| $H$-Consistency Guarantees for Regression |
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❌ |
✅ |
❌ |
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❌ |
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2 |
| $S^2$IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting |
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6 |
| $\bfΦ_\textrmFlow$: Differentiable Simulations for PyTorch, TensorFlow and Jax |
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3 |
| $\mathttVITS$ : Variational Inference Thompson Sampling for contextual bandits |
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4 |
| $\rm E(3)$-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning |
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4 |
| $\textttMoE-RBench$: Towards Building Reliable Language Models with Sparse Mixture-of-Experts |
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3 |
| $f$-Divergence Based Classification: Beyond the Use of Cross-Entropy |
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4 |
| 3D Geometric Shape Assembly via Efficient Point Cloud Matching |
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3 |
| 3D-VLA: A 3D Vision-Language-Action Generative World Model |
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4 |
| A Bayesian Approach to Online Planning |
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4 |
| A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Models |
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5 |
| A Circuit Domain Generalization Framework for Efficient Logic Synthesis in Chip Design |
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5 |
| A Closer Look at the Limitations of Instruction Tuning |
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2 |
| A Computational Framework for Solving Wasserstein Lagrangian Flows |
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4 |
| A Contextual Combinatorial Bandit Approach to Negotiation |
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3 |
| A Dense Reward View on Aligning Text-to-Image Diffusion with Preference |
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4 |
| A Differentiable Partially Observable Generalized Linear Model with Forward-Backward Message Passing |
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3 |
| A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization |
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5 |
| A Distributional Analogue to the Successor Representation |
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4 |
| A Doubly Recursive Stochastic Compositional Gradient Descent Method for Federated Multi-Level Compositional Optimization |
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4 |
| A Dual-module Framework for Counterfactual Estimation over Time |
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5 |
| A Dynamic Algorithm for Weighted Submodular Cover Problem |
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1 |
| A Dynamical Model of Neural Scaling Laws |
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2 |
| A Federated Stochastic Multi-level Compositional Minimax Algorithm for Deep AUC Maximization |
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4 |
| A Field Guide for Pacing Budget and ROS Constraints |
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2 |
| A Fine-grained Analysis of Fitted Q-evaluation: Beyond Parametric Models |
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1 |
| A Fixed-Point Approach for Causal Generative Modeling |
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5 |
| A Fresh Take on Stale Embeddings: Improving Dense Retriever Training with Corrector Networks |
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5 |
| A General Framework for Learning from Weak Supervision |
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5 |
| A General Framework for Sequential Decision-Making under Adaptivity Constraints |
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3 |
| A General Online Algorithm for Optimizing Complex Performance Metrics |
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6 |
| A General Theory for Softmax Gating Multinomial Logistic Mixture of Experts |
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❌ |
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❌ |
✅ |
✅ |
2 |
| A Generative Approach for Treatment Effect Estimation under Collider Bias: From an Out-of-Distribution Perspective |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
6 |
| A Geometric Decomposition of Finite Games: Convergence vs. Recurrence under Exponential Weights |
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❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Geometric Explanation of the Likelihood OOD Detection Paradox |
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5 |
| A Global Geometric Analysis of Maximal Coding Rate Reduction |
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3 |
| A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer |
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6 |
| A Hierarchical Adaptive Multi-Task Reinforcement Learning Framework for Multiplier Circuit Design |
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2 |
| A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts |
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4 |
| A Language Model’s Guide Through Latent Space |
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2 |
| A Linear Time and Space Local Point Cloud Geometry Encoder via Vectorized Kernel Mixture (VecKM) |
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✅ |
✅ |
❌ |
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5 |
| A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicity |
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4 |
| A Minimaximalist Approach to Reinforcement Learning from Human Feedback |
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3 |
| A Multimodal Automated Interpretability Agent |
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5 |
| A Near-Linear Time Approximation Algorithm for Beyond-Worst-Case Graph Clustering |
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1 |
| A Nearly Optimal Single Loop Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness |
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❌ |
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5 |
| A Neural-Guided Dynamic Symbolic Network for Exploring Mathematical Expressions from Data |
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6 |
| A Neural-Preconditioned Poisson Solver for Mixed Dirichlet and Neumann Boundary Conditions |
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✅ |
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4 |
| A New Branch-and-Bound Pruning Framework for $\ell_0$-Regularized Problems |
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5 |
| A New Computationally Efficient Algorithm to solve Feature Selection for Functional Data Classification in High-dimensional Spaces |
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7 |
| A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization |
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5 |
| A New Robust Partial p-Wasserstein-Based Metric for Comparing Distributions |
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❌ |
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1 |
| A New Theoretical Perspective on Data Heterogeneity in Federated Optimization |
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4 |
| A Persuasive Approach to Combating Misinformation |
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0 |
| A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs |
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❌ |
❌ |
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❌ |
1 |
| A Probabilistic Approach to Learning the Degree of Equivariance in Steerable CNNs |
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6 |
| A Provable Decision Rule for Out-of-Distribution Detection |
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2 |
| A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts |
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4 |
| A Rate-Distortion View of Uncertainty Quantification |
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5 |
| A Resilient and Accessible Distribution-Preserving Watermark for Large Language Models |
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5 |
| A Simple Early Exiting Framework for Accelerated Sampling in Diffusion Models |
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4 |
| A Single-Loop Robust Policy Gradient Method for Robust Markov Decision Processes |
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3 |
| A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules? |
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✅ |
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❌ |
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5 |
| A Space Group Symmetry Informed Network for O(3) Equivariant Crystal Tensor Prediction |
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5 |
| A Sparsity Principle for Partially Observable Causal Representation Learning |
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3 |
| A Statistical Framework for Data-dependent Retrieval-Augmented Models |
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✅ |
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❌ |
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2 |
| A Statistical Theory of Regularization-Based Continual Learning |
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❌ |
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❌ |
✅ |
2 |
| A Study of First-Order Methods with a Deterministic Relative-Error Gradient Oracle |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Subquadratic Time Algorithm for Robust Sparse Mean Estimation |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Tale of Tails: Model Collapse as a Change of Scaling Laws |
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❌ |
✅ |
❌ |
❌ |
❌ |
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2 |
| A Tensor Decomposition Perspective on Second-order RNNs |
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✅ |
❌ |
❌ |
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4 |
| A Theoretical Analysis of Backdoor Poisoning Attacks in Convolutional Neural Networks |
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❌ |
✅ |
❌ |
❌ |
❌ |
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2 |
| A Theory of Fault-Tolerant Learning |
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❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks |
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❌ |
✅ |
❌ |
❌ |
❌ |
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2 |
| A Touch, Vision, and Language Dataset for Multimodal Alignment |
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4 |
| A Unified Adaptive Testing System Enabled by Hierarchical Structure Search |
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5 |
| A Unified Framework for Learning with Nonlinear Model Classes from Arbitrary Linear Samples |
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2 |
| A Unified Linear Programming Framework for Offline Reward Learning from Human Demonstrations and Feedback |
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❌ |
❌ |
❌ |
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1 |
| A Unified View of FANOVA: A Comprehensive Bayesian Framework for Component Selection and Estimation |
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❌ |
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4 |
| A Universal Class of Sharpness-Aware Minimization Algorithms |
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4 |
| A Universal Transfer Theorem for Convex Optimization Algorithms Using Inexact First-order Oracles |
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❌ |
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1 |
| A connection between Tempering and Entropic Mirror Descent |
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3 |
| A decoder-only foundation model for time-series forecasting |
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5 |
| A fast algorithm to simulate nonlinear resistive networks |
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5 |
| A sampling theory perspective on activations for implicit neural representations |
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2 |
| A2Q+: Improving Accumulator-Aware Weight Quantization |
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3 |
| A3S: A General Active Clustering Method with Pairwise Constraints |
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5 |
| ACE: Off-Policy Actor-Critic with Causality-Aware Entropy Regularization |
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5 |
| ACM-MILP: Adaptive Constraint Modification via Grouping and Selection for Hardness-Preserving MILP Instance Generation |
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6 |
| ACPO: A Policy Optimization Algorithm for Average MDPs with Constraints |
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3 |
| AD3: Implicit Action is the Key for World Models to Distinguish the Diverse Visual Distractors |
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❌ |
❌ |
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3 |
| AI Alignment with Changing and Influenceable Reward Functions |
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❌ |
❌ |
❌ |
❌ |
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1 |
| AI Control: Improving Safety Despite Intentional Subversion |
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✅ |
❌ |
❌ |
✅ |
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4 |
| ALERT-Transformer: Bridging Asynchronous and Synchronous Machine Learning for Real-Time Event-based Spatio-Temporal Data |
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✅ |
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6 |
| AMPA: Adaptive Mixed Precision Allocation for Low-Bit Integer Training |
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5 |
| AND: Audio Network Dissection for Interpreting Deep Acoustic Models |
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6 |
| APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference |
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6 |
| AST-T5: Structure-Aware Pretraining for Code Generation and Understanding |
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✅ |
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5 |
| ATraDiff: Accelerating Online Reinforcement Learning with Imaginary Trajectories |
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❌ |
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6 |
| Absolute Policy Optimization: Enhancing Lower Probability Bound of Performance with High Confidence |
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5 |
| Accelerated Algorithms for Constrained Nonconvex-Nonconcave Min-Max Optimization and Comonotone Inclusion |
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❌ |
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1 |
| Accelerated Policy Gradient for s-rectangular Robust MDPs with Large State Spaces |
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✅ |
✅ |
❌ |
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5 |
| Accelerated Policy Gradient: On the Convergence Rates of the Nesterov Momentum for Reinforcement Learning |
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✅ |
❌ |
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❌ |
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4 |
| Accelerated Speculative Sampling Based on Tree Monte Carlo |
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❌ |
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3 |
| Accelerating Convergence in Bayesian Few-Shot Classification |
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6 |
| Accelerating Convergence of Score-Based Diffusion Models, Provably |
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❌ |
✅ |
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❌ |
❌ |
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1 |
| Accelerating Federated Learning with Quick Distributed Mean Estimation |
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5 |
| Accelerating Heterogeneous Federated Learning with Closed-form Classifiers |
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6 |
| Accelerating Iterative Retrieval-augmented Language Model Serving with Speculation |
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5 |
| Accelerating Legacy Numerical Solvers by Non-intrusive Gradient-based Meta-solving |
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5 |
| Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need |
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❌ |
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3 |
| Accelerating PDE Data Generation via Differential Operator Action in Solution Space |
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6 |
| Accelerating Parallel Sampling of Diffusion Models |
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5 |
| Accelerating Transformer Pre-training with 2:4 Sparsity |
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5 |
| Accurate LoRA-Finetuning Quantization of LLMs via Information Retention |
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✅ |
✅ |
❌ |
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5 |
| Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning |
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3 |
| Achieving Margin Maximization Exponentially Fast via Progressive Norm Rescaling |
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❌ |
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3 |
| Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Experts |
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❌ |
❌ |
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3 |
| Acquisition Conditioned Oracle for Nongreedy Active Feature Acquisition |
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❌ |
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6 |
| Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations |
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6 |
| Activation-Descent Regularization for Input Optimization of ReLU Networks |
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❌ |
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4 |
| Active Adaptive Experimental Design for Treatment Effect Estimation with Covariate Choice |
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4 |
| Active Label Correction for Semantic Segmentation with Foundation Models |
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❌ |
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5 |
| Active Preference Learning for Large Language Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Active Ranking and Matchmaking, with Perfect Matchings |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Active Statistical Inference |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Adapt and Diffuse: Sample-adaptive Reconstruction via Latent Diffusion Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Adapting Pretrained ViTs with Convolution Injector for Visuo-Motor Control |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Adapting Static Fairness to Sequential Decision-Making: Bias Mitigation Strategies towards Equal Long-term Benefit Rate |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Adaptive Accompaniment with ReaLchords |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Adaptive Advantage-Guided Policy Regularization for Offline Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Adaptive Conformal Inference by Betting |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Adaptive Group Personalization for Federated Mutual Transfer Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Adaptive Hierarchical Certification for Segmentation using Randomized Smoothing |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Adaptive Horizon Actor-Critic for Policy Learning in Contact-Rich Differentiable Simulation |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adaptive Observation Cost Control for Variational Quantum Eigensolvers |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Adaptive Online Experimental Design for Causal Discovery |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Adaptive Proximal Gradient Methods Are Universal Without Approximation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Adaptive Robust Learning using Latent Bernoulli Variables |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Adaptive Stabilization Based on Machine Learning for Column Generation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Adaptive Text Watermark for Large Language Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Adaptive-Gradient Policy Optimization: Enhancing Policy Learning in Non-Smooth Differentiable Simulations |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Adaptively Learning to Select-Rank in Online Platforms |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adaptively Perturbed Mirror Descent for Learning in Games |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Advancing DRL Agents in Commercial Fighting Games: Training, Integration, and Agent-Human Alignment |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Advancing Dynamic Sparse Training by Exploring Optimization Opportunities |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Adversarial Attacks on Combinatorial Multi-Armed Bandits |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adversarially Robust Deep Multi-View Clustering: A Novel Attack and Defense Framework |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Adversarially Robust Hypothesis Transfer Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| AegisFL: Efficient and Flexible Privacy-Preserving Byzantine-Robust Cross-silo Federated Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Agent Instructs Large Language Models to be General Zero-Shot Reasoners |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Agent-Specific Effects: A Causal Effect Propagation Analysis in Multi-Agent MDPs |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Agnostic Interactive Imitation Learning: New Theory and Practical Algorithms |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Agnostic Learning of Mixed Linear Regressions with EM and AM Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Agnostic Sample Compression Schemes for Regression |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Ai-sampler: Adversarial Learning of Markov kernels with involutive maps |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Algorithm and Hardness for Dynamic Attention Maintenance in Large Language Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Algorithmic Stability Unleashed: Generalization Bounds with Unbounded Losses |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Align Your Steps: Optimizing Sampling Schedules in Diffusion Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Aligned Objective for Soft-Pseudo-Label Generation in Supervised Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Aligning Transformers with Weisfeiler-Leman |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| All-in-one simulation-based inference |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Allocation Requires Prediction Only if Inequality Is Low |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| AlphaFold Meets Flow Matching for Generating Protein Ensembles |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| AlphaZero-Like Tree-Search can Guide Large Language Model Decoding and Training |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Ambiguity-Aware Abductive Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Ameliorate Spurious Correlations in Dataset Condensation |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Amend to Alignment: Decoupled Prompt Tuning for Mitigating Spurious Correlation in Vision-Language Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Amortized Equation Discovery in Hybrid Dynamical Systems |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Amortized Variational Deep Kernel Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Amortizing Pragmatic Program Synthesis with Rankings |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| An Analysis of Linear Time Series Forecasting Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| An Effective Dynamic Gradient Calibration Method for Continual Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| An Efficient Maximal Ancestral Graph Listing Algorithm |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| An Efficient Self-Learning Framework For Interactive Spoken Dialog Systems |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| An Embodied Generalist Agent in 3D World |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| An Empirical Study Into What Matters for Calibrating Vision-Language Models |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| An Empirical Study of Realized GNN Expressiveness |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| An Explicit Frame Construction for Normalizing 3D Point Clouds |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| An Image is Worth Multiple Words: Discovering Object Level Concepts using Multi-Concept Prompt Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| An Improved Finite-time Analysis of Temporal Difference Learning with Deep Neural Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| An Independence-promoting Loss for Music Generation with Language Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| An Infinite-Width Analysis on the Jacobian-Regularised Training of a Neural Network |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| An Information Theoretic Approach to Interaction-Grounded Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| An Information-Theoretic Analysis of In-Context Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| An Interpretable Evaluation of Entropy-based Novelty of Generative Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| An Intrinsic Vector Heat Network |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| An Iterative Min-Min Optimization Method for Sparse Bayesian Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| An LLM Compiler for Parallel Function Calling |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| An Online Optimization Perspective on First-Order and Zero-Order Decentralized Nonsmooth Nonconvex Stochastic Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| An Unsupervised Approach for Periodic Source Detection in Time Series |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| An amortized approach to non-linear mixed-effects modeling based on neural posterior estimation |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Analysis for Abductive Learning and Neural-Symbolic Reasoning Shortcuts |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Analyzing $D^α$ seeding for $k$-means |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Antibody Design Using a Score-based Diffusion Model Guided by Evolutionary, Physical and Geometric Constraints |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Any-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Applying language models to algebraic topology: generating simplicial cycles using multi-labeling in Wu’s formula |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Approximate Nearest Neighbor Search with Window Filters |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| AquaLoRA: Toward White-box Protection for Customized Stable Diffusion Models via Watermark LoRA |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Arrows of Time for Large Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| ArtWhisperer: A Dataset for Characterizing Human-AI Interactions in Artistic Creations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Assessing Large Language Models on Climate Information |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Asymmetry in Low-Rank Adapters of Foundation Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Asymptotically Optimal and Computationally Efficient Average Treatment Effect Estimation in A/B testing |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Asymptotics of Learning with Deep Structured (Random) Features |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Asymptotics of feature learning in two-layer networks after one gradient-step |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| AttNS: Attention-Inspired Numerical Solving For Limited Data Scenarios |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Attack-free Evaluating and Enhancing Adversarial Robustness on Categorical Data |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Attention Meets Post-hoc Interpretability: A Mathematical Perspective |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| AttnLRP: Attention-Aware Layer-Wise Relevance Propagation for Transformers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Attribute Based Interpretable Evaluation Metrics for Generative Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Auctionformer: A Unified Deep Learning Algorithm for Solving Equilibrium Strategies in Auction Games |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Audio Flamingo: A Novel Audio Language Model with Few-Shot Learning and Dialogue Abilities |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Auditing Private Prediction |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Augmenting Decision with Hypothesis in Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Autaptic Synaptic Circuit Enhances Spatio-temporal Predictive Learning of Spiking Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Auto-Encoding Morph-Tokens for Multimodal LLM |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Auto-Linear Phenomenon in Subsurface Imaging |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Auto-Regressive Next-Token Predictors are Universal Learners |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| AutoOS: Make Your OS More Powerful by Exploiting Large Language Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Autoencoding Conditional Neural Processes for Representation Learning |
✅ |
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✅ |
✅ |
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❌ |
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6 |
| Autoformalizing Euclidean Geometry |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Automated Evaluation of Retrieval-Augmented Language Models with Task-Specific Exam Generation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Automated Loss function Search for Class-imbalanced Node Classification |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Automated Statistical Model Discovery with Language Models |
✅ |
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✅ |
❌ |
❌ |
✅ |
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4 |
| Automating the Selection of Proxy Variables of Unmeasured Confounders |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Autonomous Sparse Mean-CVaR Portfolio Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Averaging $n$-step Returns Reduces Variance in Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| BAGEL: Bootstrapping Agents by Guiding Exploration with Language |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| BAT: Learning to Reason about Spatial Sounds with Large Language Models |
❌ |
✅ |
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❌ |
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❌ |
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4 |
| BBox-Adapter: Lightweight Adapting for Black-Box Large Language Models |
✅ |
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✅ |
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❌ |
✅ |
6 |
| BECoTTA: Input-dependent Online Blending of Experts for Continual Test-time Adaptation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| BLO-SAM: Bi-level Optimization Based Finetuning of the Segment Anything Model for Overfitting-Preventing Semantic Segmentation |
✅ |
✅ |
✅ |
✅ |
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❌ |
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6 |
| BOtied: Multi-objective Bayesian optimization with tied multivariate ranks |
✅ |
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❌ |
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❌ |
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5 |
| BRAIn: Bayesian Reward-conditioned Amortized Inference for natural language generation from feedback |
✅ |
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❌ |
❌ |
❌ |
✅ |
3 |
| BWS: Best Window Selection Based on Sample Scores for Data Pruning across Broad Ranges |
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❌ |
✅ |
❌ |
✅ |
5 |
| BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression Tasks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Bagged Deep Image Prior for Recovering Images in the Presence of Speckle Noise |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Balanced Resonate-and-Fire Neurons |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Balancing Feature Similarity and Label Variability for Optimal Size-Aware One-shot Subset Selection |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Balancing Similarity and Complementarity for Federated Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Barrier Algorithms for Constrained Non-Convex Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Batch Singular Value Polarization and Weighted Semantic Augmentation for Universal Domain Adaptation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Batch and match: black-box variational inference with a score-based divergence |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| BayOTIDE: Bayesian Online Multivariate Time Series Imputation with Functional Decomposition |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Bayesian Adaptation of Network Depth and Width for Continual Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Bayesian Design Principles for Offline-to-Online Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Bayesian Exploration Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Bayesian Knowledge Distillation: A Bayesian Perspective of Distillation with Uncertainty Quantification |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Bayesian Optimization of Function Networks with Partial Evaluations |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Bayesian Power Steering: An Effective Approach for Domain Adaptation of Diffusion Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Bayesian Program Learning by Decompiling Amortized Knowledge |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Bayesian Regret Minimization in Offline Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Be Your Own Neighborhood: Detecting Adversarial Examples by the Neighborhood Relations Built on Self-Supervised Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Behavior Generation with Latent Actions |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| BeigeMaps: Behavioral Eigenmaps for Reinforcement Learning from Images |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Benchmarking Deletion Metrics with the Principled Explanations |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Benign Overfitting in Adversarial Training of Neural Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Benign Overfitting in Two-Layer ReLU Convolutional Neural Networks for XOR Data |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Best Arm Identification for Stochastic Rising Bandits |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Best of Both Worlds Guarantees for Smoothed Online Quadratic Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Better & Faster Large Language Models via Multi-token Prediction |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Better Locally Private Sparse Estimation Given Multiple Samples Per User |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Better Safe than Sorry: Pre-training CLIP against Targeted Data Poisoning and Backdoor Attacks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| BetterV: Controlled Verilog Generation with Discriminative Guidance |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Sampling |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Beyond Implicit Bias: The Insignificance of SGD Noise in Online Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Beyond Individual Input for Deep Anomaly Detection on Tabular Data |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Beyond Point Prediction: Score Matching-based Pseudolikelihood Estimation of Neural Marked Spatio-Temporal Point Process |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Beyond Regular Grids: Fourier-Based Neural Operators on Arbitrary Domains |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Beyond Sole Strength: Customized Ensembles for Generalized Vision-Language Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Beyond the Calibration Point: Mechanism Comparison in Differential Privacy |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Beyond the Norms: Detecting Prediction Errors in Regression Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Beyond the ROC Curve: Classification Trees Using Cost-Optimal Curves, with Application to Imbalanced Datasets |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| BiE: Bi-Exponent Block Floating-Point for Large Language Models Quantization |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| BiLLM: Pushing the Limit of Post-Training Quantization for LLMs |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Modern Hopfield Model |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Bias of Stochastic Gradient Descent or the Architecture: Disentangling the Effects of Overparameterization of Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Bidirectional Reciprocative Information Communication for Few-Shot Semantic Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Bifurcated Attention for Single-Context Large-Batch Sampling |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Biharmonic Distance of Graphs and its Higher-Order Variants: Theoretical Properties with Applications to Centrality and Clustering |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Binary Decomposition: A Problem Transformation Perspective for Open-Set Semi-Supervised Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Bipartite Matching in Massive Graphs: A Tight Analysis of EDCS |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| Bivariate Causal Discovery using Bayesian Model Selection |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Block Acceleration Without Momentum: On Optimal Stepsizes of Block Gradient Descent for Least-Squares |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Boosting Offline Optimizers with Surrogate Sensitivity |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Bootstrap AutoEncoders With Contrastive Paradigm for Self-supervised Gaze Estimation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Bootstrapping Fisher Market Equilibrium and First-Price Pacing Equilibrium |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Borda Regret Minimization for Generalized Linear Dueling Bandits |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bottleneck-Minimal Indexing for Generative Document Retrieval |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Bounded and Uniform Energy-based Out-of-distribution Detection for Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Box Facets and Cut Facets of Lifted Multicut Polytopes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Boximator: Generating Rich and Controllable Motions for Video Synthesis |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Breadth-First Exploration on Adaptive Grid for Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Break the Sequential Dependency of LLM Inference Using Lookahead Decoding |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Breaking the Barrier: Enhanced Utility and Robustness in Smoothed DRL Agents |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Breaking through the learning plateaus of in-context learning in Transformer |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Bridging Data Gaps in Diffusion Models with Adversarial Noise-Based Transfer Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Bridging Environments and Language with Rendering Functions and Vision-Language Models |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Bridging Mini-Batch and Asymptotic Analysis in Contrastive Learning: From InfoNCE to Kernel-Based Losses |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Bridging Model Heterogeneity in Federated Learning via Uncertainty-based Asymmetrical Reciprocity Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Bring Your Own (Non-Robust) Algorithm to Solve Robust MDPs by Estimating The Worst Kernel |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Bringing Motion Taxonomies to Continuous Domains via GPLVM on Hyperbolic manifolds |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Building Socially-Equitable Public Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| By Tying Embeddings You Are Assuming the Distributional Hypothesis |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| ByMI: Byzantine Machine Identification with False Discovery Rate Control |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Byzantine Resilient and Fast Federated Few-Shot Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| C-RAG: Certified Generation Risks for Retrieval-Augmented Language Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| CARTE: Pretraining and Transfer for Tabular Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variables |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| CCM: Real-Time Controllable Visual Content Creation Using Text-to-Image Consistency Models |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| CF-OPT: Counterfactual Explanations for Structured Prediction |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| CHAI: Clustered Head Attention for Efficient LLM Inference |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| CHEMREASONER: Heuristic Search over a Large Language Model’s Knowledge Space using Quantum-Chemical Feedback |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| CKGConv: General Graph Convolution with Continuous Kernels |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| CLIPZyme: Reaction-Conditioned Virtual Screening of Enzymes |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| CLLMs: Consistency Large Language Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| COALA: A Practical and Vision-Centric Federated Learning Platform |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| COLD-Attack: Jailbreaking LLMs with Stealthiness and Controllability |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| COPAL: Continual Pruning in Large Language Generative Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| CRoFT: Robust Fine-Tuning with Concurrent Optimization for OOD Generalization and Open-Set OOD Detection |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| CW Complex Hypothesis for Image Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| CaM: Cache Merging for Memory-efficient LLMs Inference |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| CaPS: Collaborative and Private Synthetic Data Generation from Distributed Sources |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Calibration Bottleneck: Over-compressed Representations are Less Calibratable |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Can AI Assistants Know What They Don’t Know? |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Can Gaussian Sketching Converge Faster on a Preconditioned Landscape? |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Can Implicit Bias Imply Adversarial Robustness? |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning? |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Can Machines Learn the True Probabilities? |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Can Mamba Learn How To Learn? A Comparative Study on In-Context Learning Tasks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Can a Few Decide for Many? The Metric Distortion of Sortition |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Candidate Pseudolabel Learning: Enhancing Vision-Language Models by Prompt Tuning with Unlabeled Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| CarbonNovo: Joint Design of Protein Structure and Sequence Using a Unified Energy-based Model |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Careful with that Scalpel: Improving Gradient Surgery with an EMA |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| CasCast: Skillful High-resolution Precipitation Nowcasting via Cascaded Modelling |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Cascade-CLIP: Cascaded Vision-Language Embeddings Alignment for Zero-Shot Semantic Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Case-Based or Rule-Based: How Do Transformers Do the Math? |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Catapults in SGD: spikes in the training loss and their impact on generalization through feature learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Category-Aware Active Domain Adaptation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| CauDiTS: Causal Disentangled Domain Adaptation of Multivariate Time Series |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Causal Action Influence Aware Counterfactual Data Augmentation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Causal Customer Churn Analysis with Low-rank Tensor Block Hazard Model |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Causal Discovery via Conditional Independence Testing with Proxy Variables |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Causal Discovery with Fewer Conditional Independence Tests |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Causal Effect Identification in LiNGAM Models with Latent Confounders |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Causal Inference from Competing Treatments |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Causal Inference out of Control: Estimating Performativity without Treatment Randomization |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Causal Representation Learning Made Identifiable by Grouping of Observational Variables |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Causal Representation Learning from Multiple Distributions: A General Setting |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Causal-IQA: Towards the Generalization of Image Quality Assessment Based on Causal Inference |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Causality Based Front-door Defense Against Backdoor Attack on Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Causally Motivated Personalized Federated Invariant Learning with Shortcut-Averse Information-Theoretic Regularization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Cell2Sentence: Teaching Large Language Models the Language of Biology |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Centralized Selection with Preferences in the Presence of Biases |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Certifiably Byzantine-Robust Federated Conformal Prediction |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Chain of Code: Reasoning with a Language Model-Augmented Code Emulator |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Chain-of-Thought Predictive Control |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Challenges and Considerations in the Evaluation of Bayesian Causal Discovery |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Challenges in Training PINNs: A Loss Landscape Perspective |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| Characteristic Guidance: Non-linear Correction for Diffusion Model at Large Guidance Scale |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Characterizing Large Language Model Geometry Helps Solve Toxicity Detection and Generation |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Characterizing ResNet’s Universal Approximation Capability |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Chasing Convex Functions with Long-term Constraints |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Class-Imbalanced Graph Learning without Class Rebalancing |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Classification Under Strategic Self-Selection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
5 |
| Clifford-Steerable Convolutional Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Closing the Gap: Achieving Global Convergence (Last Iterate) of Actor-Critic under Markovian Sampling with Neural Network Parametrization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Cluster-Aware Similarity Diffusion for Instance Retrieval |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Clustered Federated Learning via Gradient-based Partitioning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| CoLoRA: Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Coactive Learning for Large Language Models using Implicit User Feedback |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Coarse-To-Fine Tensor Trains for Compact Visual Representations |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Coarse-to-Fine Highlighting: Reducing Knowledge Hallucination in Large Language Models |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Code as Reward: Empowering Reinforcement Learning with VLMs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Codebook Features: Sparse and Discrete Interpretability for Neural Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| CogBench: a large language model walks into a psychology lab |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| CogDPM: Diffusion Probabilistic Models via Cognitive Predictive Coding |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Collaborative Heterogeneous Causal Inference Beyond Meta-analysis |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Collaborative Learning with Different Labeling Functions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Collage: Light-Weight Low-Precision Strategy for LLM Training |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Collapse-Aware Triplet Decoupling for Adversarially Robust Image Retrieval |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Collective Certified Robustness against Graph Injection Attacks |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Combinatorial Approximations for Cluster Deletion: Simpler, Faster, and Better |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Combining Experimental and Historical Data for Policy Evaluation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Community-Invariant Graph Contrastive Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Compact Optimality Verification for Optimization Proxies |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Comparing Graph Transformers via Positional Encodings |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| CompeteAI: Understanding the Competition Dynamics of Large Language Model-based Agents |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Completing Visual Objects via Bridging Generation and Segmentation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Complexity Matters: Feature Learning in the Presence of Spurious Correlations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Compositional Curvature Bounds for Deep Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Compositional Few-Shot Class-Incremental Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Compositional Image Decomposition with Diffusion Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Compositional Text-to-Image Generation with Dense Blob Representations |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Compress Clean Signal from Noisy Raw Image: A Self-Supervised Approach |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Compressing Large Language Models by Joint Sparsification and Quantization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Compute Better Spent: Replacing Dense Layers with Structured Matrices |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| ConTextual: Evaluating Context-Sensitive Text-Rich Visual Reasoning in Large Multimodal Models |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Concentration Inequalities for General Functions of Heavy-Tailed Random Variables |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Conditional Common Entropy for Instrumental Variable Testing and Partial Identification |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Conditional Language Learning with Context |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count Data via Data Augmentation |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Confidence Aware Inverse Constrained Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Confidence-aware Contrastive Learning for Selective Classification |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Configurable Mirror Descent: Towards a Unification of Decision Making |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Conformal Prediction Sets Improve Human Decision Making |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Conformal Prediction for Deep Classifier via Label Ranking |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Conformal Prediction with Learned Features |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Conformal Predictions under Markovian Data |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them) |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Conformal prediction for multi-dimensional time series by ellipsoidal sets |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Conformalized Adaptive Forecasting of Heterogeneous Trajectories |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Conformalized Survival Distributions: A Generic Post-Process to Increase Calibration |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Confronting Reward Overoptimization for Diffusion Models: A Perspective of Inductive and Primacy Biases |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Connect Later: Improving Fine-tuning for Robustness with Targeted Augmentations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Connecting the Dots: Collaborative Fine-tuning for Black-Box Vision-Language Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks? |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Consistent Adversarially Robust Linear Classification: Non-Parametric Setting |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Consistent Long-Term Forecasting of Ergodic Dynamical Systems |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Consistent Submodular Maximization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Constrained Ensemble Exploration for Unsupervised Skill Discovery |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Constrained Reinforcement Learning Under Model Mismatch |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| ContPhy: Continuum Physical Concept Learning and Reasoning from Videos |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Contamination-Resilient Anomaly Detection via Adversarial Learning on Partially-Observed Normal and Anomalous Data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Contextual Feature Selection with Conditional Stochastic Gates |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Continuous Treatment Effects with Surrogate Outcomes |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Contrasting Multiple Representations with the Multi-Marginal Matching Gap |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Contrastive Learning for Clinical Outcome Prediction with Partial Data Sources |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Contrastive Predict-and-Search for Mixed Integer Linear Programs |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Contrastive Representation for Data Filtering in Cross-Domain Offline Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Controllable Prompt Tuning For Balancing Group Distributional Robustness |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Controlled Decoding from Language Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Controlling Behavioral Diversity in Multi-Agent Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet Accuracy |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Convergence Guarantees for the DeepWalk Embedding on Block Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Convergence and Complexity Guarantee for Inexact First-order Riemannian Optimization Algorithms |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Convergence of Online Learning Algorithm for a Mixture of Multiple Linear Regressions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Convergence of Some Convex Message Passing Algorithms to a Fixed Point |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Converting Transformers to Polynomial Form for Secure Inference Over Homomorphic Encryption |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Convex Relaxations of ReLU Neural Networks Approximate Global Optima in Polynomial Time |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Convex and Bilevel Optimization for Neural-Symbolic Inference and Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Cooperative Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Copula-Nested Spectral Kernel Network |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Copyright Traps for Large Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Coresets for Multiple $\ell_p$ Regression |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Correcting Diffusion-Based Perceptual Image Compression with Privileged End-to-End Decoder |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Correlation-Induced Label Prior for Semi-Supervised Multi-Label Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Counterfactual Image Editing |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Counterfactual Metarules for Local and Global Recourse |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation |
❌ |
❌ |
✅ |
❌ |
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2 |
| Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning |
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✅ |
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❌ |
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4 |
| Creative Text-to-Audio Generation via Synthesizer Programming |
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❌ |
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6 |
| Criterion Collapse and Loss Distribution Control |
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❌ |
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4 |
| Critical feature learning in deep neural networks |
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❌ |
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4 |
| Critical windows: non-asymptotic theory for feature emergence in diffusion models |
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✅ |
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2 |
| Cross-Domain Policy Adaptation by Capturing Representation Mismatch |
✅ |
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❌ |
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5 |
| Cross-domain Open-world Discovery |
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❌ |
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3 |
| Cross-view Masked Diffusion Transformers for Person Image Synthesis |
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4 |
| CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language Transformers |
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❌ |
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5 |
| CuTS: Customizable Tabular Synthetic Data Generation |
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6 |
| CurBench: Curriculum Learning Benchmark |
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5 |
| Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes |
❌ |
✅ |
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❌ |
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4 |
| D-Flow: Differentiating through Flows for Controlled Generation |
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❌ |
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5 |
| DAG-Based Column Generation for Adversarial Team Games |
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❌ |
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5 |
| DE-COP: Detecting Copyrighted Content in Language Models Training Data |
✅ |
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❌ |
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❌ |
✅ |
5 |
| DFA-RAG: Conversational Semantic Router for Large Language Model with Definite Finite Automaton |
✅ |
❌ |
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❌ |
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3 |
| DFD: Distilling the Feature Disparity Differently for Detectors |
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✅ |
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❌ |
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4 |
| DFlow: A Generative Model Combining Denoising AutoEncoder and Normalizing Flow for High Fidelity Waveform Generation |
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✅ |
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4 |
| DIDI: Diffusion-Guided Diversity for Offline Behavioral Generation |
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3 |
| DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation |
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❌ |
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4 |
| DITTO: Diffusion Inference-Time T-Optimization for Music Generation |
✅ |
❌ |
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❌ |
✅ |
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3 |
| DMTG: One-Shot Differentiable Multi-Task Grouping |
❌ |
✅ |
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❌ |
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5 |
| DNA-SE: Towards Deep Neural-Nets Assisted Semiparametric Estimation |
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✅ |
✅ |
✅ |
❌ |
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✅ |
5 |
| DNCs Require More Planning Steps |
❌ |
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❌ |
❌ |
❌ |
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1 |
| DOGE: Domain Reweighting with Generalization Estimation |
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❌ |
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4 |
| DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing Problems |
❌ |
✅ |
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✅ |
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❌ |
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5 |
| DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training |
❌ |
✅ |
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❌ |
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4 |
| DPZero: Private Fine-Tuning of Language Models without Backpropagation |
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❌ |
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6 |
| DRCT: Diffusion Reconstruction Contrastive Training towards Universal Detection of Diffusion Generated Images |
❌ |
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✅ |
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2 |
| DRED: Zero-Shot Transfer in Reinforcement Learning via Data-Regularised Environment Design |
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✅ |
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❌ |
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4 |
| DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning |
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❌ |
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5 |
| DSD-DA: Distillation-based Source Debiasing for Domain Adaptive Object Detection |
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✅ |
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4 |
| DUPLEX: Dual GAT for Complex Embedding of Directed Graphs |
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✅ |
✅ |
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❌ |
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4 |
| Data Engineering for Scaling Language Models to 128K Context |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
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3 |
| Data Poisoning Attacks against Conformal Prediction |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond |
✅ |
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✅ |
✅ |
❌ |
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5 |
| Data-Efficient Molecular Generation with Hierarchical Textual Inversion |
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6 |
| Data-efficient Large Vision Models through Sequential Autoregression |
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❌ |
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5 |
| Data-free Distillation of Diffusion Models with Bootstrapping |
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5 |
| Data-free Neural Representation Compression with Riemannian Neural Dynamics |
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5 |
| DataFreeShield: Defending Adversarial Attacks without Training Data |
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❌ |
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6 |
| DeCoOp: Robust Prompt Tuning with Out-of-Distribution Detection |
❌ |
✅ |
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5 |
| Dealing With Unbounded Gradients in Stochastic Saddle-point Optimization |
✅ |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Debating with More Persuasive LLMs Leads to More Truthful Answers |
✅ |
✅ |
✅ |
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❌ |
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5 |
| Debiased Distribution Compression |
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❌ |
✅ |
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5 |
| Debiased Offline Representation Learning for Fast Online Adaptation in Non-stationary Dynamics |
✅ |
✅ |
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❌ |
❌ |
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4 |
| Decentralized Convex Finite-Sum Optimization with Better Dependence on Condition Numbers |
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✅ |
❌ |
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❌ |
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3 |
| Deciphering RNA Secondary Structure Prediction: A Probabilistic K-Rook Matching Perspective |
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✅ |
❌ |
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5 |
| DecisionNCE: Embodied Multimodal Representations via Implicit Preference Learning |
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5 |
| Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression |
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4 |
| Decoding-time Realignment of Language Models |
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4 |
| Decomposable Submodular Maximization in Federated Setting |
✅ |
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❌ |
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❌ |
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1 |
| Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling |
❌ |
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✅ |
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❌ |
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5 |
| Decomposing and Editing Predictions by Modeling Model Computation |
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6 |
| Deconstructing the Goldilocks Zone of Neural Network Initialization |
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3 |
| Decouple then Classify: A Dynamic Multi-view Labeling Strategy with Shared and Specific Information |
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5 |
| Decoupling Feature Extraction and Classification Layers for Calibrated Neural Networks |
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✅ |
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5 |
| Decoupling Learning and Decision-Making: Breaking the $\mathcalO(\sqrtT)$ Barrier in Online Resource Allocation with First-Order Methods |
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2 |
| Deep Demonstration Tracing: Learning Generalizable Imitator Policy for Runtime Imitation from a Single Demonstration |
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3 |
| Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures |
❌ |
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4 |
| Deep Functional Factor Models: Forecasting High-Dimensional Functional Time Series via Bayesian Nonparametric Factorization |
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5 |
| Deep Fusion: Efficient Network Training via Pre-trained Initializations |
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3 |
| Deep Networks Always Grok and Here is Why |
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✅ |
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4 |
| Deep Neural Room Acoustics Primitive |
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4 |
| Deep Regression Representation Learning with Topology |
❌ |
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4 |
| Deep Stochastic Mechanics |
✅ |
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4 |
| DeepPolar: Inventing Nonlinear Large-Kernel Polar Codes via Deep Learning |
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3 |
| Deeper or Wider: A Perspective from Optimal Generalization Error with Sobolev Loss |
❌ |
❌ |
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❌ |
❌ |
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0 |
| Defense against Backdoor Attack on Pre-trained Language Models via Head Pruning and Attention Normalization |
✅ |
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6 |
| Defense against Model Extraction Attack by Bayesian Active Watermarking |
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4 |
| Defining Neural Network Architecture through Polytope Structures of Datasets |
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3 |
| Degeneration-free Policy Optimization: RL Fine-Tuning for Language Models without Degeneration |
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3 |
| Delaunay Graph: Addressing Over-Squashing and Over-Smoothing Using Delaunay Triangulation |
❌ |
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4 |
| Deletion-Anticipative Data Selection with a Limited Budget |
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6 |
| Delving into Differentially Private Transformer |
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4 |
| Delving into the Convergence of Generalized Smooth Minimax Optimization |
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4 |
| Demystifying SGD with Doubly Stochastic Gradients |
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2 |
| Denoising Autoregressive Representation Learning |
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3 |
| Dense Reward for Free in Reinforcement Learning from Human Feedback |
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5 |
| Density Ratio Estimation with Doubly Strong Robustness |
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3 |
| Density-Softmax: Efficient Test-time Model for Uncertainty Estimation and Robustness under Distribution Shifts |
✅ |
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6 |
| Designing Decision Support Systems using Counterfactual Prediction Sets |
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7 |
| DetKDS: Knowledge Distillation Search for Object Detectors |
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6 |
| Detecting Any instruction-to-answer interaction relationship:Universal Instruction-to-Answer Navigator for Med-VQA |
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4 |
| Detecting Influence Structures in Multi-Agent Reinforcement Learning |
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2 |
| Detecting and Identifying Selection Structure in Sequential Data |
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1 |
| DiJiang: Efficient Large Language Models through Compact Kernelization |
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4 |
| DiNADO: Norm-Disentangled Neurally-Decomposed Oracles for Controlling Language Models |
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3 |
| Diagnosing the Compositional Knowledge of Vision Language Models from a Game-Theoretic View |
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1 |
| DiffAug: Enhance Unsupervised Contrastive Learning with Domain-Knowledge-Free Diffusion-based Data Augmentation |
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5 |
| DiffDA: a Diffusion model for weather-scale Data Assimilation |
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6 |
| DiffFPR: Diffusion Prior for Oversampled Fourier Phase Retrieval |
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4 |
| DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching |
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5 |
| Differentiability and Optimization of Multiparameter Persistent Homology |
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3 |
| Differentiable Annealed Importance Sampling Minimizes The Jensen-Shannon Divergence Between Initial and Target Distribution |
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4 |
| Differentiable Combinatorial Scheduling at Scale |
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6 |
| Differentiable Distributionally Robust Optimization Layers |
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4 |
| Differentiable Mapper for Topological Optimization of Data Representation |
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4 |
| Differentiable Model Scaling using Differentiable Topk |
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4 |
| Differentiable Weightless Neural Networks |
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4 |
| Differentially Private Bias-Term Fine-tuning of Foundation Models |
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5 |
| Differentially Private Decentralized Learning with Random Walks |
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5 |
| Differentially Private Domain Adaptation with Theoretical Guarantees |
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4 |
| Differentially Private Post-Processing for Fair Regression |
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4 |
| Differentially Private Representation Learning via Image Captioning |
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4 |
| Differentially Private Sum-Product Networks |
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4 |
| Differentially Private Synthetic Data via Foundation Model APIs 2: Text |
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6 |
| Differentially Private Worst-group Risk Minimization |
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1 |
| Differentially private exact recovery for stochastic block models |
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1 |
| Diffuse, Sample, Project: Plug-And-Play Controllable Graph Generation |
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3 |
| Diffusion Language Models Are Versatile Protein Learners |
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3 |
| Diffusion Model-Augmented Behavioral Cloning |
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4 |
| Diffusion Models Demand Contrastive Guidance for Adversarial Purification to Advance |
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5 |
| Diffusion Models Encode the Intrinsic Dimension of Data Manifolds |
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5 |
| Diffusion Posterior Sampling is Computationally Intractable |
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1 |
| Diffusion Rejection Sampling |
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6 |
| Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations |
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4 |
| Diffusion-based Missing-view Generation With the Application on Incomplete Multi-view Clustering |
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4 |
| Diffusive Gibbs Sampling |
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4 |
| DiracDiffusion: Denoising and Incremental Reconstruction with Assured Data-Consistency |
✅ |
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6 |
| Directly Denoising Diffusion Models |
✅ |
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5 |
| Dirichlet Flow Matching with Applications to DNA Sequence Design |
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6 |
| DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents |
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6 |
| Discounted Adaptive Online Learning: Towards Better Regularization |
✅ |
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4 |
| Discovering Bias in Latent Space: An Unsupervised Debiasing Approach |
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✅ |
❌ |
✅ |
5 |
| Discovering Environments with XRM |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Discovering Features with Synergistic Interactions in Multiple Views |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Discovering Mixtures of Structural Causal Models from Time Series Data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Discovering Multiple Solutions from a Single Task in Offline Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Discrete Latent Perspective Learning for Segmentation and Detection |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Disentangled 3D Scene Generation with Layout Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Disentangled Continual Graph Neural Architecture Search with Invariant Modular Supernet |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Disentangled Graph Self-supervised Learning for Out-of-Distribution Generalization |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Disentanglement Learning via Topology |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Disguised Copyright Infringement of Latent Diffusion Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Disparate Impact on Group Accuracy of Linearization for Private Inference |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Dissecting Multimodality in VideoQA Transformer Models by Impairing Modality Fusion |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| DistiLLM: Towards Streamlined Distillation for Large Language Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Distilling Morphology-Conditioned Hypernetworks for Efficient Universal Morphology Control |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Distinguishing the Knowable from the Unknowable with Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Distributed Bilevel Optimization with Communication Compression |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Distributed High-Dimensional Quantile Regression: Estimation Efficiency and Support Recovery |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Distribution Alignment Optimization through Neural Collapse for Long-tailed Classification |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Distributional Bellman Operators over Mean Embeddings |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Distributionally Robust Data Valuation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Ditto: Quantization-aware Secure Inference of Transformers upon MPC |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Diversified Batch Selection for Training Acceleration |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Dataset |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Do Efficient Transformers Really Save Computation? |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners? |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Do Large Code Models Understand Programming Concepts? Counterfactual Analysis for Code Predicates |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Do Large Language Models Perform the Way People Expect? Measuring the Human Generalization Function |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Do Topological Characteristics Help in Knowledge Distillation? |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Do Transformer World Models Give Better Policy Gradients? |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| DoRA: Weight-Decomposed Low-Rank Adaptation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Does Label Smoothing Help Deep Partial Label Learning? |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Domain Generalisation via Imprecise Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Domain-wise Data Acquisition to Improve Performance under Distribution Shift |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Don’t Label Twice: Quantity Beats Quality when Comparing Binary Classifiers on a Budget |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Don’t be so Negative! Score-based Generative Modeling with Oracle-assisted Guidance |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Don’t trust your eyes: on the (un)reliability of feature visualizations |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| DoraemonGPT: Toward Understanding Dynamic Scenes with Large Language Models (Exemplified as A Video Agent) |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Double Momentum Method for Lower-Level Constrained Bilevel Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Double Stochasticity Gazes Faster: Snap-Shot Decentralized Stochastic Gradient Tracking Methods |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Double-Step Alternating Extragradient with Increasing Timescale Separation for Finding Local Minimax Points: Provable Improvements |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Dr. Strategy: Model-Based Generalist Agents with Strategic Dreaming |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Drug Discovery with Dynamic Goal-aware Fragments |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| DsDm: Model-Aware Dataset Selection with Datamodels |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Dual Operating Modes of In-Context Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| DynSyn: Dynamical Synergistic Representation for Efficient Learning and Control in Overactuated Embodied Systems |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Dynamic Anisotropic Smoothing for Noisy Derivative-Free Optimization |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Dynamic Correlation Clustering in Sublinear Update Time |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Dynamic Evaluation of Large Language Models by Meta Probing Agents |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Dynamic Facility Location in High Dimensional Euclidean Spaces |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Dynamic Metric Embedding into lp Space |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Dynamic Spectral Clustering with Provable Approximation Guarantee |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Dynamic Survival Analysis with Controlled Latent States |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| DéjàVu: KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| E$^2$GAN: Efficient Training of Efficient GANs for Image-to-Image Translation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| ED-Copilot: Reduce Emergency Department Wait Time with Language Model Diagnostic Assistance |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| EDISON: Enhanced Dictionary-Induced Tensorized Incomplete Multi-View Clustering with Gaussian Error Rank Minimization |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| ELF: Encoding Speaker-Specific Latent Speech Feature for Speech Synthesis |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| ELTA: An Enhancer against Long-Tail for Aesthetics-oriented Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| EMC$^2$: Efficient MCMC Negative Sampling for Contrastive Learning with Global Convergence |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| ERQ: Error Reduction for Post-Training Quantization of Vision Transformers |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| ESM All-Atom: Multi-Scale Protein Language Model for Unified Molecular Modeling |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| ESNet: Evolution and Succession Network for High-Resolution Salient Object Detection |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| ETHER: Efficient Finetuning of Large-Scale Models with Hyperplane Reflections |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| EVEREST: Efficient Masked Video Autoencoder by Removing Redundant Spatiotemporal Tokens |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Early Time Classification with Accumulated Accuracy Gap Control |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Easing Concept Bleeding in Diffusion via Entity Localization and Anchoring |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Editing Partially Observable Networks via Graph Diffusion Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Effective Federated Graph Matching |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Effects of Exponential Gaussian Distribution on (Double Sampling) Randomized Smoothing |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Efficient Algorithms for Empirical Group Distributionally Robust Optimization and Beyond |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Algorithms for Sum-Of-Minimum Optimization |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Black-box Adversarial Attacks via Bayesian Optimization Guided by a Function Prior |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Efficient Contextual Bandits with Uninformed Feedback Graphs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Contrastive Learning for Fast and Accurate Inference on Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Efficient Denoising Diffusion via Probabilistic Masking |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficient Error Certification for Physics-Informed Neural Networks |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
2 |
| Efficient Exploration for LLMs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Exploration in Average-Reward Constrained Reinforcement Learning: Achieving Near-Optimal Regret With Posterior Sampling |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank Bandits |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Efficient Mixture Learning in Black-Box Variational Inference |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficient Non-stationary Online Learning by Wavelets with Applications to Online Distribution Shift Adaptation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Efficient Online Set-valued Classification with Bandit Feedback |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Efficient PAC Learnability of Dynamical Systems Over Multilayer Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficient Pareto Manifold Learning with Low-Rank Structure |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Efficient Policy Evaluation with Offline Data Informed Behavior Policy Design |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Precision and Recall Metrics for Assessing Generative Models using Hubness-aware Sampling |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficient Stochastic Approximation of Minimax Excess Risk Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Efficient Value Iteration for s-rectangular Robust Markov Decision Processes |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Efficient World Models with Context-Aware Tokenization |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficient and Effective Time-Series Forecasting with Spiking Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| EfficientZero V2: Mastering Discrete and Continuous Control with Limited Data |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Eluder-based Regret for Stochastic Contextual MDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Embarrassingly Parallel GFlowNets |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Embodied CoT Distillation From LLM To Off-the-shelf Agents |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Emergence of In-Context Reinforcement Learning from Noise Distillation |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Emergent Equivariance in Deep Ensembles |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Emergent Representations of Program Semantics in Language Models Trained on Programs |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Empowering Graph Invariance Learning with Deep Spurious Infomax |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Enabling Few-Shot Learning with PID Control: A Layer Adaptive Optimizer |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Enabling Uncertainty Estimation in Iterative Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Encodings for Prediction-based Neural Architecture Search |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanations |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Energy-Efficient Gaussian Processes Using Low-Precision Arithmetic |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Energy-based Backdoor Defense without Task-Specific Samples and Model Retraining |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Enforcing Constraints in RNA Secondary Structure Predictions: A Post-Processing Framework Based on the Assignment Problem |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Enhancing Adversarial Robustness in SNNs with Sparse Gradients |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Enhancing Class-Imbalanced Learning with Pre-Trained Guidance through Class-Conditional Knowledge Distillation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Enhancing Cross-Modal Fine-Tuning with Gradually Intermediate Modality Generation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Enhancing Implicit Shape Generators Using Topological Regularizations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Enhancing Size Generalization in Graph Neural Networks through Disentangled Representation Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Enhancing Storage and Computational Efficiency in Federated Multimodal Learning for Large-Scale Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Enhancing Sufficient Dimension Reduction via Hellinger Correlation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Enhancing Trajectory Prediction through Self-Supervised Waypoint Distortion Prediction |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Enhancing Value Function Estimation through First-Order State-Action Dynamics in Offline Reinforcement Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Enhancing Vision Transformer: Amplifying Non-Linearity in Feedforward Network Module |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Ensemble Pruning for Out-of-distribution Generalization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Entropy-Reinforced Planning with Large Language Models for Drug Discovery |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Environment Design for Inverse Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detection |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| EquiAV: Leveraging Equivariance for Audio-Visual Contrastive Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Equilibrium of Data Markets with Externality |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Equivariant Deep Weight Space Alignment |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Equivariant Diffusion for Crystal Structure Prediction |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Equivariant Frames and the Impossibility of Continuous Canonicalization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Equivariant Graph Neural Operator for Modeling 3D Dynamics |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Error Feedback Can Accurately Compress Preconditioners |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Estimating Barycenters of Distributions with Neural Optimal Transport |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Estimating Canopy Height at Scale |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
5 |
| Estimating Unknown Population Sizes Using the Hypergeometric Distribution |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Estimating the Permanent by Nesting Importance Sampling |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Et Tu Certifications: Robustness Certificates Yield Better Adversarial Examples |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Eureka-Moments in Transformers: Multi-Step Tasks Reveal Softmax Induced Optimization Problems |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| EvGGS: A Collaborative Learning Framework for Event-based Generalizable Gaussian Splatting |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| EvIL: Evolution Strategies for Generalisable Imitation Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| EvTexture: Event-driven Texture Enhancement for Video Super-Resolution |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Evaluating Model Bias Requires Characterizing its Mistakes |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Evaluating Quantized Large Language Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Evaluating and Analyzing Relationship Hallucinations in Large Vision-Language Models |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Evaluation of LLMs on Syntax-Aware Code Fill-in-the-Middle Tasks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Evaluation of Test-Time Adaptation Under Computational Time Constraints |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Evaluation of Trajectory Distribution Predictions with Energy Score |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| EvoRainbow: Combining Improvements in Evolutionary Reinforcement Learning for Policy Search |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| EvoluNet: Advancing Dynamic Non-IID Transfer Learning on Graphs |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Evolution-Inspired Loss Functions for Protein Representation Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Evolving Subnetwork Training for Large Language Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| ExCP: Extreme LLM Checkpoint Compression via Weight-Momentum Joint Shrinking |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Exact Conversion of In-Context Learning to Model Weights in Linearized-Attention Transformers |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Exact Soft Analytical Side-Channel Attacks using Tractable Circuits |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
5 |
| Executable Code Actions Elicit Better LLM Agents |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Expand-and-Cluster: Parameter Recovery of Neural Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Expert Proximity as Surrogate Rewards for Single Demonstration Imitation Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Experts Don’t Cheat: Learning What You Don’t Know By Predicting Pairs |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Explain Temporal Black-Box Models via Functional Decomposition |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Explaining Graph Neural Networks via Structure-aware Interaction Index |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Explaining Probabilistic Models with Distributional Values |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Exploiting Code Symmetries for Learning Program Semantics |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Exploiting Human-AI Dependence for Learning to Defer |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Exploiting Negative Samples: A Catalyst for Cohort Discovery in Healthcare Analytics |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Exploration and Anti-Exploration with Distributional Random Network Distillation |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Exploration-Driven Policy Optimization in RLHF: Theoretical Insights on Efficient Data Utilization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Explorations of Self-Repair in Language Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Exploring Correlations of Self-Supervised Tasks for Graphs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Exploring Intrinsic Dimension for Vision-Language Model Pruning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Exploring Training on Heterogeneous Data with Mixture of Low-rank Adapters |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Exploring the Benefit of Activation Sparsity in Pre-training |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Exploring the Complexity of Deep Neural Networks through Functional Equivalence |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Exploring the Enigma of Neural Dynamics Through A Scattering-Transform Mixer Landscape for Riemannian Manifold |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Exploring the LLM Journey from Cognition to Expression with Linear Representations |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Exploring the Low-Pass Filtering Behavior in Image Super-Resolution |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Exponential Spectral Pursuit: An Effective Initialization Method for Sparse Phase Retrieval |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Expressivity and Generalization: Fragment-Biases for Molecular GNNs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Extending Test-Time Augmentation with Metamorphic Relations for Combinatorial Problems |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Extracting Training Data From Document-Based VQA Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Extreme Compression of Large Language Models via Additive Quantization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| FADAS: Towards Federated Adaptive Asynchronous Optimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| FAFE: Immune Complex Modeling with Geodesic Distance Loss on Noisy Group Frames |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| FESSNC: Fast Exponentially Stable and Safe Neural Controller |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| FRAG: Frequency Adapting Group for Diffusion Video Editing |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| FRAPPÉ: A Group Fairness Framework for Post-Processing Everything |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Factored-Reward Bandits with Intermediate Observations |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Failures Are Fated, But Can Be Faded: Characterizing and Mitigating Unwanted Behaviors in Large-Scale Vision and Language Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Fair Classification with Partial Feedback: An Exploration-Based Data Collection Approach |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Fair Federated Learning via the Proportional Veto Core |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fair Off-Policy Learning from Observational Data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Fair Resource Allocation in Multi-Task Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| FairProof : Confidential and Certifiable Fairness for Neural Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Faithfulness Measurable Masked Language Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Fast Adversarial Attacks on Language Models In One GPU Minute |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Fast Algorithms for Hypergraph PageRank with Applications to Semi-Supervised Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Fast Co-Training under Weak Dependence via Stream-Based Active Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Fast Decision Boundary based Out-of-Distribution Detector |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fast Peer Adaptation with Context-aware Exploration |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fast Sampling-Based Sketches for Tensors |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fast Text-to-3D-Aware Face Generation and Manipulation via Direct Cross-modal Mapping and Geometric Regularization |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fast Timing-Conditioned Latent Audio Diffusion |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fast White-Box Adversarial Streaming Without a Random Oracle |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketching |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Fast-Slow Test-Time Adaptation for Online Vision-and-Language Navigation |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Faster Adaptive Decentralized Learning Algorithms |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Faster Maximum Inner Product Search in High Dimensions |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Faster Sampling via Stochastic Gradient Proximal Sampler |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Faster Streaming and Scalable Algorithms for Finding Directed Dense Subgraphs in Large Graphs |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fault Tolerant ML: Efficient Meta-Aggregation and Synchronous Training |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Feasibility Consistent Representation Learning for Safe Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Feasible Reachable Policy Iteration |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Feature Contamination: Neural Networks Learn Uncorrelated Features and Fail to Generalize |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Feature Importance Disparities for Data Bias Investigations |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| FedBAT: Communication-Efficient Federated Learning via Learnable Binarization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| FedLMT: Tackling System Heterogeneity of Federated Learning via Low-Rank Model Training with Theoretical Guarantees |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| FedMBridge: Bridgeable Multimodal Federated Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| FedREDefense: Defending against Model Poisoning Attacks for Federated Learning using Model Update Reconstruction Error |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Federated Combinatorial Multi-Agent Multi-Armed Bandits |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Federated Continual Learning via Prompt-based Dual Knowledge Transfer |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Federated Neuro-Symbolic Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Federated Optimization with Doubly Regularized Drift Correction |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Federated Representation Learning in the Under-Parameterized Regime |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Federated Self-Explaining GNNs with Anti-shortcut Augmentations |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Feedback Efficient Online Fine-Tuning of Diffusion Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Feedback Loops With Language Models Drive In-Context Reward Hacking |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
2 |
| Feel-Good Thompson Sampling for Contextual Dueling Bandits |
✅ |
❌ |
❌ |
❌ |
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❌ |
✅ |
2 |
| Few-Shot Character Understanding in Movies as an Assessment to Meta-Learning of Theory-of-Mind |
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5 |
| Few-Shot Unsupervised Implicit Neural Shape Representation Learning with Spatial Adversaries |
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5 |
| Few-shot Adaptation to Distribution Shifts By Mixing Source and Target Embeddings |
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3 |
| Fewer Truncations Improve Language Modeling |
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6 |
| FiT: Flexible Vision Transformer for Diffusion Model |
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3 |
| FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning |
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4 |
| Finding NEM-U: Explaining unsupervised representation learning through neural network generated explanation masks |
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4 |
| Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement Learning |
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4 |
| Fine-grained Classes and How to Find Them |
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7 |
| Fine-grained Local Sensitivity Analysis of Standard Dot-Product Self-Attention |
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3 |
| Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem |
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❌ |
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5 |
| Finite Smoothing Algorithm for High-Dimensional Support Vector Machines and Quantile Regression |
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4 |
| Finite Time Logarithmic Regret Bounds for Self-Tuning Regulation |
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3 |
| Finite Volume Features, Global Geometry Representations, and Residual Training for Deep Learning-based CFD Simulation |
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❌ |
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6 |
| Finite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learning |
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✅ |
2 |
| First-Order Manifold Data Augmentation for Regression Learning |
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❌ |
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6 |
| FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction |
❌ |
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❌ |
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4 |
| Flexible Residual Binarization for Image Super-Resolution |
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4 |
| Flextron: Many-in-One Flexible Large Language Model |
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✅ |
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3 |
| Floating Anchor Diffusion Model for Multi-motif Scaffolding |
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4 |
| Flora: Low-Rank Adapters Are Secretly Gradient Compressors |
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5 |
| FlowMM: Generating Materials with Riemannian Flow Matching |
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4 |
| Fool Your (Vision and) Language Model with Embarrassingly Simple Permutations |
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5 |
| Forget Sharpness: Perturbed Forgetting of Model Biases Within SAM Dynamics |
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5 |
| Foundation Policies with Hilbert Representations |
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5 |
| Foundations of Testing for Finite-Sample Causal Discovery |
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2 |
| Fourier Controller Networks for Real-Time Decision-Making in Embodied Learning |
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7 |
| FrameQuant: Flexible Low-Bit Quantization for Transformers |
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6 |
| FreeBind: Free Lunch in Unified Multimodal Space via Knowledge Fusion |
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4 |
| From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions |
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7 |
| From Coarse to Fine: Enable Comprehensive Graph Self-supervised Learning with Multi-granular Semantic Ensemble |
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5 |
| From Fourier to Neural ODEs: Flow Matching for Modeling Complex Systems |
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5 |
| From Generalization Analysis to Optimization Designs for State Space Models |
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5 |
| From Geometry to Causality- Ricci Curvature and the Reliability of Causal Inference on Networks |
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6 |
| From Inverse Optimization to Feasibility to ERM |
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5 |
| From Neurons to Neutrons: A Case Study in Interpretability |
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5 |
| From Self-Attention to Markov Models: Unveiling the Dynamics of Generative Transformers |
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1 |
| From Vision to Audio and Beyond: A Unified Model for Audio-Visual Representation and Generation |
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3 |
| From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems |
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❌ |
1 |
| From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning |
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3 |
| FuRL: Visual-Language Models as Fuzzy Rewards for Reinforcement Learning |
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7 |
| Full-Atom Peptide Design based on Multi-modal Flow Matching |
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5 |
| Fully-Dynamic Approximate Decision Trees With Worst-Case Update Time Guarantees |
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❌ |
1 |
| Fundamental Benefit of Alternating Updates in Minimax Optimization |
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4 |
| Fundamental Limitations of Alignment in Large Language Models |
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3 |
| Fundamental Limits of Distributed Covariance Matrix Estimation Under Communication Constraints |
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❌ |
❌ |
0 |
| GALA3D: Towards Text-to-3D Complex Scene Generation via Layout-guided Generative Gaussian Splatting |
❌ |
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3 |
| GATE: How to Keep Out Intrusive Neighbors |
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4 |
| GFlowNet Training by Policy Gradients |
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4 |
| GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements |
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3 |
| GNNs Also Deserve Editing, and They Need It More Than Once |
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5 |
| GPT-4V(ision) is a Generalist Web Agent, if Grounded |
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3 |
| GPTSwarm: Language Agents as Optimizable Graphs |
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5 |
| GRATH: Gradual Self-Truthifying for Large Language Models |
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5 |
| GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection |
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5 |
| Gambling-Based Confidence Sequences for Bounded Random Vectors |
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2 |
| Gated Linear Attention Transformers with Hardware-Efficient Training |
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5 |
| Gaussian Plane-Wave Neural Operator for Electron Density Estimation |
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4 |
| Gaussian Processes on Cellular Complexes |
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3 |
| GaussianPro: 3D Gaussian Splatting with Progressive Propagation |
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4 |
| GeminiFusion: Efficient Pixel-wise Multimodal Fusion for Vision Transformer |
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5 |
| GenCO: Generating Diverse Designs with Combinatorial Constraints |
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3 |
| Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning |
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5 |
| Generalization Analysis for Multi-Label Learning |
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0 |
| Generalization Analysis of Deep Non-linear Matrix Completion |
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5 |
| Generalization Analysis of Stochastic Weight Averaging with General Sampling |
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2 |
| Generalization Bound and New Algorithm for Clean-Label Backdoor Attack |
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5 |
| Generalization Bounds for Causal Regression: Insights, Guarantees and Sensitivity Analysis |
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3 |
| Generalization Bounds for Heavy-Tailed SDEs through the Fractional Fokker-Planck Equation |
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3 |
| Generalization Error of Graph Neural Networks in the Mean-field Regime |
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5 |
| Generalization in Kernel Regression Under Realistic Assumptions |
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0 |
| Generalization to New Sequential Decision Making Tasks with In-Context Learning |
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4 |
| Generalized Neural Collapse for a Large Number of Classes |
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3 |
| Generalized Preference Optimization: A Unified Approach to Offline Alignment |
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2 |
| Generalized Smooth Variational Inequalities: Methods with Adaptive Stepsizes |
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2 |
| Generalized Sobolev Transport for Probability Measures on a Graph |
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4 |
| Generalizing Knowledge Graph Embedding with Universal Orthogonal Parameterization |
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4 |
| Generalizing Orthogonalization for Models with Non-Linearities |
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4 |
| Generating Chain-of-Thoughts with a Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought |
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3 |
| Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks |
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6 |
| Generative Active Learning for Long-tailed Instance Segmentation |
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6 |
| Generative Conditional Distributions by Neural (Entropic) Optimal Transport |
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5 |
| Generative Enzyme Design Guided by Functionally Important Sites and Small-Molecule Substrates |
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5 |
| Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design |
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5 |
| Generative Marginalization Models |
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6 |
| Generative Modeling on Manifolds Through Mixture of Riemannian Diffusion Processes |
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6 |
| Genie: Generative Interactive Environments |
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3 |
| GeoAB: Towards Realistic Antibody Design and Reliable Affinity Maturation |
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4 |
| GeoMFormer: A General Architecture for Geometric Molecular Representation Learning |
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5 |
| GeoReasoner: Geo-localization with Reasoning in Street Views using a Large Vision-Language Model |
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5 |
| Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction |
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2 |
| Geometry-Aware Instrumental Variable Regression |
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3 |
| Geometry-Calibrated DRO: Combating Over-Pessimism with Free Energy Implications |
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3 |
| Get More with LESS: Synthesizing Recurrence with KV Cache Compression for Efficient LLM Inference |
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5 |
| Getting the most out of your tokenizer for pre-training and domain adaptation |
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✅ |
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2 |
| GiLOT: Interpreting Generative Language Models via Optimal Transport |
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3 |
| Gibbs Sampling of Continuous Potentials on a Quantum Computer |
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❌ |
❌ |
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1 |
| GistScore: Learning Better Representations for In-Context Example Selection with Gist Bottlenecks |
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6 |
| GliDe with a CaPE: A Low-Hassle Method to Accelerate Speculative Decoding |
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5 |
| Global Reinforcement Learning : Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods |
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2 |
| Going beyond Compositions, DDPMs Can Produce Zero-Shot Interpolations |
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4 |
| Gradient Compressed Sensing: A Query-Efficient Gradient Estimator for High-Dimensional Zeroth-Order Optimization |
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4 |
| Gradient-based Visual Explanation for Transformer-based CLIP |
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4 |
| Gradual Divergence for Seamless Adaptation: A Novel Domain Incremental Learning Method |
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5 |
| Graph Adversarial Diffusion Convolution |
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7 |
| Graph As Point Set |
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5 |
| Graph Automorphism Group Equivariant Neural Networks |
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1 |
| Graph Distillation with Eigenbasis Matching |
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❌ |
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6 |
| Graph External Attention Enhanced Transformer |
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4 |
| Graph Generation with Diffusion Mixture |
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6 |
| Graph Geometry-Preserving Autoencoders |
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4 |
| Graph Mixup on Approximate Gromov–Wasserstein Geodesics |
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6 |
| Graph Neural Network Explanations are Fragile |
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5 |
| Graph Neural Networks Use Graphs When They Shouldn’t |
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4 |
| Graph Neural Networks with a Distribution of Parametrized Graphs |
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6 |
| Graph Neural PDE Solvers with Conservation and Similarity-Equivariance |
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5 |
| Graph Neural Stochastic Diffusion for Estimating Uncertainty in Node Classification |
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6 |
| Graph Out-of-Distribution Detection Goes Neighborhood Shaping |
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5 |
| Graph Positional and Structural Encoder |
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5 |
| Graph Structure Extrapolation for Out-of-Distribution Generalization |
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4 |
| Graph-Triggered Rising Bandits |
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2 |
| Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling |
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5 |
| Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting |
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5 |
| Graph-enhanced Large Language Models in Asynchronous Plan Reasoning |
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4 |
| Graph2Tac: Online Representation Learning of Formal Math Concepts |
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4 |
| Graphon Mean Field Games with a Representative Player: Analysis and Learning Algorithm |
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2 |
| Grokking Group Multiplication with Cosets |
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5 |
| GroupCover: A Secure, Efficient and Scalable Inference Framework for On-device Model Protection based on TEEs |
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❌ |
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5 |
| Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples |
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1 |
| Guidance with Spherical Gaussian Constraint for Conditional Diffusion |
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4 |
| Guiding LLMs The Right Way: Fast, Non-Invasive Constrained Generation |
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❌ |
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6 |
| HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding |
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5 |
| HAMLET: Graph Transformer Neural Operator for Partial Differential Equations |
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3 |
| HGAP: Boosting Permutation Invariant and Permutation Equivariant in Multi-Agent Reinforcement Learning via Graph Attention Network |
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✅ |
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2 |
| HGCN2SP: Hierarchical Graph Convolutional Network for Two-Stage Stochastic Programming |
❌ |
✅ |
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❌ |
✅ |
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4 |
| Handling Heterogeneous Curvatures in Bandit LQR Control |
✅ |
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1 |
| Hard Tasks First: Multi-Task Reinforcement Learning Through Task Scheduling |
✅ |
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3 |
| HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal |
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✅ |
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✅ |
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❌ |
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6 |
| HarmoDT: Harmony Multi-Task Decision Transformer for Offline Reinforcement Learning |
✅ |
✅ |
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❌ |
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❌ |
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5 |
| Harmonic Self-Conditioned Flow Matching for joint Multi-Ligand Docking and Binding Site Design |
✅ |
✅ |
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❌ |
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6 |
| Harmonizing Generalization and Personalization in Federated Prompt Learning |
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5 |
| Harmony in Diversity: Merging Neural Networks with Canonical Correlation Analysis |
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3 |
| HarmonyDream: Task Harmonization Inside World Models |
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4 |
| Harnessing Hierarchical Label Distribution Variations in Test Agnostic Long-tail Recognition |
✅ |
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❌ |
❌ |
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5 |
| Harnessing Neural Unit Dynamics for Effective and Scalable Class-Incremental Learning |
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✅ |
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❌ |
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5 |
| Harnessing the Power of Neural Operators with Automatically Encoded Conservation Laws |
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✅ |
✅ |
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❌ |
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5 |
| HelmFluid: Learning Helmholtz Dynamics for Interpretable Fluid Prediction |
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✅ |
✅ |
✅ |
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❌ |
✅ |
5 |
| Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions |
✅ |
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✅ |
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❌ |
✅ |
6 |
| HexGen: Generative Inference of Large Language Model over Heterogeneous Environment |
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❌ |
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❌ |
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5 |
| Hidden Traveling Waves bind Working Memory Variables in Recurrent Neural Networks |
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3 |
| Hierarchical Integral Probability Metrics: A distance on random probability measures with low sample complexity |
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❌ |
❌ |
✅ |
❌ |
❌ |
3 |
| Hierarchical Neural Operator Transformer with Learnable Frequency-aware Loss Prior for Arbitrary-scale Super-resolution |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Hierarchical Novelty Detection via Fine-Grained Evidence Allocation |
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7 |
| Hierarchical State Space Models for Continuous Sequence-to-Sequence Modeling |
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4 |
| Hieros: Hierarchical Imagination on Structured State Space Sequence World Models |
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4 |
| High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling |
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5 |
| High-Dimensional Geometric Streaming for Nearly Low Rank Data |
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3 |
| High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization |
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1 |
| High-Order Contrastive Learning with Fine-grained Comparative Levels for Sparse Ordinal Tensor Completion |
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✅ |
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❌ |
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5 |
| High-Performance Temporal Reversible Spiking Neural Networks with $\mathcalO(L)$ Training Memory and $\mathcalO(1)$ Inference Cost |
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✅ |
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4 |
| High-Probability Bound for Non-Smooth Non-Convex Stochastic Optimization with Heavy Tails |
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✅ |
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4 |
| High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise |
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2 |
| High-dimensional Linear Bandits with Knapsacks |
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✅ |
2 |
| Highway Value Iteration Networks |
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❌ |
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5 |
| Homomorphism Counts for Graph Neural Networks: All About That Basis |
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❌ |
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5 |
| How Deep Do We Need: Accelerating Training and Inference of Neural ODEs via Control Perspective |
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❌ |
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5 |
| How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model |
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3 |
| How Do Nonlinear Transformers Learn and Generalize in In-Context Learning? |
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2 |
| How Does Goal Relabeling Improve Sample Efficiency? |
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1 |
| How Far Can Fairness Constraints Help Recover From Biased Data? |
❌ |
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0 |
| How Flawed Is ECE? An Analysis via Logit Smoothing |
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4 |
| How Free is Parameter-Free Stochastic Optimization? |
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1 |
| How Graph Neural Networks Learn: Lessons from Training Dynamics |
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6 |
| How Interpretable Are Interpretable Graph Neural Networks? |
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7 |
| How Language Model Hallucinations Can Snowball |
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3 |
| How Learning by Reconstruction Produces Uninformative Features For Perception |
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2 |
| How Private are DP-SGD Implementations? |
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3 |
| How Smooth Is Attention? |
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2 |
| How Spurious Features are Memorized: Precise Analysis for Random and NTK Features |
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2 |
| How Transformers Learn Causal Structure with Gradient Descent |
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4 |
| How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers |
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2 |
| How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing |
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6 |
| How Well Can LLMs Negotiate? NegotiationArena Platform and Analysis |
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2 |
| How do Large Language Models Navigate Conflicts between Honesty and Helpfulness? |
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1 |
| How do Transformers Perform In-Context Autoregressive Learning ? |
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3 |
| How to Escape Sharp Minima with Random Perturbations |
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3 |
| How to Explore with Belief: State Entropy Maximization in POMDPs |
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3 |
| How to Leverage Diverse Demonstrations in Offline Imitation Learning |
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6 |
| How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization |
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3 |
| How to Trace Latent Generative Model Generated Images without Artificial Watermark? |
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6 |
| Human Alignment of Large Language Models through Online Preference Optimisation |
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5 |
| Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict? |
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2 |
| Human-like Category Learning by Injecting Ecological Priors from Large Language Models into Neural Networks |
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3 |
| HumanTOMATO: Text-aligned Whole-body Motion Generation |
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6 |
| Hybrid Inverse Reinforcement Learning |
✅ |
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❌ |
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4 |
| Hybrid Neural Representations for Spherical Data |
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✅ |
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4 |
| Hybrid Reinforcement Learning from Offline Observation Alone |
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❌ |
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3 |
| Hybrid$^2$ Neural ODE Causal Modeling and an Application to Glycemic Response |
✅ |
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❌ |
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5 |
| HyperFields: Towards Zero-Shot Generation of NeRFs from Text |
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2 |
| Hyperbolic Active Learning for Semantic Segmentation under Domain Shift |
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❌ |
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5 |
| Hyperbolic Geometric Latent Diffusion Model for Graph Generation |
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❌ |
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5 |
| Hyperbolic Optimizer as a Dynamical System |
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❌ |
❌ |
0 |
| Hypergraph-enhanced Dual Semi-supervised Graph Classification |
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✅ |
❌ |
❌ |
✅ |
4 |
| I/O Complexity of Attention, or How Optimal is FlashAttention? |
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1 |
| IBD-PSC: Input-level Backdoor Detection via Parameter-oriented Scaling Consistency |
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✅ |
❌ |
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6 |
| IIANet: An Intra- and Inter-Modality Attention Network for Audio-Visual Speech Separation |
❌ |
✅ |
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❌ |
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5 |
| ILILT: Implicit Learning of Inverse Lithography Technologies |
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❌ |
✅ |
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2 |
| IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation |
❌ |
❌ |
✅ |
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3 |
| IM-Unpack: Training and Inference with Arbitrarily Low Precision Integers |
✅ |
✅ |
✅ |
❌ |
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✅ |
5 |
| INViT: A Generalizable Routing Problem Solver with Invariant Nested View Transformer |
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✅ |
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❌ |
✅ |
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5 |
| IOI: Invisible One-Iteration Adversarial Attack on No-Reference Image- and Video-Quality Metrics |
✅ |
✅ |
✅ |
❌ |
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5 |
| IW-GAE: Importance weighted group accuracy estimation for improved calibration and model selection in unsupervised domain adaptation |
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❌ |
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✅ |
❌ |
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4 |
| Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to Rank |
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✅ |
❌ |
❌ |
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5 |
| Identification and Estimation for Nonignorable Missing Data: A Data Fusion Approach |
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❌ |
❌ |
❌ |
❌ |
❌ |
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1 |
| Image Clustering with External Guidance |
❌ |
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❌ |
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❌ |
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4 |
| Image Fusion via Vision-Language Model |
❌ |
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❌ |
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5 |
| Image Hijacks: Adversarial Images can Control Generative Models at Runtime |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Image Restoration Through Generalized Ornstein-Uhlenbeck Bridge |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Imitation Learning from Purified Demonstrations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Imitation Learning in Discounted Linear MDPs without exploration assumptions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Impact of Decentralized Learning on Player Utilities in Stackelberg Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Implicit Bias of AdamW: $\ell_∞$-Norm Constrained Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Implicit Bias of Policy Gradient in Linear Quadratic Control: Extrapolation to Unseen Initial States |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Implicit Compressibility of Overparametrized Neural Networks Trained with Heavy-Tailed SGD |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Implicit Regularization in Feedback Alignment Learning Mechanisms for Neural Networks |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Implicit Representations for Constrained Image Segmentation |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
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4 |
| Implicit Representations via Operator Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Implicit meta-learning may lead language models to trust more reliable sources |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Improved Bounds for Pure Private Agnostic Learning: Item-Level and User-Level Privacy |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved Communication-Privacy Trade-offs in $L_2$ Mean Estimation under Streaming Differential Privacy |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improved Differentially Private and Lazy Online Convex Optimization: Lower Regret without Smoothness Requirements |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved Dimensionality Dependence for Zeroth-Order Optimisation over Cross-Polytopes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved Generalization of Weight Space Networks via Augmentations |
❌ |
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✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
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5 |
| Improved Operator Learning by Orthogonal Attention |
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❌ |
✅ |
❌ |
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❌ |
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3 |
| Improved Stability and Generalization Guarantees of the Decentralized SGD Algorithm |
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❌ |
❌ |
❌ |
❌ |
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2 |
| Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training |
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✅ |
✅ |
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6 |
| Improving Adversarial Energy-Based Model via Diffusion Process |
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❌ |
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3 |
| Improving Antibody Humanness Prediction using Patent Data |
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6 |
| Improving Computational Complexity in Statistical Models with Local Curvature Information |
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❌ |
❌ |
❌ |
❌ |
❌ |
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1 |
| Improving Context Understanding in Multimodal Large Language Models via Multimodal Composition Learning |
❌ |
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✅ |
✅ |
❌ |
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5 |
| Improving Diffusion Models for Inverse Problems Using Optimal Posterior Covariance |
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❌ |
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❌ |
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3 |
| Improving Equivariant Graph Neural Networks on Large Geometric Graphs via Virtual Nodes Learning |
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❌ |
❌ |
✅ |
4 |
| Improving Factuality and Reasoning in Language Models through Multiagent Debate |
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❌ |
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3 |
| Improving Generalization in Offline Reinforcement Learning via Adversarial Data Splitting |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Improving Gradient-Guided Nested Sampling for Posterior Inference |
✅ |
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❌ |
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3 |
| Improving Group Robustness on Spurious Correlation Requires Preciser Group Inference |
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✅ |
✅ |
❌ |
✅ |
6 |
| Improving Instruction Following in Language Models through Proxy-Based Uncertainty Estimation |
❌ |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improving Interpretation Faithfulness for Vision Transformers |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improving Neural Additive Models with Bayesian Principles |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Improving Neural Logic Machines via Failure Reflection |
✅ |
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✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Improving Open-Ended Text Generation via Adaptive Decoding |
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✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Improving Prototypical Visual Explanations with Reward Reweighing, Reselection, and Retraining |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Improving Robustness to Multiple Spurious Correlations by Multi-Objective Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Improving SAM Requires Rethinking its Optimization Formulation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improving Sharpness-Aware Minimization by Lookahead |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Improving Token-Based World Models with Parallel Observation Prediction |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Improving Transformers with Dynamically Composable Multi-Head Attention |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Improving fine-grained understanding in image-text pre-training |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| In value-based deep reinforcement learning, a pruned network is a good network |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| In-Context Decision Transformer: Reinforcement Learning via Hierarchical Chain-of-Thought |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| In-Context Language Learning: Architectures and Algorithms |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| In-Context Learning Agents Are Asymmetric Belief Updaters |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| In-Context Principle Learning from Mistakes |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| In-Context Reinforcement Learning for Variable Action Spaces |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| In-Context Unlearning: Language Models as Few-Shot Unlearners |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| In-context Convergence of Transformers |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| In-context Learning on Function Classes Unveiled for Transformers |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Incentivized Learning in Principal-Agent Bandit Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Incorporating Information into Shapley Values: Reweighting via a Maximum Entropy Approach |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Incorporating probabilistic domain knowledge into deep multiple instance learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Incremental Topological Ordering and Cycle Detection with Predictions |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Indirectly Parameterized Concrete Autoencoders |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Individual Fairness in Graph Decomposition |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Individualized Privacy Accounting via Subsampling with Applications in Combinatorial Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Inexact Newton-type Methods for Optimisation with Nonnegativity Constraints |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| InferCept: Efficient Intercept Support for Augmented Large Language Model Inference |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Inferring Change Points in High-Dimensional Linear Regression via Approximate Message Passing |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
5 |
| Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Infinite-Horizon Distributionally Robust Regret-Optimal Control |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| InfoNet: Neural Estimation of Mutual Information without Test-Time Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Information Complexity of Stochastic Convex Optimization: Applications to Generalization, Memorization, and Tracing |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Information Flow in Self-Supervised Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Information-Directed Pessimism for Offline Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Inherent Trade-Offs between Diversity and Stability in Multi-Task Benchmarks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Initial Guessing Bias: How Untrained Networks Favor Some Classes |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| InstructSpeech: Following Speech Editing Instructions via Large Language Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Instruction Tuning for Secure Code Generation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Integrated Hardware Architecture and Device Placement Search |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Integrating Global Context Contrast and Local Sensitivity for Blind Image Quality Assessment |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Integrating Multimodal Data for Joint Generative Modeling of Complex Dynamics |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Interacting Diffusion Processes for Event Sequence Forecasting |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Interaction-based Retrieval-augmented Diffusion Models for Protein-specific 3D Molecule Generation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Interplay of ROC and Precision-Recall AUCs: Theoretical Limits and Practical Implications in Binary Classification |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Interpretability Illusions in the Generalization of Simplified Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Interpretable Deep Clustering for Tabular Data |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Interpreting Equivariant Representations |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Interpreting and Improving Diffusion Models from an Optimization Perspective |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Interpreting and Improving Large Language Models in Arithmetic Calculation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Intersecting-Boundary-Sensitive Fingerprinting for Tampering Detection of DNN Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Intersectional Unfairness Discovery |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Invariant Risk Minimization Is A Total Variation Model |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Inverse-Variance Weighting for Estimation of Heterogeneous Treatment Effects |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Investigating Pre-Training Objectives for Generalization in Vision-Based Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Irregular Multivariate Time Series Forecasting: A Transformable Patching Graph Neural Networks Approach |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods? |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Is Kernel Prediction More Powerful than Gating in Convolutional Neural Networks? |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Is Temperature Sample Efficient for Softmax Gaussian Mixture of Experts? |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Isometric Representation Learning for Disentangled Latent Space of Diffusion Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Iterated Denoising Energy Matching for Sampling from Boltzmann Densities |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-constraint |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Iterative Regularized Policy Optimization with Imperfect Demonstrations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Iterative Search Attribution for Deep Neural Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Jacobian Regularizer-based Neural Granger Causality |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Jetfire: Efficient and Accurate Transformer Pretraining with INT8 Data Flow and Per-Block Quantization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Joint Composite Latent Space Bayesian Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Junk DNA Hypothesis: Pruning Small Pre-Trained Weights $\textitIrreversibly$ and $\textitMonotonically$ Impairs “Difficult" Downstream Tasks in LLMs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Just Cluster It: An Approach for Exploration in High-Dimensions using Clustering and Pre-Trained Representations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| KISA: A Unified Keyframe Identifier and Skill Annotator for Long-Horizon Robotics Demonstrations |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| KV-Runahead: Scalable Causal LLM Inference by Parallel Key-Value Cache Generation |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Keep the Momentum: Conservation Laws beyond Euclidean Gradient Flows |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Kepler codebook |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Kernel Debiased Plug-in Estimation: Simultaneous, Automated Debiasing without Influence Functions for Many Target Parameters |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Kernel Semi-Implicit Variational Inference |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Kernel-Based Evaluation of Conditional Biological Sequence Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| KernelWarehouse: Rethinking the Design of Dynamic Convolution |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Keypoint-based Progressive Chain-of-Thought Distillation for LLMs |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| KnowFormer: Revisiting Transformers for Knowledge Graph Reasoning |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Knowledge Distillation with Auxiliary Variable |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Knowledge Graphs Can be Learned with Just Intersection Features |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Knowledge Transfer from Vision Foundation Models for Efficient Training of Small Task-specific Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Knowledge-aware Reinforced Language Models for Protein Directed Evolution |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| LAGMA: LAtent Goal-guided Multi-Agent Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| LASER: Linear Compression in Wireless Distributed Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| LCA-on-the-Line: Benchmarking Out of Distribution Generalization with Class Taxonomies |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| LESS: Selecting Influential Data for Targeted Instruction Tuning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| LIDAO: Towards Limited Interventions for Debiasing (Large) Language Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| LLM Maybe LongLM: SelfExtend LLM Context Window Without Tuning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| LLM-Empowered State Representation for Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| LLaGA: Large Language and Graph Assistant |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| LLark: A Multimodal Instruction-Following Language Model for Music |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| LPGD: A General Framework for Backpropagation through Embedded Optimization Layers |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| LQER: Low-Rank Quantization Error Reconstruction for LLMs |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph Clustering |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| LaMAGIC: Language-Model-based Topology Generation for Analog Integrated Circuits |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| LangCell: Language-Cell Pre-training for Cell Identity Understanding |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Langevin Policy for Safe Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Language Agent Tree Search Unifies Reasoning, Acting, and Planning in Language Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Game |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Language Generation with Strictly Proper Scoring Rules |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Language Models Represent Beliefs of Self and Others |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Language Models as Science Tutors |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Language Models as Semantic Indexers |
✅ |
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✅ |
✅ |
❌ |
✅ |
6 |
| Language Models with Conformal Factuality Guarantees |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Language-Driven Cross-Modal Classifier for Zero-Shot Multi-Label Image Recognition |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Language-guided Skill Learning with Temporal Variational Inference |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Large Language Models are Geographically Biased |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Large Scale Dataset Distillation with Domain Shift |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Larimar: Large Language Models with Episodic Memory Control |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Latent Logic Tree Extraction for Event Sequence Explanation from LLMs |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Latent Noise Segmentation: How Neural Noise Leads to the Emergence of Segmentation and Grouping |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Latent Optimal Paths by Gumbel Propagation for Variational Bayesian Dynamic Programming |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Latent Space Symmetry Discovery |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Latent variable model for high-dimensional point process with structured missingness |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| LayerMerge: Neural Network Depth Compression through Layer Pruning and Merging |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Layerwise Change of Knowledge in Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| LeaPformer: Enabling Linear Transformers for Autoregressive and Simultaneous Tasks via Learned Proportions |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning 1-Bit Tiny Object Detector with Discriminative Feature Refinement |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning Adaptive and View-Invariant Vision Transformer for Real-Time UAV Tracking |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Associative Memories with Gradient Descent |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Learning Causal Domain-Invariant Temporal Dynamics for Few-Shot Action Recognition |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning Causal Dynamics Models in Object-Oriented Environments |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning Causal Relations from Subsampled Time Series with Two Time-Slices |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Learning Cognitive Maps from Transformer Representations for Efficient Planning in Partially Observed Environments |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Learning Constraints from Offline Demonstrations via Superior Distribution Correction Estimation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning Coverage Paths in Unknown Environments with Deep Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Decision Policies with Instrumental Variables through Double Machine Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Decision Trees and Forests with Algorithmic Recourse |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Learning Divergence Fields for Shift-Robust Graph Representations |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Learning Exceptional Subgroups by End-to-End Maximizing KL-Divergence |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Graph Representation via Graph Entropy Maximization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning High-Frequency Functions Made Easy with Sinusoidal Positional Encoding |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning High-Order Relationships of Brain Regions |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Learning Iterative Reasoning through Energy Diffusion |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning Label Shift Correction for Test-Agnostic Long-Tailed Recognition |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Latent Dynamic Robust Representations for World Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning Latent Space Hierarchical EBM Diffusion Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Latent Structures in Network Games via Data-Dependent Gated-Prior Graph Variational Autoencoders |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Linear Block Error Correction Codes |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Low-dimensional Latent Dynamics from High-dimensional Observations: Non-asymptotics and Lower Bounds |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Mixtures of Gaussian Processes through Random Projection |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning Modality Knowledge Alignment for Cross-Modality Transfer |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Multiple Secrets in Mastermind |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Optimal Deterministic Policies with Stochastic Policy Gradients |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Optimal Projection for Forecast Reconciliation of Hierarchical Time Series |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Pseudo-Contractive Denoisers for Inverse Problems |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Learning Reward for Robot Skills Using Large Language Models via Self-Alignment |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Scale-Aware Spatio-temporal Implicit Representation for Event-based Motion Deblurring |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Shadow Variable Representation for Treatment Effect Estimation under Collider Bias |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Solution-Aware Transformers for Efficiently Solving Quadratic Assignment Problem |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Surrogates for Offline Black-Box Optimization via Gradient Matching |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Making |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Universal Predictors |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Useful Representations of Recurrent Neural Network Weight Matrices |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning a Diffusion Model Policy from Rewards via Q-Score Matching |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning and Forgetting Unsafe Examples in Large Language Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning from Integral Losses in Physics Informed Neural Networks |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning from Memory: Non-Parametric Memory Augmented Self-Supervised Learning of Visual Features |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning from Streaming Data when Users Choose |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning from Students: Applying t-Distributions to Explore Accurate and Efficient Formats for LLMs |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning in Deep Factor Graphs with Gaussian Belief Propagation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nyström method |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Learning the Target Network in Function Space |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning the Uncertainty Sets of Linear Control Systems via Set Membership: A Non-asymptotic Analysis |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning to Compile Programs to Neural Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning to Continually Learn with the Bayesian Principle |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning to Explore for Stochastic Gradient MCMC |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning to Explore in POMDPs with Informational Rewards |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning to Infer Generative Template Programs for Visual Concepts |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning to Intervene on Concept Bottlenecks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning to Model the World With Language |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning to Play Atari in a World of Tokens |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning to Predict Mutational Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning to Reach Goals via Diffusion |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning to Remove Cuts in Integer Linear Programming |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
4 |
| Learning to Route Among Specialized Experts for Zero-Shot Generalization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning to Scale Logits for Temperature-Conditional GFlowNets |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning to Stabilize Online Reinforcement Learning in Unbounded State Spaces |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning with 3D rotations, a hitchhiker’s guide to SO(3) |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Learning with Adaptive Resource Allocation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning with Partial-Label and Unlabeled Data: A Uniform Treatment for Supervision Redundancy and Insufficiency |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning-Efficient Yet Generalizable Collaborative Filtering for Item Recommendation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Less is More: on the Over-Globalizing Problem in Graph Transformers |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often! |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Let Go of Your Labels with Unsupervised Transfer |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Leverage Class-Specific Accuracy to Guide Data Generation for Improving Image Classification |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Leveraging (Biased) Information: Multi-armed Bandits with Offline Data |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Leveraging Attractor Dynamics in Spatial Navigation for Better Language Parsing |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Leveraging VLM-Based Pipelines to Annotate 3D Objects |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Libra: Building Decoupled Vision System on Large Language Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Lie Neurons: Adjoint-Equivariant Neural Networks for Semisimple Lie Algebras |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Light and Optimal Schrödinger Bridge Matching |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Lightweight Image Super-Resolution via Flexible Meta Pruning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Limited Preference Aided Imitation Learning from Imperfect Demonstrations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Linear Alignment: A Closed-form Solution for Aligning Human Preferences without Tuning and Feedback |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Linear Explanations for Individual Neurons |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Linguistic Calibration of Long-Form Generations |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Liouville Flow Importance Sampler |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Listenable Maps for Audio Classifiers |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Listening to the noise: Blind Denoising with Gibbs Diffusion |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Listwise Reward Estimation for Offline Preference-based Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| LoCoCo: Dropping In Convolutions for Long Context Compression |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| LoRA Training in the NTK Regime has No Spurious Local Minima |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| LoRA+: Efficient Low Rank Adaptation of Large Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| LoRAP: Transformer Sub-Layers Deserve Differentiated Structured Compression for Large Language Models |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Local Causal Structure Learning in the Presence of Latent Variables |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Local vs. Global Interpretability: A Computational Complexity Perspective |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Localizing Task Information for Improved Model Merging and Compression |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Locally Differentially Private Decentralized Stochastic Bilevel Optimization with Guaranteed Convergence Accuracy |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Locally Interdependent Multi-Agent MDP: Theoretical Framework for Decentralized Agents with Dynamic Dependencies |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Log Neural Controlled Differential Equations: The Lie Brackets Make A Difference |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Logistic Variational Bayes Revisited |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for Instruction Fine-Tuning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Long Range Propagation on Continuous-Time Dynamic Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Look Ahead or Look Around? A Theoretical Comparison Between Autoregressive and Masked Pretraining |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Lookbehind-SAM: k steps back, 1 step forward |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Loss Shaping Constraints for Long-Term Time Series Forecasting |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Low-Cost High-Power Membership Inference Attacks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Low-Rank Bandits via Tight Two-to-Infinity Singular Subspace Recovery |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Low-Rank Similarity Mining for Multimodal Dataset Distillation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Lyapunov-stable Neural Control for State and Output Feedback: A Novel Formulation |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| MADA: Meta-Adaptive Optimizers Through Hyper-Gradient Descent |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| MAGDi: Structured Distillation of Multi-Agent Interaction Graphs Improves Reasoning in Smaller Language Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| MAGNOLIA: Matching Algorithms via GNNs for Online Value-to-go Approximation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| MALIBO: Meta-learning for Likelihood-free Bayesian Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| MC-GTA: Metric-Constrained Model-Based Clustering using Goodness-of-fit Tests with Autocorrelations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| MD tree: a model-diagnostic tree grown on loss landscape |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| MEMORYLLM: Towards Self-Updatable Large Language Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| MF-CLR: Multi-Frequency Contrastive Learning Representation for Time Series |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| MFTN: A Multi-scale Feature Transfer Network Based on IMatchFormer for Hyperspectral Image Super-Resolution |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| MGit: A Model Versioning and Management System |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| MILP-FBGen: LP/MILP Instance Generation with Feasibility/Boundedness |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| MLI Formula: A Nearly Scale-Invariant Solution with Noise Perturbation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| MLIP: Efficient Multi-Perspective Language-Image Pretraining with Exhaustive Data Utilization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| MMPareto: Boosting Multimodal Learning with Innocent Unimodal Assistance |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| MOMENT: A Family of Open Time-series Foundation Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MS$^3$D: A RG Flow-Based Regularization for GAN Training with Limited Data |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| MS-TIP: Imputation Aware Pedestrian Trajectory Prediction |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| MaSS: Multi-attribute Selective Suppression for Utility-preserving Data Transformation from an Information-theoretic Perspective |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Maestro: Uncovering Low-Rank Structures via Trainable Decomposition |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| MagicLens: Self-Supervised Image Retrieval with Open-Ended Instructions |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MagicPose: Realistic Human Poses and Facial Expressions Retargeting with Identity-aware Diffusion |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Magicoder: Empowering Code Generation with OSS-Instruct |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Major-Minor Mean Field Multi-Agent Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| Make-A-Shape: a Ten-Million-scale 3D Shape Model |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Making Old Things New: A Unified Algorithm for Differentially Private Clustering |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Manifold Integrated Gradients: Riemannian Geometry for Feature Attribution |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Mapping the Multiverse of Latent Representations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Masked Face Recognition with Generative-to-Discriminative Representations |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Mastering Robot Manipulation with Multimodal Prompts through Pretraining and Multi-task Fine-tuning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Mastering Zero-Shot Interactions in Cooperative and Competitive Simultaneous Games |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| MathScale: Scaling Instruction Tuning for Mathematical Reasoning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Matrix Information Theory for Self-Supervised Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Matroid Semi-Bandits in Sublinear Time |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| MaxMin-RLHF: Alignment with Diverse Human Preferences |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Mean Estimation in the Add-Remove Model of Differential Privacy |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Mean Field Langevin Actor-Critic: Faster Convergence and Global Optimality beyond Lazy Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Mean-field Analysis on Two-layer Neural Networks from a Kernel Perspective |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Mean-field Chaos Diffusion Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Mean-field Underdamped Langevin Dynamics and its Spacetime Discretization |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Measures of diversity and space-filling designs for categorical data |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Measuring Stochastic Data Complexity with Boltzmann Influence Functions |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Mechanistic Design and Scaling of Hybrid Architectures |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Mechanistic Neural Networks for Scientific Machine Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Membership Inference Attacks on Diffusion Models via Quantile Regression |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Memoria: Resolving Fateful Forgetting Problem through Human-Inspired Memory Architecture |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Memorization Through the Lens of Curvature of Loss Function Around Samples |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Memory Consolidation Enables Long-Context Video Understanding |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Memory Efficient Neural Processes via Constant Memory Attention Block |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Memory-Space Visual Prompting for Efficient Vision-Language Fine-Tuning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Merging Multi-Task Models via Weight-Ensembling Mixture of Experts |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Meta Evidential Transformer for Few-Shot Open-Set Recognition |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Meta-Reinforcement Learning Robust to Distributional Shift Via Performing Lifelong In-Context Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Mimicking Better by Matching the Approximate Action Distribution |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Mind the Boundary: Coreset Selection via Reconstructing the Decision Boundary |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Minimally Modifying a Markov Game to Achieve Any Nash Equilibrium and Value |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Minimax Optimality of Score-based Diffusion Models: Beyond the Density Lower Bound Assumptions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Minimizing $f$-Divergences by Interpolating Velocity Fields |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Minimum Norm Interpolation Meets The Local Theory of Banach Spaces |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Minimum-Norm Interpolation Under Covariate Shift |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Mitigating Catastrophic Forgetting in Online Continual Learning by Modeling Previous Task Interrelations via Pareto Optimization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Mitigating Label Noise on Graphs via Topological Sample Selection |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Mitigating Oversmoothing Through Reverse Process of GNNs for Heterophilic Graphs |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Mixtures of Experts Unlock Parameter Scaling for Deep RL |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| MoMo: Momentum Models for Adaptive Learning Rates |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Mobile Attention: Mobile-Friendly Linear-Attention for Vision Transformers |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Model Alignment as Prospect Theoretic Optimization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Model Assessment and Selection under Temporal Distribution Shift |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Model-Based Minimum Bayes Risk Decoding for Text Generation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Model-Free Robust $φ$-Divergence Reinforcement Learning Using Both Offline and Online Data |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Model-based Reinforcement Learning for Confounded POMDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Model-based Reinforcement Learning for Parameterized Action Spaces |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Modeling Caption Diversity in Contrastive Vision-Language Pretraining |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Modeling Language Tokens as Functionals of Semantic Fields |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Modelling Microbial Communities with Graph Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Mol-AE: Auto-Encoder Based Molecular Representation Learning With 3D Cloze Test Objective |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Mollification Effects of Policy Gradient Methods |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Momentum Particle Maximum Likelihood |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environments |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Monotone Individual Fairness |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Monotone, Bi-Lipschitz, and Polyak-Łojasiewicz Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithms |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Moreau Envelope for Nonconvex Bi-Level Optimization: A Single-Loop and Hessian-Free Solution Strategy |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| MorphGrower: A Synchronized Layer-by-layer Growing Approach for Plausible Neuronal Morphology Generation |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Multi-Agent Reinforcement Learning Meets Leaf Sequencing in Radiotherapy |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Multi-Factor Adaptive Vision Selection for Egocentric Video Question Answering |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Multi-Patch Prediction: Adapting Language Models for Time Series Representation Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multi-Region Markovian Gaussian Process: An Efficient Method to Discover Directional Communications Across Multiple Brain Regions |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-Sender Persuasion: A Computational Perspective |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Multi-Source Conformal Inference Under Distribution Shift |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Multi-Track Message Passing: Tackling Oversmoothing and Oversquashing in Graph Learning via Preventing Heterophily Mixing |
✅ |
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✅ |
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✅ |
✅ |
✅ |
7 |
| Multi-View Clustering by Inter-cluster Connectivity Guided Reward |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multi-View Stochastic Block Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multi-group Learning for Hierarchical Groups |
✅ |
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❌ |
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❌ |
✅ |
3 |
| Multi-layer Rehearsal Feature Augmentation for Class-Incremental Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| MultiMax: Sparse and Multi-Modal Attention Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multicalibration for Confidence Scoring in LLMs |
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✅ |
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❌ |
❌ |
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3 |
| Multigroup Robustness |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Multimodal Prototyping for cancer survival prediction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multiplicative Weights Update, Area Convexity and Random Coordinate Descent for Densest Subgraph Problems |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Multiply Robust Estimation for Local Distribution Shifts with Multiple Domains |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Multiply-Robust Causal Change Attribution |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| MusicFlow: Cascaded Flow Matching for Text Guided Music Generation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| MusicRL: Aligning Music Generation to Human Preferences |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| MuxServe: Flexible Spatial-Temporal Multiplexing for Multiple LLM Serving |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| NDOT: Neuronal Dynamics-based Online Training for Spiking Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| NExT-Chat: An LMM for Chat, Detection and Segmentation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| NExT-GPT: Any-to-Any Multimodal LLM |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| NExT: Teaching Large Language Models to Reason about Code Execution |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Naive Bayes Classifiers over Missing Data: Decision and Poisoning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Nash Incentive-compatible Online Mechanism Learning via Weakly Differentially Private Online Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Nash Learning from Human Feedback |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Navigating Scaling Laws: Compute Optimality in Adaptive Model Training |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Near-Linear Time Approximation Algorithms for k-means with Outliers |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Near-Optimal Regret in Linear MDPs with Aggregate Bandit Feedback |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Near-Optimal Reinforcement Learning with Self-Play under Adaptivity Constraints |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Nearest Neighbour Score Estimators for Diffusion Generative Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Neighboring Perturbations of Knowledge Editing on Large Language Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Nesting Particle Filters for Experimental Design in Dynamical Systems |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Network Tight Community Detection |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Collapse for Cross-entropy Class-Imbalanced Learning with Unconstrained ReLU Features Model |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neural Collapse in Multi-label Learning with Pick-all-label Loss |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Collapse meets Differential Privacy: Curious behaviors of NoisyGD with Near-Perfect Representation Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neural Diffusion Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Neural Image Compression with Text-guided Encoding for both Pixel-level and Perceptual Fidelity |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Jump-Diffusion Temporal Point Processes |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Neural NeRF Compression |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Neural Networks Learn Statistics of Increasing Complexity |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Neural Operators with Localized Integral and Differential Kernels |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Neural Tangent Kernels Motivate Cross-Covariance Graphs in Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Neural Tangent Kernels for Axis-Aligned Tree Ensembles |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Neural operators meet conjugate gradients: The FCG-NO method for efficient PDE solving |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Neural-Kernel Conditional Mean Embeddings |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| NeuralIndicator: Implicit Surface Reconstruction from Neural Indicator Priors |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Neuro-Symbolic Temporal Point Processes |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
3 |
| Neuro-Visualizer: A Novel Auto-Encoder-Based Loss Landscape Visualization Method With an Application in Knowledge-Guided Machine Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Neurodegenerative Brain Network Classification via Adaptive Diffusion with Temporal Regularization |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Neuroexplicit Diffusion Models for Inpainting of Optical Flow Fields |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| New Bounds on the Cohesion of Complete-link and Other Linkage Methods for Agglomerative Clustering |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| New Sample Complexity Bounds for Sample Average Approximation in Heavy-Tailed Stochastic Programming |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| No Dimensional Sampling Coresets for Classification |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| No Double Descent in Principal Component Regression: A High-Dimensional Analysis |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| No Free Prune: Information-Theoretic Barriers to Pruning at Initialization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| No-Regret Reinforcement Learning in Smooth MDPs |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Noise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Non-Asymptotic Analysis for Single-Loop (Natural) Actor-Critic with Compatible Function Approximation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Non-Vacuous Generalization Bounds for Large Language Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Non-clairvoyant Scheduling with Partial Predictions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Non-confusing Generation of Customized Concepts in Diffusion Models |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Non-convex Stochastic Composite Optimization with Polyak Momentum |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Non-parametric Online Change Point Detection on Riemannian Manifolds |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Non-stationary Online Convex Optimization with Arbitrary Delays |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Nonlinear Filtering with Brenier Optimal Transport Maps |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Nonparametric Teaching of Implicit Neural Representations |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Not Just Pretty Pictures: Toward Interventional Data Augmentation Using Text-to-Image Generators |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Not all distributional shifts are equal: Fine-grained robust conformal inference |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Novel Spectral Algorithms for the Partial Credit Model |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| O$n$ Learning Deep O($n$)-Equivariant Hyperspheres |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| OAK: Enriching Document Representations using Auxiliary Knowledge for Extreme Classification |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| ODIN: Disentangled Reward Mitigates Hacking in RLHF |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| OLLIE: Imitation Learning from Offline Pretraining to Online Finetuning |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| OMPO: A Unified Framework for RL under Policy and Dynamics Shifts |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| OSN: Infinite Representations of Dynamic 3D Scenes from Monocular Videos |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| OSSCAR: One-Shot Structured Pruning in Vision and Language Models with Combinatorial Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| OT-CLIP: Understanding and Generalizing CLIP via Optimal Transport |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| OTMatch: Improving Semi-Supervised Learning with Optimal Transport |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Observable Propagation: Uncovering Feature Vectors in Transformers |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Off-policy Evaluation Beyond Overlap: Sharp Partial Identification Under Smoothness |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Offline Actor-Critic Reinforcement Learning Scales to Large Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Offline Imitation from Observation via Primal Wasserstein State Occupancy Matching |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Offline Inverse RL: New Solution Concepts and Provably Efficient Algorithms |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Offline Multi-Objective Optimization |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Offline Training of Language Model Agents with Functions as Learnable Weights |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Offline Transition Modeling via Contrastive Energy Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical Behaviors in Deep Off-Policy RL |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| On Computational Limits of Modern Hopfield Models: A Fine-Grained Complexity Analysis |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On Convergence of Incremental Gradient for Non-convex Smooth Functions |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Discrete Prompt Optimization for Diffusion Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Gradient-like Explanation under a Black-box Setting: When Black-box Explanations Become as Good as White-box |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| On Hypothesis Transfer Learning of Functional Linear Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| On Interpolating Experts and Multi-Armed Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On Least Square Estimation in Softmax Gating Mixture of Experts |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On Mechanistic Knowledge Localization in Text-to-Image Generative Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| On Multi-Armed Bandit with Impatient Arms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Online Experimentation without Device Identifiers |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| On PI Controllers for Updating Lagrange Multipliers in Constrained Optimization |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| On Positivity Condition for Causal Inference |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On Prompt-Driven Safeguarding for Large Language Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| On Statistical Learning Theory for Distributional Inputs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Stronger Computational Separations Between Multimodal and Unimodal Machine Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On The Complexity of First-Order Methods in Stochastic Bilevel Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On The Fairness Impacts of Hardware Selection in Machine Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| On The Statistical Complexity of Offline Decision-Making |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On Universally Optimal Algorithms for A/B Testing |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Which Nodes Does GCN Fail? Enhancing GCN From the Node Perspective |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| On a Combinatorial Problem Arising in Machine Teaching |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On a Neural Implementation of Brenier’s Polar Factorization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On dimensionality of feature vectors in MPNNs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Asymptotic Distribution of the Minimum Empirical Risk |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Calibration of Human Pose Estimation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On the Complexity of Finite-Sum Smooth Optimization under the Polyak–Łojasiewicz Condition |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| On the Consistency of Kernel Methods with Dependent Observations |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Convergence of Projected Bures-Wasserstein Gradient Descent under Euclidean Strong Convexity |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Diminishing Returns of Width for Continual Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On the Duality Between Sharpness-Aware Minimization and Adversarial Training |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Effectiveness of Supervision in Asymmetric Non-Contrastive Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On the Embedding Collapse when Scaling up Recommendation Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On the Emergence of Cross-Task Linearity in Pretraining-Finetuning Paradigm |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On the Error-Propagation of Inexact Hotelling’s Deflation for Principal Component Analysis |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Expressive Power of Spectral Invariant Graph Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| On the Feasibility of Single-Pass Full-Capacity Learning in Linear Threshold Neurons with Binary Input Vectors |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| On the Generalization of Equivariant Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On the Hardness of Probabilistic Neurosymbolic Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| On the Identifiability of Switching Dynamical Systems |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On the Implicit Bias of Adam |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Independence Assumption in Neurosymbolic Learning |
❌ |
❌ |
✅ |
❌ |
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❌ |
✅ |
2 |
| On the Last-Iterate Convergence of Shuffling Gradient Methods |
✅ |
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❌ |
❌ |
❌ |
❌ |
1 |
| On the Maximal Local Disparity of Fairness-Aware Classifiers |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| On the Minimal Degree Bias in Generalization on the Unseen for non-Boolean Functions |
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✅ |
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❌ |
❌ |
✅ |
3 |
| On the Nonlinearity of Layer Normalization |
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✅ |
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❌ |
✅ |
3 |
| On the Origins of Linear Representations in Large Language Models |
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❌ |
✅ |
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❌ |
✅ |
2 |
| On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Role of Edge Dependency in Graph Generative Models |
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❌ |
✅ |
3 |
| On the Second-Order Convergence of Biased Policy Gradient Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Tractability of SHAP Explanations under Markovian Distributions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Trajectory Regularity of ODE-based Diffusion Sampling |
✅ |
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✅ |
❌ |
✅ |
❌ |
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4 |
| On the Unexpected Effectiveness of Reinforcement Learning for Sequential Recommendation |
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❌ |
✅ |
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❌ |
❌ |
✅ |
3 |
| On the Universality of Volume-Preserving and Coupling-Based Normalizing Flows |
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❌ |
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❌ |
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1 |
| On the Weight Dynamics of Deep Normalized Networks |
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5 |
| On the sample complexity of conditional independence testing with Von Mises estimator with application to causal discovery |
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1 |
| One Meta-tuned Transformer is What You Need for Few-shot Learning |
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✅ |
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❌ |
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4 |
| One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Prompts |
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❌ |
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✅ |
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❌ |
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5 |
| One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning |
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❌ |
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✅ |
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6 |
| One for All: A Universal Generator for Concept Unlearnability via Multi-Modal Alignment |
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✅ |
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3 |
| One-Shot Strategic Classification Under Unknown Costs |
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❌ |
❌ |
❌ |
❌ |
1 |
| Online Adaptive Anomaly Thresholding with Confidence Sequences |
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❌ |
❌ |
❌ |
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3 |
| Online Algorithms with Uncertainty-Quantified Predictions |
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❌ |
❌ |
❌ |
✅ |
2 |
| Online Cascade Learning for Efficient Inference over Streams |
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✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Online Isolation Forest |
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❌ |
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❌ |
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4 |
| Online Learning and Information Exponents: The Importance of Batch size & Time/Complexity Tradeoffs |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Online Learning in Betting Markets: Profit versus Prediction |
✅ |
✅ |
❌ |
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❌ |
❌ |
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3 |
| Online Learning in CMDPs: Handling Stochastic and Adversarial Constraints |
✅ |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Learning under Budget and ROI Constraints via Weak Adaptivity |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Learning with Bounded Recall |
✅ |
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❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Online Linear Regression in Dynamic Environments via Discounting |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Matching with Stochastic Rewards: Provable Better Bound via Adversarial Reinforcement Learning |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Online Matrix Completion: A Collaborative Approach with Hott Items |
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❌ |
❌ |
❌ |
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2 |
| Online Resource Allocation with Non-Stationary Customers |
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❌ |
❌ |
❌ |
✅ |
2 |
| Online Speculative Decoding |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Online Variational Sequential Monte Carlo |
✅ |
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❌ |
❌ |
✅ |
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✅ |
4 |
| Online bipartite matching with imperfect advice |
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❌ |
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❌ |
✅ |
4 |
| Online conformal prediction with decaying step sizes |
❌ |
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✅ |
❌ |
❌ |
✅ |
4 |
| Open Ad Hoc Teamwork with Cooperative Game Theory |
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✅ |
❌ |
✅ |
❌ |
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5 |
| Open-Domain Text Evaluation via Contrastive Distribution Methods |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Open-Vocabulary Calibration for Fine-tuned CLIP |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Operator SVD with Neural Networks via Nested Low-Rank Approximation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Optimal Acceleration for Minimax and Fixed-Point Problems is Not Unique |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimal Batched Linear Bandits |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimal Coresets for Low-Dimensional Geometric Median |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Optimal Differentially Private Model Training with Public Data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Optimal Exact Recovery in Semi-Supervised Learning: A Study of Spectral Methods and Graph Convolutional Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimal Eye Surgeon: Finding image priors through sparse generators at initialization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Optimal Hessian/Jacobian-Free Nonconvex-PL Bilevel Optimization |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Optimal Kernel Choice for Score Function-based Causal Discovery |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Optimal Kernel Quantile Learning with Random Features |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Optimal Recurrent Network Topologies for Dynamical Systems Reconstruction |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Optimal Ridge Regularization for Out-of-Distribution Prediction |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimal Transport for Structure Learning Under Missing Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Optimal bounds for $\ell_p$ sensitivity sampling via $\ell_2$ augmentation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Optimally Improving Cooperative Learning in a Social Setting |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Optimistic Multi-Agent Policy Gradient |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Optimization without Retraction on the Random Generalized Stiefel Manifold |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimizing Watermarks for Large Language Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Orthogonal Bootstrap: Efficient Simulation of Input Uncertainty |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Out-of-Distribution Detection via Deep Multi-Comprehension Ensemble |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Out-of-Domain Generalization in Dynamical Systems Reconstruction |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Outlier-Efficient Hopfield Layers for Large Transformer-Based Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Outlier-aware Slicing for Post-Training Quantization in Vision Transformer |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Outlier-robust Kalman Filtering through Generalised Bayes |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Overcoming Data and Model heterogeneities in Decentralized Federated Learning via Synthetic Anchors |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Overcoming Saturation in Density Ratio Estimation by Iterated Regularization |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Overcoming the Optimizer’s Curse: Obtaining Realistic Prescriptions from Neural Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| OxyGenerator: Reconstructing Global Ocean Deoxygenation Over a Century with Deep Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| PAC-Bayesian Error Bound, via Rényi Divergence, for a Class of Linear Time-Invariant State-Space Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| PAC-Bayesian Generalization Bounds for Knowledge Graph Representation Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| PAGER: Accurate Failure Characterization in Deep Regression Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| PANDA: Expanded Width-Aware Message Passing Beyond Rewiring |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| PAPM: A Physics-aware Proxy Model for Process Systems |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| PARCv2: Physics-aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics Modeling |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| PARDEN, Can You Repeat That? Defending against Jailbreaks via Repetition |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| PASOA- PArticle baSed Bayesian Optimal Adaptive design |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Linear Programming |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| PEARL: Zero-shot Cross-task Preference Alignment and Robust Reward Learning for Robotic Manipulation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| PGODE: Towards High-quality System Dynamics Modeling |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| PICLe: Eliciting Diverse Behaviors from Large Language Models with Persona In-Context Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| PID: Prompt-Independent Data Protection Against Latent Diffusion Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| PIDformer: Transformer Meets Control Theory |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| PIPER: Primitive-Informed Preference-based Hierarchical Reinforcement Learning via Hindsight Relabeling |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| PPFLOW: Target-Aware Peptide Design with Torsional Flow Matching |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| PairNet: Training with Observed Pairs to Estimate Individual Treatment Effect |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Pairwise Alignment Improves Graph Domain Adaptation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Parallel Affine Transformation Tuning of Markov Chain Monte Carlo |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Parallelized Spatiotemporal Slot Binding for Videos |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Parameter Estimation in DAGs from Incomplete Data via Optimal Transport |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Parameter-Dependent Competitive Analysis for Online Capacitated Coverage Maximization through Boostings and Attenuations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Parameter-Efficient Fine-Tuning with Controls |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Parameter-Efficient Fine-Tuning with Discrete Fourier Transform |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Parameterized Physics-informed Neural Networks for Parameterized PDEs |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Parsimonious Learning-Augmented Approximations for Dense Instances of $\mathcalNP$-hard Problems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Partial Multi-View Multi-Label Classification via Semantic Invariance Learning and Prototype Modeling |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Partial Optimality in the Linear Ordering Problem |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Partially Stochastic Infinitely Deep Bayesian Neural Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Particle Denoising Diffusion Sampler |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Path-Guided Particle-based Sampling |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Pausing Policy Learning in Non-stationary Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| PcLast: Discovering Plannable Continuous Latent States |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Pedestrian Attribute Recognition as Label-balanced Multi-label Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data Streams |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| PerceptAnon: Exploring the Human Perception of Image Anonymization Beyond Pseudonymization for GDPR |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Perfect Alignment May be Poisonous to Graph Contrastive Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Performance Bounds for Active Binary Testing with Information Maximization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Performative Prediction with Bandit Feedback: Learning through Reparameterization |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Perturb-and-Project: Differentially Private Similarities and Marginals |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Physics and Lie symmetry informed Gaussian processes |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Physics of Language Models: Part 3.1, Knowledge Storage and Extraction |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Physics-Informed Neural Network Policy Iteration: Algorithms, Convergence, and Verification |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Pi-DUAL: Using privileged information to distinguish clean from noisy labels |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Piecewise Constant and Linear Regression Trees: An Optimal Dynamic Programming Approach |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| PinNet: Pinpoint Instructive Information for Retrieval Augmented Code-to-Text Generation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| PlanDQ: Hierarchical Plan Orchestration via D-Conductor and Q-Performer |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Planning, Fast and Slow: Online Reinforcement Learning with Action-Free Offline Data via Multiscale Planners |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Plug-and-Play image restoration with Stochastic deNOising REgularization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Plug-in Performative Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Pluvial Flood Emulation with Hydraulics-informed Message Passing |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| PointMC: Multi-instance Point Cloud Registration based on Maximal Cliques |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Policy Evaluation for Variance in Average Reward Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Policy Learning for Balancing Short-Term and Long-Term Rewards |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Policy-conditioned Environment Models are More Generalizable |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| PolySketchFormer: Fast Transformers via Sketching Polynomial Kernels |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Polynomial-based Self-Attention for Table Representation Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Position: $C^*$-Algebraic Machine Learning $-$ Moving in a New Direction |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Position: A Call for Embodied AI |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Position: A Call to Action for a Human-Centered AutoML Paradigm |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
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0 |
| Position: A Roadmap to Pluralistic Alignment |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Position: A Safe Harbor for AI Evaluation and Red Teaming |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Position: AI-Powered Autonomous Weapons Risk Geopolitical Instability and Threaten AI Research |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Position: AI/ML Influencers Have a Place in the Academic Process |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Position: Amazing Things Come From Having Many Good Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
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2 |
| Position: An Inner Interpretability Framework for AI Inspired by Lessons from Cognitive Neuroscience |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Position: Application-Driven Innovation in Machine Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
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0 |
| Position: Automatic Environment Shaping is the Next Frontier in RL |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
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0 |
| Position: Benchmarking is Limited in Reinforcement Learning Research |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Position: Beyond Personhood: Agency, Accountability, and the Limits of Anthropomorphic Ethical Analysis |
❌ |
❌ |
❌ |
❌ |
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❌ |
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0 |
| Position: Building Guardrails for Large Language Models Requires Systematic Design |
❌ |
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❌ |
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0 |
| Position: Categorical Deep Learning is an Algebraic Theory of All Architectures |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
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0 |
| Position: Compositional Generative Modeling: A Single Model is Not All You Need |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
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1 |
| Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
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1 |
| Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
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0 |
| Position: Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them? |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
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0 |
| Position: Data-driven Discovery with Large Generative Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
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1 |
| Position: Do Not Explain Vision Models Without Context |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
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1 |
| Position: Do pretrained Transformers Learn In-Context by Gradient Descent? |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
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2 |
| Position: Embracing Negative Results in Machine Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
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0 |
| Position: Enforced Amnesia as a Way to Mitigate the Potential Risk of Silent Suffering in the Conscious AI |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
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0 |
| Position: Evolving AI Collectives Enhance Human Diversity and Enable Self-Regulation |
❌ |
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1 |
| Position: Explain to Question not to Justify |
❌ |
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0 |
| Position: Exploring the Robustness of Pipeline-Parallelism-Based Decentralized Training |
❌ |
✅ |
✅ |
❌ |
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4 |
| Position: Foundation Agents as the Paradigm Shift for Decision Making |
❌ |
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✅ |
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✅ |
❌ |
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3 |
| Position: Fundamental Limitations of LLM Censorship Necessitate New Approaches |
❌ |
❌ |
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❌ |
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0 |
| Position: Future Directions in the Theory of Graph Machine Learning |
❌ |
❌ |
❌ |
❌ |
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❌ |
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0 |
| Position: Graph Foundation Models Are Already Here |
❌ |
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✅ |
❌ |
❌ |
❌ |
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1 |
| Position: Insights from Survey Methodology can Improve Training Data |
❌ |
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❌ |
❌ |
❌ |
❌ |
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0 |
| Position: Intent-aligned AI Systems Must Optimize for Agency Preservation |
❌ |
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❌ |
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1 |
| Position: Is machine learning good or bad for the natural sciences? |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Position: Key Claims in LLM Research Have a Long Tail of Footnotes |
❌ |
❌ |
❌ |
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❌ |
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0 |
| Position: LLMs Can’t Plan, But Can Help Planning in LLM-Modulo Frameworks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
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1 |
| Position: Levels of AGI for Operationalizing Progress on the Path to AGI |
❌ |
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0 |
| Position: Leverage Foundational Models for Black-Box Optimization |
✅ |
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1 |
| Position: Machine Learning-powered Assessments of the EU Digital Services Act Aid Quantify Policy Impacts on Online Harms |
❌ |
❌ |
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❌ |
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0 |
| Position: Measure Dataset Diversity, Don’t Just Claim It |
❌ |
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✅ |
❌ |
❌ |
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1 |
| Position: Mission Critical – Satellite Data is a Distinct Modality in Machine Learning |
❌ |
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✅ |
❌ |
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1 |
| Position: Near to Mid-term Risks and Opportunities of Open-Source Generative AI |
❌ |
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0 |
| Position: On the Possibilities of AI-Generated Text Detection |
❌ |
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✅ |
❌ |
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1 |
| Position: On the Societal Impact of Open Foundation Models |
❌ |
❌ |
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0 |
| Position: Open-Endedness is Essential for Artificial Superhuman Intelligence |
❌ |
❌ |
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❌ |
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0 |
| Position: Opportunities Exist for Machine Learning in Magnetic Fusion Energy |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
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1 |
| Position: Optimization in SciML Should Employ the Function Space Geometry |
❌ |
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❌ |
❌ |
❌ |
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0 |
| Position: Quo Vadis, Unsupervised Time Series Anomaly Detection? |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Position: Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Position: Relational Deep Learning - Graph Representation Learning on Relational Databases |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Position: Rethinking Post-Hoc Search-Based Neural Approaches for Solving Large-Scale Traveling Salesman Problems |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Position: Scaling Simulation is Neither Necessary Nor Sufficient for In-the-Wild Robot Manipulation |
❌ |
❌ |
❌ |
❌ |
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❌ |
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0 |
| Position: Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Position: Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
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0 |
| Position: Social Environment Design Should be Further Developed for AI-based Policy-Making |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
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2 |
| Position: Standardization of Behavioral Use Clauses is Necessary for the Adoption of Responsible Licensing of AI |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
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2 |
| Position: Stop Making Unscientific AGI Performance Claims |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Position: Technical Research and Talent is Needed for Effective AI Governance |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
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0 |
| Position: Tensor Networks are a Valuable Asset for Green AI |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
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1 |
| Position: The Causal Revolution Needs Scientific Pragmatism |
❌ |
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❌ |
❌ |
❌ |
❌ |
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0 |
| Position: The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Position: The Platonic Representation Hypothesis |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Position: The Reasonable Person Standard for AI |
❌ |
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0 |
| Position: Topological Deep Learning is the New Frontier for Relational Learning |
❌ |
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0 |
| Position: Towards Implicit Prompt For Text-To-Image Models |
❌ |
✅ |
✅ |
❌ |
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3 |
| Position: Towards Unified Alignment Between Agents, Humans, and Environment |
✅ |
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✅ |
✅ |
❌ |
✅ |
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6 |
| Position: TrustLLM: Trustworthiness in Large Language Models |
❌ |
✅ |
✅ |
❌ |
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❌ |
✅ |
3 |
| Position: Understanding LLMs Requires More Than Statistical Generalization |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
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3 |
| Position: Video as the New Language for Real-World Decision Making |
❌ |
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✅ |
❌ |
❌ |
❌ |
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2 |
| Position: What Can Large Language Models Tell Us about Time Series Analysis |
✅ |
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❌ |
❌ |
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2 |
| Position: What makes an image realistic? |
❌ |
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❌ |
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❌ |
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0 |
| Position: Why Tabular Foundation Models Should Be a Research Priority |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Position: Why We Must Rethink Empirical Research in Machine Learning |
❌ |
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❌ |
❌ |
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0 |
| Position: Will we run out of data? Limits of LLM scaling based on human-generated data |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Positional Knowledge is All You Need: Position-induced Transformer (PiT) for Operator Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Positive Concave Deep Equilibrium Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Positive and Unlabeled Learning with Controlled Probability Boundary Fence |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Post-hoc Part-Prototype Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Potential Based Diffusion Motion Planning |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Practical Hamiltonian Monte Carlo on Riemannian Manifolds via Relativity Theory |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Practical Performance Guarantees for Pipelined DNN Inference |
✅ |
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✅ |
❌ |
✅ |
✅ |
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5 |
| Pragmatic Feature Preferences: Learning Reward-Relevant Preferences from Human Input |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Pre-Training Protein Bi-level Representation Through Span Mask Strategy On 3D Protein Chains |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Precise Accuracy / Robustness Tradeoffs in Regression: Case of General Norms |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Predicting Dose-Response Curves with Deep Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Predicting Lagrangian Multipliers for Mixed Integer Linear Programs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Prediction Accuracy of Learning in Games : Follow-the-Regularized-Leader meets Heisenberg |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Prediction-powered Generalization of Causal Inferences |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Predictive Coding beyond Correlations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Predictive Dynamic Fusion |
❌ |
✅ |
✅ |
❌ |
✅ |
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4 |
| Predictive Linear Online Tracking for Unknown Targets |
✅ |
✅ |
❌ |
❌ |
✅ |
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4 |
| Predictive Performance Comparison of Decision Policies Under Confounding |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss |
❌ |
✅ |
✅ |
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✅ |
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4 |
| Premise Order Matters in Reasoning with Large Language Models |
❌ |
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✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Preventing Model Collapse in Gaussian Process Latent Variable Models |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Pricing with Contextual Elasticity and Heteroscedastic Valuation |
✅ |
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❌ |
❌ |
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2 |
| Principled Gradient-Based MCMC for Conditional Sampling of Text |
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✅ |
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❌ |
❌ |
✅ |
2 |
| Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF |
✅ |
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❌ |
❌ |
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2 |
| Principled Preferential Bayesian Optimization |
✅ |
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❌ |
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❌ |
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5 |
| Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses |
✅ |
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❌ |
❌ |
✅ |
2 |
| Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Privacy Attacks in Decentralized Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Privacy Backdoors: Stealing Data with Corrupted Pretrained Models |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Privacy Preserving Adaptive Experiment Design |
✅ |
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❌ |
❌ |
❌ |
✅ |
2 |
| Privacy Profiles for Private Selection |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Privacy-Preserving Data Release Leveraging Optimal Transport and Particle Gradient Descent |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Privacy-Preserving Embedding via Look-up Table Evaluation with Fully Homomorphic Encryption |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Privacy-Preserving Instructions for Aligning Large Language Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Private Truly-Everlasting Robust-Prediction |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Privately Learning Smooth Distributions on the Hypercube by Projections |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Proactive DP: A Multiple Target Optimization Framework for DP-SGD |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Proactive Detection of Voice Cloning with Localized Watermarking |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Probabilistic Conceptual Explainers: Trustworthy Conceptual Explanations for Vision Foundation Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Probabilistic Constrained Reinforcement Learning with Formal Interpretability |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Probabilistic Generating Circuits - Demystified |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Probabilistic Modeling of Interpersonal Coordination Processes |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Probabilistic Routing for Graph-Based Approximate Nearest Neighbor Search |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Probabilistic Subgoal Representations for Hierarchical Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Probabilistic Time Series Modeling with Decomposable Denoising Diffusion Model |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Probability Distribution of Hypervolume Improvement in Bi-objective Bayesian Optimization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Prodigy: An Expeditiously Adaptive Parameter-Free Learner |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Profile Reconstruction from Private Sketches |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Progressive Inference: Explaining Decoder-Only Sequence Classification Models Using Intermediate Predictions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Projecting Molecules into Synthesizable Chemical Spaces |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Projection-Free Online Convex Optimization with Time-Varying Constraints |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Projection-Free Variance Reduction Methods for Stochastic Constrained Multi-Level Compositional Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Prometheus: Out-of-distribution Fluid Dynamics Modeling with Disentangled Graph ODE |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Promoting External and Internal Equities Under Ex-Ante/Ex-Post Metrics in Online Resource Allocation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Prompt Sketching for Large Language Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Prompt-based Visual Alignment for Zero-shot Policy Transfer |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Prompt-guided Precise Audio Editing with Diffusion Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Prompt-tuning Latent Diffusion Models for Inverse Problems |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Promptbreeder: Self-Referential Self-Improvement via Prompt Evolution |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Prompting a Pretrained Transformer Can Be a Universal Approximator |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Prompting is a Double-Edged Sword: Improving Worst-Group Robustness of Foundation Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Prompting4Debugging: Red-Teaming Text-to-Image Diffusion Models by Finding Problematic Prompts |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Prospective Side Information for Latent MDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Prospector Heads: Generalized Feature Attribution for Large Models & Data |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Protein Conformation Generation via Force-Guided SE(3) Diffusion Models |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Proteus: Exploring Protein Structure Generation for Enhanced Designability and Efficiency |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| ProtoGate: Prototype-based Neural Networks with Global-to-local Feature Selection for Tabular Biomedical Data |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Prototypical Transformer As Unified Motion Learners |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Provable Contrastive Continual Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Provable Interactive Learning with Hindsight Instruction Feedback |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Provable Privacy with Non-Private Pre-Processing |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Provably Better Explanations with Optimized Aggregation of Feature Attributions |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Provably Efficient Long-Horizon Exploration in Monte Carlo Tree Search through State Occupancy Regularization |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Provably Efficient Partially Observable Risk-sensitive Reinforcement Learning with Hindsight Observation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Provably Efficient Reinforcement Learning for Adversarial Restless Multi-Armed Bandits with Unknown Transitions and Bandit Feedback |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Provably Robust DPO: Aligning Language Models with Noisy Feedback |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Provably Scalable Black-Box Variational Inference with Structured Variational Families |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| PruNeRF: Segment-Centric Dataset Pruning via 3D Spatial Consistency |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Pruned Pivot: Correlation Clustering Algorithm for Dynamic, Parallel, and Local Computation Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Pruner-Zero: Evolving Symbolic Pruning Metric From Scratch for Large Language Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Pseudo-Calibration: Improving Predictive Uncertainty Estimation in Unsupervised Domain Adaptation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Purifying Quantization-conditioned Backdoors via Layer-wise Activation Correction with Distribution Approximation |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Pursuing Overall Welfare in Federated Learning through Sequential Decision Making |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Q-Probe: A Lightweight Approach to Reward Maximization for Language Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Q-value Regularized Transformer for Offline Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| QBMK: Quantum-based Matching Kernels for Un-attributed Graphs |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| QORA: Zero-Shot Transfer via Interpretable Object-Relational Model Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| QUEST: Query-Aware Sparsity for Efficient Long-Context LLM Inference |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| QuIP$#$: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| QuRating: Selecting High-Quality Data for Training Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features Critics |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
4 |
| Quality-Diversity with Limited Resources |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Quality-Weighted Vendi Scores And Their Application To Diverse Experimental Design |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Quantum Algorithm for Online Exp-concave Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Quantum Algorithms and Lower Bounds for Finite-Sum Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Quantum Implicit Neural Representations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Quantum Positional Encodings for Graph Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Quantum Theory and Application of Contextual Optimal Transport |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Quasi-Monte Carlo Features for Kernel Approximation |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| R2E: Turning any Github Repository into a Programming Agent Environment |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| RAUCA: A Novel Physical Adversarial Attack on Vehicle Detectors via Robust and Accurate Camouflage Generation |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| REMEDI: Corrective Transformations for Improved Neural Entropy Estimation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanation |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| RIME: Robust Preference-based Reinforcement Learning with Noisy Preferences |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| RL-CFR: Improving Action Abstraction for Imperfect Information Extensive-Form Games with Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| RLVF: Learning from Verbal Feedback without Overgeneralization |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| RMIB: Representation Matching Information Bottleneck for Matching Text Representations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow Matching |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| RVI-SAC: Average Reward Off-Policy Deep Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Random Latent Exploration for Deep Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Random Masking Finds Winning Tickets for Parameter Efficient Fine-tuning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Random Scaling and Momentum for Non-smooth Non-convex Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Random features models: a way to study the success of naive imputation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Random matrix theory improved Fréchet mean of symmetric positive definite matrices |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Randomized Confidence Bounds for Stochastic Partial Monitoring |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Ranking-based Client Imitation Selection for Efficient Federated Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Rapid Learning without Catastrophic Forgetting in the Morris Water Maze |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Rate-Optimal Policy Optimization for Linear Markov Decision Processes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| ReDiffuser: Reliable Decision-Making Using a Diffuser with Confidence Estimation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| ReGAL: Refactoring Programs to Discover Generalizable Abstractions |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| ReLU Network with Width $d+\mathcalO(1)$ Can Achieve Optimal Approximation Rate |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| ReLUs Are Sufficient for Learning Implicit Neural Representations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Realistic Unsupervised CLIP Fine-tuning with Universal Entropy Optimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Reason for Future, Act for Now: A Principled Architecture for Autonomous LLM Agents |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Receptive Fields As Experts in Convolutional Neural Architectures |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| ReconBoost: Boosting Can Achieve Modality Reconcilement |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Recovering Labels from Local Updates in Federated Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Recovering the Pre-Fine-Tuning Weights of Generative Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Recurrent Distance Filtering for Graph Representation Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Recurrent Early Exits for Federated Learning with Heterogeneous Clients |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Reducing Balancing Error for Causal Inference via Optimal Transport |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Reducing Fine-Tuning Memory Overhead by Approximate and Memory-Sharing Backpropagation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Reducing Item Discrepancy via Differentially Private Robust Embedding Alignment for Privacy-Preserving Cross Domain Recommendation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Reducing sequential change detection to sequential estimation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Referee Can Play: An Alternative Approach to Conditional Generation via Model Inversion |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Reference Neural Operators: Learning the Smooth Dependence of Solutions of PDEs on Geometric Deformations |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Refined Coreset Selection: Towards Minimal Coreset Size under Model Performance Constraints |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Refining Minimax Regret for Unsupervised Environment Design |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Reflected Flow Matching |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Reflective Policy Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Regression Learning with Limited Observations of Multivariate Outcomes and Features |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Regression with Multi-Expert Deferral |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Regularized Q-learning through Robust Averaging |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Regularizing with Pseudo-Negatives for Continual Self-Supervised Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Reinforcement Learning and Regret Bounds for Admission Control |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Reinforcement Learning from Reachability Specifications: PAC Guarantees with Expected Conditional Distance |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Reinforcement Learning within Tree Search for Fast Macro Placement |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Reinformer: Max-Return Sequence Modeling for Offline RL |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Rejuvenating image-GPT as Strong Visual Representation Learners |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Relational DNN Verification With Cross Executional Bound Refinement |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Relational Learning in Pre-Trained Models: A Theory from Hypergraph Recovery Perspective |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Relaxing the Accurate Imputation Assumption in Doubly Robust Learning for Debiased Collaborative Filtering |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Remembering to Be Fair: Non-Markovian Fairness in Sequential Decision Making |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Removing Spurious Concepts from Neural Network Representations via Joint Subspace Estimation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Reparameterized Importance Sampling for Robust Variational Bayesian Neural Networks |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Repeat After Me: Transformers are Better than State Space Models at Copying |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Replicable Learning of Large-Margin Halfspaces |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Repoformer: Selective Retrieval for Repository-Level Code Completion |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Representation Surgery for Multi-Task Model Merging |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Representation Surgery: Theory and Practice of Affine Steering |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Representing Molecules as Random Walks Over Interpretable Grammars |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Reprompting: Automated Chain-of-Thought Prompt Inference Through Gibbs Sampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Reservoir Computing for Short High-Dimensional Time Series: an Application to SARS-CoV-2 Hospitalization Forecast |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Reshape and Adapt for Output Quantization (RAOQ): Quantization-aware Training for In-memory Computing Systems |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Residual Quantization with Implicit Neural Codebooks |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Residual-Conditioned Optimal Transport: Towards Structure-Preserving Unpaired and Paired Image Restoration |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Resisting Stochastic Risks in Diffusion Planners with the Trajectory Aggregation Tree |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Restoring balance: principled under/oversampling of data for optimal classification |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Rethinking Adversarial Robustness in the Context of the Right to be Forgotten |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Rethinking DP-SGD in Discrete Domain: Exploring Logistic Distribution in the Realm of signSGD |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Rethinking Data Shapley for Data Selection Tasks: Misleads and Merits |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Rethinking Decision Transformer via Hierarchical Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Rethinking Generative Large Language Model Evaluation for Semantic Comprehension |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
3 |
| Rethinking Guidance Information to Utilize Unlabeled Samples: A Label Encoding Perspective |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Rethinking Independent Cross-Entropy Loss For Graph-Structured Data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Rethinking Momentum Knowledge Distillation in Online Continual Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Rethinking Optimization and Architecture for Tiny Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided Diffusion |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Rethinking Transformers in Solving POMDPs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Rethinking the Flat Minima Searching in Federated Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Retrieval Across Any Domains via Large-scale Pre-trained Model |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Retrieval-Augmented Score Distillation for Text-to-3D Generation |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Revealing Vision-Language Integration in the Brain with Multimodal Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Revealing the Dark Secrets of Extremely Large Kernel ConvNets on Robustness |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Revisit the Essence of Distilling Knowledge through Calibration |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Revisiting Character-level Adversarial Attacks for Language Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Revisiting Context Aggregation for Image Matting |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Revisiting Inexact Fixed-Point Iterations for Min-Max Problems: Stochasticity and Structured Nonconvexity |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Revisiting Scalable Hessian Diagonal Approximations for Applications in Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Revisiting the Power of Prompt for Visual Tuning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Revisiting the Role of Language Priors in Vision-Language Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Reward Model Learning vs. Direct Policy Optimization: A Comparative Analysis of Learning from Human Preferences |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Reward Shaping for Reinforcement Learning with An Assistant Reward Agent |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Reward-Free Kernel-Based Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Reweighted Solutions for Weighted Low Rank Approximation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Rich-Observation Reinforcement Learning with Continuous Latent Dynamics |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Riemannian Accelerated Zeroth-order Algorithm: Improved Robustness and Lower Query Complexity |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Riemannian coordinate descent algorithms on matrix manifolds |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Risk Aware Benchmarking of Large Language Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Risk Estimation in a Markov Cost Process: Lower and Upper Bounds |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Risk-Sensitive Policy Optimization via Predictive CVaR Policy Gradient |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Risk-Sensitive Reward-Free Reinforcement Learning with CVaR |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| RoboCodeX: Multimodal Code Generation for Robotic Behavior Synthesis |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| RoboDreamer: Learning Compositional World Models for Robot Imagination |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| RoboMP$^2$: A Robotic Multimodal Perception-Planning Framework with Multimodal Large Language Models |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Robust Classification via a Single Diffusion Model |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Robust Data-driven Prescriptiveness Optimization |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
5 |
| Robust Graph Matching when Nodes are Corrupt |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Robust Inverse Constrained Reinforcement Learning under Model Misspecification |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Robust Inverse Graphics via Probabilistic Inference |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Robust Learning-Augmented Dictionaries |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robust Multi-Task Learning with Excess Risks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Robust Sparse Estimation for Gaussians with Optimal Error under Huber Contamination |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Robust Stable Spiking Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust Universal Adversarial Perturbations |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Robust Yet Efficient Conformal Prediction Sets |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Robust and Conjugate Gaussian Process Regression |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Robustly Learning Single-Index Models via Alignment Sharpness |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse Training Data |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Robustness of Nonlinear Representation Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Rolling Diffusion Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Roping in Uncertainty: Robustness and Regularization in Markov Games |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Rotational Equilibrium: How Weight Decay Balances Learning Across Neural Networks |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Run-Time Task Composition with Safety Semantics |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration via Shift Reduction Lemmas |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| S$Ω$I: Score-based O-INFORMATION Estimation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| S3O: A Dual-Phase Approach for Reconstructing Dynamic Shape and Skeleton of Articulated Objects from Single Monocular Video |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| SAM as the Guide: Mastering Pseudo-Label Refinement in Semi-Supervised Referring Expression Segmentation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| SAM-E: Leveraging Visual Foundation Model with Sequence Imitation for Embodied Manipulation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| SAPG: Split and Aggregate Policy Gradients |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| SCoRe: Submodular Combinatorial Representation Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| SFC: Achieve Accurate Fast Convolution under Low-precision Arithmetic |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| SHINE: Shielding Backdoors in Deep Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| SILVER: Single-loop variance reduction and application to federated learning |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| SIN: Selective and Interpretable Normalization for Long-Term Time Series Forecasting |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| SLAB: Efficient Transformers with Simplified Linear Attention and Progressive Re-parameterized Batch Normalization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| SLOG: An Inductive Spectral Graph Neural Network Beyond Polynomial Filter |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| SMaRt: Improving GANs with Score Matching Regularity |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| SPABA: A Single-Loop and Probabilistic Stochastic Bilevel Algorithm Achieving Optimal Sample Complexity |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| SPADE: Sparsity-Guided Debugging for Deep Neural Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models |
❌ |
✅ |
✅ |
❌ |
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3 |
| SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models |
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✅ |
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3 |
| SSL4Q: Semi-Supervised Learning of Quantum Data with Application to Quantum State Classification |
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❌ |
✅ |
1 |
| STEER: Assessing the Economic Rationality of Large Language Models |
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4 |
| STELLA: Continual Audio-Video Pre-training with SpatioTemporal Localized Alignment |
✅ |
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❌ |
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5 |
| SaVeR: Optimal Data Collection Strategy for Safe Policy Evaluation in Tabular MDP |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants |
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❌ |
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2 |
| Safe Reinforcement Learning using Finite-Horizon Gradient-based Estimation |
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❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Safe and Robust Subgame Exploitation in Imperfect Information Games |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models |
✅ |
✅ |
✅ |
❌ |
✅ |
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5 |
| Saliency strikes back: How filtering out high frequencies improves white-box explanations |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
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2 |
| Sample Average Approximation for Conditional Stochastic Optimization with Dependent Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sample Complexity Bounds for Estimating Probability Divergences under Invariances |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Sample as you Infer: Predictive Coding with Langevin Dynamics |
✅ |
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✅ |
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✅ |
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4 |
| Sample-Efficient Multiagent Reinforcement Learning with Reset Replay |
✅ |
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✅ |
❌ |
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✅ |
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5 |
| Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sample-specific Masks for Visual Reprogramming-based Prompting |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Sampling in Unit Time with Kernel Fisher-Rao Flow |
❌ |
✅ |
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✅ |
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3 |
| Sampling is as easy as keeping the consistency: convergence guarantee for Consistency Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Sampling-based Multi-dimensional Recalibration |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scalable AI Safety via Doubly-Efficient Debate |
✅ |
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❌ |
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✅ |
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3 |
| Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers |
❌ |
✅ |
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4 |
| Scalable Multiple Kernel Clustering: Learning Clustering Structure from Expectation |
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4 |
| Scalable Online Exploration via Coverability |
✅ |
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❌ |
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✅ |
4 |
| Scalable Pre-training of Large Autoregressive Image Models |
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4 |
| Scalable Safe Policy Improvement for Factored Multi-Agent MDPs |
✅ |
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✅ |
❌ |
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❌ |
✅ |
4 |
| Scalable Wasserstein Gradient Flow for Generative Modeling through Unbalanced Optimal Transport |
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✅ |
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4 |
| Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency |
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5 |
| Scale-Free Image Keypoints Using Differentiable Persistent Homology |
❌ |
✅ |
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5 |
| Scaling Beyond the GPU Memory Limit for Large Mixture-of-Experts Model Training |
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4 |
| Scaling Down Deep Learning with MNIST-1D |
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✅ |
4 |
| Scaling Exponents Across Parameterizations and Optimizers |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Scaling Laws for Fine-Grained Mixture of Experts |
❌ |
✅ |
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4 |
| Scaling Laws for the Value of Individual Data Points in Machine Learning |
❌ |
✅ |
✅ |
❌ |
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❌ |
✅ |
3 |
| Scaling Rectified Flow Transformers for High-Resolution Image Synthesis |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Scaling Tractable Probabilistic Circuits: A Systems Perspective |
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✅ |
✅ |
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❌ |
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6 |
| Scene Graph Generation Strategy with Co-occurrence Knowledge and Learnable Term Frequency |
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✅ |
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❌ |
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5 |
| SceneCraft: An LLM Agent for Synthesizing 3D Scenes as Blender Code |
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❌ |
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❌ |
❌ |
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3 |
| SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Score identity Distillation: Exponentially Fast Distillation of Pretrained Diffusion Models for One-Step Generation |
✅ |
✅ |
✅ |
❌ |
✅ |
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5 |
| Score-Based Causal Discovery of Latent Variable Causal Models |
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❌ |
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2 |
| Scribble-Supervised Semantic Segmentation with Prototype-based Feature Augmentation |
❌ |
✅ |
✅ |
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✅ |
❌ |
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5 |
| SeMOPO: Learning High-quality Model and Policy from Low-quality Offline Visual Datasets |
✅ |
✅ |
✅ |
❌ |
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6 |
| Second-Order Uncertainty Quantification: A Distance-Based Approach |
❌ |
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1 |
| See More Details: Efficient Image Super-Resolution by Experts Mining |
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5 |
| Seesaw: Compensating for Nonlinear Reduction with Linear Computations for Private Inference |
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5 |
| Seizing Serendipity: Exploiting the Value of Past Success in Off-Policy Actor-Critic |
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4 |
| SelMatch: Effectively Scaling Up Dataset Distillation via Selection-Based Initialization and Partial Updates by Trajectory Matching |
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4 |
| Selecting Large Language Model to Fine-tune via Rectified Scaling Law |
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❌ |
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6 |
| Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup |
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❌ |
✅ |
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4 |
| Self-Alignment of Large Language Models via Monopolylogue-based Social Scene Simulation |
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4 |
| Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes |
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5 |
| Self-Composing Policies for Scalable Continual Reinforcement Learning |
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4 |
| Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction |
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5 |
| Self-Correcting Self-Consuming Loops for Generative Model Training |
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4 |
| Self-Driven Entropy Aggregation for Byzantine-Robust Heterogeneous Federated Learning |
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5 |
| Self-Infilling Code Generation |
✅ |
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3 |
| Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models |
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5 |
| Self-Rewarding Language Models |
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3 |
| Self-Supervised Coarsening of Unstructured Grid with Automatic Differentiation |
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3 |
| Self-Supervised Interpretable End-to-End Learning via Latent Functional Modularity |
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5 |
| Self-attention Networks Localize When QK-eigenspectrum Concentrates |
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3 |
| Self-cognitive Denoising in the Presence of Multiple Noisy Label Sources |
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4 |
| SelfIE: Self-Interpretation of Large Language Model Embeddings |
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✅ |
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4 |
| SelfVC: Voice Conversion With Iterative Refinement using Self Transformations |
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4 |
| Semantic-Aware Human Object Interaction Image Generation |
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❌ |
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5 |
| Semantically-correlated memories in a dense associative model |
❌ |
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4 |
| Sequence Compression Speeds Up Credit Assignment in Reinforcement Learning |
✅ |
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3 |
| Sequential Asynchronous Action Coordination in Multi-Agent Systems: A Stackelberg Decision Transformer Approach |
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4 |
| Sequential Disentanglement by Extracting Static Information From A Single Sequence Element |
❌ |
✅ |
✅ |
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❌ |
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4 |
| Sequential Kernel Goodness-of-fit Testing |
✅ |
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❌ |
❌ |
❌ |
❌ |
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2 |
| Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models |
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❌ |
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5 |
| Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
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0 |
| Sharpness-Aware Data Generation for Zero-shot Quantization |
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❌ |
❌ |
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3 |
| Shifted Interpolation for Differential Privacy |
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4 |
| Short-Long Convolutions Help Hardware-Efficient Linear Attention to Focus on Long Sequences |
❌ |
❌ |
✅ |
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4 |
| Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs |
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3 |
| SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States |
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5 |
| SiT: Symmetry-invariant Transformers for Generalisation in Reinforcement Learning |
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4 |
| Sign Gradient Descent-based Neuronal Dynamics: ANN-to-SNN Conversion Beyond ReLU Network |
✅ |
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❌ |
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5 |
| Sign Rank Limitations for Inner Product Graph Decoders |
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❌ |
✅ |
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3 |
| Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs |
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5 |
| SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign Decoding |
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3 |
| SimPro: A Simple Probabilistic Framework Towards Realistic Long-Tailed Semi-Supervised Learning |
✅ |
✅ |
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4 |
| Simple Ingredients for Offline Reinforcement Learning |
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4 |
| Simple linear attention language models balance the recall-throughput tradeoff |
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5 |
| Simplicity Bias of Two-Layer Networks beyond Linearly Separable Data |
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4 |
| Simplicity Bias via Global Convergence of Sharpness Minimization |
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1 |
| Simulation of Graph Algorithms with Looped Transformers |
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4 |
| Simulation-Based Inference with Quantile Regression |
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❌ |
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6 |
| Simultaneous identification of models and parameters of scientific simulators |
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7 |
| Single-Model Attribution of Generative Models Through Final-Layer Inversion |
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5 |
| Single-Trajectory Distributionally Robust Reinforcement Learning |
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3 |
| Size-invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection |
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5 |
| Skill Set Optimization: Reinforcing Language Model Behavior via Transferable Skills |
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4 |
| SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals |
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4 |
| Sliced Wasserstein with Random-Path Projecting Directions |
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5 |
| Sliced-Wasserstein Estimation with Spherical Harmonics as Control Variates |
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6 |
| Slicedit: Zero-Shot Video Editing With Text-to-Image Diffusion Models Using Spatio-Temporal Slices |
✅ |
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4 |
| Slicing Mutual Information Generalization Bounds for Neural Networks |
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3 |
| Sliding Down the Stairs: How Correlated Latent Variables Accelerate Learning with Neural Networks |
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3 |
| Slot Abstractors: Toward Scalable Abstract Visual Reasoning |
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6 |
| Slow and Steady Wins the Race: Maintaining Plasticity with Hare and Tortoise Networks |
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5 |
| Small-loss Adaptive Regret for Online Convex Optimization |
✅ |
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❌ |
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1 |
| Smooth Min-Max Monotonic Networks |
❌ |
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5 |
| Smooth Tchebycheff Scalarization for Multi-Objective Optimization |
✅ |
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✅ |
✅ |
❌ |
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5 |
| Smoothing Proximal Gradient Methods for Nonsmooth Sparsity Constrained Optimization: Optimality Conditions and Global Convergence |
✅ |
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5 |
| Smoothness Adaptive Hypothesis Transfer Learning |
✅ |
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✅ |
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5 |
| Sobolev Space Regularised Pre Density Models |
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5 |
| Socialized Learning: Making Each Other Better Through Multi-Agent Collaboration |
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7 |
| Soft Prompt Recovers Compressed LLMs, Transferably |
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6 |
| Solving Hierarchical Information-Sharing Dec-POMDPs: An Extensive-Form Game Approach |
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3 |
| Solving Poisson Equations using Neural Walk-on-Spheres |
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4 |
| SparQ Attention: Bandwidth-Efficient LLM Inference |
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6 |
| Sparse Cocktail: Every Sparse Pattern Every Sparse Ratio All At Once |
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6 |
| Sparse Dimensionality Reduction Revisited |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference |
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5 |
| Sparse Model Inversion: Efficient Inversion of Vision Transformers for Data-Free Applications |
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4 |
| Sparse and Structured Hopfield Networks |
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5 |
| Sparse is Enough in Fine-tuning Pre-trained Large Language Models |
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5 |
| Sparse-IFT: Sparse Iso-FLOP Transformations for Maximizing Training Efficiency |
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5 |
| Sparse-to-dense Multimodal Image Registration via Multi-Task Learning |
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4 |
| SparseTSF: Modeling Long-term Time Series Forecasting with *1k* Parameters |
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6 |
| Sparser, Better, Deeper, Stronger: Improving Static Sparse Training with Exact Orthogonal Initialization |
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5 |
| Sparsest Models Elude Pruning: An Exposé of Pruning’s Current Capabilities |
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3 |
| Spectral Phase Transition and Optimal PCA in Block-Structured Spiked Models |
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| Spectral Preconditioning for Gradient Methods on Graded Non-convex Functions |
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3 |
| Speech Self-Supervised Learning Using Diffusion Model Synthetic Data |
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5 |
| Spider: A Unified Framework for Context-dependent Concept Segmentation |
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4 |
| Spike Distance Function as a Learning Objective for Spike Prediction |
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6 |
| SpikeLM: Towards General Spike-Driven Language Modeling via Elastic Bi-Spiking Mechanisms |
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5 |
| SpikeZIP-TF: Conversion is All You Need for Transformer-based SNN |
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3 |
| Split-Ensemble: Efficient OOD-aware Ensemble via Task and Model Splitting |
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4 |
| Split-and-Denoise: Protect large language model inference with local differential privacy |
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5 |
| Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text |
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4 |
| SqueezeLLM: Dense-and-Sparse Quantization |
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5 |
| Stability Evaluation through Distributional Perturbation Analysis |
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5 |
| Stability and Generalization for Stochastic Recursive Momentum-based Algorithms for (Strongly-)Convex One to $K$-Level Stochastic Optimizations |
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2 |
| Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms |
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1 |
| Stability and Multigroup Fairness in Ranking with Uncertain Predictions |
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2 |
| Stability-Informed Initialization of Neural Ordinary Differential Equations |
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3 |
| Stabilizing Policy Gradients for Stochastic Differential Equations via Consistency with Perturbation Process |
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3 |
| Stable Differentiable Causal Discovery |
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6 |
| StableMask: Refining Causal Masking in Decoder-only Transformer |
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6 |
| StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization |
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2 |
| StackSight: Unveiling WebAssembly through Large Language Models and Neurosymbolic Chain-of-Thought Decompilation |
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3 |
| Stacking Deep Set Networks and Pooling by Quantiles |
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3 |
| Standardized Interpretable Fairness Measures for Continuous Risk Scores |
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2 |
| State-Constrained Zero-Sum Differential Games with One-Sided Information |
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3 |
| State-Free Inference of State-Space Models: The *Transfer Function* Approach |
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6 |
| Stationarity without mean reversion in improper Gaussian processes |
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2 |
| Stationary Latent Weight Inference for Unreliable Observations from Online Test-Time Adaptation |
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5 |
| Statistical Inference Under Constrained Selection Bias |
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3 |
| Statistical Properties of Robust Satisficing |
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1 |
| Statistical Test for Attention Maps in Vision Transformers |
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4 |
| Statistically Optimal Generative Modeling with Maximum Deviation from the Empirical Distribution |
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4 |
| Stay on Topic with Classifier-Free Guidance |
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3 |
| Stealing part of a production language model |
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3 |
| Stealthy Imitation: Reward-guided Environment-free Policy Stealing |
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5 |
| Stereo Risk: A Continuous Modeling Approach to Stereo Matching |
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5 |
| Stereographic Spherical Sliced Wasserstein Distances |
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5 |
| Stochastic Bandits with ReLU Neural Networks |
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3 |
| Stochastic Conditional Diffusion Models for Robust Semantic Image Synthesis |
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5 |
| Stochastic Gradient Flow Dynamics of Test Risk and its Exact Solution for Weak Features |
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3 |
| Stochastic Interpolants with Data-Dependent Couplings |
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5 |
| Stochastic Localization via Iterative Posterior Sampling |
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5 |
| Stochastic Optimization with Arbitrary Recurrent Data Sampling |
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3 |
| Stochastic Q-learning for Large Discrete Action Spaces |
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5 |
| Stochastic Quantum Sampling for Non-Logconcave Distributions and Estimating Partition Functions |
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1 |
| Stochastic Weakly Convex Optimization beyond Lipschitz Continuity |
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2 |
| Stochastic positional embeddings improve masked image modeling |
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5 |
| Stop Regressing: Training Value Functions via Classification for Scalable Deep RL |
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4 |
| StrWAEs to Invariant Representations |
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6 |
| Straight-Through Meets Sparse Recovery: the Support Exploration Algorithm |
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4 |
| StrokeNUWA—Tokenizing Strokes for Vector Graphic Synthesis |
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5 |
| Structure Your Data: Towards Semantic Graph Counterfactuals |
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5 |
| Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks |
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6 |
| Structure-based drug design by denoising voxel grids |
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5 |
| Structured Chemistry Reasoning with Large Language Models |
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5 |
| Structured Inverse-Free Natural Gradient Descent: Memory-Efficient & Numerically-Stable KFAC |
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4 |
| Studying K-FAC Heuristics by Viewing Adam through a Second-Order Lens |
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7 |
| StyDeSty: Min-Max Stylization and Destylization for Single Domain Generalization |
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4 |
| SuDA: Support-based Domain Adaptation for Sim2Real Hinge Joint Tracking with Flexible Sensors |
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3 |
| Sub-token ViT Embedding via Stochastic Resonance Transformers |
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4 |
| Subequivariant Reinforcement Learning in 3D Multi-Entity Physical Environments |
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4 |
| Subgoal-based Demonstration Learning for Formal Theorem Proving |
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6 |
| Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products |
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5 |
| Subhomogeneous Deep Equilibrium Models |
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4 |
| Submodular framework for structured-sparse optimal transport |
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6 |
| Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation |
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1 |
| Successor Features for Efficient Multi-Subject Controlled Text Generation |
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4 |
| Superpoint Gaussian Splatting for Real-Time High-Fidelity Dynamic Scene Reconstruction |
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3 |
| Superposition Prompting: Improving and Accelerating Retrieval-Augmented Generation |
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6 |
| Supervised Matrix Factorization: Local Landscape Analysis and Applications |
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4 |
| SurfPro: Functional Protein Design Based on Continuous Surface |
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4 |
| Surface-VQMAE: Vector-quantized Masked Auto-encoders on Molecular Surfaces |
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5 |
| Surprisingly Strong Performance Prediction with Neural Graph Features |
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5 |
| Swallowing the Bitter Pill: Simplified Scalable Conformer Generation |
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5 |
| Switchable Decision: Dynamic Neural Generation Networks |
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5 |
| Switched Flow Matching: Eliminating Singularities via Switching ODEs |
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5 |
| Switching the Loss Reduces the Cost in Batch Reinforcement Learning |
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3 |
| SyCoCa: Symmetrizing Contrastive Captioners with Attentive Masking for Multimodal Alignment |
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4 |
| Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion |
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5 |
| Symmetric Matrix Completion with ReLU Sampling |
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4 |
| Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial Optimization |
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5 |
| Symmetry Induces Structure and Constraint of Learning |
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2 |
| Synergistic Integration of Coordinate Network and Tensorial Feature for Improving Neural Radiance Fields from Sparse Inputs |
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4 |
| TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision |
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5 |
| TERD: A Unified Framework for Safeguarding Diffusion Models Against Backdoors |
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5 |
| TIC-TAC: A Framework For Improved Covariance Estimation In Deep Heteroscedastic Regression |
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4 |
| TSLANet: Rethinking Transformers for Time Series Representation Learning |
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6 |
| TVE: Learning Meta-attribution for Transferable Vision Explainer |
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5 |
| TabLog: Test-Time Adaptation for Tabular Data Using Logic Rules |
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4 |
| Tabular Insights, Visual Impacts: Transferring Expertise from Tables to Images |
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3 |
| Tackling Non-Stationarity in Reinforcement Learning via Causal-Origin Representation |
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5 |
| Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and More |
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5 |
| Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains |
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4 |
| Tandem Transformers for Inference Efficient LLMs |
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3 |
| Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation |
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1 |
| Task Groupings Regularization: Data-Free Meta-Learning with Heterogeneous Pre-trained Models |
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4 |
| Task-aware Orthogonal Sparse Network for Exploring Shared Knowledge in Continual Learning |
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4 |
| Taylor Videos for Action Recognition |
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6 |
| Tell, Don’t Show: Language Guidance Eases Transfer Across Domains in Images and Videos |
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4 |
| Temporal Logic Specification-Conditioned Decision Transformer for Offline Safe Reinforcement Learning |
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3 |
| Temporal Spiking Neural Networks with Synaptic Delay for Graph Reasoning |
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5 |
| Test-Time Degradation Adaptation for Open-Set Image Restoration |
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4 |
| Test-Time Model Adaptation with Only Forward Passes |
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6 |
| Test-Time Regret Minimization in Meta Reinforcement Learning |
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1 |
| Testing the Feasibility of Linear Programs with Bandit Feedback |
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3 |
| The Balanced-Pairwise-Affinities Feature Transform |
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5 |
| The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents |
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2 |
| The Computational Complexity of Finding Second-Order Stationary Points |
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1 |
| The Effect of Weight Precision on the Neuron Count in Deep ReLU Networks |
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1 |
| The Emergence of Reproducibility and Consistency in Diffusion Models |
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3 |
| The Entropy Enigma: Success and Failure of Entropy Minimization |
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4 |
| The Expressive Power of Path-Based Graph Neural Networks |
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5 |
| The Fundamental Limits of Least-Privilege Learning |
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3 |
| The Good, The Bad, and Why: Unveiling Emotions in Generative AI |
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2 |
| The Illusion of State in State-Space Models |
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1 |
| The Linear Representation Hypothesis and the Geometry of Large Language Models |
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3 |
| The Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithm |
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5 |
| The Merit of River Network Topology for Neural Flood Forecasting |
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5 |
| The Non-linear $F$-Design and Applications to Interactive Learning |
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1 |
| The Perception-Robustness Tradeoff in Deterministic Image Restoration |
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3 |
| The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks |
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4 |
| The Pitfalls of Next-Token Prediction |
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2 |
| The Privacy Power of Correlated Noise in Decentralized Learning |
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4 |
| The Relative Value of Prediction in Algorithmic Decision Making |
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| The Role of Learning Algorithms in Collective Action |
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4 |
| The Stronger the Diffusion Model, the Easier the Backdoor: Data Poisoning to Induce Copyright BreachesWithout Adjusting Finetuning Pipeline |
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| The Surprising Effectiveness of Skip-Tuning in Diffusion Sampling |
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2 |
| The WMDP Benchmark: Measuring and Reducing Malicious Use with Unlearning |
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6 |
| Theoretical Analysis of Learned Database Operations under Distribution Shift through Distribution Learnability |
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| Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians |
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1 |
| Theoretical insights for diffusion guidance: A case study for Gaussian mixture models |
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2 |
| Theory of Consistency Diffusion Models: Distribution Estimation Meets Fast Sampling |
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| Thermometer: Towards Universal Calibration for Large Language Models |
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6 |
| Think Before You Act: Decision Transformers with Working Memory |
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5 |
| Tight Partial Identification of Causal Effects with Marginal Distribution of Unmeasured Confounders |
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3 |
| Tilt and Average : Geometric Adjustment of the Last Layer for Recalibration |
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6 |
| Tilt your Head: Activating the Hidden Spatial-Invariance of Classifiers |
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6 |
| Tilting the Odds at the Lottery: the Interplay of Overparameterisation and Curricula in Neural Networks |
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2 |
| Time Series Diffusion in the Frequency Domain |
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5 |
| Time Weaver: A Conditional Time Series Generation Model |
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5 |
| Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning |
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5 |
| TimeMIL: Advancing Multivariate Time Series Classification via a Time-aware Multiple Instance Learning |
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6 |
| TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling |
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5 |
| TimeX++: Learning Time-Series Explanations with Information Bottleneck |
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6 |
| Timer: Generative Pre-trained Transformers Are Large Time Series Models |
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5 |
| TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce Edge |
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7 |
| To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO |
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7 |
| To Each (Textual Sequence) Its Own: Improving Memorized-Data Unlearning in Large Language Models |
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4 |
| To the Max: Reinventing Reward in Reinforcement Learning |
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4 |
| Token-Specific Watermarking with Enhanced Detectability and Semantic Coherence for Large Language Models |
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5 |
| Token-level Direct Preference Optimization |
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4 |
| Topological Neural Networks go Persistent, Equivariant, and Continuous |
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5 |
| Total Variation Distance Meets Probabilistic Inference |
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1 |
| Total Variation Floodgate for Variable Importance Inference in Classification |
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5 |
| Toward Adaptive Reasoning in Large Language Models with Thought Rollback |
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3 |
| Toward Availability Attacks in 3D Point Clouds |
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5 |
| Towards AutoAI: Optimizing a Machine Learning System with Black-box and Differentiable Components |
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4 |
| Towards Causal Foundation Model: on Duality between Optimal Balancing and Attention |
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5 |
| Towards Certified Unlearning for Deep Neural Networks |
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6 |
| Towards Compositionality in Concept Learning |
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4 |
| Towards Efficient Exact Optimization of Language Model Alignment |
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4 |
| Towards Efficient Spiking Transformer: a Token Sparsification Framework for Training and Inference Acceleration |
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4 |
| Towards Efficient Training and Evaluation of Robust Models against $l_0$ Bounded Adversarial Perturbations |
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5 |
| Towards General Algorithm Discovery for Combinatorial Optimization: Learning Symbolic Branching Policy from Bipartite Graph |
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7 |
| Towards General Neural Surrogate Solvers with Specialized Neural Accelerators |
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3 |
| Towards Generalization beyond Pointwise Learning: A Unified Information-theoretic Perspective |
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4 |
| Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles |
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2 |
| Towards Interpretable Deep Local Learning with Successive Gradient Reconciliation |
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4 |
| Towards Modular LLMs by Building and Reusing a Library of LoRAs |
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4 |
| Towards Neural Architecture Search through Hierarchical Generative Modeling |
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6 |
| Towards Optimal Adversarial Robust Q-learning with Bellman Infinity-error |
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4 |
| Towards Realistic Model Selection for Semi-supervised Learning |
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4 |
| Towards Resource-friendly, Extensible and Stable Incomplete Multi-view Clustering |
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2 |
| Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption |
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1 |
| Towards Scalable and Versatile Weight Space Learning |
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5 |
| Towards Theoretical Understanding of Learning Large-scale Dependent Data via Random Features |
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4 |
| Towards Theoretical Understandings of Self-Consuming Generative Models |
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2 |
| Towards Understanding Inductive Bias in Transformers: A View From Infinity |
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2 |
| Towards Understanding the Word Sensitivity of Attention Layers: A Study via Random Features |
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3 |
| Towards Unified Multi-granularity Text Detection with Interactive Attention |
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3 |
| Towards a Better Theoretical Understanding of Independent Subnetwork Training |
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3 |
| Towards a Self-contained Data-driven Global Weather Forecasting Framework |
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5 |
| Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model |
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4 |
| Towards efficient deep spiking neural networks construction with spiking activity based pruning |
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3 |
| Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms |
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3 |
| Trainable Transformer in Transformer |
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4 |
| Trained Random Forests Completely Reveal your Dataset |
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5 |
| Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial Optimization |
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4 |
| Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning |
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5 |
| Training-Free Long-Context Scaling of Large Language Models |
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5 |
| Transferable Facial Privacy Protection against Blind Face Restoration via Domain-Consistent Adversarial Obfuscation |
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2 |
| Transferring Knowledge From Large Foundation Models to Small Downstream Models |
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6 |
| Transformers Get Stable: An End-to-End Signal Propagation Theory for Language Models |
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4 |
| Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions In Context |
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1 |
| Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape |
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2 |
| Transformers Provably Learn Sparse Token Selection While Fully-Connected Nets Cannot |
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2 |
| Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality |
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5 |
| Transformers, parallel computation, and logarithmic depth |
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4 |
| Transforming and Combining Rewards for Aligning Large Language Models |
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3 |
| Transitional Uncertainty with Layered Intermediate Predictions |
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5 |
| Translating Subgraphs to Nodes Makes Simple GNNs Strong and Efficient for Subgraph Representation Learning |
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5 |
| Translation Equivariant Transformer Neural Processes |
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3 |
| Transolver: A Fast Transformer Solver for PDEs on General Geometries |
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4 |
| Transport of Algebraic Structure to Latent Embeddings |
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6 |
| TravelPlanner: A Benchmark for Real-World Planning with Language Agents |
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4 |
| Triadic-OCD: Asynchronous Online Change Detection with Provable Robustness, Optimality, and Convergence |
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3 |
| Triple Changes Estimator for Targeted Policies |
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3 |
| Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers |
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5 |
| Tripod: Three Complementary Inductive Biases for Disentangled Representation Learning |
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5 |
| TroVE: Inducing Verifiable and Efficient Toolboxes for Solving Programmatic Tasks |
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3 |
| Truly No-Regret Learning in Constrained MDPs |
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4 |
| Trust Regions for Explanations via Black-Box Probabilistic Certification |
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5 |
| Trust the Model Where It Trusts Itself - Model-Based Actor-Critic with Uncertainty-Aware Rollout Adaption |
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4 |
| Trustless Audits without Revealing Data or Models |
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3 |
| Trustworthy Actionable Perturbations |
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5 |
| Trustworthy Alignment of Retrieval-Augmented Large Language Models via Reinforcement Learning |
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5 |
| Tuning-Free Stochastic Optimization |
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1 |
| Tuning-free Estimation and Inference of Cumulative Distribution Function under Local Differential Privacy |
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3 |
| Turnstile $\ell_p$ leverage score sampling with applications |
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4 |
| Two Fists, One Heart: Multi-Objective Optimization Based Strategy Fusion for Long-tailed Learning |
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5 |
| Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness |
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6 |
| Two Heads are Actually Better than One: Towards Better Adversarial Robustness via Transduction and Rejection |
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5 |
| Two Stones Hit One Bird: Bilevel Positional Encoding for Better Length Extrapolation |
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5 |
| Two Tales of Single-Phase Contrastive Hebbian Learning |
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4 |
| Two-Stage Shadow Inclusion Estimation: An IV Approach for Causal Inference under Latent Confounding and Collider Bias |
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6 |
| Two-sided Competing Matching Recommendation Markets With Quota and Complementary Preferences Constraints |
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3 |
| Two-timescale Derivative Free Optimization for Performative Prediction with Markovian Data |
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4 |
| UGrid: An Efficient-And-Rigorous Neural Multigrid Solver for Linear PDEs |
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3 |
| ULAREF: A Unified Label Refinement Framework for Learning with Inaccurate Supervision |
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5 |
| ULTRAFEEDBACK: Boosting Language Models with Scaled AI Feedback |
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2 |
| UP2ME: Univariate Pre-training to Multivariate Fine-tuning as a General-purpose Framework for Multivariate Time Series Analysis |
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5 |
| UPAM: Unified Prompt Attack in Text-to-Image Generation Models Against Both Textual Filters and Visual Checkers |
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2 |
| UPOCR: Towards Unified Pixel-Level OCR Interface |
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5 |
| USTAD: Unified Single-model Training Achieving Diverse Scores for Information Retrieval |
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3 |
| Unbiased Multi-Label Learning from Crowdsourced Annotations |
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5 |
| Uncertainty Estimation by Density Aware Evidential Deep Learning |
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5 |
| Uncertainty for Active Learning on Graphs |
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5 |
| Uncertainty-Aware Reward-Free Exploration with General Function Approximation |
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4 |
| Understanding Adam Optimizer via Online Learning of Updates: Adam is FTRL in Disguise |
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1 |
| Understanding Diffusion Models by Feynman’s Path Integral |
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4 |
| Understanding Finetuning for Factual Knowledge Extraction |
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3 |
| Understanding Forgetting in Continual Learning with Linear Regression |
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1 |
| Understanding Heterophily for Graph Neural Networks |
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3 |
| Understanding Inter-Concept Relationships in Concept-Based Models |
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6 |
| Understanding MLP-Mixer as a wide and sparse MLP |
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3 |
| Understanding Reasoning Ability of Language Models From the Perspective of Reasoning Paths Aggregation |
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5 |
| Understanding Retrieval-Augmented Task Adaptation for Vision-Language Models |
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4 |
| Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation |
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3 |
| Understanding Stochastic Natural Gradient Variational Inference |
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3 |
| Understanding Unimodal Bias in Multimodal Deep Linear Networks |
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3 |
| Understanding and Diagnosing Deep Reinforcement Learning |
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3 |
| Understanding the Effects of Iterative Prompting on Truthfulness |
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2 |
| Understanding the Impact of Introducing Constraints at Inference Time on Generalization Error |
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0 |
| Understanding the Learning Dynamics of Alignment with Human Feedback |
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2 |
| Understanding the Training Speedup from Sampling with Approximate Losses |
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4 |
| UniAudio: Towards Universal Audio Generation with Large Language Models |
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4 |
| UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning |
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❌ |
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4 |
| Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-Decoding |
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❌ |
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6 |
| Unified Training of Universal Time Series Forecasting Transformers |
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5 |
| Uniform Memory Retrieval with Larger Capacity for Modern Hopfield Models |
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❌ |
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5 |
| Uniformly Stable Algorithms for Adversarial Training and Beyond |
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❌ |
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❌ |
✅ |
4 |
| Unifying Bayesian Flow Networks and Diffusion Models through Stochastic Differential Equations |
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❌ |
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4 |
| Unifying Image Processing as Visual Prompting Question Answering |
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❌ |
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4 |
| Universal Consistency of Wide and Deep ReLU Neural Networks and Minimax Optimal Convergence Rates for Kolmogorov-Donoho Optimal Function Classes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
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0 |
| Universal Gradient Methods for Stochastic Convex Optimization |
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❌ |
❌ |
❌ |
✅ |
3 |
| Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues |
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❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Unlock the Cognitive Generalization of Deep Reinforcement Learning via Granular Ball Representation |
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6 |
| Unlocking the Power of Spatial and Temporal Information in Medical Multimodal Pre-training |
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5 |
| Unmasking Vulnerabilities: Cardinality Sketches under Adaptive Inputs |
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❌ |
✅ |
2 |
| Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning |
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3 |
| Unsupervised Concept Discovery Mitigates Spurious Correlations |
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6 |
| Unsupervised Domain Adaptation for Anatomical Structure Detection in Ultrasound Images |
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5 |
| Unsupervised Episode Generation for Graph Meta-learning |
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❌ |
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6 |
| Unsupervised Evaluation of Code LLMs with Round-Trip Correctness |
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3 |
| Unsupervised Parameter-free Simplicial Representation Learning with Scattering Transforms |
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4 |
| Unsupervised Representation Learning of Brain Activity via Bridging Voxel Activity and Functional Connectivity |
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3 |
| Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings |
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4 |
| Unveiling Privacy, Memorization, and Input Curvature Links |
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4 |
| Unveiling and Harnessing Hidden Attention Sinks: Enhancing Large Language Models without Training through Attention Calibration |
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4 |
| Unveiling the Cycloid Trajectory of EM Iterations in Mixed Linear Regression |
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1 |
| Unveiling the Dynamics of Information Interplay in Supervised Learning |
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2 |
| Unveiling the Potential of AI for Nanomaterial Morphology Prediction |
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❌ |
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5 |
| Use Your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits Coupled with Transformers |
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5 |
| Using AI Uncertainty Quantification to Improve Human Decision-Making |
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4 |
| Using Left and Right Brains Together: Towards Vision and Language Planning |
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1 |
| Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs |
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2 |
| VNN: Verification-Friendly Neural Networks with Hard Robustness Guarantees |
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6 |
| VQDNA: Unleashing the Power of Vector Quantization for Multi-Species Genomic Sequence Modeling |
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4 |
| Vague Prototype-Oriented Diffusion Model for Multi-Class Anomaly Detection |
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6 |
| Value-Evolutionary-Based Reinforcement Learning |
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5 |
| Vanilla Bayesian Optimization Performs Great in High Dimensions |
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❌ |
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3 |
| Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models |
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❌ |
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6 |
| Variational Inference with Coverage Guarantees in Simulation-Based Inference |
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6 |
| Variational Learning is Effective for Large Deep Networks |
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6 |
| Variational Linearized Laplace Approximation for Bayesian Deep Learning |
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5 |
| Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-Shifts |
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3 |
| Variational Schrödinger Diffusion Models |
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6 |
| Various Lengths, Constant Speed: Efficient Language Modeling with Lightning Attention |
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6 |
| Vector Quantization Pretraining for EEG Time Series with Random Projection and Phase Alignment |
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5 |
| Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent Parametric Partial Differential Equations |
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5 |
| Verification of Machine Unlearning is Fragile |
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6 |
| Verifying message-passing neural networks via topology-based bounds tightening |
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6 |
| ViP: A Differentially Private Foundation Model for Computer Vision |
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4 |
| Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization |
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5 |
| Video-of-Thought: Step-by-Step Video Reasoning from Perception to Cognition |
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5 |
| VideoPoet: A Large Language Model for Zero-Shot Video Generation |
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3 |
| VideoPrism: A Foundational Visual Encoder for Video Understanding |
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4 |
| Viewing Transformers Through the Lens of Long Convolutions Layers |
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4 |
| VinT-6D: A Large-Scale Object-in-hand Dataset from Vision, Touch and Proprioception |
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4 |
| Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model |
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6 |
| Vision Transformers as Probabilistic Expansion from Learngene |
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3 |
| VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context |
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4 |
| Visual Representation Learning with Stochastic Frame Prediction |
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4 |
| Visual Transformer with Differentiable Channel Selection: An Information Bottleneck Inspired Approach |
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6 |
| Visual-Text Cross Alignment: Refining the Similarity Score in Vision-Language Models |
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6 |
| Vocabulary for Universal Approximation: A Linguistic Perspective of Mapping Compositions |
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0 |
| VoroNav: Voronoi-based Zero-shot Object Navigation with Large Language Model |
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6 |
| WARM: On the Benefits of Weight Averaged Reward Models |
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3 |
| WAVES: Benchmarking the Robustness of Image Watermarks |
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4 |
| WISER: Weak Supervision and Supervised Representation Learning to Improve Drug Response Prediction in Cancer |
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6 |
| Wasserstein Wormhole: Scalable Optimal Transport Distance with Transformer |
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6 |
| Watermark Stealing in Large Language Models |
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3 |
| Watermarks in the Sand: Impossibility of Strong Watermarking for Language Models |
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5 |
| Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision |
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4 |
| Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation |
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3 |
| Weakly-Supervised Residual Evidential Learning for Multi-Instance Uncertainty Estimation |
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4 |
| WebLINX: Real-World Website Navigation with Multi-Turn Dialogue |
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5 |
| Weighted distance nearest neighbor condensing |
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2 |
| Weisfeiler Leman for Euclidean Equivariant Machine Learning |
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7 |
| Weisfeiler-Leman at the margin: When more expressivity matters |
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5 |
| What Can Transformer Learn with Varying Depth? Case Studies on Sequence Learning Tasks |
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1 |
| What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding |
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3 |
| What Will My Model Forget? Forecasting Forgotten Examples in Language Model Refinement |
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4 |
| What Would Gauss Say About Representations? Probing Pretrained Image Models using Synthetic Gaussian Benchmarks |
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4 |
| What is Dataset Distillation Learning? |
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3 |
| What is the Long-Run Distribution of Stochastic Gradient Descent? A Large Deviations Analysis |
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0 |
| What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation |
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5 |
| What’s the score? Automated Denoising Score Matching for Nonlinear Diffusions |
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3 |
| When Do Skills Help Reinforcement Learning? A Theoretical Analysis of Temporal Abstractions |
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4 |
| When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models |
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4 |
| When Representations Align: Universality in Representation Learning Dynamics |
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2 |
| When Will Gradient Regularization Be Harmful? |
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4 |
| When and How Does In-Distribution Label Help Out-of-Distribution Detection? |
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3 |
| When is Transfer Learning Possible? |
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4 |
| Which Frequencies do CNNs Need? Emergent Bottleneck Structure in Feature Learning |
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2 |
| Whispering Experts: Neural Interventions for Toxicity Mitigation in Language Models |
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4 |
| Why Do Animals Need Shaping? A Theory of Task Composition and Curriculum Learning |
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1 |
| Why Do You Grok? A Theoretical Analysis on Grokking Modular Addition |
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3 |
| Why Larger Language Models Do In-context Learning Differently? |
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2 |
| Why do Variational Autoencoders Really Promote Disentanglement? |
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3 |
| Winner-takes-all learners are geometry-aware conditional density estimators |
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5 |
| WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks? |
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5 |
| Wukong: Towards a Scaling Law for Large-Scale Recommendation |
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3 |
| X-Oscar: A Progressive Framework for High-quality Text-guided 3D Animatable Avatar Generation |
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4 |
| Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge Enhancement |
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5 |
| Zero-Shot Reinforcement Learning via Function Encoders |
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5 |
| Zero-Shot Unsupervised and Text-Based Audio Editing Using DDPM Inversion |
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5 |
| Zero-Sum Positional Differential Games as a Framework for Robust Reinforcement Learning: Deep Q-Learning Approach |
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2 |
| Zeroth-Order Methods for Constrained Nonconvex Nonsmooth Stochastic Optimization |
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3 |
| convSeq: Fast and Scalable Method for Detecting Patterns in Spike Data |
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5 |
| diff History for Neural Language Agents |
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5 |
| eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data |
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4 |
| tinyBenchmarks: evaluating LLMs with fewer examples |
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4 |
| tnGPS: Discovering Unknown Tensor Network Structure Search Algorithms via Large Language Models (LLMs) |
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5 |
| video-SALMONN: Speech-Enhanced Audio-Visual Large Language Models |
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3 |
| xT: Nested Tokenization for Larger Context in Large Images |
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4 |