| "Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shifts |
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5 |
| $H$-Consistency Bounds for Pairwise Misranking Loss Surrogates |
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✅ |
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2 |
| $\pi$-Tuning: Transferring Multimodal Foundation Models with Optimal Multi-task Interpolation |
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5 |
| 2D-Shapley: A Framework for Fragmented Data Valuation |
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5 |
| A Category-theoretical Meta-analysis of Definitions of Disentanglement |
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0 |
| A Closer Look at Few-shot Classification Again |
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4 |
| A Closer Look at Self-Supervised Lightweight Vision Transformers |
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5 |
| A Closer Look at the Intervention Procedure of Concept Bottleneck Models |
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6 |
| A Complete Expressiveness Hierarchy for Subgraph GNNs via Subgraph Weisfeiler-Lehman Tests |
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5 |
| A Conditional Normalizing Flow for Accelerated Multi-Coil MR Imaging |
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5 |
| A Connection between One-Step RL and Critic Regularization in Reinforcement Learning |
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3 |
| A Coupled Flow Approach to Imitation Learning |
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4 |
| A Critical Revisit of Adversarial Robustness in 3D Point Cloud Recognition with Diffusion-Driven Purification |
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4 |
| A Critical View of Vision-Based Long-Term Dynamics Prediction Under Environment Misalignment |
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5 |
| A Deep Conjugate Direction Method for Iteratively Solving Linear Systems |
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5 |
| A Distribution Optimization Framework for Confidence Bounds of Risk Measures |
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3 |
| A Fast Optimistic Method for Monotone Variational Inequalities |
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4 |
| A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel |
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3 |
| A Flexible Diffusion Model |
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3 |
| A Framework for Adapting Offline Algorithms to Solve Combinatorial Multi-Armed Bandit Problems with Bandit Feedback |
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3 |
| A Fully First-Order Method for Stochastic Bilevel Optimization |
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4 |
| A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems |
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5 |
| A General Representation Learning Framework with Generalization Performance Guarantees |
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6 |
| A Generalization of ViT/MLP-Mixer to Graphs |
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5 |
| A Gromov-Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening |
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6 |
| A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining |
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5 |
| A Hybrid Quantum-Classical Approach based on the Hadamard Transform for the Convolutional Layer |
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5 |
| A Kernel Stein Test of Goodness of Fit for Sequential Models |
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3 |
| A Kernel-Based View of Language Model Fine-Tuning |
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4 |
| A Kernelized Stein Discrepancy for Biological Sequences |
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3 |
| A Large-Scale Study of Probabilistic Calibration in Neural Network Regression |
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5 |
| A Law of Robustness beyond Isoperimetry |
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0 |
| A Mathematical Model for Curriculum Learning for Parities |
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✅ |
2 |
| A Model-Based Method for Minimizing CVaR and Beyond |
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3 |
| A Model-free Closeness-of-influence Test for Features in Supervised Learning |
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3 |
| A Modern Look at the Relationship between Sharpness and Generalization |
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4 |
| A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints |
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3 |
| A Nearly-Optimal Bound for Fast Regression with $\ell_∞$ Guarantee |
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0 |
| A Neural PDE Solver with Temporal Stencil Modeling |
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3 |
| A New PHO-rmula for Improved Performance of Semi-Structured Networks |
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5 |
| A Picture of the Space of Typical Learnable Tasks |
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3 |
| A Reinforcement Learning Framework for Dynamic Mediation Analysis |
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4 |
| A Robust Optimisation Perspective on Counterexample-Guided Repair of Neural Networks |
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7 |
| A Robust Test for the Stationarity Assumption in Sequential Decision Making |
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4 |
| A Scalable Frank-Wolfe-Based Algorithm for the Max-Cut SDP |
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5 |
| A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models |
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5 |
| A Statistical Perspective on Retrieval-Based Models |
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3 |
| A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs |
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4 |
| A Study on Transformer Configuration and Training Objective |
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4 |
| A Theoretical Analysis of the Learning Dynamics under Class Imbalance |
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6 |
| A Three-regime Model of Network Pruning |
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5 |
| A Toy Model of Universality: Reverse Engineering how Networks Learn Group Operations |
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3 |
| A Two-Stage Active Learning Algorithm for k-Nearest Neighbors |
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1 |
| A Unified Audio-Visual Learning Framework for Localization, Separation, and Recognition |
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4 |
| A Unified Optimization Framework of ANN-SNN Conversion: Towards Optimal Mapping from Activation Values to Firing Rates |
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5 |
| A Unifying Framework to the Analysis of Interaction Methods using Synergy Functions |
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0 |
| A Universal Unbiased Method for Classification from Aggregate Observations |
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4 |
| A Watermark for Large Language Models |
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4 |
| A new near-linear time algorithm for k-nearest neighbor search using a compressed cover tree |
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1 |
| A theory of continuous generative flow networks |
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4 |
| A theory of representation learning gives a deep generalisation of kernel methods |
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3 |
| A/B Testing in Network Data with Covariate-Adaptive Randomization |
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4 |
| ACAT: Adversarial Counterfactual Attention for Classification and Detection in Medical Imaging |
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6 |
| AbODE: Ab initio antibody design using conjoined ODEs |
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3 |
| Abstract-to-Executable Trajectory Translation for One-Shot Task Generalization |
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1 |
| Abstracting Imperfect Information Away from Two-Player Zero-Sum Games |
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1 |
| Accelerated Cyclic Coordinate Dual Averaging with Extrapolation for Composite Convex Optimization |
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❌ |
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4 |
| Accelerated Infeasibility Detection of Constrained Optimization and Fixed-Point Iterations |
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❌ |
❌ |
❌ |
❌ |
✅ |
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2 |
| Accelerated Primal-Dual Methods for Convex-Strongly-Concave Saddle Point Problems |
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❌ |
✅ |
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3 |
| Accelerated Stochastic Optimization Methods under Quasar-convexity |
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✅ |
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❌ |
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4 |
| Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time |
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4 |
| Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations |
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3 |
| Achieving Hierarchy-Free Approximation for Bilevel Programs with Equilibrium Constraints |
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4 |
| Achieving High Accuracy with PINNs via Energy Natural Gradient Descent |
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4 |
| Achieving Linear Speedup in Non-IID Federated Bilevel Learning |
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4 |
| Action Matching: Learning Stochastic Dynamics from Samples |
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4 |
| Active Learning based Structural Inference |
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6 |
| Active Policy Improvement from Multiple Black-box Oracles |
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4 |
| Active Ranking of Experts Based on their Performances in Many Tasks |
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2 |
| Active causal structure learning with advice |
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4 |
| Actor-Critic Alignment for Offline-to-Online Reinforcement Learning |
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4 |
| AdaBoost is not an Optimal Weak to Strong Learner |
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1 |
| AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation |
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5 |
| AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners |
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3 |
| Adapting to game trees in zero-sum imperfect information games |
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4 |
| Adaptive Annealed Importance Sampling with Constant Rate Progress |
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4 |
| Adaptive Barrier Smoothing for First-Order Policy Gradient with Contact Dynamics |
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2 |
| Adaptive Compositional Continual Meta-Learning |
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6 |
| Adaptive Computation with Elastic Input Sequence |
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6 |
| Adaptive Coordination in Social Embodied Rearrangement |
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5 |
| Adaptive Estimation of Graphical Models under Total Positivity |
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3 |
| Adaptive IMLE for Few-shot Pretraining-free Generative Modelling |
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5 |
| Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions |
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2 |
| Adaptive Smoothing Gradient Learning for Spiking Neural Networks |
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5 |
| Adaptive Whitening in Neural Populations with Gain-modulating Interneurons |
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4 |
| Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift |
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5 |
| Additive Causal Bandits with Unknown Graph |
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3 |
| Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm |
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❌ |
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5 |
| Adversarial Cheap Talk |
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5 |
| Adversarial Collaborative Learning on Non-IID Features |
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4 |
| Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples |
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5 |
| Adversarial Learning of Distributional Reinforcement Learning |
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✅ |
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❌ |
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2 |
| Adversarial Parameter Attack on Deep Neural Networks |
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5 |
| Adversarial Policies Beat Superhuman Go AIs |
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4 |
| Adversarial robustness of amortized Bayesian inference |
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5 |
| Adversarially Robust PAC Learnability of Real-Valued Functions |
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1 |
| Algorithmic Collective Action in Machine Learning |
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✅ |
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2 |
| Algorithmic Stability of Heavy-Tailed SGD with General Loss Functions |
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❌ |
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❌ |
❌ |
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0 |
| Algorithms for bounding contribution for histogram estimation under user-level privacy |
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3 |
| Aligning Language Models with Preferences through $f$-divergence Minimization |
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4 |
| All in a Row: Compressed Convolution Networks for Graphs |
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6 |
| Alternately Optimized Graph Neural Networks |
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6 |
| Alternating Local Enumeration (TnALE): Solving Tensor Network Structure Search with Fewer Evaluations |
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5 |
| An Adaptive Entropy-Regularization Framework for Multi-Agent Reinforcement Learning |
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5 |
| An Effective Meaningful Way to Evaluate Survival Models |
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4 |
| An Information-Theoretic Analysis of Nonstationary Bandit Learning |
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1 |
| An Instrumental Variable Approach to Confounded Off-Policy Evaluation |
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2 |
| An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning |
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3 |
| An SDE for Modeling SAM: Theory and Insights |
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✅ |
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❌ |
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2 |
| Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression: Fast Convergence and Partial Participation |
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✅ |
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❌ |
❌ |
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4 |
| Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels |
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❌ |
✅ |
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2 |
| Analyzing Diffusion as Serial Reproduction |
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✅ |
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❌ |
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4 |
| Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano |
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3 |
| Anchor Sampling for Federated Learning with Partial Client Participation |
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6 |
| Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization |
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❌ |
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6 |
| Anti-Exploration by Random Network Distillation |
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❌ |
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❌ |
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5 |
| Applied Online Algorithms with Heterogeneous Predictors |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Approximate Causal Effect Identification under Weak Confounding |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Approximate Stein Classes for Truncated Density Estimation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Approximately Optimal Core Shapes for Tensor Decompositions |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Approximation Algorithms for Fair Range Clustering |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Approximation and Estimation Ability of Transformers for Sequence-to-Sequence Functions with Infinite Dimensional Input |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Architecture-Agnostic Masked Image Modeling -- From ViT back to CNN |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Are Diffusion Models Vulnerable to Membership Inference Attacks? |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Are Equivariant Equilibrium Approximators Beneficial? |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Are Gaussian Data All You Need? The Extents and Limits of Universality in High-Dimensional Generalized Linear Estimation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Are Large Kernels Better Teachers than Transformers for ConvNets? |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Are Neurons Actually Collapsed? On the Fine-Grained Structure in Neural Representations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Are Random Decompositions all we need in High Dimensional Bayesian Optimisation? |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Are labels informative in semi-supervised learning? Estimating and leveraging the missing-data mechanism. |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Atari-5: Distilling the Arcade Learning Environment down to Five Games |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Attribute-Efficient PAC Learning of Low-Degree Polynomial Threshold Functions with Nasty Noise |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Attributing Image Generative Models using Latent Fingerprints |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| AudioLDM: Text-to-Audio Generation with Latent Diffusion Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| AutoCoreset: An Automatic Practical Coreset Construction Framework |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Automated Search for Conjectures on Mathematical Constants using Analysis of Integer Sequences |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Automatic Data Augmentation via Invariance-Constrained Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Automatic Intrinsic Reward Shaping for Exploration in Deep Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Automatically Auditing Large Language Models via Discrete Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Automatically marginalized MCMC in probabilistic programming |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Autoregressive Diffusion Model for Graph Generation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Auxiliary Learning as an Asymmetric Bargaining Game |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Auxiliary Modality Learning with Generalized Curriculum Distillation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Averaged Method of Multipliers for Bi-Level Optimization without Lower-Level Strong Convexity |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| BEATs: Audio Pre-Training with Acoustic Tokenizers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic Programming |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| BPipe: Memory-Balanced Pipeline Parallelism for Training Large Language Models |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Bag of Tricks for Training Data Extraction from Language Models |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Bandit Multi-linear DR-Submodular Maximization and Its Applications on Adversarial Submodular Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Bandit Online Linear Optimization with Hints and Queries |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Bandits with Knapsacks: Advice on Time-Varying Demands |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Banker Online Mirror Descent: A Universal Approach for Delayed Online Bandit Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Bayes-optimal Learning of Deep Random Networks of Extensive-width |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Bayesian Design Principles for Frequentist Sequential Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Bayesian Estimation of Differential Privacy |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Bayesian Neural Networks Avoid Encoding Complex and Perturbation-Sensitive Concepts |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bayesian Progressive Deep Topic Model with Knowledge Informed Textual Data Coarsening Process |
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❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bayesian Reparameterization of Reward-Conditioned Reinforcement Learning with Energy-based Models |
✅ |
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✅ |
❌ |
✅ |
❌ |
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4 |
| Bayesian online change point detection with Hilbert space approximate Student-t process |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Beam Tree Recursive Cells |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Behavior Contrastive Learning for Unsupervised Skill Discovery |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Benign Overfitting in Deep Neural Networks under Lazy Training |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Benign Overfitting in Two-layer ReLU Convolutional Neural Networks |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Best Arm Identification in Multi-Agent Multi-Armed Bandits |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Best of Both Worlds Policy Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Better Diffusion Models Further Improve Adversarial Training |
❌ |
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✅ |
✅ |
✅ |
❌ |
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5 |
| Better Training of GFlowNets with Local Credit and Incomplete Trajectories |
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✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering |
❌ |
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✅ |
❌ |
❌ |
❌ |
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3 |
| Beyond In-Domain Scenarios: Robust Density-Aware Calibration |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Beyond Lipschitz Smoothness: A Tighter Analysis for Nonconvex Optimization |
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✅ |
❌ |
✅ |
❌ |
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4 |
| Beyond Reward: Offline Preference-guided Policy Optimization |
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✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Beyond Uniform Lipschitz Condition in Differentially Private Optimization |
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✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Beyond the Edge of Stability via Two-step Gradient Updates |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Beyond the Universal Law of Robustness: Sharper Laws for Random Features and Neural Tangent Kernels |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Bi-directional Masks for Efficient N:M Sparse Training |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| BiBench: Benchmarking and Analyzing Network Binarization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| BiRT: Bio-inspired Replay in Vision Transformers for Continual Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Biases in Evaluation of Molecular Optimization Methods and Bias Reduction Strategies |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Bidirectional Adaptation for Robust Semi-Supervised Learning with Inconsistent Data Distributions |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Bidirectional Learning for Offline Model-based Biological Sequence Design |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Bidirectional Looking with A Novel Double Exponential Moving Average to Adaptive and Non-adaptive Momentum Optimizers |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Bigger, Better, Faster: Human-level Atari with human-level efficiency |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Bilevel Optimization with Coupled Decision-Dependent Distributions |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Bit Allocation using Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Block Subsampled Randomized Hadamard Transform for Nyström Approximation on Distributed Architectures |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Blossom: an Anytime Algorithm for Computing Optimal Decision Trees |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Boosting Graph Contrastive Learning via Graph Contrastive Saliency |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Boosting Offline Reinforcement Learning with Action Preference Query |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bootstrap in High Dimension with Low Computation |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Bootstrapped Representations in Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Brainformers: Trading Simplicity for Efficiency |
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✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Brauer’s Group Equivariant Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Building Neural Networks on Matrix Manifolds: A Gyrovector Space Approach |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Buying Information for Stochastic Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| CAB: Comprehensive Attention Benchmarking on Long Sequence Modeling |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| CLIPood: Generalizing CLIP to Out-of-Distributions |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| CLUSTSEG: Clustering for Universal Segmentation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| CLUTR: Curriculum Learning via Unsupervised Task Representation Learning |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
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4 |
| CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
4 |
| COLA: Orchestrating Error Coding and Learning for Robust Neural Network Inference Against Hardware Defects |
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✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| COMCAT: Towards Efficient Compression and Customization of Attention-Based Vision Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| CRISP: Curriculum based Sequential neural decoders for Polar code family |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
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3 |
| CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Calibrating Multimodal Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Can Forward Gradient Match Backpropagation? |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Can Large Language Models Reason about Program Invariants? |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Can Neural Network Memorization Be Localized? |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models? |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| CataBEEM: Integrating Latent Interaction Categories in Node-wise Community Detection Models for Network Data |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Causal Bounds in Quasi-Markovian Graphs |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Causal Discovery with Latent Confounders Based on Higher-Order Cumulants |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Causal Isotonic Calibration for Heterogeneous Treatment Effects |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
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4 |
| Causal Modeling of Policy Interventions From Treatment-Outcome Sequences |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Causal Proxy Models for Concept-based Model Explanations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Causal Strategic Classification: A Tale of Two Shifts |
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✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Causal Structure Learning for Latent Intervened Non-stationary Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Cell-Free Latent Go-Explore |
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✅ |
✅ |
❌ |
✅ |
✅ |
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6 |
| Certified Robust Neural Networks: Generalization and Corruption Resistance |
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✅ |
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❌ |
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5 |
| Certifying Ensembles: A General Certification Theory with S-Lipschitzness |
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❌ |
✅ |
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❌ |
✅ |
2 |
| Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| Change is Hard: A Closer Look at Subpopulation Shift |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Chemically Transferable Generative Backmapping of Coarse-Grained Proteins |
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✅ |
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✅ |
❌ |
✅ |
6 |
| ChiPFormer: Transferable Chip Placement via Offline Decision Transformer |
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✅ |
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❌ |
✅ |
5 |
| CircuitNet: A Generic Neural Network to Realize Universal Circuit Motif Modeling |
❌ |
❌ |
✅ |
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❌ |
❌ |
✅ |
3 |
| ClimaX: A foundation model for weather and climate |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Cluster Explanation via Polyhedral Descriptions |
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❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| ClusterFuG: Clustering Fully connected Graphs by Multicut |
✅ |
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❌ |
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6 |
| CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification |
✅ |
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✅ |
❌ |
❌ |
❌ |
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3 |
| CoDi: Co-evolving Contrastive Diffusion Models for Mixed-type Tabular Synthesis |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| CocktailSGD: Fine-tuning Foundation Models over 500Mbps Networks |
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✅ |
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4 |
| CodeIPPrompt: Intellectual Property Infringement Assessment of Code Language Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Coder Reviewer Reranking for Code Generation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Cold Analysis of Rao-Blackwellized Straight-Through Gumbel-Softmax Gradient Estimator |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Collaborative Causal Inference with Fair Incentives |
❌ |
✅ |
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❌ |
✅ |
❌ |
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3 |
| Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits |
✅ |
❌ |
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❌ |
❌ |
❌ |
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2 |
| Combinatorial Neural Bandits |
✅ |
❌ |
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❌ |
❌ |
❌ |
✅ |
2 |
| Communication-Constrained Bandits under Additive Gaussian Noise |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation |
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❌ |
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3 |
| Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects |
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❌ |
❌ |
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3 |
| Competing for Shareable Arms in Multi-Player Multi-Armed Bandits |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Competitive Gradient Optimization |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
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2 |
| Complementary Attention for Multi-Agent Reinforcement Learning |
❌ |
✅ |
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✅ |
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3 |
| Complexity of Block Coordinate Descent with Proximal Regularization and Applications to Wasserstein CP-dictionary Learning |
✅ |
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3 |
| Composer: Creative and Controllable Image Synthesis with Composable Conditions |
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✅ |
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2 |
| Compositional Exemplars for In-context Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Compositional Score Modeling for Simulation-Based Inference |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Compressed Decentralized Proximal Stochastic Gradient Method for Nonconvex Composite Problems with Heterogeneous Data |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Compressing Tabular Data via Latent Variable Estimation |
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✅ |
❌ |
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4 |
| Computational Asymmetries in Robust Classification |
❌ |
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✅ |
✅ |
✅ |
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6 |
| Computational Doob h-transforms for Online Filtering of Discretely Observed Diffusions |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings |
✅ |
❌ |
✅ |
❌ |
❌ |
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✅ |
3 |
| ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction |
❌ |
✅ |
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❌ |
✅ |
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3 |
| Concept-based Explanations for Out-of-Distribution Detectors |
✅ |
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✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Concurrent Shuffle Differential Privacy Under Continual Observation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Conditional Graph Information Bottleneck for Molecular Relational Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Conditional Tree Matching for Inference-Time Adaptation of Tree Prediction Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Conditionally Strongly Log-Concave Generative Models |
✅ |
✅ |
✅ |
❌ |
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❌ |
✅ |
4 |
| Cones: Concept Neurons in Diffusion Models for Customized Generation |
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❌ |
❌ |
✅ |
❌ |
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3 |
| Confidence and Dispersity Speak: Characterizing Prediction Matrix for Unsupervised Accuracy Estimation |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Conformal Inference is (almost) Free for Neural Networks Trained with Early Stopping |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Conformal Prediction Sets for Graph Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Conformal Prediction for Federated Uncertainty Quantification Under Label Shift |
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✅ |
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❌ |
❌ |
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4 |
| Conformal Prediction with Missing Values |
✅ |
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✅ |
❌ |
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5 |
| Conformalization of Sparse Generalized Linear Models |
✅ |
✅ |
✅ |
❌ |
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3 |
| Consistency Models |
✅ |
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✅ |
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4 |
| Consistency of Multiple Kernel Clustering |
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✅ |
❌ |
✅ |
❌ |
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3 |
| Constant Matters: Fine-grained Error Bound on Differentially Private Continual Observation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Constrained Causal Bayesian Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Constrained Decision Transformer for Offline Safe Reinforcement Learning |
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3 |
| Constrained Efficient Global Optimization of Expensive Black-box Functions |
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2 |
| Constrained Monotonic Neural Networks |
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✅ |
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4 |
| Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching |
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4 |
| Constrained Phi-Equilibria |
✅ |
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1 |
| Context Consistency Regularization for Label Sparsity in Time Series |
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✅ |
✅ |
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❌ |
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6 |
| Context-Aware Bayesian Network Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning |
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3 |
| Contextual Combinatorial Bandits with Probabilistically Triggered Arms |
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3 |
| Contextual Conservative Interleaving Bandits |
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3 |
| Contextual Reliability: When Different Features Matter in Different Contexts |
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✅ |
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3 |
| Continual Learners are Incremental Model Generalizers |
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3 |
| Continual Learning in Linear Classification on Separable Data |
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1 |
| Continual Task Allocation in Meta-Policy Network via Sparse Prompting |
✅ |
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✅ |
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4 |
| Continual Vision-Language Representation Learning with Off-Diagonal Information |
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✅ |
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✅ |
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4 |
| Continuation Path Learning for Homotopy Optimization |
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5 |
| Continuous Spatiotemporal Transformer |
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6 |
| Continuously Parameterized Mixture Models |
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3 |
| ContraBAR: Contrastive Bayes-Adaptive Deep RL |
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2 |
| Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining |
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4 |
| Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning |
✅ |
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4 |
| Contrastive Learning Meets Homophily: Two Birds with One Stone |
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4 |
| Controllability-Aware Unsupervised Skill Discovery |
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❌ |
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5 |
| Controllable Neural Symbolic Regression |
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4 |
| Controlled Differential Equations on Long Sequences via Non-standard Wavelets |
✅ |
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✅ |
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❌ |
❌ |
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5 |
| Controlled Text Generation with Natural Language Instructions |
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✅ |
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❌ |
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4 |
| Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network |
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❌ |
✅ |
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2 |
| Controlling Type Confounding in Ad Hoc Teamwork with Instance-wise Teammate Feedback Rectification |
✅ |
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❌ |
❌ |
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3 |
| Convergence of First-Order Methods for Constrained Nonconvex Optimization with Dependent Data |
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✅ |
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3 |
| Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
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1 |
| Convex Geometry of ReLU-layers, Injectivity on the Ball and Local Reconstruction |
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✅ |
✅ |
❌ |
❌ |
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4 |
| Cooperation in the Latent Space: The Benefits of Adding Mixture Components in Variational Autoencoders |
❌ |
✅ |
✅ |
✅ |
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5 |
| Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation |
✅ |
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❌ |
❌ |
❌ |
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1 |
| Cooperative Open-ended Learning Framework for Zero-Shot Coordination |
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4 |
| Coordinate Descent Methods for Fractional Minimization |
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❌ |
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5 |
| Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets |
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❌ |
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3 |
| Correcting discount-factor mismatch in on-policy policy gradient methods |
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4 |
| Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes |
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❌ |
❌ |
❌ |
❌ |
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1 |
| Counterfactual Analysis in Dynamic Latent State Models |
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5 |
| Counterfactual Identifiability of Bijective Causal Models |
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✅ |
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3 |
| Coupled Variational Autoencoder |
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3 |
| Covariate balancing using the integral probability metric for causal inference |
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❌ |
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6 |
| Crafting Training Degradation Distribution for the Accuracy-Generalization Trade-off in Real-World Super-Resolution |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
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2 |
| Cramming: Training a Language Model on a single GPU in one day. |
❌ |
✅ |
✅ |
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✅ |
❌ |
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5 |
| Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss |
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2 |
| Cross-Entropy Loss Functions: Theoretical Analysis and Applications |
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3 |
| Cross-Modal Fine-Tuning: Align then Refine |
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6 |
| CrossSplit: Mitigating Label Noise Memorization through Data Splitting |
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6 |
| Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments |
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4 |
| Curious Replay for Model-based Adaptation |
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6 |
| Curriculum Co-disentangled Representation Learning across Multiple Environments for Social Recommendation |
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4 |
| Cut your Losses with Squentropy |
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✅ |
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❌ |
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3 |
| Cyclic Block Coordinate Descent With Variance Reduction for Composite Nonconvex Optimization |
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❌ |
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4 |
| D2Match: Leveraging Deep Learning and Degeneracy for Subgraph Matching |
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4 |
| DADAO: Decoupled Accelerated Decentralized Asynchronous Optimization |
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2 |
| DDGR: Continual Learning with Deep Diffusion-based Generative Replay |
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3 |
| DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning |
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5 |
| DIVISION: Memory Efficient Training via Dual Activation Precision |
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7 |
| DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian Inference |
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4 |
| DRCFS: Doubly Robust Causal Feature Selection |
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4 |
| DRew: Dynamically Rewired Message Passing with Delay |
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5 |
| DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation |
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5 |
| DSGD-CECA: Decentralized SGD with Communication-Optimal Exact Consensus Algorithm |
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5 |
| DUET: 2D Structured and Approximately Equivariant Representations |
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4 |
| Data Efficient Neural Scaling Law via Model Reusing |
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3 |
| Data Feedback Loops: Model-driven Amplification of Dataset Biases |
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✅ |
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❌ |
❌ |
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5 |
| Data Poisoning Attacks Against Multimodal Encoders |
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❌ |
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❌ |
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3 |
| Data Representations’ Study of Latent Image Manifolds |
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2 |
| Data Structures for Density Estimation |
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4 |
| Data-Copying in Generative Models: A Formal Framework |
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3 |
| Data-Driven Subgroup Identification for Linear Regression |
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6 |
| Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the Least |
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4 |
| Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value |
❌ |
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✅ |
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5 |
| Dataset Distillation with Convexified Implicit Gradients |
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5 |
| DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution Models |
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5 |
| Decentralized SGD and Average-direction SAM are Asymptotically Equivalent |
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4 |
| Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexity |
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4 |
| Decoding Layer Saliency in Language Transformers |
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3 |
| DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design |
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6 |
| Deep Anomaly Detection under Labeling Budget Constraints |
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4 |
| Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach |
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4 |
| Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search |
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3 |
| Deep Graph Representation Learning and Optimization for Influence Maximization |
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4 |
| Deep Laplacian-based Options for Temporally-Extended Exploration |
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3 |
| Deep Latent State Space Models for Time-Series Generation |
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4 |
| Deep Perturbation Learning: Enhancing the Network Performance via Image Perturbations |
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5 |
| Deep Regression Unlearning |
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5 |
| Deep Temporal Sets with Evidential Reinforced Attentions for Unique Behavioral Pattern Discovery |
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✅ |
❌ |
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❌ |
✅ |
4 |
| Defects of Convolutional Decoder Networks in Frequency Representation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Delay-Adapted Policy Optimization and Improved Regret for Adversarial MDP with Delayed Bandit Feedback |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Delay-agnostic Asynchronous Coordinate Update Algorithm |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Delayed Bandits: When Do Intermediate Observations Help? |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Delayed Feedback in Kernel Bandits |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Delving into Noisy Label Detection with Clean Data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Demonstration-free Autonomous Reinforcement Learning via Implicit and Bidirectional Curriculum |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Demystifying Disagreement-on-the-Line in High Dimensions |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Demystifying Uneven Vulnerability of Link Stealing Attacks against Graph Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Denoising MCMC for Accelerating Diffusion-Based Generative Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Detecting Out-of-distribution Data through In-distribution Class Prior |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
4 |
| Deterministic equivalent and error universality of deep random features learning |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| DevFormer: A Symmetric Transformer for Context-Aware Device Placement |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Diagnosis, Feedback, Adaptation: A Human-in-the-Loop Framework for Test-Time Policy Adaptation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Difference of submodular minimization via DC programming |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Difference-in-Differences Meets Tree-based Methods: Heterogeneous Treatment Effects Estimation with Unmeasured Confounding |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Differentiable Multi-Target Causal Bayesian Experimental Design |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Differentiable Simulations for Enhanced Sampling of Rare Events |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Differentiable Tree Operations Promote Compositional Generalization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Differentiable and Transportable Structure Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Differential Privacy has Bounded Impact on Fairness in Classification |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Differential Privacy, Linguistic Fairness, and Training Data Influence: Impossibility and Possibility Theorems for Multilingual Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Differentially Private Distributed Bayesian Linear Regression with MCMC |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Differentially Private Hierarchical Clustering with Provable Approximation Guarantees |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Differentially Private Optimization on Large Model at Small Cost |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Differentially Private Sharpness-Aware Training |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Differentially Private Stochastic Convex Optimization under a Quantile Loss Function |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Diffusion Based Representation Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Diffusion Models are Minimax Optimal Distribution Estimators |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines? |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Diffusion Models for Black-Box Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Dimension-independent Certified Neural Network Watermarks via Mollifier Smoothing |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Dimensionality Reduction for General KDE Mode Finding |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Dink-Net: Neural Clustering on Large Graphs |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Direct Parameterization of Lipschitz-Bounded Deep Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Directed Chain Generative Adversarial Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Dirichlet Diffusion Score Model for Biological Sequence Generation |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| DiscoBAX: Discovery of optimal intervention sets in genomic experiment design |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Discover and Cure: Concept-aware Mitigation of Spurious Correlation |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Discover-Then-Rank Unlabeled Support Vectors in the Dual Space for Multi-Class Active Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Discovering Object-Centric Generalized Value Functions From Pixels |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Discrete Continuous Optimization Framework for Simultaneous Clustering and Training in Mixture Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Discrete Key-Value Bottleneck |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Disentangled Generative Models for Robust Prediction of System Dynamics |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Disentangled Multi-Fidelity Deep Bayesian Active Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Disentangled Multiplex Graph Representation Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Distance Weighted Supervised Learning for Offline Interaction Data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Distilling Internet-Scale Vision-Language Models into Embodied Agents |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Distortion and Uncertainty Aware Loss for Panoramic Depth Completion |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Distributed Linear Bandits under Communication Constraints |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Distribution Free Domain Generalization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Distribution Free Prediction Sets for Node Classification |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Distribution-dependent McDiarmid-type Inequalities for Functions of Unbounded Interaction |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Distributional Offline Policy Evaluation with Predictive Error Guarantees |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior Inference |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Divide and Conquer Dynamic Programming: An Almost Linear Time Change Point Detection Methodology in High Dimensions |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World Modelling |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Do Machine Learning Models Learn Statistical Rules Inferred from Data? |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Do Perceptually Aligned Gradients Imply Robustness? |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Do You Remember? Overcoming Catastrophic Forgetting for Fake Audio Detection |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the Machiavelli Benchmark |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| DoCoFL: Downlink Compression for Cross-Device Federated Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| DoG is SGD’s Best Friend: A Parameter-Free Dynamic Step Size Schedule |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Does Continual Learning Equally Forget All Parameters? |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Does Sparsity Help in Learning Misspecified Linear Bandits? |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Does a Neural Network Really Encode Symbolic Concepts? |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Domain Adaptation for Time Series Under Feature and Label Shifts |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Double-Weighting for Covariate Shift Adaptation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Doubly Adversarial Federated Bandits |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Doubly Optimal No-Regret Learning in Monotone Games |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Dropout Reduces Underfitting |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Dual Focal Loss for Calibration |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Dual Propagation: Accelerating Contrastive Hebbian Learning with Dyadic Neurons |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| DualHSIC: HSIC-Bottleneck and Alignment for Continual Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Dynamic Constrained Submodular Optimization with Polylogarithmic Update Time |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Dynamical Linear Bandits |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Dynamics-inspired Neuromorphic Visual Representation Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| E$(n)$ Equivariant Message Passing Simplicial Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| EF21-P and Friends: Improved Theoretical Communication Complexity for Distributed Optimization with Bidirectional Compression |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| ELSA: Efficient Label Shift Adaptation through the Lens of Semiparametric Models |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| EM-Network: Oracle Guided Self-distillation for Sequence Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Effective Neural Topic Modeling with Embedding Clustering Regularization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Effective Structured Prompting by Meta-Learning and Representative Verbalizer |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Effective and Efficient Structural Inference with Reservoir Computing |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Effectively Using Public Data in Privacy Preserving Machine Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficient Algorithms for Exact Graph Matching on Correlated Stochastic Block Models with Constant Correlation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Efficient Exploration via Epistemic-Risk-Seeking Policy Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Graph Field Integrators Meet Point Clouds |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Efficient Latency-Aware CNN Depth Compression via Two-Stage Dynamic Programming |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Efficient Learning of Mesh-Based Physical Simulation with Bi-Stride Multi-Scale Graph Neural Network |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Efficient List-Decodable Regression using Batches |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficient Online Reinforcement Learning with Offline Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Efficient Parametric Approximations of Neural Network Function Space Distance |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Personalized Federated Learning via Sparse Model-Adaptation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Efficient Quantum Algorithms for Quantum Optimal Control |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Efficient RL via Disentangled Environment and Agent Representations |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Efficient Rate Optimal Regret for Adversarial Contextual MDPs Using Online Function Approximation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Efficient Sequence Transduction by Jointly Predicting Tokens and Durations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Efficient Training of Language Models using Few-Shot Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Efficient displacement convex optimization with particle gradient descent |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Efficient preconditioned stochastic gradient descent for estimation in latent variable models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Efficiently predicting high resolution mass spectra with graph neural networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Eliminating Adversarial Noise via Information Discard and Robust Representation Restoration |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Emergence of Adaptive Circadian Rhythms in Deep Reinforcement Learning |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Emergence of Sparse Representations from Noise |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Emergent Agentic Transformer from Chain of Hindsight Experience |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Emergent Asymmetry of Precision and Recall for Measuring Fidelity and Diversity of Generative Models in High Dimensions |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Enabling First-Order Gradient-Based Learning for Equilibrium Computation in Markets |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| End-to-End Full-Atom Antibody Design |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| End-to-End Learning for Stochastic Optimization: A Bayesian Perspective |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| End-to-End Multi-Object Detection with a Regularized Mixture Model |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| End-to-end Differentiable Clustering with Associative Memories |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| End-to-end Training of Deep Boltzmann Machines by Unbiased Contrastive Divergence with Local Mode Initialization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Entropy-driven Unsupervised Keypoint Representation Learning in Videos |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Equivariance with Learned Canonicalization Functions |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Equivariant Architectures for Learning in Deep Weight Spaces |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Equivariant Polynomials for Graph Neural Networks |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Escaping saddle points in zeroth-order optimization: the power of two-point estimators |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Estimating Causal Effects using a Multi-task Deep Ensemble |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Estimating Heterogeneous Treatment Effects: Mutual Information Bounds and Learning Algorithms |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Estimating Joint Treatment Effects by Combining Multiple Experiments |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Estimating Possible Causal Effects with Latent Variables via Adjustment |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Estimating the Contamination Factor’s Distribution in Unsupervised Anomaly Detection |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Estimation Beyond Data Reweighting: Kernel Method of Moments |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Evaluating Self-Supervised Learning via Risk Decomposition |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Evaluating Unsupervised Denoising Requires Unsupervised Metrics |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Eventual Discounting Temporal Logic Counterfactual Experience Replay |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Everyone’s Preference Changes Differently: A Weighted Multi-Interest Model For Retrieval |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Evidential Interactive Learning for Medical Image Captioning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Evolving Semantic Prototype Improves Generative Zero-Shot Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Ewald-based Long-Range Message Passing for Molecular Graphs |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Exact Inference in High-order Structured Prediction |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Existence and Estimation of Critical Batch Size for Training Generative Adversarial Networks with Two Time-Scale Update Rule |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Expectation-Complete Graph Representations with Homomorphisms |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Expected Gradients of Maxout Networks and Consequences to Parameter Initialization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Exphormer: Sparse Transformers for Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Explainability as statistical inference |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Explainable Data-Driven Optimization: From Context to Decision and Back Again |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Explaining Reinforcement Learning with Shapley Values |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Explaining the effects of non-convergent MCMC in the training of Energy-Based Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Explore and Exploit the Diverse Knowledge in Model Zoo for Domain Generalization |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Exploring Chemical Space with Score-based Out-of-distribution Generation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Exploring Model Dynamics for Accumulative Poisoning Discovery |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Exploring the Benefits of Training Expert Language Models over Instruction Tuning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Exponential Smoothing for Off-Policy Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Extending Conformal Prediction to Hidden Markov Models with Exact Validity via de Finetti’s Theorem for Markov Chains |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Extrapolated Random Tree for Regression |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Extrapolative Controlled Sequence Generation via Iterative Refinement |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| FAENet: Frame Averaging Equivariant GNN for Materials Modeling |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| FAIRER: Fairness as Decision Rationale Alignment |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| FARE: Provably Fair Representation Learning with Practical Certificates |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| FLEX: an Adaptive Exploration Algorithm for Nonlinear Systems |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| FREDIS: A Fusion Framework of Refinement and Disambiguation for Unreliable Partial Label Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Facial Expression Recognition with Adaptive Frame Rate based on Multiple Testing Correction |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Fair Densities via Boosting the Sufficient Statistics of Exponential Families |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Fair Neighbor Embedding |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fair and Accurate Decision Making through Group-Aware Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Fair and Optimal Classification via Post-Processing |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Fair yet Asymptotically Equal Collaborative Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Fairness in Matching under Uncertainty |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fairness in Streaming Submodular Maximization over a Matroid Constraint |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Fast $(1+\varepsilon)$-Approximation Algorithms for Binary Matrix Factorization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Fast Algorithms for Distributed k-Clustering with Outliers |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fast Combinatorial Algorithms for Min Max Correlation Clustering |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Fast Excess Risk Rates via Offset Rademacher Complexity |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Fast Federated Machine Unlearning with Nonlinear Functional Theory |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Fast Inference from Transformers via Speculative Decoding |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fast Online Node Labeling for Very Large Graphs |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Fast Online Value-Maximizing Prediction Sets with Conformal Cost Control |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Fast Private Kernel Density Estimation via Locality Sensitive Quantization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Fast Rates for Maximum Entropy Exploration |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Fast Rates in Time-Varying Strongly Monotone Games |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fast Sampling of Diffusion Models via Operator Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fast as CHITA: Neural Network Pruning with Combinatorial Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Faster Rates of Convergence to Stationary Points in Differentially Private Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| FeDXL: Provable Federated Learning for Deep X-Risk Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Feature Directions Matter: Long-Tailed Learning via Rotated Balanced Representation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Feature Expansion for Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Feature Programming for Multivariate Time Series Prediction |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Feature learning in deep classifiers through Intermediate Neural Collapse |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Featured Graph Coarsening with Similarity Guarantees |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| FedCR: Personalized Federated Learning Based on Across-Client Common Representation with Conditional Mutual Information Regularization |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| FedDisco: Federated Learning with Discrepancy-Aware Collaboration |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| FedHPO-Bench: A Benchmark Suite for Federated Hyperparameter Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Federated Adversarial Learning: A Framework with Convergence Analysis |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Federated Conformal Predictors for Distributed Uncertainty Quantification |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Federated Heavy Hitter Recovery under Linear Sketching |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Federated Linear Contextual Bandits with User-level Differential Privacy |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Federated Online and Bandit Convex Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Few-Sample Feature Selection via Feature Manifold Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Few-bit Backward: Quantized Gradients of Activation Functions for Memory Footprint Reduction |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Fighting Fire with Fire: Contrastive Debiasing without Bias-free Data via Generative Bias-transformation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Finding Generalization Measures by Contrasting Signal and Noise |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Fisher Information Embedding for Node and Graph Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Flash: Concept Drift Adaptation in Federated Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Flexible Phase Dynamics for Bio-Plausible Contrastive Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| For Pre-Trained Vision Models in Motor Control, Not All Policy Learning Methods are Created Equal |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Forget Unlearning: Towards True Data-Deletion in Machine Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Formalizing Preferences Over Runtime Distributions |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Forward-Backward Gaussian Variational Inference via JKO in the Bures-Wasserstein Space |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fourmer: An Efficient Global Modeling Paradigm for Image Restoration |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fractional Denoising for 3D Molecular Pre-training |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Free-Form Variational Inference for Gaussian Process State-Space Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| From Adaptive Query Release to Machine Unlearning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| From Hypergraph Energy Functions to Hypergraph Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| From Perception to Programs: Regularize, Overparameterize, and Amortize |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| From Relational Pooling to Subgraph GNNs: A Universal Framework for More Expressive Graph Neural Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| From Robustness to Privacy and Back |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| From Temporal to Contemporaneous Iterative Causal Discovery in the Presence of Latent Confounders |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fully Dynamic Submodular Maximization over Matroids |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Fully-Adaptive Composition in Differential Privacy |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Function-Space Regularization in Neural Networks: A Probabilistic Perspective |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Functional Neural Networks: Shift invariant models for functional data with applications to EEG classification |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fundamental Tradeoffs in Learning with Prior Information |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Future-conditioned Unsupervised Pretraining for Decision Transformer |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| GC-Flow: A Graph-Based Flow Network for Effective Clustering |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| GFlowNet-EM for Learning Compositional Latent Variable Models |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| GFlowOut: Dropout with Generative Flow Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| GLOBE-CE: A Translation Based Approach for Global Counterfactual Explanations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| GNN&GBDT-Guided Fast Optimizing Framework for Large-scale Integer Programming |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| GNOT: A General Neural Operator Transformer for Operator Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| GOAT: A Global Transformer on Large-scale Graphs |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| GREAD: Graph Neural Reaction-Diffusion Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Gaussian processes at the Helm(holtz): A more fluid model for ocean currents |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| GeCoNeRF: Few-shot Neural Radiance Fields via Geometric Consistency |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| General Covariance Data Augmentation for Neural PDE Solvers |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| General Sequential Episodic Memory Model |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Generalization Analysis for Contrastive Representation Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Generalization Bounds using Data-Dependent Fractal Dimensions |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Generalization on the Unseen, Logic Reasoning and Degree Curriculum |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Generalized Disparate Impact for Configurable Fairness Solutions in ML |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Generalized Implicit Follow-The-Regularized-Leader |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Generalized Polyak Step Size for First Order Optimization with Momentum |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Generalized Reductions: Making any Hierarchical Clustering Fair and Balanced with Low Cost |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Generalized Teacher Forcing for Learning Chaotic Dynamics |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Generalized-Smooth Nonconvex Optimization is As Efficient As Smooth Nonconvex Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Generalizing Neural Wave Functions |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Generated Graph Detection |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Generating Language Corrections for Teaching Physical Control Tasks |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue Clouds |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Generating Private Synthetic Data with Genetic Algorithms |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Generative Adversarial Symmetry Discovery |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Generative Decoding of Visual Stimuli |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Generative Graph Dictionary Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
5 |
| Generative Pretraining for Black-Box Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Geometric Autoencoders - What You See is What You Decode |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Geometric Clifford Algebra Networks |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Geometric Latent Diffusion Models for 3D Molecule Generation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Gibbsian Polar Slice Sampling |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Git-Theta: A Git Extension for Collaborative Development of Machine Learning Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Global Context Vision Transformers |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Global Optimization with Parametric Function Approximation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Global Selection of Contrastive Batches via Optimization on Sample Permutations |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Global optimality for Euclidean CCCP under Riemannian convexity |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Global optimality of Elman-type RNNs in the mean-field regime |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Go Beyond Imagination: Maximizing Episodic Reachability with World Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Gradient Descent Converges Linearly for Logistic Regression on Separable Data |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Gradient Descent Finds the Global Optima of Two-Layer Physics-Informed Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Gradient Descent Monotonically Decreases the Sharpness of Gradient Flow Solutions in Scalar Networks and Beyond |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Gradient Descent in Neural Networks as Sequential Learning in Reproducing Kernel Banach Space |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Gradient-Free Structured Pruning with Unlabeled Data |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Gradient-based Wang-Landau Algorithm: A Novel Sampler for Output Distribution of Neural Networks over the Input Space |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Graph Contrastive Backdoor Attacks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Graph Generative Model for Benchmarking Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Graph Inductive Biases in Transformers without Message Passing |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Graph Mixup with Soft Alignments |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Graph Neural Networks with Learnable and Optimal Polynomial Bases |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Graph Neural Tangent Kernel: Convergence on Large Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Graph Positional Encoding via Random Feature Propagation |
✅ |
❌ |
✅ |
✅ |
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❌ |
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5 |
| Graph Reinforcement Learning for Network Control via Bi-Level Optimization |
❌ |
✅ |
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❌ |
✅ |
❌ |
✅ |
4 |
| Graph Switching Dynamical Systems |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Graphically Structured Diffusion Models |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Great Models Think Alike: Improving Model Reliability via Inter-Model Latent Agreement |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Grounding Language Models to Images for Multimodal Inputs and Outputs |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Group Equivariant Fourier Neural Operators for Partial Differential Equations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| GuardHFL: Privacy Guardian for Heterogeneous Federated Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Guiding Pretraining in Reinforcement Learning with Large Language Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| H-Likelihood Approach to Deep Neural Networks with Temporal-Spatial Random Effects for High-Cardinality Categorical Features |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic Encryption |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| HOPE: High-order Graph ODE For Modeling Interacting Dynamics |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Half-Hop: A graph upsampling approach for slowing down message passing |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Hardware-Aware Compression with Random Operation Access Specific Tile (ROAST) Hashing |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Harmonic Neural Networks |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Hidden Symmetries of ReLU Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Hiding Data Helps: On the Benefits of Masking for Sparse Coding |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Hierarchical Diffusion for Offline Decision Making |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Hierarchical Imitation Learning with Vector Quantized Models |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Hierarchical Neural Coding for Controllable CAD Model Generation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Hierarchies of Reward Machines |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| High Fidelity Image Counterfactuals with Probabilistic Causal Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| High Probability Convergence of Stochastic Gradient Methods |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| High-dimensional Clustering onto Hamiltonian Cycle |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| High-dimensional Location Estimation via Norm Concentration for Subgamma Vectors |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Hindsight Learning for MDPs with Exogenous Inputs |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Homomorphism AutoEncoder -- Learning Group Structured Representations from Observed Transitions |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Horizon-Free and Variance-Dependent Reinforcement Learning for Latent Markov Decision Processes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Horizon-free Learning for Markov Decision Processes and Games: Stochastically Bounded Rewards and Improved Bounds |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| How Bad is Top-$K$ Recommendation under Competing Content Creators? |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| How Do Transformers Learn Topic Structure: Towards a Mechanistic Understanding |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| How Does Information Bottleneck Help Deep Learning? |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| How Jellyfish Characterise Alternating Group Equivariant Neural Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| How Many Perturbations Break This Model? Evaluating Robustness Beyond Adversarial Accuracy |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| How Powerful are Shallow Neural Networks with Bandlimited Random Weights? |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| How much does Initialization Affect Generalization? |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| How to address monotonicity for model risk management? |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Human-Timescale Adaptation in an Open-Ended Task Space |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Hyena Hierarchy: Towards Larger Convolutional Language Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| HyperTuning: Toward Adapting Large Language Models without Back-propagation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Hyperbolic Image-text Representations |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Hyperbolic Representation Learning: Revisiting and Advancing |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Hyperparameters in Reinforcement Learning and How To Tune Them |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Hypervolume Knowledge Gradient: A Lookahead Approach for Multi-Objective Bayesian Optimization with Partial Information |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Hypothesis Transfer Learning with Surrogate Classification Losses: Generalization Bounds through Algorithmic Stability |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| I$^2$SB: Image-to-Image Schrödinger Bridge |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| ILLUME: Rationalizing Vision-Language Models through Human Interactions |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| IRNeXt: Rethinking Convolutional Network Design for Image Restoration |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Identifiability and Generalizability in Constrained Inverse Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Identifiability of Label Noise Transition Matrix |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Identification of the Adversary from a Single Adversarial Example |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Identifying Interpretable Subspaces in Image Representations |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Identifying Useful Learnwares for Heterogeneous Label Spaces |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Image Restoration with Mean-Reverting Stochastic Differential Equations |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Image Shortcut Squeezing: Countering Perturbative Availability Poisons with Compression |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Image generation with shortest path diffusion |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Implicit Graph Neural Networks: A Monotone Operator Viewpoint |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Implicit Jacobian regularization weighted with impurity of probability output |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Implicit Neural Spatial Representations for Time-dependent PDEs |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Implicit Regularization Leads to Benign Overfitting for Sparse Linear Regression |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Importance Weighted Expectation-Maximization for Protein Sequence Design |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Improved Active Multi-Task Representation Learning via Lasso |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improved Algorithms for Multi-period Multi-class Packing Problems with Bandit Feedback |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Improved Algorithms for White-Box Adversarial Streams |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved Analysis of Score-based Generative Modeling: User-Friendly Bounds under Minimal Smoothness Assumptions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Improved Learning-Augmented Algorithms for the Multi-Option Ski Rental Problem via Best-Possible Competitive Analysis |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Improved Online Conformal Prediction via Strongly Adaptive Online Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Improved Online Learning Algorithms for CTR Prediction in Ad Auctions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved Policy Evaluation for Randomized Trials of Algorithmic Resource Allocation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEs |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Improving Adversarial Robustness Through the Contrastive-Guided Diffusion Process |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Improving Adversarial Robustness of Deep Equilibrium Models with Explicit Regulations Along the Neural Dynamics |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Improving Bi-level Optimization Based Methods with Inspiration from Humans’ Classroom Study Techniques |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Improving Expert Predictions with Conformal Prediction |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Improving Fair Training under Correlation Shifts |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Improving Graph Generation by Restricting Graph Bandwidth |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Improving Graph Neural Networks with Learnable Propagation Operators |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Improving Visual Prompt Tuning for Self-supervised Vision Transformers |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Improving l1-Certified Robustness via Randomized Smoothing by Leveraging Box Constraints |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Improving the Model Consistency of Decentralized Federated Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| In Search for a Generalizable Method for Source Free Domain Adaptation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| InGram: Inductive Knowledge Graph Embedding via Relation Graphs |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| IncDSI: Incrementally Updatable Document Retrieval |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Incentivizing Exploration with Linear Contexts and Combinatorial Actions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Individually Fair Learning with One-Sided Feedback |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Inferring Relational Potentials in Interacting Systems |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Infinite Action Contextual Bandits with Reusable Data Exhaust |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Inflow, Outflow, and Reciprocity in Machine Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| InfoOT: Information Maximizing Optimal Transport |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Information-Theoretic State Space Model for Multi-View Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Infusing Lattice Symmetry Priors in Attention Mechanisms for Sample-Efficient Abstract Geometric Reasoning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Input Perturbation Reduces Exposure Bias in Diffusion Models |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Input uncertainty propagation through trained neural networks |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Instant Soup: Cheap Pruning Ensembles in A Single Pass Can Draw Lottery Tickets from Large Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Instrumental Variable Estimation of Average Partial Causal Effects |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Integrating Prior Knowledge in Contrastive Learning with Kernel |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Interactive Object Placement with Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Internally Rewarded Reinforcement Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Internet Explorer: Targeted Representation Learning on the Open Web |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Interpretable Neural-Symbolic Concept Reasoning |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Interval Bound Interpolation for Few-shot Learning with Few Tasks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Interventional Causal Representation Learning |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Intrinsic Sliced Wasserstein Distances for Comparing Collections of Probability Distributions on Manifolds and Graphs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Invariance in Policy Optimisation and Partial Identifiability in Reward Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Inverse Reinforcement Learning without Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Investigating the Role of Model-Based Learning in Exploration and Transfer |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Is Consensus Acceleration Possible in Decentralized Optimization over Slowly Time-Varying Networks? |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Is Learning Summary Statistics Necessary for Likelihood-free Inference? |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Is Overfitting Necessary for Implicit Video Representation? |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Iterative Approximate Cross-Validation |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| JAWS-X: Addressing Efficiency Bottlenecks of Conformal Prediction Under Standard and Feedback Covariate Shift |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Jump-Start Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| K-SHAP: Policy Clustering Algorithm for Anonymous Multi-Agent State-Action Pairs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| KDEformer: Accelerating Transformers via Kernel Density Estimation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Kernel Logistic Regression Approximation of an Understandable ReLU Neural Network |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Kernel QuantTree |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Kernel Sufficient Dimension Reduction and Variable Selection for Compositional Data via Amalgamation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| LEVER: Learning to Verify Language-to-Code Generation with Execution |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| LIV: Language-Image Representations and Rewards for Robotic Control |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| LSDS++ : Dual Sampling for Accelerated k-means++ |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Label differential privacy and private training data release |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Language Instructed Reinforcement Learning for Human-AI Coordination |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Large Language Models Can Be Easily Distracted by Irrelevant Context |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Large Language Models Struggle to Learn Long-Tail Knowledge |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Last Switch Dependent Bandits with Monotone Payoff Functions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Latent Traversals in Generative Models as Potential Flows |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Layered State Discovery for Incremental Autonomous Exploration |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Lazy Agents: A New Perspective on Solving Sparse Reward Problem in Multi-agent Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| LeadFL: Client Self-Defense against Model Poisoning in Federated Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learn to Accumulate Evidence from All Training Samples: Theory and Practice |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learnability and Algorithm for Continual Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Affinity with Hyperbolic Representation for Spatial Propagation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Antidote Data to Individual Unfairness |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Belief Representations for Partially Observable Deep RL |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Compiler Pass Orders using Coreset and Normalized Value Prediction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Control by Iterative Inversion |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Control-Oriented Dynamical Structure from Data |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Controllable Degradation for Real-World Super-Resolution via Constrained Flows |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Learning Deductive Reasoning from Synthetic Corpus based on Formal Logic |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Deep Time-index Models for Time Series Forecasting |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Dense Correspondences between Photos and Sketches |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Distributions over Quantum Measurement Outcomes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Dynamic Query Combinations for Transformer-based Object Detection and Segmentation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Functional Distributions with Private Labels |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning GFlowNets From Partial Episodes For Improved Convergence And Stability |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Globally Smooth Functions on Manifolds |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning Hidden Markov Models When the Locations of Missing Observations are Unknown |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Instance-Specific Augmentations by Capturing Local Invariances |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Intuitive Policies Using Action Features |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Learning Lightweight Object Detectors via Multi-Teacher Progressive Distillation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Mixtures of Gaussians with Censored Data |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Mixtures of Markov Chains and MDPs |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Learning Neural Constitutive Laws from Motion Observations for Generalizable PDE Dynamics |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Learning Neural PDE Solvers with Parameter-Guided Channel Attention |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning Perturbations to Explain Time Series Predictions |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Physical Models that Can Respect Conservation Laws |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Preconditioners for Conjugate Gradient PDE Solvers |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Learning Prescriptive ReLU Networks |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Learning Rate Schedules in the Presence of Distribution Shift |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Representations without Compositional Assumptions |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mapping |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Subpocket Prototypes for Generalizable Structure-based Drug Design |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Temporally AbstractWorld Models without Online Experimentation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Unforeseen Robustness from Out-of-distribution Data Using Equivariant Domain Translator |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Unnormalized Statistical Models via Compositional Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning for Edge-Weighted Online Bipartite Matching with Robustness Guarantees |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning in POMDPs is Sample-Efficient with Hindsight Observability |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning the Dynamics of Sparsely Observed Interacting Systems |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning the Right Layers a Data-Driven Layer-Aggregation Strategy for Semi-Supervised Learning on Multilayer Graphs |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning to Bid in Repeated First-Price Auctions with Budgets |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning to Boost Training by Periodic Nowcasting Near Future Weights |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning to Decouple Complex Systems |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning to Design Analog Circuits to Meet Threshold Specifications |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Model |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning to Initiate and Reason in Event-Driven Cascading Processes |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning to Learn from APIs: Black-Box Data-Free Meta-Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning to Maximize Mutual Information for Dynamic Feature Selection |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning to Optimize Differentiable Games |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagement |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning useful representations for shifting tasks and distributions |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning-Rate-Free Learning by D-Adaptation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning-augmented private algorithms for multiple quantile release |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| LegendreTron: Uprising Proper Multiclass Loss Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Less is More: Task-aware Layer-wise Distillation for Language Model Compression |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Leveraging Demonstrations to Improve Online Learning: Quality Matters |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Leveraging Offline Data in Online Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Leveraging Proxy of Training Data for Test-Time Adaptation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Lifelong Language Pretraining with Distribution-Specialized Experts |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Likelihood Adjusted Semidefinite Programs for Clustering Heterogeneous Data |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| LinSATNet: The Positive Linear Satisfiability Neural Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Linear CNNs Discover the Statistical Structure of the Dataset Using Only the Most Dominant Frequencies |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Linear Causal Disentanglement via Interventions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Linear Time GPs for Inferring Latent Trajectories from Neural Spike Trains |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Linear optimal partial transport embedding |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Linearly Constrained Bilevel Optimization: A Smoothed Implicit Gradient Approach |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Linkless Link Prediction via Relational Distillation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| LipsNet: A Smooth and Robust Neural Network with Adaptive Lipschitz Constant for High Accuracy Optimal Control |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Local Vertex Colouring Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Locally Regularized Neural Differential Equations: Some Black Boxes were meant to remain closed! |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Long Horizon Temperature Scaling |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Long-Tailed Recognition by Mutual Information Maximization between Latent Features and Ground-Truth Labels |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Long-Term Rhythmic Video Soundtracker |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| LongCoder: A Long-Range Pre-trained Language Model for Code Completion |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Lookahead When It Matters: Adaptive Non-causal Transformers for Streaming Neural Transducers |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| LookupFFN: Making Transformers Compute-lite for CPU inference |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Looped Transformers as Programmable Computers |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Loss Balancing for Fair Supervised Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Loss-Guided Diffusion Models for Plug-and-Play Controllable Generation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Lottery Tickets in Evolutionary Optimization: On Sparse Backpropagation-Free Trainability |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Low Complexity Homeomorphic Projection to Ensure Neural-Network Solution Feasibility for Optimization over (Non-)Convex Set |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Low-Switching Policy Gradient with Exploration via Online Sensitivity Sampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Low-Variance Gradient Estimation in Unrolled Computation Graphs with ES-Single |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Lower Bounds for Learning in Revealing POMDPs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Lowering the Pre-training Tax for Gradient-based Subset Training: A Lightweight Distributed Pre-Training Toolkit |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| MABe22: A Multi-Species Multi-Task Benchmark for Learned Representations of Behavior |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| MAGANet: Achieving Combinatorial Generalization by Modeling a Group Action |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| MANSA: Learning Fast and Slow in Multi-Agent Systems |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| MEWL: Few-shot multimodal word learning with referential uncertainty |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MG-GNN: Multigrid Graph Neural Networks for Learning Multilevel Domain Decomposition Methods |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| MODeL: Memory Optimizations for Deep Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Machine Learning Force Fields with Data Cost Aware Training |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Magneto: A Foundation Transformer |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Margin-based Neural Network Watermarking |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Margin-based sampling in high dimensions: When being active is less efficient than staying passive |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Marginalization is not Marginal: No Bad VAE Local Minima when Learning Optimal Sparse Representations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Markovian Gaussian Process Variational Autoencoders |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Masked Trajectory Models for Prediction, Representation, and Control |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Master-ASR: Achieving Multilingual Scalability and Low-Resource Adaptation in ASR with Modular Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Matrix Estimation for Individual Fairness |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Maximal Initial Learning Rates in Deep ReLU Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Maximum Optimality Margin: A Unified Approach for Contextual Linear Programming and Inverse Linear Programming |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Measuring the Impact of Programming Language Distribution |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Mechanistic Mode Connectivity |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Memory-Based Dual Gaussian Processes for Sequential Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Memory-Based Meta-Learning on Non-Stationary Distributions |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Men Also Do Laundry: Multi-Attribute Bias Amplification |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Meta Optimal Transport |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Meta-Learning the Inductive Bias of Simple Neural Circuits |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Meta-learning Parameterized Skills |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| MetaDiffuser: Diffusion Model as Conditional Planner for Offline Meta-RL |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer Tasks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Metagenomic Binning using Connectivity-constrained Variational Autoencoders |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| MetricGAN-OKD: Multi-Metric Optimization of MetricGAN via Online Knowledge Distillation for Speech Enhancement |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Mimetic Initialization of Self-Attention Layers |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Minimalistic Predictions to Schedule Jobs with Online Precedence Constraints |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Minimax estimation of discontinuous optimal transport maps: The semi-discrete case |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Minimizing Trajectory Curvature of ODE-based Generative Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Minimum Width of Leaky-ReLU Neural Networks for Uniform Universal Approximation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Mirror Sinkhorn: Fast Online Optimization on Transport Polytopes |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Mitigating Memorization of Noisy Labels by Clipping the Model Prediction |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Mitigating Spurious Correlations in Multi-modal Models during Fine-tuning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| MixFlows: principled variational inference via mixed flows |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Mixing Predictions for Online Metric Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Mixture Proportion Estimation Beyond Irreducibility |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Moccasin: Efficient Tensor Rematerialization for Neural Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Modality-Agnostic Variational Compression of Implicit Neural Representations |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Model Transferability with Responsive Decision Subjects |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Model-Aware Contrastive Learning: Towards Escaping the Dilemmas |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Model-Bellman Inconsistency for Model-based Offline Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Model-Free Robust Average-Reward Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Model-agnostic Measure of Generalization Difficulty |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Model-based Offline Reinforcement Learning with Count-based Conservatism |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Model-based Reinforcement Learning with Scalable Composite Policy Gradient Estimators |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| ModelDiff: A Framework for Comparing Learning Algorithms |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Modeling Dynamic Environments with Scene Graph Memory |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Moderately Distributional Exploration for Domain Generalization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation |
❌ |
✅ |
✅ |
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❌ |
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✅ |
4 |
| Momentum Ensures Convergence of SIGNSGD under Weaker Assumptions |
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❌ |
❌ |
❌ |
✅ |
3 |
| Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| MonoFlow: Rethinking Divergence GANs via the Perspective of Wasserstein Gradient Flows |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| MonoNeRF: Learning Generalizable NeRFs from Monocular Videos without Camera Poses |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Monotonic Location Attention for Length Generalization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Motion Question Answering via Modular Motion Programs |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Mu$^2$SLAM: Multitask, Multilingual Speech and Language Models |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Multi-Agent Best Arm Identification with Private Communications |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-Agent Learning from Learners |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Multi-Environment Pretraining Enables Transfer to Action Limited Datasets |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Multi-Fidelity Covariance Estimation in the Log-Euclidean Geometry |
✅ |
✅ |
❌ |
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❌ |
❌ |
✅ |
3 |
| Multi-Layer Neural Networks as Trainable Ladders of Hilbert Spaces |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Multi-Modal Classifiers for Open-Vocabulary Object Detection |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Multi-Objective GFlowNets |
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✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Multi-Objective Population Based Training |
✅ |
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✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-Task Differential Privacy Under Distribution Skew |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Multi-Task Off-Policy Learning from Bandit Feedback |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-Task Structural Learning using Local Task Similarity induced Neuron Creation and Removal |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Multi-User Reinforcement Learning with Low Rank Rewards |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Multi-View Masked World Models for Visual Robotic Manipulation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Multi-agent Online Scheduling: MMS Allocations for Indivisible Items |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Multi-channel Autobidding with Budget and ROI Constraints |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Multi-class Graph Clustering via Approximated Effective $p$-Resistance |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Multi-task Hierarchical Adversarial Inverse Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Multi-task Representation Learning for Pure Exploration in Linear Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| MultiAdam: Parameter-wise Scale-invariant Optimizer for Multiscale Training of Physics-informed Neural Networks |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
4 |
| MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| MultiRobustBench: Benchmarking Robustness Against Multiple Attacks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multicalibration as Boosting for Regression |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Multiple Thinking Achieving Meta-Ability Decoupling for Object Navigation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Multiplier Bootstrap-based Exploration |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multiply Robust Off-policy Evaluation and Learning under Truncation by Death |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Multisample Flow Matching: Straightening Flows with Minibatch Couplings |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Muse: Text-To-Image Generation via Masked Generative Transformers |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
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3 |
| MyoDex: A Generalizable Prior for Dexterous Manipulation |
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❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| N$\text{A}^{\text{2}}$Q: Neural Attention Additive Model for Interpretable Multi-Agent Q-Learning |
✅ |
✅ |
✅ |
❌ |
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❌ |
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5 |
| NNSplitter: An Active Defense Solution for DNN Model via Automated Weight Obfuscation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation |
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✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| NTK-approximating MLP Fusion for Efficient Language Model Fine-tuning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data |
✅ |
✅ |
✅ |
❌ |
✅ |
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5 |
| Naive imputation implicitly regularizes high-dimensional linear models |
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❌ |
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1 |
| NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations |
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❌ |
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3 |
| Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR |
✅ |
❌ |
❌ |
❌ |
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❌ |
❌ |
1 |
| Near-Optimal $Φ$-Regret Learning in Extensive-Form Games |
✅ |
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❌ |
❌ |
❌ |
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3 |
| Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime |
✅ |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Near-Optimal Cryptographic Hardness of Agnostically Learning Halfspaces and ReLU Regression under Gaussian Marginals |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Near-Optimal Quantum Coreset Construction Algorithms for Clustering |
✅ |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints |
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❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path |
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❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes |
✅ |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Nearly Optimal Algorithms with Sublinear Computational Complexity for Online Kernel Regression |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Nearly Optimal Competitive Ratio for Online Allocation Problems with Two-sided Resource Constraints and Finite Requests |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Nearly-tight Bounds for Deep Kernel Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-aware Diffusion |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Nested Elimination: A Simple Algorithm for Best-Item Identification From Choice-Based Feedback |
✅ |
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✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization |
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❌ |
❌ |
❌ |
✅ |
2 |
| Network Effects in Performative Prediction Games |
✅ |
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❌ |
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3 |
| Neural Algorithmic Reasoning with Causal Regularisation |
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✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
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2 |
| Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series |
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✅ |
❌ |
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6 |
| Neural Diffusion Processes |
✅ |
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✅ |
❌ |
✅ |
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✅ |
5 |
| Neural FIM for learning Fisher information metrics from point cloud data |
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✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Neural Inverse Operators for Solving PDE Inverse Problems |
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✅ |
✅ |
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❌ |
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5 |
| Neural Latent Aligner: Cross-trial Alignment for Learning Representations of Complex, Naturalistic Neural Data |
❌ |
❌ |
✅ |
✅ |
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4 |
| Neural Markov Jump Processes |
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❌ |
✅ |
✅ |
❌ |
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5 |
| Neural Network Accelerated Implicit Filtering: Integrating Neural Network Surrogates With Provably Convergent Derivative Free Optimization Methods |
✅ |
✅ |
✅ |
❌ |
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❌ |
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5 |
| Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective |
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❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Neural Prediction Errors enable Analogical Visual Reasoning in Human Standard Intelligence Tests |
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4 |
| Neural Status Registers |
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❌ |
❌ |
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✅ |
3 |
| Neural Stochastic Differential Games for Time-series Analysis |
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✅ |
❌ |
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❌ |
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4 |
| Neural Wasserstein Gradient Flows for Discrepancies with Riesz Kernels |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally Coupled Oscillatory Recurrent Neural Networks |
❌ |
✅ |
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❌ |
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4 |
| Neural networks trained with SGD learn distributions of increasing complexity |
❌ |
✅ |
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❌ |
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❌ |
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3 |
| Neural signature kernels as infinite-width-depth-limits of controlled ResNets |
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✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| NeuralSlice: Neural 3D Triangle Mesh Reconstruction via Slicing 4D Tetrahedral Meshes |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Neuro-Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Never mind the metrics---what about the uncertainty? Visualising binary confusion matrix metric distributions to put performance in perspective |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| New metrics and search algorithms for weighted causal DAGs |
✅ |
✅ |
❌ |
❌ |
✅ |
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4 |
| No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Non-autoregressive Conditional Diffusion Models for Time Series Prediction |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Non-stationary Reinforcement Learning under General Function Approximation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Nonlinear Advantage: Trained Networks Might Not Be As Complex as You Think |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Nonlinear Causal Discovery with Latent Confounders |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Nonparametric Density Estimation under Distribution Drift |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Nonparametric Extensions of Randomized Response for Private Confidence Sets |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Nonparametric Generative Modeling with Conditional Sliced-Wasserstein Flows |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Nonparametric Iterative Machine Teaching |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Normalizing Flows for Interventional Density Estimation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Not all Strongly Rayleigh Distributions Have Small Probabilistic Generating Circuits |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Nugget: Neural Agglomerative Embeddings of Text |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| OCD: Learning to Overfit with Conditional Diffusion Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| ODS: Test-Time Adaptation in the Presence of Open-World Data Shift |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Off-Policy Average Reward Actor-Critic with Deterministic Policy Search |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Offline Learning in Markov Games with General Function Approximation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Offline Meta Reinforcement Learning with In-Distribution Online Adaptation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Offline Reinforcement Learning with Closed-Form Policy Improvement Operators |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Omnipredictors for Constrained Optimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On Bridging the Gap between Mean Field and Finite Width Deep Random Multilayer Perceptron with Batch Normalization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Computing Optimal Tree Ensembles |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On Coresets for Clustering in Small Dimensional Euclidean spaces |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On Data Manifolds Entailed by Structural Causal Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| On Distribution Dependent Sub-Logarithmic Query Time of Learned Indexing |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Enhancing Expressive Power via Compositions of Single Fixed-Size ReLU Network |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Excess Mass Behavior in Gaussian Mixture Models with Orlicz-Wasserstein Distances |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Heterogeneous Treatment Effects in Heterogeneous Causal Graphs |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| On Investigating the Conservative Property of Score-Based Generative Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| On Kinetic Optimal Probability Paths for Generative Models |
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❌ |
✅ |
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✅ |
❌ |
✅ |
3 |
| On Many-Actions Policy Gradient |
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✅ |
✅ |
❌ |
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❌ |
✅ |
4 |
| On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| On Penalty-based Bilevel Gradient Descent Method |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| On Pitfalls of Test-Time Adaptation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline |
❌ |
✅ |
✅ |
❌ |
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❌ |
✅ |
3 |
| On Preemption and Learning in Stochastic Scheduling |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| On Provable Copyright Protection for Generative Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On Regularization and Inference with Label Constraints |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Sampling with Approximate Transport Maps |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On Second-Order Scoring Rules for Epistemic Uncertainty Quantification |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| On Uni-Modal Feature Learning in Supervised Multi-Modal Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| On User-Level Private Convex Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Complexity of Bayesian Generalization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On the Connection Between MPNN and Graph Transformer |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On the Convergence Rate of Gaussianization with Random Rotations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Convergence of Federated Averaging with Cyclic Client Participation |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| On the Convergence of Gradient Flow on Multi-layer Linear Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Convergence of SARSA with Linear Function Approximation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Effectiveness of Offline RL for Dialogue Response Generation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On the Estimation of Gaussian Mixture Copula Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On the Expressive Power of Geometric Graph Neural Networks |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Forward Invariance of Neural ODEs |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| On the Functional Similarity of Robust and Non-Robust Neural Representations |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On the Generalization of Multi-modal Contrastive Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On the Global Convergence of Fitted Q-Iteration with Two-layer Neural Network Parametrization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Global Convergence of Risk-Averse Policy Gradient Methods with Expected Conditional Risk Measures |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Identifiability and Estimation of Causal Location-Scale Noise Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Impact of Algorithmic Recourse on Social Segregation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| On the Impact of Knowledge Distillation for Model Interpretability |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| On the Initialization of Graph Neural Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On the Occupancy Measure of Non-Markovian Policies in Continuous MDPs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Optimality of Misspecified Kernel Ridge Regression |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| On the Power of Foundation Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Power of Pre-training for Generalization in RL: Provable Benefits and Hardness |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Privacy-Robustness-Utility Trilemma in Distributed Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Relationship Between Explanation and Prediction: A Causal View |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Robustness of Randomized Ensembles to Adversarial Perturbations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| On the Robustness of Text Vectorizers |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Role of Attention in Prompt-tuning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Statistical Benefits of Temporal Difference Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Stepwise Nature of Self-Supervised Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Training Instability of Shuffling SGD with Batch Normalization |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| On the Within-Group Fairness of Screening Classifiers |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| On the convergence of the MLE as an estimator of the learning rate in the Exp3 algorithm |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| One-Shot Compression of Large Edge-Exchangeable Graphs using Bits-Back Coding |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| One-Shot Federated Conformal Prediction |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| One-Step Estimator for Permuted Sparse Recovery |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| One-shot Imitation in a Non-Stationary Environment via Multi-Modal Skill |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| One-sided Matrix Completion from Two Observations Per Row |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Online Learning in Stackelberg Games with an Omniscient Follower |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Learning with Feedback Graphs: The True Shape of Regret |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Local Differential Private Quantile Inference via Self-normalization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Online Mechanism Design for Information Acquisition |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Nonstochastic Control with Adversarial and Static Constraints |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Online Platt Scaling with Calibeating |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Online Prototype Alignment for Few-shot Policy Transfer |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Online Restless Bandits with Unobserved States |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Open-VCLIP: Transforming CLIP to an Open-vocabulary Video Model via Interpolated Weight Optimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Open-Vocabulary Universal Image Segmentation with MaskCLIP |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| OpenFE: Automated Feature Generation with Expert-level Performance |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Opponent-Limited Online Search for Imperfect Information Games |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Optimal Arms Identification with Knapsacks |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimal Convergence Rates for Agnostic Nyström Kernel Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal LP Rounding and Linear-Time Approximation Algorithms for Clustering Edge-Colored Hypergraphs |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Optimal No-Regret Learning for One-Sided Lipschitz Functions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimal Rates and Efficient Algorithms for Online Bayesian Persuasion |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal Sets and Solution Paths of ReLU Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Optimal Shrinkage for Distributed Second-Order Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Optimal Stochastic Non-smooth Non-convex Optimization through Online-to-Non-convex Conversion |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal randomized multilevel Monte Carlo for repeatedly nested expectations |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimally-weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimistic Planning by Regularized Dynamic Programming |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimization for Amortized Inverse Problems |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimizing DDPM Sampling with Shortcut Fine-Tuning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Optimizing Hyperparameters with Conformal Quantile Regression |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Optimizing Mode Connectivity for Class Incremental Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Optimizing NOTEARS Objectives via Topological Swaps |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimizing the Collaboration Structure in Cross-Silo Federated Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Orthogonality-Enforced Latent Space in Autoencoders: An Approach to Learning Disentangled Representations |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Oscillation-free Quantization for Low-bit Vision Transformers |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Out-of-Domain Robustness via Targeted Augmentations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Over-parametrization via Lifting for Low-rank Matrix Sensing: Conversion of Spurious Solutions to Strict Saddle Points |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Overcoming Simplicity Bias in Deep Networks using a Feature Sieve |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| PAC Generalization via Invariant Representations |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| PAC Prediction Sets for Large Language Models of Code |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| PAC-Bayesian Generalization Bounds for Adversarial Generative Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| PAC-Bayesian Offline Contextual Bandits With Guarantees |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| PAL: Program-aided Language Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| PASTA: Pessimistic Assortment Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| PCA-based Multi-Task Learning: a Random Matrix Approach |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| PFGM++: Unlocking the Potential of Physics-Inspired Generative Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| PFNs4BO: In-Context Learning for Bayesian Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| PLay: Parametrically Conditioned Layout Generation using Latent Diffusion |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| POUF: Prompt-Oriented Unsupervised Fine-tuning for Large Pre-trained Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| PPG Reloaded: An Empirical Study on What Matters in Phasic Policy Gradient |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| PWSHAP: A Path-Wise Explanation Model for Targeted Variables |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| PaLM-E: An Embodied Multimodal Language Model |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Paging with Succinct Predictions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Parallel $Q$-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Parallel Neurosymbolic Integration with Concordia |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Parallel Online Clustering of Bandits via Hedonic Game |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Parameter-Level Soft-Masking for Continual Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Pareto Regret Analyses in Multi-objective Multi-armed Bandit |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Partial Optimality in Cubic Correlation Clustering |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Partially Observable Multi-agent RL with (Quasi-)Efficiency: The Blessing of Information Sharing |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Patch-level Contrastive Learning via Positional Query for Visual Pre-training |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Path Neural Networks: Expressive and Accurate Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Performative Recommendation: Diversifying Content via Strategic Incentives |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Performative Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Personalized Federated Learning under Mixture of Distributions |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Personalized Federated Learning with Inferred Collaboration Graphs |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Personalized Subgraph Federated Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Perturbation Analysis of Neural Collapse |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Phase Transitions in the Detection of Correlated Databases |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Phase-aware Adversarial Defense for Improving Adversarial Robustness |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| PixelAsParam: A Gradient View on Diffusion Sampling with Guidance |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Poisoning Generative Replay in Continual Learning to Promote Forgetting |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Poisoning Language Models During Instruction Tuning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Polarity Is All You Need to Learn and Transfer Faster |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Policy Contrastive Imitation Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Policy Gradient in Robust MDPs with Global Convergence Guarantee |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Policy Regularization with Dataset Constraint for Offline Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Polyhedral Complex Extraction from ReLU Networks using Edge Subdivision |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Polynomial Preconditioning for Gradient Methods |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Posterior Sampling for Deep Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Pre-computed memory or on-the-fly encoding? A hybrid approach to retrieval augmentation makes the most of your compute |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Pre-training for Speech Translation: CTC Meets Optimal Transport |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| PreNAS: Preferred One-Shot Learning Towards Efficient Neural Architecture Search |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Predictable MDP Abstraction for Unsupervised Model-Based RL |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Predicting Ordinary Differential Equations with Transformers |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Predicting Rare Events by Shrinking Towards Proportional Odds |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Predictive Flows for Faster Ford-Fulkerson |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Prefer to Classify: Improving Text Classifiers via Auxiliary Preference Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Pretraining Language Models with Human Preferences |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Pricing Experimental Design: Causal Effect, Expected Revenue and Tail Risk |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Principled Acceleration of Iterative Numerical Methods Using Machine Learning |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Principled Offline RL in the Presence of Rich Exogenous Information |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Private Federated Learning with Autotuned Compression |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Private Statistical Estimation of Many Quantiles |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Probabilistic Attention-to-Influence Neural Models for Event Sequences |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Probabilistic Categorical Adversarial Attack and Adversarial Training |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Probabilistic Concept Bottleneck Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Probabilistic Imputation for Time-series Classification with Missing Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Probably Anytime-Safe Stochastic Combinatorial Semi-Bandits |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Progressive Purification for Instance-Dependent Partial Label Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Projected Tensor Power Method for Hypergraph Community Recovery |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Prometheus: Taming Sample and Communication Complexities in Constrained Decentralized Stochastic Bilevel Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| PromptBoosting: Black-Box Text Classification with Ten Forward Passes |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Prompting Large Language Model for Machine Translation: A Case Study |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Propensity Matters: Measuring and Enhancing Balancing for Recommendation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Proper Losses for Discrete Generative Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Proper Scoring Rules for Survival Analysis |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Properties of the Mallows Model Depending on the Number of Alternatives: A Warning for an Experimentalist |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Protecting Language Generation Models via Invisible Watermarking |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Prototype-oriented unsupervised anomaly detection for multivariate time series |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Provable Benefit of Mixup for Finding Optimal Decision Boundaries |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Provable Data Subset Selection For Efficient Neural Networks Training |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Provable Dynamic Fusion for Low-Quality Multimodal Data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Provable Multi-instance Deep AUC Maximization with Stochastic Pooling |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Provable Reset-free Reinforcement Learning by No-Regret Reduction |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Provably Invariant Learning without Domain Information |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Provably Learning Object-Centric Representations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Provably and Practically Efficient Neural Contextual Bandits |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Proximal Causal Learning of Conditional Average Treatment Effects |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Pruning via Sparsity-indexed ODE: a Continuous Sparsity Viewpoint |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| QAS-Bench: Rethinking Quantum Architecture Search and A Benchmark |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| QASA: Advanced Question Answering on Scientific Articles |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Quantifying Human Priors over Social and Navigation Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Quantifying the Knowledge in GNNs for Reliable Distillation into MLPs |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Quantifying the Variability Collapse of Neural Networks |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Quantile Credit Assignment |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Quantitative Universal Approximation Bounds for Deep Belief Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Quantized Distributed Training of Large Models with Convergence Guarantees |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Quantum 3D Graph Learning with Applications to Molecule Embedding |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Quantum Lower Bounds for Finding Stationary Points of Nonconvex Functions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Quantum Policy Gradient Algorithm with Optimized Action Decoding |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Quantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks with Quantum Computation |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Quantum Speedups for Zero-Sum Games via Improved Dynamic Gibbs Sampling |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| QuantumDARTS: Differentiable Quantum Architecture Search for Variational Quantum Algorithms |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| RACE: Improve Multi-Agent Reinforcement Learning with Representation Asymmetry and Collaborative Evolution |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| RGE: A Repulsive Graph Rectification for Node Classification via Influence |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| RLEG: Vision-Language Representation Learning with Diffusion-based Embedding Generation |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| RLSbench: Domain Adaptation Under Relaxed Label Shift |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| RSC: Accelerate Graph Neural Networks Training via Randomized Sparse Computations |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Raising the Cost of Malicious AI-Powered Image Editing |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Random Grid Neural Processes for Parametric Partial Differential Equations |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Random Shuffle Transformer for Image Restoration |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Random Teachers are Good Teachers |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Randomized Schur Complement Views for Graph Contrastive Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| RankMe: Assessing the Downstream Performance of Pretrained Self-Supervised Representations by Their Rank |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| ReDi: Efficient Learning-Free Diffusion Inference via Trajectory Retrieval |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Reachability-Aware Laplacian Representation in Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Reasons for the Superiority of Stochastic Estimators over Deterministic Ones: Robustness, Consistency and Perceptual Quality |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Recasting Self-Attention with Holographic Reduced Representations |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Reconstructive Neuron Pruning for Backdoor Defense |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Recovering Top-Two Answers and Confusion Probability in Multi-Choice Crowdsourcing |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Recovery Bounds on Class-Based Optimal Transport: A Sum-of-Norms Regularization Framework |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Refined Regret for Adversarial MDPs with Linear Function Approximation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Reflected Diffusion Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Regression with Label Permutation in Generalized Linear Model |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Regression with Sensor Data Containing Incomplete Observations |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Regret Bounds for Markov Decision Processes with Recursive Optimized Certainty Equivalents |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Regret Minimization and Convergence to Equilibria in General-sum Markov Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Regret-Minimizing Double Oracle for Extensive-Form Games |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Regularization-free Diffeomorphic Temporal Alignment Nets |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Regularizing Towards Soft Equivariance Under Mixed Symmetries |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Reinforcement Learning Can Be More Efficient with Multiple Rewards |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Reinforcement Learning from Passive Data via Latent Intentions |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Reinforcement Learning in Low-rank MDPs with Density Features |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Reinforcement Learning with History Dependent Dynamic Contexts |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Relevant Walk Search for Explaining Graph Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Reliable Measures of Spread in High Dimensional Latent Spaces |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Reparameterized Policy Learning for Multimodal Trajectory Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Repository-Level Prompt Generation for Large Language Models of Code |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Representation-Driven Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Representer Point Selection for Explaining Regularized High-dimensional Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Reprogramming Pretrained Language Models for Antibody Sequence Infilling |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Restoration based Generative Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-type Samplers |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Resurrecting Recurrent Neural Networks for Long Sequences |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Rethink DARTS Search Space and Renovate a New Benchmark |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Rethinking Backdoor Attacks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Rethinking Visual Reconstruction: Experience-Based Content Completion Guided by Visual Cues |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Rethinking Warm-Starts with Predictions: Learning Predictions Close to Sets of Optimal Solutions for Faster $\text{L}$-/$\text{L}^\natural$-Convex Function Minimization |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Rethinking Weak Supervision in Helping Contrastive Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Retrieval-Augmented Multimodal Language Modeling |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Retrosynthetic Planning with Dual Value Networks |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
5 |
| Revisiting Bellman Errors for Offline Model Selection |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Revisiting Data-Free Knowledge Distillation with Poisoned Teachers |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Revisiting Discriminative vs. Generative Classifiers: Theory and Implications |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Revisiting Domain Randomization via Relaxed State-Adversarial Policy Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvature |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Revisiting Pseudo-Label for Single-Positive Multi-Label Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Revisiting Sampling for Combinatorial Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Revisiting Simple Regret: Fast Rates for Returning a Good Arm |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Revisiting Structured Variational Autoencoders |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Revisiting Weighted Aggregation in Federated Learning with Neural Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Revisiting the Linear-Programming Framework for Offline RL with General Function Approximation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Reward-Mixing MDPs with Few Latent Contexts are Learnable |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Rigid Body Flows for Sampling Molecular Crystal Structures |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Robust Budget Pacing with a Single Sample |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Robust Camera Pose Refinement for Multi-Resolution Hash Encoding |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robust Collaborative Learning with Linear Gradient Overhead |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Robust Explanation for Free or At the Cost of Faithfulness |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robust Non-Linear Feedback Coding via Power-Constrained Deep Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Robust Perception through Equivariance |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Robust Satisficing MDPs |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Robust Situational Reinforcement Learning in Face of Context Disturbances |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Robust Speech Recognition via Large-Scale Weak Supervision |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Robust Subtask Learning for Compositional Generalization |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust Weak Supervision with Variational Auto-Encoders |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robust Weight Signatures: Gaining Robustness as Easy as Patching Weights? |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust and Scalable Bayesian Online Changepoint Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Robust and private stochastic linear bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Robustly Learning a Single Neuron via Sharpness |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robustness in Multimodal Learning under Train-Test Modality Mismatch |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Rockmate: an Efficient, Fast, Automatic and Generic Tool for Re-materialization in PyTorch |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
4 |
| Rotation and Translation Invariant Representation Learning with Implicit Neural Representations |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Run-off Election: Improved Provable Defense against Data Poisoning Attacks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| SAAL: Sharpness-Aware Active Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| SAM operates far from home: eigenvalue regularization as a dynamical phenomenon |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| SDDM: Score-Decomposed Diffusion Models on Manifolds for Unpaired Image-to-Image Translation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SE(3) diffusion model with application to protein backbone generation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| SGD with AdaGrad Stepsizes: Full Adaptivity with High Probability to Unknown Parameters, Unbounded Gradients and Affine Variance |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| SGD with Large Step Sizes Learns Sparse Features |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SLAMB: Accelerated Large Batch Training with Sparse Communication |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| SRATTA: Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning. |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| STEERING : Stein Information Directed Exploration for Model-Based Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| STEP: Learning N:M Structured Sparsity Masks from Scratch with Precondition |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Safe Offline Reinforcement Learning with Real-Time Budget Constraints |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sample Complexity Bounds for Learning High-dimensional Simplices in Noisy Regimes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Sample Complexity of Probability Divergences under Group Symmetry |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Sample and Predict Your Latent: Modality-free Sequential Disentanglement via Contrastive Estimation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Sampling-Based Accuracy Testing of Posterior Estimators for General Inference |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Sampling-based Nyström Approximation and Kernel Quadrature |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Scalable Adaptive Computation for Iterative Generation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scalable Safe Policy Improvement via Monte Carlo Tree Search |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Scalable Set Encoding with Universal Mini-Batch Consistency and Unbiased Full Set Gradient Approximation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Scaling Laws for Generative Mixed-Modal Language Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Scaling Laws for Multilingual Neural Machine Translation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Scaling Laws for Reward Model Overoptimization |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Scaling Spherical CNNs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Scaling Vision Transformers to 22 Billion Parameters |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Scaling of Class-wise Training Losses for Post-hoc Calibration |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Second-Order Optimization with Lazy Hessians |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Second-order regression models exhibit progressive sharpening to the edge of stability |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Secure Federated Correlation Test and Entropy Estimation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| SeedGNN: Graph Neural Network for Supervised Seeded Graph Matching |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| SegCLIP: Patch Aggregation with Learnable Centers for Open-Vocabulary Semantic Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Self-Interpretable Time Series Prediction with Counterfactual Explanations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Self-Repellent Random Walks on General Graphs - Achieving Minimal Sampling Variance via Nonlinear Markov Chains |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Self-supervised Neural Factor Analysis for Disentangling Utterance-level Speech Representations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Self-supervised learning of Split Invariant Equivariant representations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SemSup-XC: Semantic Supervision for Zero and Few-shot Extreme Classification |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Semi Bandit dynamics in Congestion Games: Convergence to Nash Equilibrium and No-Regret Guarantees. |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Semi-Dual Unbalanced Quadratic Optimal Transport: fast statistical rates and convergent algorithm. |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Semi-Offline Reinforcement Learning for Optimized Text Generation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Semi-Parametric Contextual Pricing Algorithm using Cox Proportional Hazards Model |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Semiparametrically Efficient Off-Policy Evaluation in Linear Markov Decision Processes |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Sequence Modeling with Multiresolution Convolutional Memory |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Sequential Changepoint Detection via Backward Confidence Sequences |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sequential Counterfactual Risk Minimization |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Sequential Kernelized Independence Testing |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sequential Monte Carlo Learning for Time Series Structure Discovery |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Sequential Predictive Conformal Inference for Time Series |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Sequential Strategic Screening |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Sequential Underspecified Instrument Selection for Cause-Effect Estimation |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Set-membership Belief State-based Reinforcement Learning for POMDPs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Settling the Reward Hypothesis |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Shape-Guided Dual-Memory Learning for 3D Anomaly Detection |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Shapley Based Residual Decomposition for Instance Analysis |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Sharp Variance-Dependent Bounds in Reinforcement Learning: Best of Both Worlds in Stochastic and Deterministic Environments |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sharper Bounds for $\ell_p$ Sensitivity Sampling |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Shiftable Context: Addressing Training-Inference Context Mismatch in Simultaneous Speech Translation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Short-lived High-volume Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Shortest Edit Path Crossover: A Theory-driven Solution to the Permutation Problem in Evolutionary Neural Architecture Search |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Simple Disentanglement of Style and Content in Visual Representations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Simple Embodied Language Learning as a Byproduct of Meta-Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Simple Hardware-Efficient Long Convolutions for Sequence Modeling |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Simple and Fast Group Robustness by Automatic Feature Reweighting |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Simplex Random Features |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Simplified Temporal Consistency Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Simplifying Momentum-based Positive-definite Submanifold Optimization with Applications to Deep Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| SinDDM: A Single Image Denoising Diffusion Model |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| SinFusion: Training Diffusion Models on a Single Image or Video |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Single Point-Based Distributed Zeroth-Order Optimization with a Non-Convex Stochastic Objective Function |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Ske2Grid: Skeleton-to-Grid Representation Learning for Action Recognition |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Sketch-Flip-Merge: Mergeable Sketches for Private Distinct Counting |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sketched Ridgeless Linear Regression: The Role of Downsampling |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Sketching Meets Differential Privacy: Fast Algorithm for Dynamic Kronecker Projection Maintenance |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Slot-VAE: Object-Centric Scene Generation with Slot Attention |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| SlotGAT: Slot-based Message Passing for Heterogeneous Graphs |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Smart Initial Basis Selection for Linear Programs |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Smooth Non-stationary Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Social learning spontaneously emerges by searching optimal heuristics with deep reinforcement learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Solving High-Dimensional PDEs with Latent Spectral Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Solving Linear Programs with Fast Online Learning Algorithms |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| SpENCNN: Orchestrating Encoding and Sparsity for Fast Homomorphically Encrypted Neural Network Inference |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Sparse Learning of Dynamical Systems in RKHS: An Operator-Theoretic Approach |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| SparseGPT: Massive Language Models Can be Accurately Pruned in One-Shot |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Spatial Implicit Neural Representations for Global-Scale Species Mapping |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation |
✅ |
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❌ |
✅ |
✅ |
✅ |
✅ |
6 |
| Special Properties of Gradient Descent with Large Learning Rates |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Specializing Smaller Language Models towards Multi-Step Reasoning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Speed-Oblivious Online Scheduling: Knowing (Precise) Speeds is not Necessary |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| SpeedDETR: Speed-aware Transformers for End-to-end Object Detection |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Speeding Up Bellman Ford via Minimum Violation Permutations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SpotEM: Efficient Video Search for Episodic Memory |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Spurious Valleys and Clustering Behavior of Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Stabilizing GANs’ Training with Brownian Motion Controller |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Stabilizing Transformer Training by Preventing Attention Entropy Collapse |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Stable Estimation of Heterogeneous Treatment Effects |
✅ |
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✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Stable and Consistent Prediction of 3D Characteristic Orientation via Invariant Residual Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| State and parameter learning with PARIS particle Gibbs |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
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4 |
| Statistical Foundations of Prior-Data Fitted Networks |
❌ |
✅ |
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❌ |
❌ |
✅ |
✅ |
3 |
| Statistical Indistinguishability of Learning Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Statistical Inference and A/B Testing for First-Price Pacing Equilibria |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Statistical Inference on Multi-armed Bandits with Delayed Feedback |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Statistical Learning under Heterogeneous Distribution Shift |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Stein Variational Goal Generation for adaptive Exploration in Multi-Goal Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Stochastic Gradient Descent under Markovian Sampling Schemes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Stochastic Gradient Descent-Induced Drift of Representation in a Two-Layer Neural Network |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Stochastic Gradient Succeeds for Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Strategic Classification with Unknown User Manipulations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Stratified Adversarial Robustness with Rejection |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Streaming Active Learning with Deep Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Streaming Submodular Maximization with Differential Privacy |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| StriderNet: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Structural Re-weighting Improves Graph Domain Adaptation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Structure Learning of Latent Factors via Clique Search on Correlation Thresholded Graphs |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Structure-informed Language Models Are Protein Designers |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Structured Cooperative Learning with Graphical Model Priors |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Subequivariant Graph Reinforcement Learning in 3D Environments |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Submodular Order Functions and Assortment Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Subsample Ridge Ensembles: Equivalences and Generalized Cross-Validation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Subset Selection Based On Multiple Rankings in the Presence of Bias: Effectiveness of Fairness Constraints for Multiwinner Voting Score Functions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Subset-Based Instance Optimality in Private Estimation |
✅ |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Superhuman Fairness |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Supervised Metric Learning to Rank for Retrieval via Contextual Similarity Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Supported Trust Region Optimization for Offline Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| SurCo: Learning Linear SURrogates for COmbinatorial Nonlinear Optimization Problems |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| SurProGenes: Survival Risk-Ordered Representation of Cancer Patients and Genes for the Identification of Prognostic Genes |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Surface Snapping Optimization Layer for Single Image Object Shape Reconstruction |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
2 |
| Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Surrogate Module Learning: Reduce the Gradient Error Accumulation in Training Spiking Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Symmetry-Aware Robot Design with Structured Subgroups |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Synthetic Data, Real Errors: How (Not) to Publish and Use Synthetic Data |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Synthetic data for model selection |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| System Identification of Neural Systems: If We Got It Right, Would We Know? |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| TAN Without a Burn: Scaling Laws of DP-SGD |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| TGRL: An Algorithm for Teacher Guided Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| TIDE: Time Derivative Diffusion for Deep Learning on Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| TIPS: Topologically Important Path Sampling for Anytime Neural Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| TR0N: Translator Networks for 0-Shot Plug-and-Play Conditional Generation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| TRAK: Attributing Model Behavior at Scale |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| TabDDPM: Modelling Tabular Data with Diffusion Models |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| TabLeak: Tabular Data Leakage in Federated Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Taming graph kernels with random features |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Target-Aware Generative Augmentations for Single-Shot Adaptation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Target-based Surrogates for Stochastic Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Task-Specific Skill Localization in Fine-tuned Language Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Task-specific experimental design for treatment effect estimation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Taxonomy-Structured Domain Adaptation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Team Belief DAG: Generalizing the Sequence Form to Team Games for Fast Computation of Correlated Team Max-Min Equilibria via Regret Minimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Temporal Label Smoothing for Early Event Prediction |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Temporally Consistent Transformers for Video Generation |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Tensor Gaussian Process with Contraction for Multi-Channel Imaging Analysis |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Test-Time Style Shifting: Handling Arbitrary Styles in Domain Generalization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Test-time Adaptation with Slot-Centric Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Text Generation with Diffusion Language Models: A Pre-training Approach with Continuous Paragraph Denoise |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Text-To-4D Dynamic Scene Generation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Text-To-Concept (and Back) via Cross-Model Alignment |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| The Acquisition of Physical Knowledge in Generative Neural Networks |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
3 |
| The Benefits of Mixup for Feature Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Benefits of Model-Based Generalization in Reinforcement Learning |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Catalog Problem: Clustering and Ordering Variable-Sized Sets |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| The Computational Complexity of Concise Hypersphere Classification |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Dormant Neuron Phenomenon in Deep Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| The Edge of Orthogonality: A Simple View of What Makes BYOL Tick |
✅ |
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✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| The Fast Johnson-Lindenstrauss Transform Is Even Faster |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Flan Collection: Designing Data and Methods for Effective Instruction Tuning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| The Hessian perspective into the Nature of Convolutional Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Ideal Continual Learner: An Agent That Never Forgets |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Impact of Exploration on Convergence and Performance of Multi-Agent Q-Learning Dynamics |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The Implicit Regularization of Dynamical Stability in Stochastic Gradient Descent |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The Monge Gap: A Regularizer to Learn All Transport Maps |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| The Numerical Stability of Hyperbolic Representation Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Persistent Laplacian for Data Science: Evaluating Higher-Order Persistent Spectral Representations of Data |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| The Power of Learned Locally Linear Models for Nonlinear Policy Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Power of Uniform Sampling for k-Median |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
2 |
| The Price of Differential Privacy under Continual Observation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| The Regret of Exploration and the Control of Bad Episodes in Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Role of Entropy and Reconstruction in Multi-View Self-Supervised Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| The SSL Interplay: Augmentations, Inductive Bias, and Generalization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Saddle-Point Method in Differential Privacy |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Statistical Scope of Multicalibration |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| The Test of Tests: A Framework for Differentially Private Hypothesis Testing |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Unreasonable Effectiveness of Few-shot Learning for Machine Translation |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| The Value of Out-of-Distribution Data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| The Wisdom of Hindsight Makes Language Models Better Instruction Followers |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| The case for 4-bit precision: k-bit Inference Scaling Laws |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Theoretical Behavior of XAI Methods in the Presence of Suppressor Variables |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Theoretical Bounds on the Network Community Profile from Low-rank Semi-definite Programming |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Theory on Forgetting and Generalization of Continual Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Thompson Sampling with Diffusion Generative Prior |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Thompson Sampling with Less Exploration is Fast and Optimal |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Tied-Augment: Controlling Representation Similarity Improves Data Augmentation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Tight Certification of Adversarially Trained Neural Networks via Nonconvex Low-Rank Semidefinite Relaxations |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Tight Data Access Bounds for Private Top-$k$ Selection |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Tight Regret Bounds for Single-pass Streaming Multi-armed Bandits |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Tight and fast generalization error bound of graph embedding in metric space |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Tighter Analysis for ProxSkip |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Tighter Bounds on the Expressivity of Transformer Encoders |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Tighter Information-Theoretic Generalization Bounds from Supersamples |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Tighter Lower Bounds for Shuffling SGD: Random Permutations and Beyond |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Tilted Sparse Additive Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Topological Point Cloud Clustering |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Topological Singularity Detection at Multiple Scales |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Topologically Faithful Image Segmentation via Induced Matching of Persistence Barcodes |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Total Variation Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Toward Efficient Gradient-Based Value Estimation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Toward Large Kernel Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Towards Constituting Mathematical Structures for Learning to Optimize |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Towards Controlled Data Augmentations for Active Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Towards Deep Attention in Graph Neural Networks: Problems and Remedies |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Towards Explaining Distribution Shifts |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Towards Learning Geometric Eigen-Lengths Crucial for Fitting Tasks |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Towards Omni-generalizable Neural Methods for Vehicle Routing Problems |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Towards Quantum Machine Learning for Constrained Combinatorial Optimization: a Quantum QAP Solver |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Towards Reliable Neural Specifications |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Towards Robust Graph Incremental Learning on Evolving Graphs |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Towards Robust and Safe Reinforcement Learning with Benign Off-policy Data |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Towards Sustainable Learning: Coresets for Data-efficient Deep Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Towards Theoretical Understanding of Inverse Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Towards Trustworthy Explanation: On Causal Rationalization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Towards Unbiased Training in Federated Open-world Semi-supervised Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Towards Understanding Ensemble Distillation in Federated Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Towards Understanding Generalization of Graph Neural Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Towards Understanding Generalization of Macro-AUC in Multi-label Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Towards Understanding and Improving GFlowNet Training |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Towards Understanding and Reducing Graph Structural Noise for GNNs |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Towards a Persistence Diagram that is Robust to Noise and Varied Densities |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Towards a better understanding of representation dynamics under TD-learning |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Towards credible visual model interpretation with path attribution |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Tractable Control for Autoregressive Language Generation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Trading-Off Payments and Accuracy in Online Classification with Paid Stochastic Experts |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Trainability, Expressivity and Interpretability in Gated Neural ODEs |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Training Deep Surrogate Models with Large Scale Online Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Training Normalizing Flows from Dependent Data |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Training-Free Neural Active Learning with Initialization-Robustness Guarantees |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Transcendental Idealism of Planner: Evaluating Perception from Planning Perspective for Autonomous Driving |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Transformed Distribution Matching for Missing Value Imputation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Transformer-based Stagewise Decomposition for Large-Scale Multistage Stochastic Optimization |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
4 |
| Transformers Learn In-Context by Gradient Descent |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Transformers Meet Directed Graphs |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Transformers as Algorithms: Generalization and Stability in In-context Learning |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Trapdoor Normalization with Irreversible Ownership Verification |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Traversing Between Modes in Function Space for Fast Ensembling |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Trompt: Towards a Better Deep Neural Network for Tabular Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Truncating Trajectories in Monte Carlo Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Trustworthy Policy Learning under the Counterfactual No-Harm Criterion |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Tuning Computer Vision Models With Task Rewards |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Two-Scale Gradient Descent Ascent Dynamics Finds Mixed Nash Equilibria of Continuous Games: A Mean-Field Perspective |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| UMD: Unsupervised Model Detection for X2X Backdoor Attacks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| UPSCALE: Unconstrained Channel Pruning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Uncertain Evidence in Probabilistic Models and Stochastic Simulators |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Uncertainty Estimation by Fisher Information-based Evidential Deep Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Uncertainty Estimation for Molecules: Desiderata and Methods |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Unconstrained Online Learning with Unbounded Losses |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Uncovering Adversarial Risks of Test-Time Adaptation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Under-Counted Tensor Completion with Neural Incorporation of Attributes |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Understand and Modularize Generator Optimization in ELECTRA-style Pretraining |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Understanding Backdoor Attacks through the Adaptability Hypothesis |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Understanding Gradient Regularization in Deep Learning: Efficient Finite-Difference Computation and Implicit Bias |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Understanding Int4 Quantization for Language Models: Latency Speedup, Composability, and Failure Cases |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Understanding Oversquashing in GNNs through the Lens of Effective Resistance |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Understanding Plasticity in Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Understanding Self-Distillation in the Presence of Label Noise |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Understanding Self-Predictive Learning for Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Understanding and Defending Patched-based Adversarial Attacks for Vision Transformer |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Understanding and Generalizing Contrastive Learning from the Inverse Optimal Transport Perspective |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Understanding the Complexity Gains of Single-Task RL with a Curriculum |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Understanding the Distillation Process from Deep Generative Models to Tractable Probabilistic Circuits |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Understanding the Impact of Adversarial Robustness on Accuracy Disparity |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Understanding the Role of Feedback in Online Learning with Switching Costs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Unearthing InSights into Mars: Unsupervised Source Separation with Limited Data |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Unifying Molecular and Textual Representations via Multi-task Language Modelling |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Unifying Nesterov’s Accelerated Gradient Methods for Convex and Strongly Convex Objective Functions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Unit Scaling: Out-of-the-Box Low-Precision Training |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Universal Morphology Control via Contextual Modulation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Universal Physics-Informed Neural Networks: Symbolic Differential Operator Discovery with Sparse Data |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability |
✅ |
✅ |
✅ |
✅ |
✅ |
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7 |
| Unlocking Slot Attention by Changing Optimal Transport Costs |
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4 |
| Unscented Autoencoder |
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5 |
| Unsupervised Out-of-Distribution Detection with Diffusion Inpainting |
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4 |
| Unsupervised Skill Discovery for Learning Shared Structures across Changing Environments |
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3 |
| Unveiling The Mask of Position-Information Pattern Through the Mist of Image Features |
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4 |
| Unveiling the Latent Space Geometry of Push-Forward Generative Models |
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4 |
| User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems |
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1 |
| User-level Private Stochastic Convex Optimization with Optimal Rates |
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1 |
| Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies |
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3 |
| Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy |
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❌ |
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❌ |
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3 |
| VA-learning as a more efficient alternative to Q-learning |
✅ |
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❌ |
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❌ |
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3 |
| VIMA: Robot Manipulation with Multimodal Prompts |
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7 |
| Variance Control for Distributional Reinforcement Learning |
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4 |
| Variational Autoencoding Neural Operators |
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3 |
| Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills |
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3 |
| Variational Mixture of HyperGenerators for Learning Distributions over Functions |
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5 |
| Variational Open-Domain Question Answering |
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6 |
| Variational Sparse Inverse Cholesky Approximation for Latent Gaussian Processes via Double Kullback-Leibler Minimization |
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4 |
| Vector Quantized Wasserstein Auto-Encoder |
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4 |
| Vector-Valued Control Variates |
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4 |
| VectorMapNet: End-to-end Vectorized HD Map Learning |
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5 |
| Vertical Federated Graph Neural Network for Recommender System |
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6 |
| Von Mises Mixture Distributions for Molecular Conformation Generation |
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4 |
| WL meet VC |
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4 |
| Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap |
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1 |
| Wasserstein Barycenter Matching for Graph Size Generalization of Message Passing Neural Networks |
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7 |
| Weak Proxies are Sufficient and Preferable for Fairness with Missing Sensitive Attributes |
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4 |
| Weakly Supervised Regression with Interval Targets |
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3 |
| Weighted Flow Diffusion for Local Graph Clustering with Node Attributes: an Algorithm and Statistical Guarantees |
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4 |
| Weighted Sampling without Replacement for Deep Top-$k$ Classification |
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4 |
| Weighted Tallying Bandits: Overcoming Intractability via Repeated Exposure Optimality |
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2 |
| What Can Be Learnt With Wide Convolutional Neural Networks? |
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5 |
| What Makes Entities Similar? A Similarity Flooding Perspective for Multi-sourced Knowledge Graph Embeddings |
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6 |
| What can online reinforcement learning with function approximation benefit from general coverage conditions? |
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1 |
| What do CNNs Learn in the First Layer and Why? A Linear Systems Perspective |
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2 |
| What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL? |
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5 |
| When Personalization Harms Performance: Reconsidering the Use of Group Attributes in Prediction |
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3 |
| When Sparsity Meets Contrastive Models: Less Graph Data Can Bring Better Class-Balanced Representations |
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4 |
| When and How Does Known Class Help Discover Unknown Ones? Provable Understanding Through Spectral Analysis |
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4 |
| When do Minimax-fair Learning and Empirical Risk Minimization Coincide? |
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4 |
| When does Privileged information Explain Away Label Noise? |
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4 |
| When is Realizability Sufficient for Off-Policy Reinforcement Learning? |
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0 |
| Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression |
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2 |
| Which Invariance Should We Transfer? A Causal Minimax Learning Approach |
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7 |
| Which Tricks are Important for Learning to Rank? |
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3 |
| Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise? |
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5 |
| Who Needs to Know? Minimal Knowledge for Optimal Coordination |
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3 |
| Whose Opinions Do Language Models Reflect? |
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3 |
| Why Is Public Pretraining Necessary for Private Model Training? |
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3 |
| Why Random Pruning Is All We Need to Start Sparse |
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5 |
| Why Target Networks Stabilise Temporal Difference Methods |
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2 |
| Why do Nearest Neighbor Language Models Work? |
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4 |
| Why does Throwing Away Data Improve Worst-Group Error? |
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3 |
| Width and Depth Limits Commute in Residual Networks |
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1 |
| Wrapped Cauchy Distributed Angular Softmax for Long-Tailed Visual Recognition |
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4 |
| X-Paste: Revisiting Scalable Copy-Paste for Instance Segmentation using CLIP and StableDiffusion |
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4 |
| XTab: Cross-table Pretraining for Tabular Transformers |
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5 |
| dugMatting: Decomposed-Uncertainty-Guided Matting |
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5 |
| mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video |
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5 |
| simple diffusion: End-to-end diffusion for high resolution images |
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4 |
| spred: Solving L1 Penalty with SGD |
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5 |