Customization by Tracking Objective Dynamics |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Isometric Quotient Variational Auto-Encoders for Structure-Preserving Representation Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Iterative Reachability Estimation for Safe Reinforcement Learning |
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❌ |
✅ |
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❌ |
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5 |
| Iteratively Learn Diverse Strategies with State Distance Information |
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❌ |
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❌ |
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4 |
| Jaccard Metric Losses: Optimizing the Jaccard Index with Soft Labels |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Jailbroken: How Does LLM Safety Training Fail? |
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❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Jigsaw: Learning to Assemble Multiple Fractured Objects |
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❌ |
✅ |
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4 |
| Joint Attribute and Model Generalization Learning for Privacy-Preserving Action Recognition |
✅ |
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✅ |
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❌ |
❌ |
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4 |
| Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Joint Data-Task Generation for Auxiliary Learning |
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❌ |
❌ |
❌ |
✅ |
3 |
| Joint Feature and Differentiable $ k $-NN Graph Learning using Dirichlet Energy |
✅ |
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✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Joint Prompt Optimization of Stacked LLMs using Variational Inference |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Joint Training of Deep Ensembles Fails Due to Learner Collusion |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Joint processing of linguistic properties in brains and language models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| K-Nearest-Neighbor Local Sampling Based Conditional Independence Testing |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| KAKURENBO: Adaptively Hiding Samples in Deep Neural Network Training |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| KD-Zero: Evolving Knowledge Distiller for Any Teacher-Student Pairs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Keep Various Trajectories: Promoting Exploration of Ensemble Policies in Continuous Control |
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❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Kernel Quadrature with Randomly Pivoted Cholesky |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Kernel-Based Tests for Likelihood-Free Hypothesis Testing |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Kernelized Cumulants: Beyond Kernel Mean Embeddings |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Kernelized Reinforcement Learning with Order Optimal Regret Bounds |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Keypoint-Augmented Self-Supervised Learning for Medical Image Segmentation with Limited Annotation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Kiki or Bouba? Sound Symbolism in Vision-and-Language Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Kissing to Find a Match: Efficient Low-Rank Permutation Representation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Knowledge Diffusion for Distillation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Knowledge Distillation Performs Partial Variance Reduction |
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❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Knowledge Distillation for High Dimensional Search Index |
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❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Tasks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Koopman Kernel Regression |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded Rewards |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inference |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| L-CAD: Language-based Colorization with Any-level Descriptions using Diffusion Priors |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| L2T-DLN: Learning to Teach with Dynamic Loss Network |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| LANCE: Stress-testing Visual Models by Generating Language-guided Counterfactual Images |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| LART: Neural Correspondence Learning with Latent Regularization Transformer for 3D Motion Transfer |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| LD2: Scalable Heterophilous Graph Neural Network with Decoupled Embeddings |
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✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
5 |
| LEACE: Perfect linear concept erasure in closed form |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| LEPARD: Learning Explicit Part Discovery for 3D Articulated Shape Reconstruction |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| LICO: Explainable Models with Language-Image COnsistency |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| LIMA: Less Is More for Alignment |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| LLM-Pruner: On the Structural Pruning of Large Language Models |
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✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| LLMScore: Unveiling the Power of Large Language Models in Text-to-Image Synthesis Evaluation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
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3 |
| LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object Recognition |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| LaFTer: Label-Free Tuning of Zero-shot Classifier using Language and Unlabeled Image Collections |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Label Correction of Crowdsourced Noisy Annotations with an Instance-Dependent Noise Transition Model |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Label Poisoning is All You Need |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Label Robust and Differentially Private Linear Regression: Computational and Statistical Efficiency |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Label-Only Model Inversion Attacks via Knowledge Transfer |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Label-efficient Segmentation via Affinity Propagation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Labeling Neural Representations with Inverse Recognition |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| LambdaBeam: Neural Program Search with Higher-Order Functions and Lambdas |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Langevin Quasi-Monte Carlo |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Language Is Not All You Need: Aligning Perception with Language Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Language Model Alignment with Elastic Reset |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Language Model Tokenizers Introduce Unfairness Between Languages |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning |
❌ |
✅ |
✅ |
✅ |
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❌ |
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5 |
| Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Language Models Meet World Models: Embodied Experiences Enhance Language Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Language Models are Weak Learners |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Language Models can Solve Computer Tasks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Language Quantized AutoEncoders: Towards Unsupervised Text-Image Alignment |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Language Semantic Graph Guided Data-Efficient Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Language-based Action Concept Spaces Improve Video Self-Supervised Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Language-driven Scene Synthesis using Multi-conditional Diffusion Model |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Large Language Models Are Semi-Parametric Reinforcement Learning Agents |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Large Language Models Are Zero-Shot Time Series Forecasters |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Large Language Models are Visual Reasoning Coordinators |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Large Language Models as Commonsense Knowledge for Large-Scale Task Planning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Large Language Models can Implement Policy Iteration |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Large Language Models of Code Fail at Completing Code with Potential Bugs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Large language models implicitly learn to straighten neural sentence trajectories to construct a predictive representation of natural language. |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Large language models transition from integrating across position-yoked, exponential windows to structure-yoked, power-law windows |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Large-Scale Distributed Learning via Private On-Device LSH |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Latent Diffusion for Language Generation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Latent Field Discovery in Interacting Dynamical Systems with Neural Fields |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Latent Graph Inference with Limited Supervision |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Latent SDEs on Homogeneous Spaces |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Latent Space Translation via Semantic Alignment |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Latent exploration for Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Layer-Neighbor Sampling --- Defusing Neighborhood Explosion in GNNs |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| LayoutGPT: Compositional Visual Planning and Generation with Large Language Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| LayoutPrompter: Awaken the Design Ability of Large Language Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learn to Categorize or Categorize to Learn? Self-Coding for Generalized Category Discovery |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Adaptive Tensorial Density Fields for Clean Cryo-ET Reconstruction |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Better with Less: Effective Augmentation for Sample-Efficient Visual Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Causal Models under Independent Changes |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Curves for Deep Structured Gaussian Feature Models |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Learning Curves for Noisy Heterogeneous Feature-Subsampled Ridge Ensembles |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
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4 |
| Learning Cuts via Enumeration Oracles |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Learning DAGs from Data with Few Root Causes |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Dense Flow Field for Highly-accurate Cross-view Camera Localization |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Learning Descriptive Image Captioning via Semipermeable Maximum Likelihood Estimation |
✅ |
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✅ |
✅ |
✅ |
❌ |
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6 |
| Learning Dictionary for Visual Attention |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Learning Domain-Aware Detection Head with Prompt Tuning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Efficient Coding of Natural Images with Maximum Manifold Capacity Representations |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Efficient Surrogate Dynamic Models with Graph Spline Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Energy-Based Prior Model with Diffusion-Amortized MCMC |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Learning Energy-based Model via Dual-MCMC Teaching |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Environment-Aware Affordance for 3D Articulated Object Manipulation under Occlusions |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Learning Exponential Families from Truncated Samples |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Fine-grained View-Invariant Representations from Unpaired Ego-Exo Videos via Temporal Alignment |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning From Biased Soft Labels |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Functional Transduction |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Learning Generalizable Agents via Saliency-guided Features Decorrelation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Interpretable Low-dimensional Representation via Physical Symmetry |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Invariant Molecular Representation in Latent Discrete Space |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Invariant Representations of Graph Neural Networks via Cluster Generalization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Invariant Representations with a Nonparametric Nadaraya-Watson Head |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Large Graph Property Prediction via Graph Segment Training |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning Large-Scale MTP$_2$ Gaussian Graphical Models via Bridge-Block Decomposition |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Large-scale Neural Fields via Context Pruned Meta-Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Layer-wise Equivariances Automatically using Gradients |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Linear Causal Representations from Interventions under General Nonlinear Mixing |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning List-Level Domain-Invariant Representations for Ranking |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning Mask-aware CLIP Representations for Zero-Shot Segmentation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Mixtures of Gaussians Using the DDPM Objective |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Modulated Transformation in GANs |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Motion Refinement for Unsupervised Face Animation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Multi-agent Behaviors from Distributed and Streaming Demonstrations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Neural Implicit through Volume Rendering with Attentive Depth Fusion Priors |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Nonparametric Latent Causal Graphs with Unknown Interventions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Learning Provably Robust Estimators for Inverse Problems via Jittering |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Rate Free Sampling in Constrained Domains |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning Re-sampling Methods with Parameter Attribution for Image Super-resolution |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Regularized Monotone Graphon Mean-Field Games |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Learning Reliable Logical Rules with SATNet |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Learning Repeatable Speech Embeddings Using An Intra-class Correlation Regularizer |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Robust Statistics for Simulation-based Inference under Model Misspecification |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Learning Rule-Induced Subgraph Representations for Inductive Relation Prediction |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Sample Difficulty from Pre-trained Models for Reliable Prediction |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Score-based Grasping Primitive for Human-assisting Dexterous Grasping |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Shared Safety Constraints from Multi-task Demonstrations |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning Space-Time Continuous Latent Neural PDEs from Partially Observed States |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Time-Invariant Representations for Individual Neurons from Population Dynamics |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning To Dive In Branch And Bound |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Learning Topology-Agnostic EEG Representations with Geometry-Aware Modeling |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Trajectories are Generalization Indicators |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Transformer Programs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Universal Policies via Text-Guided Video Generation |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Learning Unseen Modality Interaction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Visual Prior via Generative Pre-Training |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning World Models with Identifiable Factorization |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Learning a 1-layer conditional generative model in total variation |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning a Neuron by a Shallow ReLU Network: Dynamics and Implicit Bias for Correlated Inputs |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning and Collusion in Multi-unit Auctions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning and processing the ordinal information of temporal sequences in recurrent neural circuits |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Learning better with Dale’s Law: A Spectral Perspective |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Learning from Active Human Involvement through Proxy Value Propagation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning from Both Structural and Textual Knowledge for Inductive Knowledge Graph Completion |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Learning from Rich Semantics and Coarse Locations for Long-tailed Object Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning from Visual Observation via Offline Pretrained State-to-Go Transformer |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning in the Presence of Low-dimensional Structure: A Spiked Random Matrix Perspective |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning non-Markovian Decision-Making from State-only Sequences |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| Learning the Efficient Frontier |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning threshold neurons via edge of stability |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning to Augment Distributions for Out-of-distribution Detection |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning to Compress Prompts with Gist Tokens |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Learning to Configure Separators in Branch-and-Cut |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning to Discover Skills through Guidance |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning to Group Auxiliary Datasets for Molecule |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Learning to Influence Human Behavior with Offline Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Learning to Modulate pre-trained Models in RL |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning to Parameterize Visual Attributes for Open-set Fine-grained Retrieval |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning to Reason and Memorize with Self-Notes |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Learning to Receive Help: Intervention-Aware Concept Embedding Models |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning to Tokenize for Generative Retrieval |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Learning via Wasserstein-Based High Probability Generalisation Bounds |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning with Explanation Constraints |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Leave No Stone Unturned: Mine Extra Knowledge for Imbalanced Facial Expression Recognition |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Lending Interaction Wings to Recommender Systems with Conversational Agents |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Leveraging Early-Stage Robustness in Diffusion Models for Efficient and High-Quality Image Synthesis |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Leveraging Locality and Robustness to Achieve Massively Scalable Gaussian Process Regression |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Leveraging Vision-Centric Multi-Modal Expertise for 3D Object Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Leveraging sparse and shared feature activations for disentangled representation learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Leveraging the two-timescale regime to demonstrate convergence of neural networks |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Lexinvariant Language Models |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Lie Point Symmetry and Physics-Informed Networks |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Lift Yourself Up: Retrieval-augmented Text Generation with Self-Memory |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| LightSpeed: Light and Fast Neural Light Fields on Mobile Devices |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Lightweight Vision Transformer with Bidirectional Interaction |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Likelihood Ratio Confidence Sets for Sequential Decision Making |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Likelihood-Based Diffusion Language Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Limits, approximation and size transferability for GNNs on sparse graphs via graphops |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Linear Time Algorithms for k-means with Multi-Swap Local Search |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Linguistic Binding in Diffusion Models: Enhancing Attribute Correspondence through Attention Map Alignment |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| LinkerNet: Fragment Poses and Linker Co-Design with 3D Equivariant Diffusion |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| List and Certificate Complexities in Replicable Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| LoCoOp: Few-Shot Out-of-Distribution Detection via Prompt Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Local Convergence of Gradient Methods for Min-Max Games: Partial Curvature Generically Suffices |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Locality Sensitive Hashing in Fourier Frequency Domain For Soft Set Containment Search |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Locality-Aware Generalizable Implicit Neural Representation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Localized Symbolic Knowledge Distillation for Visual Commonsense Models |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| LogSpecT: Feasible Graph Learning Model from Stationary Signals with Recovery Guarantees |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Logarithmic-Regret Quantum Learning Algorithms for Zero-Sum Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Long Sequence Hopfield Memory |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Long-Term Fairness with Unknown Dynamics |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Look Ma, No Hands! Agent-Environment Factorization of Egocentric Videos |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Lookaround Optimizer: $k$ steps around, 1 step average |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Lookup Table meets Local Laplacian Filter: Pyramid Reconstruction Network for Tone Mapping |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Loss Decoupling for Task-Agnostic Continual Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Loss Dynamics of Temporal Difference Reinforcement Learning |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Lossy Image Compression with Conditional Diffusion Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Lovász Principle for Unsupervised Graph Representation Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Low Tensor Rank Learning of Neural Dynamics |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
5 |
| Lower Bounds on Adaptive Sensing for Matrix Recovery |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| LuminAIRe: Illumination-Aware Conditional Image Repainting for Lighting-Realistic Generation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| MADG: Margin-based Adversarial Learning for Domain Generalization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| MAG-GNN: Reinforcement Learning Boosted Graph Neural Network |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| MAViL: Masked Audio-Video Learners |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| MCUFormer: Deploying Vision Tranformers on Microcontrollers with Limited Memory |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detection |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MG-ViT: A Multi-Granularity Method for Compact and Efficient Vision Transformers |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| MGDD: A Meta Generator for Fast Dataset Distillation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| MIM4DD: Mutual Information Maximization for Dataset Distillation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| MIMEx: Intrinsic Rewards from Masked Input Modeling |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| MIMONets: Multiple-Input-Multiple-Output Neural Networks Exploiting Computation in Superposition |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| MKOR: Momentum-Enabled Kronecker-Factor-Based Optimizer Using Rank-1 Updates |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| MMD-Fuse: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under nonparametrized geometrical variability |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| MVDiffusion: Enabling Holistic Multi-view Image Generation with Correspondence-Aware Diffusion |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Machine learning detects terminal singularities |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Macro Placement by Wire-Mask-Guided Black-Box Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Make Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Make the U in UDA Matter: Invariant Consistency Learning for Unsupervised Domain Adaptation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Making Scalable Meta Learning Practical |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Many-body Approximation for Non-negative Tensors |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Marginal Density Ratio for Off-Policy Evaluation in Contextual Bandits |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Marich: A Query-efficient Distributionally Equivalent Model Extraction Attack |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| MarioGPT: Open-Ended Text2Level Generation through Large Language Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Markovian Sliced Wasserstein Distances: Beyond Independent Projections |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Mask Propagation for Efficient Video Semantic Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Masked Image Residual Learning for Scaling Deeper Vision Transformers |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Masked Space-Time Hash Encoding for Efficient Dynamic Scene Reconstruction |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Mass-Producing Failures of Multimodal Systems with Language Models |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| MathNAS: If Blocks Have a Role in Mathematical Architecture Design |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
5 |
| Matrix Compression via Randomized Low Rank and Low Precision Factorization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Max-Margin Token Selection in Attention Mechanism |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Max-Sliced Mutual Information |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Maximization of Average Precision for Deep Learning with Adversarial Ranking Robustness |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Maximum Average Randomly Sampled: A Scale Free and Non-parametric Algorithm for Stochastic Bandits |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Maximum Independent Set: Self-Training through Dynamic Programming |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Maximum State Entropy Exploration using Predecessor and Successor Representations |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| May the Force be with You: Unified Force-Centric Pre-Training for 3D Molecular Conformations |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| MeCo: Zero-Shot NAS with One Data and Single Forward Pass via Minimum Eigenvalue of Correlation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| MeGraph: Capturing Long-Range Interactions by Alternating Local and Hierarchical Aggregation on Multi-Scaled Graph Hierarchy |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Mechanic: A Learning Rate Tuner |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Mechanism Design for Collaborative Normal Mean Estimation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Med-UniC: Unifying Cross-Lingual Medical Vision-Language Pre-Training by Diminishing Bias |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Meek Separators and Their Applications in Targeted Causal Discovery |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Meet in the Middle: A New Pre-training Paradigm |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Memory Efficient Optimizers with 4-bit States |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Memory-Constrained Algorithms for Convex Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Meta-AdaM: An Meta-Learned Adaptive Optimizer with Momentum for Few-Shot Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Meta-Adapter: An Online Few-shot Learner for Vision-Language Model |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Meta-Learning Adversarial Bandit Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Meta-Learning with Neural Bandit Scheduler |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Meta-in-context learning in large language models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Meta-learning families of plasticity rules in recurrent spiking networks using simulation-based inference |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Metis: Understanding and Enhancing In-Network Regular Expressions |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Metropolis Sampling for Constrained Diffusion Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Michelangelo: Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Minimax Forward and Backward Learning of Evolving Tasks with Performance Guarantees |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Minimax Optimal Rate for Parameter Estimation in Multivariate Deviated Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Minimax Risks and Optimal Procedures for Estimation under Functional Local Differential Privacy |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Minimax-Optimal Location Estimation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Minimum Description Length and Generalization Guarantees for Representation Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Minimum norm interpolation by perceptra: Explicit regularization and implicit bias |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Minimum-Risk Recalibration of Classifiers |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Mip-Grid: Anti-aliased Grid Representations for Neural Radiance Fields |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Mirror Diffusion Models for Constrained and Watermarked Generation |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Mitigating Over-smoothing in Transformers via Regularized Nonlocal Functionals |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Mitigating Source Bias for Fairer Weak Supervision |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Mitigating Test-Time Bias for Fair Image Retrieval |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Mitigating the Effect of Incidental Correlations on Part-based Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Mitigating the Popularity Bias of Graph Collaborative Filtering: A Dimensional Collapse Perspective |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| MixFormerV2: Efficient Fully Transformer Tracking |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
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5 |
| Mixed Samples as Probes for Unsupervised Model Selection in Domain Adaptation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Mixed-Initiative Multiagent Apprenticeship Learning for Human Training of Robot Teams |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Mixture Weight Estimation and Model Prediction in Multi-source Multi-target Domain Adaptation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Mnemosyne: Learning to Train Transformers with Transformers |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment Tasks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| MoVie: Visual Model-Based Policy Adaptation for View Generalization |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Modality-Agnostic Self-Supervised Learning with Meta-Learned Masked Auto-Encoder |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Modality-Independent Teachers Meet Weakly-Supervised Audio-Visual Event Parser |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Mode Connectivity in Auction Design |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Model Shapley: Equitable Model Valuation with Black-box Access |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Model Sparsity Can Simplify Machine Unlearning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Model Spider: Learning to Rank Pre-Trained Models Efficiently |
✅ |
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✅ |
✅ |
✅ |
❌ |
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6 |
| Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Model-Based Control with Sparse Neural Dynamics |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Model-Free Active Exploration in Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Model-Free Reinforcement Learning with the Decision-Estimation Coefficient |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Model-enhanced Vector Index |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Model-free Posterior Sampling via Learning Rate Randomization |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Modeling Dynamics over Meshes with Gauge Equivariant Nonlinear Message Passing |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Modeling Human Visual Motion Processing with Trainable Motion Energy Sensing and a Self-attention Network |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Modulated Neural ODEs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Module-wise Adaptive Distillation for Multimodality Foundation Models |
✅ |
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✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Module-wise Training of Neural Networks via the Minimizing Movement Scheme |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Molecule Joint Auto-Encoding: Trajectory Pretraining with 2D and 3D Diffusion |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Moment Matching Denoising Gibbs Sampling |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| MomentDiff: Generative Video Moment Retrieval from Random to Real |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Momentum Provably Improves Error Feedback! |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth Clues |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Monte Carlo Tree Search with Boltzmann Exploration |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Moral Responsibility for AI Systems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| MosaicBERT: A Bidirectional Encoder Optimized for Fast Pretraining |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Most Neural Networks Are Almost Learnable |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| MotionGPT: Human Motion as a Foreign Language |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Multi Time Scale World Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-Agent First Order Constrained Optimization in Policy Space |
✅ |
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✅ |
❌ |
✅ |
❌ |
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4 |
| Multi-Agent Learning with Heterogeneous Linear Contextual Bandits |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Multi-Agent Meta-Reinforcement Learning: Sharper Convergence Rates with Task Similarity |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multi-Fidelity Multi-Armed Bandits Revisited |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-Head Adapter Routing for Cross-Task Generalization |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Multi-Modal Inverse Constrained Reinforcement Learning from a Mixture of Demonstrations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Multi-Object Representation Learning via Feature Connectivity and Object-Centric Regularization |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Multi-Objective Intrinsic Reward Learning for Conversational Recommender Systems |
✅ |
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✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multi-Player Zero-Sum Markov Games with Networked Separable Interactions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Multi-Prompt Alignment for Multi-Source Unsupervised Domain Adaptation |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Multi-Step Generalized Policy Improvement by Leveraging Approximate Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Multi-Swap k-Means++ |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Multi-body SE(3) Equivariance for Unsupervised Rigid Segmentation and Motion Estimation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Multi-modal Queried Object Detection in the Wild |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multi-resolution Spectral Coherence for Graph Generation with Score-based Diffusion |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Multi-scale Diffusion Denoised Smoothing |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Multi-task Graph Neural Architecture Search with Task-aware Collaboration and Curriculum |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Multi-task Representation Learning for Pure Exploration in Bilinear Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Multi-task learning with summary statistics |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| MultiFusion: Fusing Pre-Trained Models for Multi-Lingual, Multi-Modal Image Generation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| MultiMoDN—Multimodal, Multi-Task, Interpretable Modular Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multiclass Boosting: Simple and Intuitive Weak Learning Criteria |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
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1 |
| Multimodal Deep Learning Model Unveils Behavioral Dynamics of V1 Activity in Freely Moving Mice |
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| Multinomial Logistic Regression: Asymptotic Normality on Null Covariates in High-Dimensions |
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| Multiplication-Free Transformer Training via Piecewise Affine Operations |
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| Multiply Robust Federated Estimation of Targeted Average Treatment Effects |
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| Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning |
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| Mutual Information Regularized Offline Reinforcement Learning |
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5 |
| Mutual-Information Regularized Multi-Agent Policy Iteration |
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| NAP: Neural 3D Articulated Object Prior |
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| NAR-Former V2: Rethinking Transformer for Universal Neural Network Representation Learning |
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5 |
| NAS-X: Neural Adaptive Smoothing via Twisting |
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| NCDL: A Framework for Deep Learning on non-Cartesian Lattices |
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| NEO-KD: Knowledge-Distillation-Based Adversarial Training for Robust Multi-Exit Neural Networks |
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| NICE: NoIse-modulated Consistency rEgularization for Data-Efficient GANs |
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| NPCL: Neural Processes for Uncertainty-Aware Continual Learning |
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| NU-MCC: Multiview Compressive Coding with Neighborhood Decoder and Repulsive UDF |
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5 |
| NVFi: Neural Velocity Fields for 3D Physics Learning from Dynamic Videos |
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5 |
| Nash Regret Guarantees for Linear Bandits |
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2 |
| Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation |
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4 |
| Natural Language Instruction-following with Task-related Language Development and Translation |
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3 |
| Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Federated Object Detection |
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4 |
| Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance Assessment |
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| NeRF Revisited: Fixing Quadrature Instability in Volume Rendering |
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| NeRF-IBVS: Visual Servo Based on NeRF for Visual Localization and Navigation |
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| Near Optimal Reconstruction of Spherical Harmonic Expansions |
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2 |
| Near-Linear Time Algorithm for the Chamfer Distance |
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4 |
| Near-Optimal $k$-Clustering in the Sliding Window Model |
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4 |
| Near-Optimal Algorithms for Gaussians with Huber Contamination: Mean Estimation and Linear Regression |
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| Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise |
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| Near-optimal learning with average Hölder smoothness |
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0 |
| Nearest Neighbour with Bandit Feedback |
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| Nearly Optimal Bounds for Cyclic Forgetting |
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| Nearly Optimal VC-Dimension and Pseudo-Dimension Bounds for Deep Neural Network Derivatives |
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| Nearly Tight Bounds For Differentially Private Multiway Cut |
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3 |
| Necessary and Sufficient Conditions for Optimal Decision Trees using Dynamic Programming |
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6 |
| NetHack is Hard to Hack |
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4 |
| Networks are Slacking Off: Understanding Generalization Problem in Image Deraining |
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| Neural (Tangent Kernel) Collapse |
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| Neural Algorithmic Reasoning Without Intermediate Supervision |
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| Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb |
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| Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization |
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| Neural Data Transformer 2: Multi-context Pretraining for Neural Spiking Activity |
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| Neural Fields with Hard Constraints of Arbitrary Differential Order |
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| Neural Foundations of Mental Simulation: Future Prediction of Latent Representations on Dynamic Scenes |
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| Neural Frailty Machine: Beyond proportional hazard assumption in neural survival regressions |
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| Neural Functional Transformers |
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| Neural Graph Generation from Graph Statistics |
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| Neural Harmonics: Bridging Spectral Embedding and Matrix Completion in Self-Supervised Learning |
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| Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential Equations |
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| Neural Image Compression: Generalization, Robustness, and Spectral Biases |
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| Neural Injective Functions for Multisets, Measures and Graphs via a Finite Witness Theorem |
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| Neural Lad: A Neural Latent Dynamics Framework for Times Series Modeling |
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| Neural Latent Geometry Search: Product Manifold Inference via Gromov-Hausdorff-Informed Bayesian Optimization |
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| Neural Lighting Simulation for Urban Scenes |
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| Neural Lyapunov Control for Discrete-Time Systems |
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6 |
| Neural Modulation for Flash Memory: An Unsupervised Learning Framework for Improved Reliability |
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| Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement |
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5 |
| Neural Oscillators are Universal |
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| Neural Polarizer: A Lightweight and Effective Backdoor Defense via Purifying Poisoned Features |
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| Neural Priming for Sample-Efficient Adaptation |
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| Neural Processes with Stability |
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| Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data |
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| Neural Sampling in Hierarchical Exponential-family Energy-based Models |
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| Neural Sculpting: Uncovering hierarchically modular task structure in neural networks through pruning and network analysis |
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| Neural approximation of Wasserstein distance via a universal architecture for symmetric and factorwise group invariant functions |
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| Neural-Logic Human-Object Interaction Detection |
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| NeuralGF: Unsupervised Point Normal Estimation by Learning Neural Gradient Function |
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| Neuro-symbolic Learning Yielding Logical Constraints |
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| NeuroGF: A Neural Representation for Fast Geodesic Distance and Path Queries |
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| New Bounds for Hyperparameter Tuning of Regression Problems Across Instances |
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| New Complexity-Theoretic Frontiers of Tractability for Neural Network Training |
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| Newton–Cotes Graph Neural Networks: On the Time Evolution of Dynamic Systems |
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| No Change, No Gain: Empowering Graph Neural Networks with Expected Model Change Maximization for Active Learning |
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| No Representation Rules Them All in Category Discovery |
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| No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models |
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| No-Regret Learning in Dynamic Competition with Reference Effects Under Logit Demand |
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| No-Regret Learning with Unbounded Losses: The Case of Logarithmic Pooling |
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| No-Regret Online Prediction with Strategic Experts |
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| No-Regret Online Reinforcement Learning with Adversarial Losses and Transitions |
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| No-regret Algorithms for Fair Resource Allocation |
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4 |
| Noether Embedding: Efficient Learning of Temporal Regularities |
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4 |
| Noise-Adaptive Thompson Sampling for Linear Contextual Bandits |
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2 |
| Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstruction |
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5 |
| Non-Asymptotic Analysis of a UCB-based Top Two Algorithm |
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| Non-Convex Bilevel Optimization with Time-Varying Objective Functions |
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3 |
| Non-Rigid Shape Registration via Deep Functional Maps Prior |
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| Non-Smooth Weakly-Convex Finite-sum Coupled Compositional Optimization |
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4 |
| Non-Stationary Bandits with Auto-Regressive Temporal Dependency |
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3 |
| Non-adversarial training of Neural SDEs with signature kernel scores |
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4 |
| Non-autoregressive Machine Translation with Probabilistic Context-free Grammar |
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6 |
| Non-stationary Experimental Design under Linear Trends |
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1 |
| Nonparametric Boundary Geometry in Physics Informed Deep Learning |
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3 |
| Nonparametric Identifiability of Causal Representations from Unknown Interventions |
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3 |
| Nonparametric Teaching for Multiple Learners |
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4 |
| Norm-based Generalization Bounds for Sparse Neural Networks |
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3 |
| Norm-guided latent space exploration for text-to-image generation |
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| Normalization Layers Are All That Sharpness-Aware Minimization Needs |
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4 |
| Normalization-Equivariant Neural Networks with Application to Image Denoising |
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5 |
| Normalizing flow neural networks by JKO scheme |
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5 |
| Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts |
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6 |
| Not All Out-of-Distribution Data Are Harmful to Open-Set Active Learning |
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5 |
| NuTrea: Neural Tree Search for Context-guided Multi-hop KGQA |
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| OBJECT 3DIT: Language-guided 3D-aware Image Editing |
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4 |
| ODE-based Recurrent Model-free Reinforcement Learning for POMDPs |
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4 |
| OKRidge: Scalable Optimal k-Sparse Ridge Regression |
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6 |
| Object-Centric Learning for Real-World Videos by Predicting Temporal Feature Similarities |
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6 |
| Object-Centric Slot Diffusion |
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5 |
| Object-centric Learning with Cyclic Walks between Parts and Whole |
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5 |
| Off-Policy Evaluation for Human Feedback |
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4 |
| Offline Imitation Learning with Variational Counterfactual Reasoning |
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| Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage |
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| Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization |
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5 |
| Offline RL with Discrete Proxy Representations for Generalizability in POMDPs |
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| Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management |
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3 |
| Offline Reinforcement Learning with Differential Privacy |
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4 |
| On Calibrating Diffusion Probabilistic Models |
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3 |
| On Certified Generalization in Structured Prediction |
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| On Class Distributions Induced by Nearest Neighbor Graphs for Node Classification of Tabular Data |
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5 |
| On Computing Pairwise Statistics with Local Differential Privacy |
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| On Convergence of Polynomial Approximations to the Gaussian Mixture Entropy |
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1 |
| On Differentially Private Sampling from Gaussian and Product Distributions |
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1 |
| On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes |
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| On Evaluating Adversarial Robustness of Large Vision-Language Models |
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5 |
| On Generalization Bounds for Projective Clustering |
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3 |
| On Imitation in Mean-field Games |
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1 |
| On Learning Latent Models with Multi-Instance Weak Supervision |
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3 |
| On Learning Necessary and Sufficient Causal Graphs |
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2 |
| On Masked Pre-training and the Marginal Likelihood |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| On Measuring Fairness in Generative Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| On Private and Robust Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On Proper Learnability between Average- and Worst-case Robustness |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Robust Streaming for Learning with Experts: Algorithms and Lower Bounds |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| On Sample-Efficient Offline Reinforcement Learning: Data Diversity, Posterior Sampling and Beyond |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On Separate Normalization in Self-supervised Transformers |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Single-Index Models beyond Gaussian Data |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On Slicing Optimality for Mutual Information |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Sparse Modern Hopfield Model |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| On Transfer of Adversarial Robustness from Pretraining to Downstream Tasks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On kernel-based statistical learning theory in the mean field limit |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On permutation symmetries in Bayesian neural network posteriors: a variational perspective |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On quantum backpropagation, information reuse, and cheating measurement collapse |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On skip connections and normalisation layers in deep optimisation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On student-teacher deviations in distillation: does it pay to disobey? |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| On the Ability of Graph Neural Networks to Model Interactions Between Vertices |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| On the Adversarial Robustness of Out-of-distribution Generalization Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| On the Asymptotic Learning Curves of Kernel Ridge Regression under Power-law Decay |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Complexity of Differentially Private Best-Arm Identification with Fixed Confidence |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| On the Connection between Pre-training Data Diversity and Fine-tuning Robustness |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Consistency of Maximum Likelihood Estimation of Probabilistic Principal Component Analysis |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Constrained Time-Series Generation Problem |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| On the Convergence and Sample Complexity Analysis of Deep Q-Networks with $\epsilon$-Greedy Exploration |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Convergence of Black-Box Variational Inference |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On the Convergence of CART under Sufficient Impurity Decrease Condition |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Convergence of Encoder-only Shallow Transformers |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Convergence of No-Regret Learning Dynamics in Time-Varying Games |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Exploitability of Instruction Tuning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On the Exploration of Local Significant Differences For Two-Sample Test |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On the Generalization Error of Stochastic Mirror Descent for Quadratically-Bounded Losses: an Improved Analysis |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Generalization Properties of Diffusion Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Gini-impurity Preservation For Privacy Random Forests |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On the Identifiability and Interpretability of Gaussian Process Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Identifiability of Sparse ICA without Assuming Non-Gaussianity |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| On the Implicit Bias of Linear Equivariant Steerable Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Importance of Exploration for Generalization in Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| On the Importance of Feature Separability in Predicting Out-Of-Distribution Error |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Interplay between Social Welfare and Tractability of Equilibria |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Last-iterate Convergence in Time-varying Zero-sum Games: Extra Gradient Succeeds where Optimism Fails |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Learnability of Multilabel Ranking |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Minimax Regret for Online Learning with Feedback Graphs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| On the Overlooked Structure of Stochastic Gradients |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| On the Pareto Front of Multilingual Neural Machine Translation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Planning Abilities of Large Language Models - A Critical Investigation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On the Power of SVD in the Stochastic Block Model |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Powerfulness of Textual Outlier Exposure for Visual OoD Detection |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On the Properties of Kullback-Leibler Divergence Between Multivariate Gaussian Distributions |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Relationship Between Relevance and Conflict in Online Social Link Recommendations |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On the Robustness of Mechanism Design under Total Variation Distance |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Robustness of Removal-Based Feature Attributions |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On the Role of Entanglement and Statistics in Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Role of Noise in the Sample Complexity of Learning Recurrent Neural Networks: Exponential Gaps for Long Sequences |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Role of Randomization in Adversarially Robust Classification |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Size and Approximation Error of Distilled Datasets |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Stability-Plasticity Dilemma in Continual Meta-Learning: Theory and Algorithm |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| On the Statistical Consistency of Risk-Sensitive Bayesian Decision-Making |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Sublinear Regret of GP-UCB |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Trade-off of Intra-/Inter-class Diversity for Supervised Pre-training |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| On the Variance, Admissibility, and Stability of Empirical Risk Minimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the choice of Perception Loss Function for Learned Video Compression |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the impact of activation and normalization in obtaining isometric embeddings at initialization |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On the spectral bias of two-layer linear networks |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| On-the-Fly Adapting Code Summarization on Trainable Cost-Effective Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| One Fits All: Power General Time Series Analysis by Pretrained LM |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| One Less Reason for Filter Pruning: Gaining Free Adversarial Robustness with Structured Grouped Kernel Pruning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| One Risk to Rule Them All: A Risk-Sensitive Perspective on Model-Based Offline Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| One-Step Diffusion Distillation via Deep Equilibrium Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| One-for-All: Bridge the Gap Between Heterogeneous Architectures in Knowledge Distillation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| One-step differentiation of iterative algorithms |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Online (Multinomial) Logistic Bandit: Improved Regret and Constant Computation Cost |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Online Ad Allocation with Predictions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Online Ad Procurement in Non-stationary Autobidding Worlds |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Adaptive Policy Selection in Time-Varying Systems: No-Regret via Contractive Perturbations |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Online Clustering of Bandits with Misspecified User Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Online Constrained Meta-Learning: Provable Guarantees for Generalization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Online Control for Meta-optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Online Convex Optimization with Unbounded Memory |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Online Corrupted User Detection and Regret Minimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Online Inventory Problems: Beyond the i.i.d. Setting with Online Convex Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Online Learning under Adversarial Nonlinear Constraints |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Online List Labeling with Predictions |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Online Map Vectorization for Autonomous Driving: A Rasterization Perspective |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Online Nonstochastic Model-Free Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Online PCA in Converging Self-consistent Field Equations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Online POMDP Planning with Anytime Deterministic Guarantees |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Online Performative Gradient Descent for Learning Nash Equilibria in Decision-Dependent Games |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Online Pricing for Multi-User Multi-Item Markets |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Online RL in Linearly $q^\pi$-Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online learning of long-range dependencies |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Online robust non-stationary estimation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Open Compound Domain Adaptation with Object Style Compensation for Semantic Segmentation |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Open Visual Knowledge Extraction via Relation-Oriented Multimodality Model Prompting |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| OpenMask3D: Open-Vocabulary 3D Instance Segmentation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Opening the Vocabulary of Egocentric Actions |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Operation-Level Early Stopping for Robustifying Differentiable NAS |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Operator Learning with Neural Fields: Tackling PDEs on General Geometries |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Optimal Algorithms for the Inhomogeneous Spiked Wigner Model |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Optimal Block-wise Asymmetric Graph Construction for Graph-based Semi-supervised Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Optimal Convergence Rate for Exact Policy Mirror Descent in Discounted Markov Decision Processes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Optimal Excess Risk Bounds for Empirical Risk Minimization on $p$-Norm Linear Regression |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Optimal Exploration for Model-Based RL in Nonlinear Systems |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimal Learners for Realizable Regression: PAC Learning and Online Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal Parameter and Neuron Pruning for Out-of-Distribution Detection |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Optimal Preconditioning and Fisher Adaptive Langevin Sampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimal Rates for Bandit Nonstochastic Control |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound Framework |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimal Time Complexities of Parallel Stochastic Optimization Methods Under a Fixed Computation Model |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Optimal Transport Model Distributional Robustness |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimal Transport for Treatment Effect Estimation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimal Transport-Guided Conditional Score-Based Diffusion Model |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Optimal Treatment Allocation for Efficient Policy Evaluation in Sequential Decision Making |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimal Treatment Regimes for Proximal Causal Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Optimal Unbiased Randomizers for Regression with Label Differential Privacy |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Optimal and Fair Encouragement Policy Evaluation and Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Optimal approximation using complex-valued neural networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Optimal cross-learning for contextual bandits with unknown context distributions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal privacy guarantees for a relaxed threat model: Addressing sub-optimal adversaries in differentially private machine learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Optimal testing using combined test statistics across independent studies |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Optimality in Mean Estimation: Beyond Worst-Case, Beyond Sub-Gaussian, and Beyond $1+\alpha$ Moments |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimality of Message-Passing Architectures for Sparse Graphs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimistic Active Exploration of Dynamical Systems |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Optimistic Exploration in Reinforcement Learning Using Symbolic Model Estimates |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Optimistic Meta-Gradients |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimistic Rates for Multi-Task Representation Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Optimization and Bayes: A Trade-off for Overparameterized Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimization of Inter-group criteria for clustering with minimum size constraints |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Optimization or Architecture: How to Hack Kalman Filtering |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Optimized Covariance Design for AB Test on Social Network under Interference |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Optimizing Prompts for Text-to-Image Generation |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Method |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimizing over trained GNNs via symmetry breaking |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Oracle Complexity of Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Functional Constrained Optimization |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Order Matters in the Presence of Dataset Imbalance for Multilingual Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Ordering-based Conditions for Global Convergence of Policy Gradient Methods |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Orthogonal Non-negative Tensor Factorization based Multi-view Clustering |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Outlier-Robust Gromov-Wasserstein for Graph Data |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Outlier-Robust Wasserstein DRO |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
4 |
| Overcoming Recency Bias of Normalization Statistics in Continual Learning: Balance and Adaptation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| P-Flow: A Fast and Data-Efficient Zero-Shot TTS through Speech Prompting |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| PAC Learning Linear Thresholds from Label Proportions |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| PAC-Bayesian Spectrally-Normalized Bounds for Adversarially Robust Generalization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| PAPR: Proximity Attention Point Rendering |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| PCF-GAN: generating sequential data via the characteristic function of measures on the path space |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| PDF: Point Diffusion Implicit Function for Large-scale Scene Neural Representation |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| PDP: Parameter-free Differentiable Pruning is All You Need |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| PERFOGRAPH: A Numerical Aware Program Graph Representation for Performance Optimization and Program Analysis |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| PETAL: Physics Emulation Through Averaged Linearizations for Solving Inverse Problems |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| PGDiff: Guiding Diffusion Models for Versatile Face Restoration via Partial Guidance |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| PHOTOSWAP: Personalized Subject Swapping in Images |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| PLANNER: Generating Diversified Paragraph via Latent Language Diffusion Model |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| POMDP Planning for Object Search in Partially Unknown Environment |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| POP-3D: Open-Vocabulary 3D Occupancy Prediction from Images |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| PPi: Pretraining Brain Signal Model for Patient-independent Seizure Detection |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| PRED: Pre-training via Semantic Rendering on LiDAR Point Clouds |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning. |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| PRODIGY: Enabling In-context Learning Over Graphs |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| PROTES: Probabilistic Optimization with Tensor Sampling |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| PTQD: Accurate Post-Training Quantization for Diffusion Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| PUCA: Patch-Unshuffle and Channel Attention for Enhanced Self-Supervised Image Denoising |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| PUe: Biased Positive-Unlabeled Learning Enhancement by Causal Inference |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| PackQViT: Faster Sub-8-bit Vision Transformers via Full and Packed Quantization on the Mobile |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| PaintSeg: Painting Pixels for Training-free Segmentation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Pairwise Causality Guided Transformers for Event Sequences |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| PanoGRF: Generalizable Spherical Radiance Fields for Wide-baseline Panoramas |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| PanoGen: Text-Conditioned Panoramic Environment Generation for Vision-and-Language Navigation |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| ParaFuzz: An Interpretability-Driven Technique for Detecting Poisoned Samples in NLP |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Parallel Sampling of Diffusion Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependencies |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Parallel Submodular Function Minimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Parallel-mentoring for Offline Model-based Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Parameter-efficient Tuning of Large-scale Multimodal Foundation Model |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Parameterizing Context: Unleashing the Power of Parameter-Efficient Fine-Tuning and In-Context Tuning for Continual Table Semantic Parsing |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Parameterizing Non-Parametric Meta-Reinforcement Learning Tasks via Subtask Decomposition |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Pareto Frontiers in Deep Feature Learning: Data, Compute, Width, and Luck |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Parsel🐍: Algorithmic Reasoning with Language Models by Composing Decompositions |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Partial Label Learning with Dissimilarity Propagation guided Candidate Label Shrinkage |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Partial Matrix Completion |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Partial Multi-Label Learning with Probabilistic Graphical Disambiguation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Participatory Personalization in Classification |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Particle-based Variational Inference with Generalized Wasserstein Gradient Flow |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Parts of Speech–Grounded Subspaces in Vision-Language Models |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Passive learning of active causal strategies in agents and language models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Patch n’ Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Path Regularization: A Convexity and Sparsity Inducing Regularization for Parallel ReLU Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Path following algorithms for $\ell_2$-regularized $M$-estimation with approximation guarantee |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Paxion: Patching Action Knowledge in Video-Language Foundation Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Payoff-based Learning with Matrix Multiplicative Weights in Quantum Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Penalising the biases in norm regularisation enforces sparsity |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Pengi: An Audio Language Model for Audio Tasks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Penguin: Parallel-Packed Homomorphic Encryption for Fast Graph Convolutional Network Inference |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Percentile Criterion Optimization in Offline Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Perceptual Kalman Filters: Online State Estimation under a Perfect Perceptual-Quality Constraint |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Perceptual adjustment queries and an inverted measurement paradigm for low-rank metric learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Performance Bounds for Policy-Based Average Reward Reinforcement Learning Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Permutation Equivariant Neural Functionals |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Personalized Dictionary Learning for Heterogeneous Datasets |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Persuading Farsighted Receivers in MDPs: the Power of Honesty |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Perturbation Towards Easy Samples Improves Targeted Adversarial Transferability |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Phase diagram of early training dynamics in deep neural networks: effect of the learning rate, depth, and width |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Physics-Driven ML-Based Modelling for Correcting Inverse Estimation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Physics-Informed Bayesian Optimization of Variational Quantum Circuits |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Pitfall of Optimism: Distributional Reinforcement Learning by Randomizing Risk Criterion |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| PlanE: Representation Learning over Planar Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Plug-and-Play Stability for Intracortical Brain-Computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| PoET: A generative model of protein families as sequences-of-sequences |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Point Cloud Completion with Pretrained Text-to-Image Diffusion Models |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| PointGPT: Auto-regressively Generative Pre-training from Point Clouds |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Policy Finetuning in Reinforcement Learning via Design of Experiments using Offline Data |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Policy Gradient for Rectangular Robust Markov Decision Processes |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Policy Optimization for Continuous Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Policy Space Diversity for Non-Transitive Games |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Polyhedron Attention Module: Learning Adaptive-order Interactions |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Polynomial-Time Linear-Swap Regret Minimization in Imperfect-Information Sequential Games |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Polynomially Over-Parameterized Convolutional Neural Networks Contain Structured Strong Winning Lottery Tickets |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Post Hoc Explanations of Language Models Can Improve Language Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Post-processing Private Synthetic Data for Improving Utility on Selected Measures |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Posterior Sampling for Competitive RL: Function Approximation and Partial Observation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Posthoc privacy guarantees for collaborative inference with modified Propose-Test-Release |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| PrObeD: Proactive Object Detection Wrapper |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Practical Contextual Bandits with Feedback Graphs |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Practical Differentially Private Hyperparameter Tuning with Subsampling |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Practical Equivariances via Relational Conditional Neural Processes |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Practical Sharpness-Aware Minimization Cannot Converge All the Way to Optima |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Practical and Asymptotically Exact Conditional Sampling in Diffusion Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Pre-RMSNorm and Pre-CRMSNorm Transformers: Equivalent and Efficient Pre-LN Transformers |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Pre-Training Protein Encoder via Siamese Sequence-Structure Diffusion Trajectory Prediction |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| PreDiff: Precipitation Nowcasting with Latent Diffusion Models |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Precise asymptotic generalization for multiclass classification with overparameterized linear models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Precision-Recall Divergence Optimization for Generative Modeling with GANs and Normalizing Flows |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Preconditioning Matters: Fast Global Convergence of Non-convex Matrix Factorization via Scaled Gradient Descent |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Predict-then-Calibrate: A New Perspective of Robust Contextual LP |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Predicting Global Label Relationship Matrix for Graph Neural Networks under Heterophily |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Predicting a Protein's Stability under a Million Mutations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Prediction and Control in Continual Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Preference-grounded Token-level Guidance for Language Model Fine-tuning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Prefix-Tree Decoding for Predicting Mass Spectra from Molecules |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| PrimDiffusion: Volumetric Primitives Diffusion for 3D Human Generation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Principled Weight Initialisation for Input-Convex Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Prioritizing Samples in Reinforcement Learning with Reducible Loss |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception? |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Privacy Auditing with One (1) Training Run |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Private Distribution Learning with Public Data: The View from Sample Compression |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Private Everlasting Prediction |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Private Federated Frequency Estimation: Adapting to the Hardness of the Instance |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Private estimation algorithms for stochastic block models and mixture models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| ProPILE: Probing Privacy Leakage in Large Language Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Probabilistic Exponential Integrators |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Probabilistic Inference in Reinforcement Learning Done Right |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Probabilistic Invariant Learning with Randomized Linear Classifiers |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Probabilistic Weight Fixing: Large-scale training of neural network weight uncertainties for quantisation. |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costs |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Progressive Ensemble Distillation: Building Ensembles for Efficient Inference |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Projection-Free Methods for Solving Nonconvex-Concave Saddle Point Problems |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Projection-Free Methods for Stochastic Simple Bilevel Optimization with Convex Lower-level Problem |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Projection-Free Online Convex Optimization via Efficient Newton Iterations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Promises and Pitfalls of Threshold-based Auto-labeling |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Prompt-augmented Temporal Point Process for Streaming Event Sequence |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| PromptIR: Prompting for All-in-One Image Restoration |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| PromptRestorer: A Prompting Image Restoration Method with Degradation Perception |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Propagating Knowledge Updates to LMs Through Distillation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
2 |
| Protein Design with Guided Discrete Diffusion |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| ProteinNPT: Improving Protein Property Prediction and Design with Non-Parametric Transformers |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided Diffusion |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Prototype-based Aleatoric Uncertainty Quantification for Cross-modal Retrieval |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Prototypical Variational Autoencoder for 3D Few-shot Object Detection |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Provable Advantage of Curriculum Learning on Parity Targets with Mixed Inputs |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Provable Guarantees for Neural Networks via Gradient Feature Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Provable Training for Graph Contrastive Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Provable benefits of score matching |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Provable convergence guarantees for black-box variational inference |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Provably (More) Sample-Efficient Offline RL with Options |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Provably Bounding Neural Network Preimages |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Provably Efficient Algorithm for Nonstationary Low-Rank MDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Provably Efficient Offline Goal-Conditioned Reinforcement Learning with General Function Approximation and Single-Policy Concentrability |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Provably Efficient Offline Reinforcement Learning in Regular Decision Processes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Provably Fast Convergence of Independent Natural Policy Gradient for Markov Potential Games |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Provably Fast Finite Particle Variants of SVGD via Virtual Particle Stochastic Approximation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Provably Robust Temporal Difference Learning for Heavy-Tailed Rewards |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Provably Safe Reinforcement Learning with Step-wise Violation Constraints |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Proximity-Informed Calibration for Deep Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Pruning vs Quantization: Which is Better? |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
3 |
| Pseudo-Likelihood Inference |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Public Opinion Field Effect Fusion in Representation Learning for Trending Topics Diffusion |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Punctuation-level Attack: Single-shot and Single Punctuation Can Fool Text Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Puzzlefusion: Unleashing the Power of Diffusion Models for Spatial Puzzle Solving |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| PyNeRF: Pyramidal Neural Radiance Fields |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Q-DM: An Efficient Low-bit Quantized Diffusion Model |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| QLoRA: Efficient Finetuning of Quantized LLMs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| QuACK: Accelerating Gradient-Based Quantum Optimization with Koopman Operator Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| QuIP: 2-Bit Quantization of Large Language Models With Guarantees |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| QuadAttac$K$: A Quadratic Programming Approach to Learning Ordered Top-$K$ Adversarial Attacks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| QuantSR: Accurate Low-bit Quantization for Efficient Image Super-Resolution |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Quantification of Uncertainty with Adversarial Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Quantifying & Modeling Multimodal Interactions: An Information Decomposition Framework |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Quantifying the Cost of Learning in Queueing Systems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Quantum Bayesian Optimization |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Quantum speedups for stochastic optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Quasi-Monte Carlo Graph Random Features |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Query-based Temporal Fusion with Explicit Motion for 3D Object Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| R-divergence for Estimating Model-oriented Distribution Discrepancy |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| RADAR: Robust AI-Text Detection via Adversarial Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| RDumb: A simple approach that questions our progress in continual test-time adaptation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| RECESS Vaccine for Federated Learning: Proactive Defense Against Model Poisoning Attacks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| RECKONING: Reasoning through Dynamic Knowledge Encoding |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| REFINE: A Fine-Grained Medication Recommendation System Using Deep Learning and Personalized Drug Interaction Modeling |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| RETVec: Resilient and Efficient Text Vectorizer |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| REx: Data-Free Residual Quantization Error Expansion |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| RGMIL: Guide Your Multiple-Instance Learning Model with Regressor |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| RH-BrainFS: Regional Heterogeneous Multimodal Brain Networks Fusion Strategy |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| RL-based Stateful Neural Adaptive Sampling and Denoising for Real-Time Path Tracing |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| RRHF: Rank Responses to Align Language Models with Human Feedback |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| RanPAC: Random Projections and Pre-trained Models for Continual Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Random Cuts are Optimal for Explainable k-Medians |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Random-Access Infinite Context Length for Transformers |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Randomized and Deterministic Maximin-share Approximations for Fractionally Subadditive Valuations |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| RangePerception: Taming LiDAR Range View for Efficient and Accurate 3D Object Detection |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Rank-1 Matrix Completion with Gradient Descent and Small Random Initialization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Rank-DETR for High Quality Object Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Rank-N-Contrast: Learning Continuous Representations for Regression |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| RayDF: Neural Ray-surface Distance Fields with Multi-view Consistency |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| ReDS: Offline RL With Heteroskedastic Datasets via Support Constraints |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| ReHLine: Regularized Composite ReLU-ReHU Loss Minimization with Linear Computation and Linear Convergence |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| ReSync: Riemannian Subgradient-based Robust Rotation Synchronization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| ReTR: Modeling Rendering Via Transformer for Generalizable Neural Surface Reconstruction |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Reading Relevant Feature from Global Representation Memory for Visual Object Tracking |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Real-Time Motion Prediction via Heterogeneous Polyline Transformer with Relative Pose Encoding |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Real-World Image Super-Resolution as Multi-Task Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Real-World Image Variation by Aligning Diffusion Inversion Chain |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Recaptured Raw Screen Image and Video Demoiréing via Channel and Spatial Modulations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Recasting Continual Learning as Sequence Modeling |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Recommender Systems with Generative Retrieval |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Reconciling Competing Sampling Strategies of Network Embedding |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Recovering Simultaneously Structured Data via Non-Convex Iteratively Reweighted Least Squares |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| Recovering Unbalanced Communities in the Stochastic Block Model with Application to Clustering with a Faulty Oracle |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Recovering from Out-of-sample States via Inverse Dynamics in Offline Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Recurrent Hypernetworks are Surprisingly Strong in Meta-RL |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Recurrent Temporal Revision Graph Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Recursion in Recursion: Two-Level Nested Recursion for Length Generalization with Scalability |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Red Teaming Deep Neural Networks with Feature Synthesis Tools |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Reduced Policy Optimization for Continuous Control with Hard Constraints |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Reducing Blackwell and Average Optimality to Discounted MDPs via the Blackwell Discount Factor |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Reducing Shape-Radiance Ambiguity in Radiance Fields with a Closed-Form Color Estimation Method |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Reference-Based POMDPs |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Refined Mechanism Design for Approximately Structured Priors via Active Regression |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Reflexion: language agents with verbal reinforcement learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| RegBN: Batch Normalization of Multimodal Data with Regularization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Regression with Cost-based Rejection |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Regret Matching+: (In)Stability and Fast Convergence in Games |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Regret Minimization via Saddle Point Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Regret-Optimal Model-Free Reinforcement Learning for Discounted MDPs with Short Burn-In Time |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Regularity as Intrinsic Reward for Free Play |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Regularization properties of adversarially-trained linear regression |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Regularized Behavior Cloning for Blocking the Leakage of Past Action Information |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Regularizing Neural Networks with Meta-Learning Generative Models |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Rehearsal Learning for Avoiding Undesired Future |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Reinforcement Learning with Fast and Forgetful Memory |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Reinforcement Learning with Simple Sequence Priors |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Reinforcement-Enhanced Autoregressive Feature Transformation: Gradient-steered Search in Continuous Space for Postfix Expressions |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Reining Generalization in Offline Reinforcement Learning via Representation Distinction |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Relative Entropic Optimal Transport: a (Prior-aware) Matching Perspective to (Unbalanced) Classification |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Relax, it doesn’t matter how you get there: A new self-supervised approach for multi-timescale behavior analysis |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Reliable Off-Policy Learning for Dosage Combinations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Reliable learning in challenging environments |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Removing Hidden Confounding in Recommendation: A Unified Multi-Task Learning Approach |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Repetition In Repetition Out: Towards Understanding Neural Text Degeneration from the Data Perspective |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Replicability in Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Replicable Clustering |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Replicable Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Representation Learning via Consistent Assignment of Views over Random Partitions |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Representational Strengths and Limitations of Transformers |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Reproducibility in Multiple Instance Learning: A Case For Algorithmic Unit Tests |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Res-Tuning: A Flexible and Efficient Tuning Paradigm via Unbinding Tuner from Backbone |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| ResMem: Learn what you can and memorize the rest |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Resetting the Optimizer in Deep RL: An Empirical Study |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Residual Alignment: Uncovering the Mechanisms of Residual Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Residual Q-Learning: Offline and Online Policy Customization without Value |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Resilient Constrained Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysis |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| ResoNet: Noise-Trained Physics-Informed MRI Off-Resonance Correction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Resolving the Tug-of-War: A Separation of Communication and Learning in Federated Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Responsible AI (RAI) Games and Ensembles |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Restart Sampling for Improving Generative Processes |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Restless Bandits with Average Reward: Breaking the Uniform Global Attractor Assumption |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Retaining Beneficial Information from Detrimental Data for Neural Network Repair |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Rethinking Conditional Diffusion Sampling with Progressive Guidance |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Rethinking Gauss-Newton for learning over-parameterized models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Rethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial? |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Rethinking the Backward Propagation for Adversarial Transferability |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Rethinking the Role of Token Retrieval in Multi-Vector Retrieval |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Retrieval-Augmented Multiple Instance Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Reusable Slotwise Mechanisms |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Reusing Pretrained Models by Multi-linear Operators for Efficient Training |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| RevColV2: Exploring Disentangled Representations in Masked Image Modeling |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Reverse Engineering Self-Supervised Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Reversible and irreversible bracket-based dynamics for deep graph neural networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Revisit Weakly-Supervised Audio-Visual Video Parsing from the Language Perspective |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Revisit the Power of Vanilla Knowledge Distillation: from Small Scale to Large Scale |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Revisiting Adversarial Robustness Distillation from the Perspective of Robust Fairness |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Revisiting Area Convexity: Faster Box-Simplex Games and Spectrahedral Generalizations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Revisiting Implicit Differentiation for Learning Problems in Optimal Control |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot Classification |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Revisiting Scalarization in Multi-Task Learning: A Theoretical Perspective |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Revisiting Visual Model Robustness: A Frequency Long-Tailed Distribution View |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Revisiting the Minimalist Approach to Offline Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Reward Finetuning for Faster and More Accurate Unsupervised Object Discovery |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Reward Imputation with Sketching for Contextual Batched Bandits |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Reward Scale Robustness for Proximal Policy Optimization via DreamerV3 Tricks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Rewiring Neurons in Non-Stationary Environments |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Rewrite Caption Semantics: Bridging Semantic Gaps for Language-Supervised Semantic Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Riemannian Laplace approximations for Bayesian neural networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Riemannian Projection-free Online Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Riemannian Residual Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Riemannian SAM: Sharpness-Aware Minimization on Riemannian Manifolds |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Riemannian stochastic optimization methods avoid strict saddle points |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Rigorous Runtime Analysis of MOEA/D for Solving Multi-Objective Minimum Weight Base Problems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Risk-Averse Active Sensing for Timely Outcome Prediction under Cost Pressure |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| RoboCLIP: One Demonstration is Enough to Learn Robot Policies |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Robust Bayesian Satisficing |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Robust Concept Erasure via Kernelized Rate-Distortion Maximization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Robust Contrastive Language-Image Pretraining against Data Poisoning and Backdoor Attacks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Robust Data Valuation with Weighted Banzhaf Values |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Robust Knowledge Transfer in Tiered Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust Learning for Smoothed Online Convex Optimization with Feedback Delay |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Robust Learning with Progressive Data Expansion Against Spurious Correlation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Robust Lipschitz Bandits to Adversarial Corruptions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robust Matrix Sensing in the Semi-Random Model |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Robust Mean Estimation Without Moments for Symmetric Distributions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Robust Model Reasoning and Fitting via Dual Sparsity Pursuit |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Robust and Actively Secure Serverless Collaborative Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Robust covariance estimation with missing values and cell-wise contamination |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Robust low-rank training via approximate orthonormal constraints |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Robustifying Generalizable Implicit Shape Networks with a Tunable Non-Parametric Model |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Robustness Guarantees for Adversarially Trained Neural Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Rotating Features for Object Discovery |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Rubik's Cube: High-Order Channel Interactions with a Hierarchical Receptive Field |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| S-CLIP: Semi-supervised Vision-Language Learning using Few Specialist Captions |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| SA-Solver: Stochastic Adams Solver for Fast Sampling of Diffusion Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SALSA VERDE: a machine learning attack on LWE with sparse small secrets |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| SAME: Uncovering GNN Black Box with Structure-aware Shapley-based Multipiece Explanations |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| SAMoSSA: Multivariate Singular Spectrum Analysis with Stochastic Autoregressive Noise |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| SANFlow: Semantic-Aware Normalizing Flow for Anomaly Detection |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| SE(3) Equivariant Augmented Coupling Flows |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| SEEDS: Exponential SDE Solvers for Fast High-Quality Sampling from Diffusion Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| SEENN: Towards Temporal Spiking Early Exit Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SEGA: Instructing Text-to-Image Models using Semantic Guidance |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SHAP-IQ: Unified Approximation of any-order Shapley Interactions |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| SHOT: Suppressing the Hessian along the Optimization Trajectory for Gradient-Based Meta-Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| SLIBO-Net: Floorplan Reconstruction via Slicing Box Representation with Local Geometry Regularization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SLM: A Smoothed First-Order Lagrangian Method for Structured Constrained Nonconvex Optimization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| SLaM: Student-Label Mixing for Distillation with Unlabeled Examples |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| SNEkhorn: Dimension Reduction with Symmetric Entropic Affinities |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| SOAR: Improved Indexing for Approximate Nearest Neighbor Search |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| SOC: Semantic-Assisted Object Cluster for Referring Video Object Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SODA: Robust Training of Test-Time Data Adaptors |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| SOL: Sampling-based Optimal Linear bounding of arbitrary scalar functions |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| SPA: A Graph Spectral Alignment Perspective for Domain Adaptation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SPACE: Single-round Participant Amalgamation for Contribution Evaluation in Federated Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| SPAE: Semantic Pyramid AutoEncoder for Multimodal Generation with Frozen LLMs |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| SPRING: Studying Papers and Reasoning to play Games |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SQ Lower Bounds for Learning Mixtures of Linear Classifiers |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| SQ Lower Bounds for Non-Gaussian Component Analysis with Weaker Assumptions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| STEVE-1: A Generative Model for Text-to-Behavior in Minecraft |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| STORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| STREAMER: Streaming Representation Learning and Event Segmentation in a Hierarchical Manner |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| STXD: Structural and Temporal Cross-Modal Distillation for Multi-View 3D Object Detection |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| SUBP: Soft Uniform Block Pruning for 1$\times$N Sparse CNNs Multithreading Acceleration |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| SaVeNet: A Scalable Vector Network for Enhanced Molecular Representation Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Saddle-to-Saddle Dynamics in Diagonal Linear Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| SafeDICE: Offline Safe Imitation Learning with Non-Preferred Demonstrations |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Safety Verification of Decision-Tree Policies in Continuous Time |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sample Complexity of Forecast Aggregation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Sample Complexity of Goal-Conditioned Hierarchical Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Sample Efficient Reinforcement Learning in Mixed Systems through Augmented Samples and Its Applications to Queueing Networks |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Sample based Explanations via Generalized Representers |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sample-Conditioned Hypothesis Stability Sharpens Information-Theoretic Generalization Bounds |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Sample-Efficient and Safe Deep Reinforcement Learning via Reset Deep Ensemble Agents |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sample-efficient Multi-objective Molecular Optimization with GFlowNets |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Sampling from Structured Log-Concave Distributions via a Soft-Threshold Dikin Walk |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sampling weights of deep neural networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| SatLM: Satisfiability-Aided Language Models Using Declarative Prompting |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Saving 100x Storage: Prototype Replay for Reconstructing Training Sample Distribution in Class-Incremental Semantic Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Scalable Fair Influence Maximization |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Scalable Membership Inference Attacks via Quantile Regression |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Scalable Transformer for PDE Surrogate Modeling |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Scalarization for Multi-Task and Multi-Domain Learning at Scale |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Scale Alone Does not Improve Mechanistic Interpretability in Vision Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Scale-Space Hypernetworks for Efficient Biomedical Image Analysis |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Scale-teaching: Robust Multi-scale Training for Time Series Classification with Noisy Labels |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| ScaleLong: Towards More Stable Training of Diffusion Model via Scaling Network Long Skip Connection |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Scaling Data-Constrained Language Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scaling Laws for Hyperparameter Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Scaling MLPs: A Tale of Inductive Bias |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Scaling Open-Vocabulary Object Detection |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Scaling Riemannian Diffusion Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Scaling laws for language encoding models in fMRI |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Scan and Snap: Understanding Training Dynamics and Token Composition in 1-layer Transformer |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Scattering Vision Transformer: Spectral Mixing Matters |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Scenario Diffusion: Controllable Driving Scenario Generation With Diffusion |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| SceneScape: Text-Driven Consistent Scene Generation |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Schema-learning and rebinding as mechanisms of in-context learning and emergence |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scissorhands: Exploiting the Persistence of Importance Hypothesis for LLM KV Cache Compression at Test Time |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Score-based Data Assimilation |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Score-based Generative Modeling through Stochastic Evolution Equations in Hilbert Spaces |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Score-based Generative Models with Lévy Processes |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Score-based Source Separation with Applications to Digital Communication Signals |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Searching for Optimal Per-Coordinate Step-sizes with Multidimensional Backtracking |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Secure Out-of-Distribution Task Generalization with Energy-Based Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Segment Any Point Cloud Sequences by Distilling Vision Foundation Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Segment Anything in 3D with NeRFs |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Segment Anything in High Quality |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Segment Everything Everywhere All at Once |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Selective Amnesia: A Continual Learning Approach to Forgetting in Deep Generative Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Selective Sampling and Imitation Learning via Online Regression |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Self-Adaptive Motion Tracking against On-body Displacement of Flexible Sensors |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Self-Chained Image-Language Model for Video Localization and Question Answering |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Self-Consistent Velocity Matching of Probability Flows |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Self-Correcting Bayesian Optimization through Bayesian Active Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Self-Evaluation Guided Beam Search for Reasoning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Self-Predictive Universal AI |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Self-Refine: Iterative Refinement with Self-Feedback |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Self-Supervised Learning of Representations for Space Generates Multi-Modular Grid Cells |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Self-Supervised Learning with Lie Symmetries for Partial Differential Equations |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Self-Supervised Motion Magnification by Backpropagating Through Optical Flow |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Self-Supervised Reinforcement Learning that Transfers using Random Features |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Self-Supervised Visual Acoustic Matching |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Self-Weighted Contrastive Learning among Multiple Views for Mitigating Representation Degeneration |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Self-supervised Graph Neural Networks via Low-Rank Decomposition |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Self-supervised Object-Centric Learning for Videos |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Self-supervised video pretraining yields robust and more human-aligned visual representations |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Semantic HELM: A Human-Readable Memory for Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Semantic Image Synthesis with Unconditional Generator |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Semantic segmentation of sparse irregular point clouds for leaf/wood discrimination |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Semi-Implicit Denoising Diffusion Models (SIDDMs) |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Semi-Supervised Contrastive Learning for Deep Regression with Ordinal Rankings from Spectral Seriation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Semi-Supervised Domain Generalization with Known and Unknown Classes |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Sensitivity in Translation Averaging |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Separable Physics-Informed Neural Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Sequential Memory with Temporal Predictive Coding |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sequential Predictive Two-Sample and Independence Testing |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Sequential Preference Ranking for Efficient Reinforcement Learning from Human Feedback |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Sequential Subset Matching for Dataset Distillation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Setting the Trap: Capturing and Defeating Backdoors in Pretrained Language Models through Honeypots |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Shape Non-rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape Matching via Unsupervised Functional Map Regularized Reconstruction |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Shared Adversarial Unlearning: Backdoor Mitigation by Unlearning Shared Adversarial Examples |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Sharp Bounds for Generalized Causal Sensitivity Analysis |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Sharp Calibrated Gaussian Processes |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Sharp Recovery Thresholds of Tensor PCA Spectral Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sharp Spectral Rates for Koopman Operator Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sharpness Minimization Algorithms Do Not Only Minimize Sharpness To Achieve Better Generalization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Sharpness-Aware Minimization Leads to Low-Rank Features |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Sheaf Hypergraph Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
3 |
| SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| ShiftAddViT: Mixture of Multiplication Primitives Towards Efficient Vision Transformer |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Should Under-parameterized Student Networks Copy or Average Teacher Weights? |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Should We Learn Most Likely Functions or Parameters? |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Siamese Masked Autoencoders |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
3 |
| Similarity-based cooperative equilibrium |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Simple and Asymmetric Graph Contrastive Learning without Augmentations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Simple and Controllable Music Generation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Simple, Scalable and Effective Clustering via One-Dimensional Projections |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Simplicity Bias in 1-Hidden Layer Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Simplifying Neural Network Training Under Class Imbalance |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Simultaneous embedding of multiple attractor manifolds in a recurrent neural network using constrained gradient optimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditions |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Single-Pass Pivot Algorithm for Correlation Clustering. Keep it simple! |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Single-Stage Visual Query Localization in Egocentric Videos |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sketchy: Memory-efficient Adaptive Regularization with Frequent Directions |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Skill-it! A data-driven skills framework for understanding and training language models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Slimmed Asymmetrical Contrastive Learning and Cross Distillation for Lightweight Model Training |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Slot-guided Volumetric Object Radiance Fields |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| SlotDiffusion: Object-Centric Generative Modeling with Diffusion Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Slow and Weak Attractor Computation Embedded in Fast and Strong E-I Balanced Neural Dynamics |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Small Total-Cost Constraints in Contextual Bandits with Knapsacks, with Application to Fairness |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Small batch deep reinforcement learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| SmooSeg: Smoothness Prior for Unsupervised Semantic Segmentation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Smooth Flipping Probability for Differential Private Sign Random Projection Methods |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Smooth, exact rotational symmetrization for deep learning on point clouds |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SmoothHess: ReLU Network Feature Interactions via Stein's Lemma |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Smoothed Analysis of Sequential Probability Assignment |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Smoothed Online Learning for Prediction in Piecewise Affine Systems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| SnapFusion: Text-to-Image Diffusion Model on Mobile Devices within Two Seconds |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| SoTTA: Robust Test-Time Adaptation on Noisy Data Streams |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Social Motion Prediction with Cognitive Hierarchies |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Soft-Unification in Deep Probabilistic Logic |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Softmax Output Approximation for Activation Memory-Efficient Training of Attention-based Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Solving Inverse Physics Problems with Score Matching |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Solving a Class of Non-Convex Minimax Optimization in Federated Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Sorting with Predictions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Sounding Bodies: Modeling 3D Spatial Sound of Humans Using Body Pose and Audio |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Sparse Deep Learning for Time Series Data: Theory and Applications |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Sparse Modular Activation for Efficient Sequence Modeling |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Sparse Parameterization for Epitomic Dataset Distillation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| SparseProp: Efficient Event-Based Simulation and Training of Sparse Recurrent Spiking Neural Networks |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Sparsity-Preserving Differentially Private Training of Large Embedding Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Spatial-frequency channels, shape bias, and adversarial robustness |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Spatially Resolved Gene Expression Prediction from Histology Images via Bi-modal Contrastive Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Spatio-Angular Convolutions for Super-resolution in Diffusion MRI |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SpecTr: Fast Speculative Decoding via Optimal Transport |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Spectral Co-Distillation for Personalized Federated Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Spectral Entry-wise Matrix Estimation for Low-Rank Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Spectral Evolution and Invariance in Linear-width Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Speculative Decoding with Big Little Decoder |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Spike-driven Transformer |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Spiking PointNet: Spiking Neural Networks for Point Clouds |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Spontaneous symmetry breaking in generative diffusion models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Spuriosity Didn’t Kill the Classifier: Using Invariant Predictions to Harness Spurious Features |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Spuriosity Rankings: Sorting Data to Measure and Mitigate Biases |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Squared Neural Families: A New Class of Tractable Density Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| StEik: Stabilizing the Optimization of Neural Signed Distance Functions and Finer Shape Representation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Stability Guarantees for Feature Attributions with Multiplicative Smoothing |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Stability and Generalization of the Decentralized Stochastic Gradient Descent Ascent Algorithm |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stability of Random Forests and Coverage of Random-Forest Prediction Intervals |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
5 |
| Stable Diffusion is Unstable |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Stable Nonconvex-Nonconcave Training via Linear Interpolation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stable Vectorization of Multiparameter Persistent Homology using Signed Barcodes as Measures |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Stable and low-precision training for large-scale vision-language models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| StableFDG: Style and Attention Based Learning for Federated Domain Generalization |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Star-Shaped Denoising Diffusion Probabilistic Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| State Regularized Policy Optimization on Data with Dynamics Shift |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| State Sequences Prediction via Fourier Transform for Representation Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| State-Action Similarity-Based Representations for Off-Policy Evaluation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| State-space models with layer-wise nonlinearity are universal approximators with exponential decaying memory |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| StateMask: Explaining Deep Reinforcement Learning through State Mask |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Static and Sequential Malicious Attacks in the Context of Selective Forgetting |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Statistical Analysis of Quantum State Learning Process in Quantum Neural Networks |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Statistical Insights into HSIC in High Dimensions |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Statistical Knowledge Assessment for Large Language Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Statistical Limits of Adaptive Linear Models: Low-Dimensional Estimation and Inference |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Statistical and Computational Trade-off in Multi-Agent Multi-Armed Bandits |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Statistically Valid Variable Importance Assessment through Conditional Permutations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Stein $\Pi$-Importance Sampling |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Stochastic Approximation Algorithms for Systems of Interacting Particles |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Stochastic Approximation Approaches to Group Distributionally Robust Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Stochastic Collapse: How Gradient Noise Attracts SGD Dynamics Towards Simpler Subnetworks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Stochastic Multi-armed Bandits: Optimal Trade-off among Optimality, Consistency, and Tail Risk |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Strategic Apple Tasting |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Strategic Behavior in Two-sided Matching Markets with Prediction-enhanced Preference-formation |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Strategic Classification under Unknown Personalized Manipulation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Strategic Data Sharing between Competitors |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Strategic Distribution Shift of Interacting Agents via Coupled Gradient Flows |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Strategyproof Voting under Correlated Beliefs |
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0 |
| StreamNet: Memory-Efficient Streaming Tiny Deep Learning Inference on the Microcontroller |
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2 |
| Streaming Algorithms and Lower Bounds for Estimating Correlation Clustering Cost |
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2 |
| Streaming Factor Trajectory Learning for Temporal Tensor Decomposition |
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4 |
| Streaming PCA for Markovian Data |
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1 |
| Strong and Precise Modulation of Human Percepts via Robustified ANNs |
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5 |
| Structural Pruning for Diffusion Models |
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4 |
| Structure Learning with Adaptive Random Neighborhood Informed MCMC |
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5 |
| Structure from Duplicates: Neural Inverse Graphics from a Pile of Objects |
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3 |
| Structure of universal formulas |
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0 |
| Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data |
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❌ |
✅ |
6 |
| Structured Federated Learning through Clustered Additive Modeling |
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3 |
| Structured Neural Networks for Density Estimation and Causal Inference |
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4 |
| Structured Neural-PI Control with End-to-End Stability and Output Tracking Guarantees |
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4 |
| Structured Prediction with Stronger Consistency Guarantees |
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0 |
| Structured Semidefinite Programming for Recovering Structured Preconditioners |
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1 |
| Structured State Space Models for In-Context Reinforcement Learning |
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5 |
| Structured Voronoi Sampling |
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❌ |
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6 |
| Students Parrot Their Teachers: Membership Inference on Model Distillation |
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4 |
| StyleDrop: Text-to-Image Synthesis of Any Style |
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5 |
| StyleGAN knows Normal, Depth, Albedo, and More |
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3 |
| StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models |
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❌ |
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6 |
| Sub-optimality of the Naive Mean Field approximation for proportional high-dimensional Linear Regression |
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✅ |
2 |
| Subclass-Dominant Label Noise: A Counterexample for the Success of Early Stopping |
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✅ |
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❌ |
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6 |
| Subject-driven Text-to-Image Generation via Apprenticeship Learning |
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4 |
| Subspace Identification for Multi-Source Domain Adaptation |
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✅ |
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1 |
| Successor-Predecessor Intrinsic Exploration |
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3 |
| Suggesting Variable Order for Cylindrical Algebraic Decomposition via Reinforcement Learning |
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❌ |
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6 |
| Supervised Pretraining Can Learn In-Context Reinforcement Learning |
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4 |
| Supply-Side Equilibria in Recommender Systems |
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2 |
| Supported Value Regularization for Offline Reinforcement Learning |
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5 |
| Survival Instinct in Offline Reinforcement Learning |
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❌ |
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5 |
| Survival Permanental Processes for Survival Analysis with Time-Varying Covariates |
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6 |
| SutraNets: Sub-series Autoregressive Networks for Long-Sequence, Probabilistic Forecasting |
❌ |
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3 |
| Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration |
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1 |
| SwapPrompt: Test-Time Prompt Adaptation for Vision-Language Models |
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✅ |
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3 |
| Swarm Reinforcement Learning for Adaptive Mesh Refinement |
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3 |
| SwiFT: Swin 4D fMRI Transformer |
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6 |
| SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks |
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4 |
| Switching Autoregressive Low-rank Tensor Models |
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5 |
| Switching Temporary Teachers for Semi-Supervised Semantic Segmentation |
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4 |
| Symbol-LLM: Leverage Language Models for Symbolic System in Visual Human Activity Reasoning |
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4 |
| Symbolic Discovery of Optimization Algorithms |
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5 |
| SyncDiffusion: Coherent Montage via Synchronized Joint Diffusions |
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3 |
| SyncTREE: Fast Timing Analysis for Integrated Circuit Design through a Physics-informed Tree-based Graph Neural Network |
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6 |
| Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions |
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0 |
| Synthetic Experience Replay |
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5 |
| Synthetic-to-Real Pose Estimation with Geometric Reconstruction |
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✅ |
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3 |
| Systematic Visual Reasoning through Object-Centric Relational Abstraction |
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✅ |
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3 |
| T2T: From Distribution Learning in Training to Gradient Search in Testing for Combinatorial Optimization |
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4 |
| TART: A plug-and-play Transformer module for task-agnostic reasoning |
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5 |
| TD Convergence: An Optimization Perspective |
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1 |
| TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph |
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6 |
| TIES-Merging: Resolving Interference When Merging Models |
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6 |
| TMT-VIS: Taxonomy-aware Multi-dataset Joint Training for Video Instance Segmentation |
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4 |
| TOA: Task-oriented Active VQA |
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5 |
| TRIAGE: Characterizing and auditing training data for improved regression |
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5 |
| TabMT: Generating tabular data with masked transformers |
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5 |
| Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds |
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2 |
| Tailoring Self-Attention for Graph via Rooted Subtrees |
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5 |
| Taking the neural sampling code very seriously: A data-driven approach for evaluating generative models of the visual system |
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3 |
| Tame a Wild Camera: In-the-Wild Monocular Camera Calibration |
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3 |
| Taming Local Effects in Graph-based Spatiotemporal Forecasting |
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5 |
| Tanh Works Better with Asymmetry |
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4 |
| Tanimoto Random Features for Scalable Molecular Machine Learning |
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3 |
| Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models |
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6 |
| Task-Robust Pre-Training for Worst-Case Downstream Adaptation |
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5 |
| Task-aware Distributed Source Coding under Dynamic Bandwidth |
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4 |
| Task-aware world model learning with meta weighting via bi-level optimization |
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6 |
| TaskMet: Task-driven Metric Learning for Model Learning |
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4 |
| Taylor TD-learning |
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5 |
| Team-PSRO for Learning Approximate TMECor in Large Team Games via Cooperative Reinforcement Learning |
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4 |
| TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery |
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6 |
| Temperature Balancing, Layer-wise Weight Analysis, and Neural Network Training |
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5 |
| Template-free Articulated Neural Point Clouds for Reposable View Synthesis |
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4 |
| Tempo Adaptation in Non-stationary Reinforcement Learning |
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2 |
| Temporal Causal Mediation through a Point Process: Direct and Indirect Effects of Healthcare Interventions |
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3 |
| Temporal Conditioning Spiking Latent Variable Models of the Neural Response to Natural Visual Scenes |
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4 |
| Temporal Continual Learning with Prior Compensation for Human Motion Prediction |
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6 |
| Temporal Dynamic Quantization for Diffusion Models |
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5 |
| Temporal Robustness against Data poisoning |
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2 |
| Temporally Disentangled Representation Learning under Unknown Nonstationarity |
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3 |
| TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials |
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6 |
| Test-Time Amendment with a Coarse Classifier for Fine-Grained Classification |
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3 |
| Test-Time Distribution Normalization for Contrastively Learned Visual-language Models |
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5 |
| Test-time Training for Matching-based Video Object Segmentation |
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5 |
| Tester-Learners for Halfspaces: Universal Algorithms |
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1 |
| Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples |
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4 |
| TexQ: Zero-shot Network Quantization with Texture Feature Distribution Calibration |
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4 |
| Text Alignment Is An Efficient Unified Model for Massive NLP Tasks |
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5 |
| Text Promptable Surgical Instrument Segmentation with Vision-Language Models |
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5 |
| Text-to-Image Diffusion Models are Zero Shot Classifiers |
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3 |
| TextDiffuser: Diffusion Models as Text Painters |
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4 |
| Textually Pretrained Speech Language Models |
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4 |
| The Adversarial Consistency of Surrogate Risks for Binary Classification |
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0 |
| The Bayesian Stability Zoo |
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1 |
| The Behavior and Convergence of Local Bayesian Optimization |
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3 |
| The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning |
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4 |
| The Best of Both Worlds in Network Population Games: Reaching Consensus and Convergence to Equilibrium |
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3 |
| The CLIP Model is Secretly an Image-to-Prompt Converter |
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4 |
| The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks |
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5 |
| The Contextual Lasso: Sparse Linear Models via Deep Neural Networks |
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6 |
| The Crucial Role of Normalization in Sharpness-Aware Minimization |
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2 |
| The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model |
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1 |
| The Distortion of Binomial Voting Defies Expectation |
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0 |
| The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks |
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0 |
| The Emergence of Essential Sparsity in Large Pre-trained Models: The Weights that Matter |
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3 |
| The Equivalence of Dynamic and Strategic Stability under Regularized Learning in Games |
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1 |
| The Exact Sample Complexity Gain from Invariances for Kernel Regression |
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0 |
| The Gain from Ordering in Online Learning |
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1 |
| The Geometry of Neural Nets' Parameter Spaces Under Reparametrization |
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2 |
| The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs |
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5 |
| The Grand Illusion: The Myth of Software Portability and Implications for ML Progress. |
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4 |
| The Graph Pencil Method: Mapping Subgraph Densities to Stochastic Block Models |
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2 |
| The Impact of Positional Encoding on Length Generalization in Transformers |
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4 |
| The Learnability of In-Context Learning |
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0 |
| The Memory-Perturbation Equation: Understanding Model's Sensitivity to Data |
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4 |
| The Pick-to-Learn Algorithm: Empowering Compression for Tight Generalization Bounds and Improved Post-training Performance |
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6 |
| The Pursuit of Human Labeling: A New Perspective on Unsupervised Learning |
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4 |
| The Quantization Model of Neural Scaling |
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4 |
| The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions |
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4 |
| The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance |
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4 |
| The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classification |
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3 |
| The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit |
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3 |
| The Simplicity Bias in Multi-Task RNNs: Shared Attractors, Reuse of Dynamics, and Geometric Representation |
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0 |
| The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| The Target-Charging Technique for Privacy Analysis across Interactive Computations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| The Transient Nature of Emergent In-Context Learning in Transformers |
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❌ |
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❌ |
✅ |
4 |
| The Tunnel Effect: Building Data Representations in Deep Neural Networks |
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✅ |
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❌ |
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3 |
| The Utility of “Even if” Semifactual Explanation to Optimise Positive Outcomes |
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❌ |
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5 |
| The emergence of clusters in self-attention dynamics |
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0 |
| The expressive power of pooling in Graph Neural Networks |
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✅ |
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✅ |
✅ |
❌ |
✅ |
5 |
| The geometry of hidden representations of large transformer models |
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4 |
| The noise level in linear regression with dependent data |
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❌ |
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0 |
| The probability flow ODE is provably fast |
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❌ |
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❌ |
❌ |
✅ |
3 |
| The s-value: evaluating stability with respect to distributional shifts |
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✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Theoretical Analysis of the Inductive Biases in Deep Convolutional Networks |
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❌ |
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1 |
| Theoretical and Practical Perspectives on what Influence Functions Do |
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❌ |
✅ |
✅ |
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❌ |
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4 |
| Theoretically Guaranteed Bidirectional Data Rectification for Robust Sequential Recommendation |
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✅ |
✅ |
❌ |
✅ |
5 |
| Thin and deep Gaussian processes |
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✅ |
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✅ |
❌ |
❌ |
❌ |
3 |
| Thinker: Learning to Plan and Act |
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❌ |
✅ |
❌ |
✅ |
5 |
| This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations |
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✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Thought Cloning: Learning to Think while Acting by Imitating Human Thinking |
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✅ |
❌ |
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❌ |
✅ |
5 |
| Three Iterations of (d − 1)-WL Test Distinguish Non Isometric Clouds of d-dimensional Points |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Three Towers: Flexible Contrastive Learning with Pretrained Image Models |
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❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance |
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✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Thrust: Adaptively Propels Large Language Models with External Knowledge |
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✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Tight Bounds for Volumetric Spanners and Applications |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Tight Risk Bounds for Gradient Descent on Separable Data |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Time Series Kernels based on Nonlinear Vector AutoRegressive Delay Embeddings |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Time Series as Images: Vision Transformer for Irregularly Sampled Time Series |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Time-Independent Information-Theoretic Generalization Bounds for SGLD |
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❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Time-Reversed Dissipation Induces Duality Between Minimizing Gradient Norm and Function Value |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Time-uniform confidence bands for the CDF under nonstationarity |
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✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics |
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✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis |
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❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning |
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✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Token-Scaled Logit Distillation for Ternary Weight Generative Language Models |
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✅ |
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❌ |
✅ |
❌ |
✅ |
4 |
| Toolformer: Language Models Can Teach Themselves to Use Tools |
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❌ |
✅ |
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❌ |
❌ |
✅ |
3 |
| ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Tools for Verifying Neural Models' Training Data |
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❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Top-Ambiguity Samples Matter: Understanding Why Deep Ensemble Works in Selective Classification |
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❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| TopP&R: Robust Support Estimation Approach for Evaluating Fidelity and Diversity in Generative Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| TopoSRL: Topology preserving self-supervised Simplicial Representation Learning |
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✅ |
✅ |
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❌ |
❌ |
❌ |
4 |
| Topological Obstructions and How to Avoid Them |
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❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Topological Parallax: A Geometric Specification for Deep Perception Models |
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✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
4 |
| Topological RANSAC for instance verification and retrieval without fine-tuning |
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✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Topology-Aware Uncertainty for Image Segmentation |
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✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Toward Better PAC-Bayes Bounds for Uniformly Stable Algorithms |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Toward Re-Identifying Any Animal |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Toward Understanding Generative Data Augmentation |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Towards A Richer 2D Understanding of Hands at Scale |
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❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Towards Accelerated Model Training via Bayesian Data Selection |
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✅ |
✅ |
❌ |
✅ |
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5 |
| Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity |
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✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Towards Automated Circuit Discovery for Mechanistic Interpretability |
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❌ |
✅ |
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6 |
| Towards Better Dynamic Graph Learning: New Architecture and Unified Library |
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❌ |
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5 |
| Towards Characterizing the First-order Query Complexity of Learning (Approximate) Nash Equilibria in Zero-sum Matrix Games |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Towards Combinatorial Generalization for Catalysts: A Kohn-Sham Charge-Density Approach |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
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3 |
| Towards Consistent Video Editing with Text-to-Image Diffusion Models |
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❌ |
✅ |
❌ |
✅ |
✅ |
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4 |
| Towards Data-Agnostic Pruning At Initialization: What Makes a Good Sparse Mask? |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Towards Data-Algorithm Dependent Generalization: a Case Study on Overparameterized Linear Regression |
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❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Towards Distribution-Agnostic Generalized Category Discovery |
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✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Towards Efficient Image Compression Without Autoregressive Models |
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❌ |
✅ |
❌ |
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❌ |
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3 |
| Towards Efficient Pre-Trained Language Model via Feature Correlation Distillation |
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❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Towards Efficient and Accurate Winograd Convolution via Full Quantization |
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❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Towards Evaluating Transfer-based Attacks Systematically, Practically, and Fairly |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior |
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✅ |
❌ |
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4 |
| Towards Free Data Selection with General-Purpose Models |
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✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation |
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✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Towards Higher Ranks via Adversarial Weight Pruning |
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✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Towards Hybrid-grained Feature Interaction Selection for Deep Sparse Network |
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✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Towards In-context Scene Understanding |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Towards Label Position Bias in Graph Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Towards Label-free Scene Understanding by Vision Foundation Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Towards Last-layer Retraining for Group Robustness with Fewer Annotations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Towards Optimal Caching and Model Selection for Large Model Inference |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Towards Optimal Effective Resistance Estimation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Towards Personalized Federated Learning via Heterogeneous Model Reassembly |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Towards Revealing the Mystery behind Chain of Thought: A Theoretical Perspective |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Towards Robust and Expressive Whole-body Human Pose and Shape Estimation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Towards Self-Interpretable Graph-Level Anomaly Detection |
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✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Towards Semi-Structured Automatic ICD Coding via Tree-based Contrastive Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Towards Stable Backdoor Purification through Feature Shift Tuning |
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✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Towards Symmetry-Aware Generation of Periodic Materials |
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✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Towards Test-Time Refusals via Concept Negation |
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❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Towards Unbounded Machine Unlearning |
✅ |
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✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Towards a Unified Analysis of Kernel-based Methods Under Covariate Shift |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Towards a Unified Framework of Contrastive Learning for Disentangled Representations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Towards a fuller understanding of neurons with Clustered Compositional Explanations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Towards robust and generalizable representations of extracellular data using contrastive learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Towards the Difficulty for a Deep Neural Network to Learn Concepts of Different Complexities |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Tracking Most Significant Shifts in Nonparametric Contextual Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Tracr: Compiled Transformers as a Laboratory for Interpretability |
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✅ |
❌ |
❌ |
✅ |
❌ |
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3 |
| Trade-off Between Efficiency and Consistency for Removal-based Explanations |
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✅ |
❌ |
✅ |
❌ |
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5 |
| Trading-off price for data quality to achieve fair online allocation |
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❌ |
❌ |
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❌ |
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3 |
| Train 'n Trade: Foundations of Parameter Markets |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Train Faster, Perform Better: Modular Adaptive Training in Over-Parameterized Models |
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✅ |
❌ |
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4 |
| Train Hard, Fight Easy: Robust Meta Reinforcement Learning |
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❌ |
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5 |
| Train Once and Explain Everywhere: Pre-training Interpretable Graph Neural Networks |
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✅ |
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❌ |
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6 |
| Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning |
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✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Training Chain-of-Thought via Latent-Variable Inference |
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❌ |
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5 |
| Training Energy-Based Normalizing Flow with Score-Matching Objectives |
❌ |
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✅ |
✅ |
✅ |
❌ |
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5 |
| Training Fully Connected Neural Networks is $\exists\mathbb{R}$-Complete |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Training Neural Networks is NP-Hard in Fixed Dimension |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Training Private Models That Know What They Don’t Know |
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✅ |
❌ |
❌ |
❌ |
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4 |
| Training Transformers with 4-bit Integers |
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✅ |
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❌ |
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5 |
| Training Transitive and Commutative Multimodal Transformers with LoReTTa |
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5 |
| Training Your Image Restoration Network Better with Random Weight Network as Optimization Function |
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❌ |
✅ |
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❌ |
❌ |
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2 |
| Training biologically plausible recurrent neural networks on cognitive tasks with long-term dependencies |
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❌ |
✅ |
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❌ |
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2 |
| Training neural operators to preserve invariant measures of chaotic attractors |
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❌ |
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2 |
| Training on Foveated Images Improves Robustness to Adversarial Attacks |
❌ |
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✅ |
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6 |
| Training shallow ReLU networks on noisy data using hinge loss: when do we overfit and is it benign? |
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✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Training-free Diffusion Model Adaptation for Variable-Sized Text-to-Image Synthesis |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Trajectory Alignment: Understanding the Edge of Stability Phenomenon via Bifurcation Theory |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Trans-Dimensional Generative Modeling via Jump Diffusion Models |
✅ |
✅ |
✅ |
❌ |
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❌ |
✅ |
5 |
| TransHP: Image Classification with Hierarchical Prompting |
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✅ |
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✅ |
✅ |
❌ |
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5 |
| Transfer Learning with Affine Model Transformation |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Transfer learning for atomistic simulations using GNNs and kernel mean embeddings |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings |
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❌ |
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❌ |
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5 |
| Transformed Low-Rank Parameterization Can Help Robust Generalization for Tensor Neural Networks |
❌ |
✅ |
✅ |
❌ |
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❌ |
✅ |
4 |
| Transformer as a hippocampal memory consolidation model based on NMDAR-inspired nonlinearity |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Transformer-based Planning for Symbolic Regression |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Transformers are uninterpretable with myopic methods: a case study with bounded Dyck grammars |
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❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Transformers learn through gradual rank increase |
✅ |
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✅ |
❌ |
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❌ |
✅ |
4 |
| Transformers learn to implement preconditioned gradient descent for in-context learning |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Transformers over Directed Acyclic Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Transient Neural Radiance Fields for Lidar View Synthesis and 3D Reconstruction |
❌ |
✅ |
✅ |
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❌ |
✅ |
4 |
| Transition-constant Normalization for Image Enhancement |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Transitivity Recovering Decompositions: Interpretable and Robust Fine-Grained Relationships |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Transportability for Bandits with Data from Different Environments |
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| Tree Variational Autoencoders |
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| Tree of Thoughts: Deliberate Problem Solving with Large Language Models |
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| Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters |
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5 |
| Tree-Rings Watermarks: Invisible Fingerprints for Diffusion Images |
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4 |
| TriRE: A Multi-Mechanism Learning Paradigm for Continual Knowledge Retention and Promotion |
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6 |
| Trial matching: capturing variability with data-constrained spiking neural networks |
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4 |
| Triangulation Residual Loss for Data-efficient 3D Pose Estimation |
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4 |
| Triple Eagle: Simple, Fast and Practical Budget-Feasible Mechanisms |
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5 |
| TrojLLM: A Black-box Trojan Prompt Attack on Large Language Models |
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3 |
| Truly Scale-Equivariant Deep Nets with Fourier Layers |
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3 |
| Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection |
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6 |
| Truncating Trajectories in Monte Carlo Policy Evaluation: an Adaptive Approach |
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3 |
| Trust Region-Based Safe Distributional Reinforcement Learning for Multiple Constraints |
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5 |
| Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery |
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4 |
| Tuning Multi-mode Token-level Prompt Alignment across Modalities |
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5 |
| Two Heads are Better Than One: A Simple Exploration Framework for Efficient Multi-Agent Reinforcement Learning |
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4 |
| Two Sides of One Coin: the Limits of Untuned SGD and the Power of Adaptive Methods |
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3 |
| Two Sides of The Same Coin: Bridging Deep Equilibrium Models and Neural ODEs via Homotopy Continuation |
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5 |
| Two-Stage Learning to Defer with Multiple Experts |
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2 |
| Two-Stage Predict+Optimize for MILPs with Unknown Parameters in Constraints |
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4 |
| Type-to-Track: Retrieve Any Object via Prompt-based Tracking |
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5 |
| UE4-NeRF:Neural Radiance Field for Real-Time Rendering of Large-Scale Scene |
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4 |
| UNSSOR: Unsupervised Neural Speech Separation by Leveraging Over-determined Training Mixtures |
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5 |
| UP-DP: Unsupervised Prompt Learning for Data Pre-Selection with Vision-Language Models |
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4 |
| UP-NeRF: Unconstrained Pose Prior-Free Neural Radiance Field |
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4 |
| UltraRE: Enhancing RecEraser for Recommendation Unlearning via Error Decomposition |
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5 |
| Unbalanced Low-rank Optimal Transport Solvers |
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7 |
| Unbiased Compression Saves Communication in Distributed Optimization: When and How Much? |
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4 |
| Unbiased constrained sampling with Self-Concordant Barrier Hamiltonian Monte Carlo |
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4 |
| Unbiased learning of deep generative models with structured discrete representations |
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6 |
| Unbounded Differentially Private Quantile and Maximum Estimation |
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4 |
| Uncertainty Estimation for Safety-critical Scene Segmentation via Fine-grained Reward Maximization |
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5 |
| Uncertainty Quantification over Graph with Conformalized Graph Neural Networks |
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6 |
| Uncertainty Quantification via Neural Posterior Principal Components |
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4 |
| Uncertainty-Aware Alignment Network for Cross-Domain Video-Text Retrieval |
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4 |
| Uncertainty-Aware Instance Reweighting for Off-Policy Learning |
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5 |
| Unconstrained Dynamic Regret via Sparse Coding |
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4 |
| Uncoupled and Convergent Learning in Two-Player Zero-Sum Markov Games with Bandit Feedback |
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1 |
| Uncovering Meanings of Embeddings via Partial Orthogonality |
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3 |
| Uncovering Prototypical Knowledge for Weakly Open-Vocabulary Semantic Segmentation |
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6 |
| Uncovering and Quantifying Social Biases in Code Generation |
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4 |
| Uncovering motifs of concurrent signaling across multiple neuronal populations |
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3 |
| Uncovering the Hidden Dynamics of Video Self-supervised Learning under Distribution Shifts |
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5 |
| Understanding Contrastive Learning via Distributionally Robust Optimization |
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5 |
| Understanding Deep Gradient Leakage via Inversion Influence Functions |
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4 |
| Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation |
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4 |
| Understanding Few-Shot Learning: Measuring Task Relatedness and Adaptation Difficulty via Attributes |
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4 |
| Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization |
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5 |
| Understanding Multi-phase Optimization Dynamics and Rich Nonlinear Behaviors of ReLU Networks |
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3 |
| Understanding Neural Network Binarization with Forward and Backward Proximal Quantizers |
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3 |
| Understanding and Addressing the Pitfalls of Bisimulation-based Representations in Offline Reinforcement Learning |
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5 |
| Understanding and Improving Ensemble Adversarial Defense |
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4 |
| Understanding and Improving Feature Learning for Out-of-Distribution Generalization |
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7 |
| Understanding and Mitigating Copying in Diffusion Models |
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| Understanding the Latent Space of Diffusion Models through the Lens of Riemannian Geometry |
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4 |
| Understanding the Limitations of Deep Models for Molecular property prediction: Insights and Solutions |
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4 |
| Understanding the detrimental class-level effects of data augmentation |
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5 |
| Understanding, Predicting and Better Resolving Q-Value Divergence in Offline-RL |
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3 |
| Undirected Probabilistic Model for Tensor Decomposition |
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6 |
| Unexpected Improvements to Expected Improvement for Bayesian Optimization |
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4 |
| Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models |
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4 |
| Uni3DETR: Unified 3D Detection Transformer |
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5 |
| UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild |
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4 |
| UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models |
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5 |
| UniT: A Unified Look at Certified Robust Training against Text Adversarial Perturbation |
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5 |
| UniTSFace: Unified Threshold Integrated Sample-to-Sample Loss for Face Recognition |
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| Unified 3D Segmenter As Prototypical Classifiers |
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5 |
| Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems |
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4 |
| Unified Enhancement of Privacy Bounds for Mixture Mechanisms via $f$-Differential Privacy |
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| Unified Lower Bounds for Interactive High-dimensional Estimation under Information Constraints |
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1 |
| Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective |
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5 |
| Unified Segment-to-Segment Framework for Simultaneous Sequence Generation |
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5 |
| Uniform Convergence with Square-Root Lipschitz Loss |
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| Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent |
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| Unifying GANs and Score-Based Diffusion as Generative Particle Models |
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7 |
| Unifying Predictions of Deterministic and Stochastic Physics in Mesh-reduced Space with Sequential Flow Generative Model |
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2 |
| Universal Gradient Descent Ascent Method for Nonconvex-Nonconcave Minimax Optimization |
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3 |
| Universal Online Learning with Gradient Variations: A Multi-layer Online Ensemble Approach |
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1 |
| Universal Prompt Tuning for Graph Neural Networks |
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3 |
| Universality and Limitations of Prompt Tuning |
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3 |
| Universality laws for Gaussian mixtures in generalized linear models |
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2 |
| Unleash the Potential of Image Branch for Cross-modal 3D Object Detection |
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5 |
| Unleashing the Full Potential of Product Quantization for Large-Scale Image Retrieval |
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5 |
| Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift |
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6 |
| Unleashing the Power of Randomization in Auditing Differentially Private ML |
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5 |
| Unlimiformer: Long-Range Transformers with Unlimited Length Input |
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5 |
| Unlocking Deterministic Robustness Certification on ImageNet |
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5 |
| Unlocking Feature Visualization for Deep Network with MAgnitude Constrained Optimization |
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4 |
| Unpaired Multi-Domain Causal Representation Learning |
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3 |
| Unsupervised Anomaly Detection with Rejection |
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5 |
| Unsupervised Behavior Extraction via Random Intent Priors |
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4 |
| Unsupervised Graph Neural Architecture Search with Disentangled Self-Supervision |
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3 |
| Unsupervised Image Denoising with Score Function |
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4 |
| Unsupervised Learning for Solving the Travelling Salesman Problem |
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4 |
| Unsupervised Optical Flow Estimation with Dynamic Timing Representation for Spike Camera |
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3 |
| Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction |
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4 |
| Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation |
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5 |
| Unsupervised Semantic Correspondence Using Stable Diffusion |
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4 |
| Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective |
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5 |
| Use perturbations when learning from explanations |
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5 |
| User-Level Differential Privacy With Few Examples Per User |
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1 |
| Using Imperfect Surrogates for Downstream Inference: Design-based Supervised Learning for Social Science Applications of Large Language Models |
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4 |
| Utilitarian Algorithm Configuration |
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4 |
| V-InFoR: A Robust Graph Neural Networks Explainer for Structurally Corrupted Graphs |
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3 |
| VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset |
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5 |
| VCC: Scaling Transformers to 128K Tokens or More by Prioritizing Important Tokens |
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5 |
| VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models |
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6 |
| VOCE: Variational Optimization with Conservative Estimation for Offline Safe Reinforcement Learning |
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3 |
| VPGTrans: Transfer Visual Prompt Generator across LLMs |
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4 |
| VPP: Efficient Conditional 3D Generation via Voxel-Point Progressive Representation |
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4 |
| VRA: Variational Rectified Activation for Out-of-distribution Detection |
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5 |
| VaRT: Variational Regression Trees |
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4 |
| VanillaNet: the Power of Minimalism in Deep Learning |
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5 |
| Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies |
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4 |
| Variational Annealing on Graphs for Combinatorial Optimization |
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5 |
| Variational Gaussian Processes with Decoupled Conditionals |
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4 |
| Variational Gaussian processes for linear inverse problems |
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2 |
| Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing |
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3 |
| Variational Inference with Gaussian Score Matching |
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4 |
| Variational Monte Carlo on a Budget — Fine-tuning pre-trained Neural Wavefunctions |
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4 |
| Variational Weighting for Kernel Density Ratios |
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4 |
| VeriX: Towards Verified Explainability of Deep Neural Networks |
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5 |
| Versatile Energy-Based Probabilistic Models for High Energy Physics |
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4 |
| ViCA-NeRF: View-Consistency-Aware 3D Editing of Neural Radiance Fields |
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3 |
| ViSt3D: Video Stylization with 3D CNN |
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3 |
| Video Dynamics Prior: An Internal Learning Approach for Robust Video Enhancements |
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4 |
| Video Prediction Models as Rewards for Reinforcement Learning |
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5 |
| Video-Mined Task Graphs for Keystep Recognition in Instructional Videos |
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3 |
| VideoComposer: Compositional Video Synthesis with Motion Controllability |
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3 |
| VillanDiffusion: A Unified Backdoor Attack Framework for Diffusion Models |
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5 |
| VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks |
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4 |
| Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution |
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5 |
| Visual Instruction Inversion: Image Editing via Image Prompting |
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4 |
| Visual Instruction Tuning |
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5 |
| Visual Programming for Step-by-Step Text-to-Image Generation and Evaluation |
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6 |
| Vocabulary-free Image Classification |
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4 |
| Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale |
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3 |
| Volume Feature Rendering for Fast Neural Radiance Field Reconstruction |
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3 |
| VoxDet: Voxel Learning for Novel Instance Detection |
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4 |
| Vulnerabilities in Video Quality Assessment Models: The Challenge of Adversarial Attacks |
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4 |
| WITRAN: Water-wave Information Transmission and Recurrent Acceleration Network for Long-range Time Series Forecasting |
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6 |
| WalkLM: A Uniform Language Model Fine-tuning Framework for Attributed Graph Embedding |
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4 |
| Wasserstein Gradient Flows for Optimizing Gaussian Mixture Policies |
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2 |
| Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schrödinger Equation |
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5 |
| Wasserstein distributional robustness of neural networks |
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5 |
| Waypoint Transformer: Reinforcement Learning via Supervised Learning with Intermediate Targets |
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3 |
| Weakly Coupled Deep Q-Networks |
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4 |
| Weakly Supervised 3D Open-vocabulary Segmentation |
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4 |
| Weakly-Supervised Audio-Visual Segmentation |
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3 |
| Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo Labeling and Multi-scale Feature Grouping |
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4 |
| Weighted ROC Curve in Cost Space: Extending AUC to Cost-Sensitive Learning |
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5 |
| Weitzman's Rule for Pandora's Box with Correlations |
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1 |
| What Can We Learn from Unlearnable Datasets? |
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5 |
| What Distributions are Robust to Indiscriminate Poisoning Attacks for Linear Learners? |
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2 |
| What Do Deep Saliency Models Learn about Visual Attention? |
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2 |
| What Knowledge Gets Distilled in Knowledge Distillation? |
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3 |
| What Makes Data Suitable for a Locally Connected Neural Network? A Necessary and Sufficient Condition Based on Quantum Entanglement. |
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6 |
| What Makes Good Examples for Visual In-Context Learning? |
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3 |
| What Planning Problems Can A Relational Neural Network Solve? |
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4 |
| What Truly Matters in Trajectory Prediction for Autonomous Driving? |
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5 |
| What You See is What You Read? Improving Text-Image Alignment Evaluation |
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5 |
| What can a Single Attention Layer Learn? A Study Through the Random Features Lens |
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1 |
| What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding |
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3 |
| What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization |
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5 |
| What is the Inductive Bias of Flatness Regularization? A Study of Deep Matrix Factorization Models |
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| What’s Left? Concept Grounding with Logic-Enhanced Foundation Models |
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5 |
| When Can We Track Significant Preference Shifts in Dueling Bandits? |
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3 |
| When Demonstrations meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning |
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5 |
| When Do Graph Neural Networks Help with Node Classification? Investigating the Homophily Principle on Node Distinguishability |
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| When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment |
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| When Does Confidence-Based Cascade Deferral Suffice? |
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4 |
| When Does Optimizing a Proper Loss Yield Calibration? |
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1 |
| When Visual Prompt Tuning Meets Source-Free Domain Adaptive Semantic Segmentation |
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4 |
| When are ensembles really effective? |
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1 |
| When can Regression-Adjusted Control Variate Help? Rare Events, Sobolev Embedding and Minimax Optimality |
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| When is Agnostic Reinforcement Learning Statistically Tractable? |
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1 |
| Where Did I Come From? Origin Attribution of AI-Generated Images |
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6 |
| Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence? |
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5 |
| Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects |
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| Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness |
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4 |
| White-Box Transformers via Sparse Rate Reduction |
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6 |
| Why Did This Model Forecast This Future? Information-Theoretic Saliency for Counterfactual Explanations of Probabilistic Regression Models |
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4 |
| Why Does Sharpness-Aware Minimization Generalize Better Than SGD? |
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4 |
| Why think step by step? Reasoning emerges from the locality of experience |
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| Wide Neural Networks as Gaussian Processes: Lessons from Deep Equilibrium Models |
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3 |
| Window-Based Distribution Shift Detection for Deep Neural Networks |
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5 |
| Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization |
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3 |
| Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model |
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5 |
| Worst-case Performance of Popular Approximate Nearest Neighbor Search Implementations: Guarantees and Limitations |
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4 |
| Would I have gotten that reward? Long-term credit assignment by counterfactual contribution analysis |
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4 |
| XAGen: 3D Expressive Human Avatars Generation |
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1 |
| You Only Condense Once: Two Rules for Pruning Condensed Datasets |
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4 |
| Your representations are in the network: composable and parallel adaptation for large scale models |
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3 |
| Zero-One Laws of Graph Neural Networks |
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2 |
| Zero-Regret Performative Prediction Under Inequality Constraints |
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2 |
| Zero-Shot Anomaly Detection via Batch Normalization |
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5 |
| Zero-shot Visual Relation Detection via Composite Visual Cues from Large Language Models |
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2 |
| Zero-shot causal learning |
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6 |
| Zero-sum Polymatrix Markov Games: Equilibrium Collapse and Efficient Computation of Nash Equilibria |
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1 |
| Zeroth-Order Methods for Nondifferentiable, Nonconvex, and Hierarchical Federated Optimization |
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3 |
| ZipLM: Inference-Aware Structured Pruning of Language Models |
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6 |
| ZoomTrack: Target-aware Non-uniform Resizing for Efficient Visual Tracking |
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5 |
| f-Policy Gradients: A General Framework for Goal-Conditioned RL using f-Divergences |
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2 |
| iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models |
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5 |
| k-Median Clustering via Metric Embedding: Towards Better Initialization with Differential Privacy |
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3 |
| xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data |
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3 |
| “Why Not Looking backward?” A Robust Two-Step Method to Automatically Terminate Bayesian Optimization |
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3 |