Reproducibility Index

Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..

International Conference on Machine Learning (ICML) - 2022

Documentation Rate of Empirical Papers by Reproducibility Variable

Distribution of Empirical Papers by Number of Documented Variables

Website:

Venue	Year	Papers	Reproducibility Score Reproducibility Score based on Gundersen et al. (2025). See Methods for details.	Documentation Score Documentation Score is the average score over the seven reproducibility variables for empirical research papers. See Methods for details.	% Empirical Percentage of papers that are empirical research vs theoretical research.	% Industry Percentage of empirical research papers with at least one author from Industry.	Website
ICML	2022	1233	0.58	3.97	92.94%	42.84%

Search Papers

	Pseudocode	Open Source Code	Open Datasets	Dataset Splits	Hardware Specification	Software Dependencies	Experiment Setup
$p$-Laplacian Based Graph Neural Networks	❌	✅	✅	✅	❌	❌	✅	4
(Non-)Convergence Results for Predictive Coding Networks	❌	❌	❌	❌	❌	❌	❌	0
3D Infomax improves GNNs for Molecular Property Prediction	❌	✅	✅	✅	✅	❌	✅	5
3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design	❌	✅	✅	✅	❌	❌	✅	4
3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation	✅	✅	✅	❌	✅	✅	✅	6
A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks	❌	✅	✅	❌	✅	❌	✅	4
A Closer Look at Smoothness in Domain Adversarial Training	✅	✅	✅	✅	✅	❌	✅	6
A Completely Tuning-Free and Robust Approach to Sparse Precision Matrix Estimation	✅	❌	✅	❌	❌	✅	✅	4
A Consistent and Efficient Evaluation Strategy for Attribution Methods	❌	✅	✅	❌	✅	❌	✅	4
A Context-Integrated Transformer-Based Neural Network for Auction Design	✅	✅	❌	❌	❌	❌	✅	3
A Convergence Theory for SVGD in the Population Limit under Talagrand’s Inequality T1	✅	❌	❌	❌	❌	❌	❌	1
A Convergent and Dimension-Independent Min-Max Optimization Algorithm	✅	❌	✅	❌	✅	❌	✅	4
A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications	❌	✅	✅	✅	✅	❌	✅	5
A Difference Standardization Method for Mutual Transfer Learning	✅	❌	✅	✅	✅	❌	✅	5
A Differential Entropy Estimator for Training Neural Networks	✅	✅	✅	✅	✅	❌	✅	6
A Dynamical System Perspective for Lipschitz Neural Networks	✅	✅	✅	❌	❌	❌	✅	4
A Framework for Learning to Request Rich and Contextually Useful Information from Humans	❌	✅	✅	✅	❌	❌	✅	4
A Functional Information Perspective on Model Interpretation	❌	✅	✅	✅	❌	❌	❌	3
A General Recipe for Likelihood-free Bayesian Optimization	❌	✅	✅	❌	❌	❌	✅	3
A Hierarchical Bayesian Approach to Inverse Reinforcement Learning with Symbolic Reward Machines	✅	❌	✅	❌	❌	❌	✅	3
A Hierarchical Transitive-Aligned Graph Kernel for Un-attributed Graphs	❌	❌	✅	✅	❌	❌	✅	3
A Joint Exponential Mechanism For Differentially Private Top-$k$	✅	✅	✅	❌	❌	❌	✅	4
A Langevin-like Sampler for Discrete Distributions	✅	✅	✅	✅	❌	❌	✅	5
A Marriage between Adversarial Team Games and 2-player Games: Enabling Abstractions, No-regret Learning, and Subgame Solving	✅	❌	✅	❌	✅	❌	✅	4
A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes	❌	✅	✅	❌	❌	❌	✅	3
A Model-Agnostic Randomized Learning Framework based on Random Hypothesis Subspace Sampling	✅	✅	✅	❌	❌	❌	✅	4
A Modern Self-Referential Weight Matrix That Learns to Modify Itself	❌	✅	✅	✅	✅	❌	✅	5
A Multi-objective / Multi-task Learning Framework Induced by Pareto Stationarity	✅	❌	✅	❌	❌	❌	❌	2
A Natural Actor-Critic Framework for Zero-Sum Markov Games	✅	❌	✅	❌	❌	❌	✅	3
A Neural Tangent Kernel Perspective of GANs	❌	✅	✅	❌	✅	✅	✅	5
A New Perspective on the Effects of Spectrum in Graph Neural Networks	❌	✅	✅	✅	❌	❌	✅	4
A Parametric Class of Approximate Gradient Updates for Policy Optimization	❌	❌	✅	❌	❌	❌	✅	2
A Psychological Theory of Explainability	❌	✅	✅	✅	❌	❌	❌	3
A Random Matrix Analysis of Data Stream Clustering: Coping With Limited Memory Resources	✅	✅	✅	❌	❌	❌	❌	3
A Reduction from Linear Contextual Bandits Lower Bounds to Estimations Lower Bounds	❌	❌	❌	❌	❌	❌	❌	0
A Regret Minimization Approach to Multi-Agent Control	✅	❌	❌	❌	❌	❌	✅	2
A Resilient Distributed Boosting Algorithm	✅	❌	❌	❌	❌	❌	❌	1
A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions	❌	✅	✅	❌	❌	❌	❌	2
A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games	✅	❌	❌	❌	❌	❌	❌	1
A Simple Guard for Learned Optimizers	✅	❌	✅	✅	✅	✅	✅	6
A Simple Reward-free Approach to Constrained Reinforcement Learning	✅	❌	❌	❌	❌	❌	❌	1
A Simple Unified Framework for High Dimensional Bandit Problems	✅	❌	❌	❌	❌	❌	❌	1
A Simple yet Universal Strategy for Online Convex Optimization	✅	❌	❌	❌	❌	❌	✅	2
A Single-Loop Gradient Descent and Perturbed Ascent Algorithm for Nonconvex Functional Constrained Optimization	✅	❌	✅	❌	✅	❌	✅	4
A State-Distribution Matching Approach to Non-Episodic Reinforcement Learning	✅	❌	✅	❌	❌	❌	✅	3
A Statistical Manifold Framework for Point Cloud Data	❌	✅	✅	✅	❌	❌	✅	4
A Stochastic Multi-Rate Control Framework For Modeling Distributed Optimization Algorithms	❌	❌	❌	❌	❌	❌	✅	1
A Study of Face Obfuscation in ImageNet	❌	✅	✅	✅	✅	❌	✅	5
A Study on the Ramanujan Graph Property of Winning Lottery Tickets	✅	❌	✅	❌	❌	❌	✅	3
A Temporal-Difference Approach to Policy Gradient Estimation	✅	✅	✅	❌	❌	❌	✅	4
A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization	✅	✅	❌	❌	❌	❌	✅	3
A Theoretical Comparison of Graph Neural Network Extensions	❌	❌	❌	❌	❌	❌	❌	0
A Tighter Analysis of Spectral Clustering, and Beyond	❌	✅	✅	❌	✅	❌	❌	3
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources	✅	✅	✅	✅	✅	❌	✅	6
A Unified View on PAC-Bayes Bounds for Meta-Learning	❌	❌	✅	❌	❌	❌	✅	2
A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks	❌	✅	✅	✅	✅	❌	✅	5
A data-driven approach for learning to control computers	❌	❌	✅	❌	❌	❌	✅	2
A deep convolutional neural network that is invariant to time rescaling	❌	❌	✅	❌	❌	❌	✅	2
A new similarity measure for covariate shift with applications to nonparametric regression	❌	❌	❌	❌	❌	❌	❌	0
A query-optimal algorithm for finding counterfactuals	✅	❌	❌	❌	❌	❌	❌	1
A$^3$T: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and Editing	❌	✅	✅	✅	❌	❌	✅	4
AGNAS: Attention-Guided Micro and Macro-Architecture Search	✅	✅	✅	✅	✅	❌	✅	6
ASAP.SGD: Instance-based Adaptiveness to Staleness in Asynchronous SGD	✅	✅	✅	❌	✅	❌	✅	5
Accelerated Federated Learning with Decoupled Adaptive Optimization	✅	❌	✅	❌	✅	❌	✅	4
Accelerated Gradient Methods for Geodesically Convex Optimization: Tractable Algorithms and Convergence Analysis	✅	✅	❌	❌	❌	❌	✅	3
Accelerated, Optimal and Parallel: Some results on model-based stochastic optimization	❌	❌	❌	❌	❌	❌	✅	1
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders	✅	✅	✅	✅	✅	❌	✅	6
Accelerating Shapley Explanation via Contributive Cooperator Selection	✅	✅	✅	✅	✅	❌	✅	6
Accurate Quantization of Measures via Interacting Particle-based Optimization	✅	✅	❌	❌	❌	❌	✅	3
Achieving Fairness at No Utility Cost via Data Reweighing with Influence	✅	✅	✅	✅	❌	✅	✅	6
Achieving Minimax Rates in Pool-Based Batch Active Learning	✅	❌	❌	❌	❌	❌	❌	1
Action-Sufficient State Representation Learning for Control with Structural Constraints	✅	❌	✅	❌	❌	❌	✅	3
Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets	✅	✅	✅	❌	❌	❌	✅	4
Active Multi-Task Representation Learning	✅	❌	✅	❌	❌	❌	❌	2
Active Nearest Neighbor Regression Through Delaunay Refinement	✅	✅	✅	❌	✅	❌	✅	5
Active Sampling for Min-Max Fairness	✅	✅	✅	✅	❌	❌	✅	5
Active fairness auditing	✅	❌	✅	❌	❌	❌	❌	2
ActiveHedge: Hedge meets Active Learning	✅	❌	❌	❌	❌	❌	✅	2
Actor-Critic based Improper Reinforcement Learning	✅	❌	❌	❌	❌	❌	✅	2
AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems	✅	❌	✅	❌	❌	❌	✅	3
AdaGrad Avoids Saddle Points	❌	❌	❌	❌	❌	❌	❌	0
Adapting k-means Algorithms for Outliers	✅	❌	✅	❌	❌	❌	✅	3
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning	✅	❌	✅	❌	✅	❌	✅	4
Adapting to Mixing Time in Stochastic Optimization with Markovian Data	✅	❌	❌	❌	❌	❌	✅	2
Adaptive Accelerated (Extra-)Gradient Methods with Variance Reduction	✅	❌	✅	❌	❌	❌	✅	3
Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits	✅	❌	❌	❌	❌	❌	❌	1
Adaptive Conformal Predictions for Time Series	✅	✅	❌	✅	❌	❌	✅	4
Adaptive Data Analysis with Correlated Observations	✅	❌	❌	❌	❌	❌	❌	1
Adaptive Gaussian Process Change Point Detection	✅	✅	✅	❌	✅	❌	✅	5
Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum	✅	✅	✅	❌	✅	❌	✅	5
Adaptive Model Design for Markov Decision Process	✅	❌	❌	❌	❌	❌	✅	2
Adaptive Random Walk Gradient Descent for Decentralized Optimization	✅	❌	✅	❌	❌	❌	✅	3
Adaptive Second Order Coresets for Data-efficient Machine Learning	✅	✅	✅	❌	✅	❌	✅	5
Additive Gaussian Processes Revisited	✅	✅	✅	✅	❌	❌	✅	5
Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning	❌	✅	✅	❌	❌	✅	✅	4
Adversarial Attack and Defense for Non-Parametric Two-Sample Tests	✅	✅	✅	❌	✅	✅	✅	6
Adversarial Attacks on Gaussian Process Bandits	❌	✅	✅	❌	❌	✅	✅	4
Adversarial Masking for Self-Supervised Learning	❌	❌	✅	❌	❌	❌	✅	2
Adversarial Robustness against Multiple and Single $l_p$-Threat Models via Quick Fine-Tuning of Robust Classifiers	❌	✅	✅	❌	✅	❌	✅	4
Adversarial Vulnerability of Randomized Ensembles	✅	✅	✅	❌	✅	❌	✅	5
Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization	❌	❌	✅	❌	❌	❌	✅	2
Adversarially Trained Actor Critic for Offline Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Adversarially trained neural representations are already as robust as biological neural representations	❌	❌	✅	❌	❌	❌	✅	2
Agnostic Learnability of Halfspaces via Logistic Loss	❌	❌	❌	❌	❌	❌	❌	0
Algorithms for the Communication of Samples	✅	❌	❌	❌	❌	❌	✅	2
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution	❌	✅	✅	❌	✅	✅	✅	5
An Analytical Update Rule for General Policy Optimization	❌	❌	❌	❌	❌	❌	❌	0
An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings	❌	✅	❌	❌	❌	❌	✅	2
An Equivalence Between Data Poisoning and Byzantine Gradient Attacks	❌	✅	✅	❌	❌	❌	✅	3
An Exact Symbolic Reduction of Linear Smart Predict+Optimize to Mixed Integer Linear Programming	✅	✅	✅	❌	❌	❌	✅	4
An Initial Alignment between Neural Network and Target is Needed for Gradient Descent to Learn	❌	❌	✅	❌	❌	❌	✅	2
An Intriguing Property of Geophysics Inversion	❌	❌	✅	✅	✅	❌	✅	4
An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees	✅	✅	✅	❌	❌	❌	✅	4
Analysis of Stochastic Processes through Replay Buffers	✅	❌	❌	❌	❌	❌	❌	1
Analyzing and Mitigating Interference in Neural Architecture Search	❌	❌	✅	✅	✅	❌	✅	4
Anarchic Federated Learning	✅	❌	✅	❌	❌	❌	✅	3
Antibody-Antigen Docking and Design via Hierarchical Structure Refinement	❌	✅	✅	❌	❌	❌	✅	3
Anticorrelated Noise Injection for Improved Generalization	❌	❌	✅	❌	❌	❌	✅	2
AnyMorph: Learning Transferable Polices By Inferring Agent Morphology	❌	✅	✅	✅	✅	❌	✅	5
Anytime Information Cascade Popularity Prediction via Self-Exciting Processes	❌	✅	✅	✅	✅	✅	❌	5
Approximate Bayesian Computation with Domain Expert in the Loop	✅	✅	❌	❌	❌	❌	✅	3
Approximate Frank-Wolfe Algorithms over Graph-structured Support Sets	✅	✅	✅	❌	❌	✅	✅	5
Approximately Equivariant Networks for Imperfectly Symmetric Dynamics	❌	✅	✅	❌	❌	❌	✅	3
Architecture Agnostic Federated Learning for Neural Networks	✅	❌	✅	✅	❌	❌	✅	4
Asking for Knowledge (AFK): Training RL Agents to Query External Knowledge Using Language	❌	✅	✅	✅	✅	❌	✅	5
Asymptotically-Optimal Gaussian Bandits with Side Observations	✅	❌	❌	❌	❌	❌	❌	1
Attentional Meta-learners for Few-shot Polythetic Classification	✅	✅	✅	✅	❌	❌	✅	5
Augment with Care: Contrastive Learning for Combinatorial Problems	❌	✅	✅	✅	❌	❌	✅	4
AutoIP: A United Framework to Integrate Physics into Gaussian Processes	❌	❌	✅	❌	❌	✅	✅	3
AutoSNN: Towards Energy-Efficient Spiking Neural Networks	✅	✅	✅	✅	✅	❌	✅	6
Auxiliary Learning with Joint Task and Data Scheduling	✅	✅	✅	✅	❌	❌	✅	5
BAMDT: Bayesian Additive Semi-Multivariate Decision Trees for Nonparametric Regression	✅	✅	✅	✅	❌	❌	✅	5
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation	❌	✅	✅	✅	❌	❌	✅	4
BabelTower: Learning to Auto-parallelized Program Translation	❌	❌	✅	✅	✅	❌	✅	4
Balancing Discriminability and Transferability for Source-Free Domain Adaptation	✅	❌	✅	❌	❌	❌	✅	3
Balancing Sample Efficiency and Suboptimality in Inverse Reinforcement Learning	❌	❌	✅	❌	❌	❌	✅	2
Batch Greenkhorn Algorithm for Entropic-Regularized Multimarginal Optimal Transport: Linear Rate of Convergence and Iteration Complexity	✅	✅	✅	❌	✅	❌	✅	5
Batched Dueling Bandits	✅	❌	✅	❌	✅	❌	✅	4
Bayesian Continuous-Time Tucker Decomposition	✅	❌	✅	✅	❌	❌	✅	4
Bayesian Deep Embedding Topic Meta-Learner	✅	❌	✅	✅	❌	❌	✅	4
Bayesian Imitation Learning for End-to-End Mobile Manipulation	❌	❌	❌	❌	❌	❌	✅	1
Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense	✅	❌	✅	❌	❌	❌	✅	3
Bayesian Model Selection, the Marginal Likelihood, and Generalization	❌	❌	✅	✅	❌	❌	✅	3
Bayesian Nonparametric Learning for Point Processes with Spatial Homogeneity: A Spatial Analysis of NBA Shot Locations	✅	❌	✅	✅	❌	❌	✅	4
Bayesian Nonparametrics for Offline Skill Discovery	✅	✅	✅	❌	❌	❌	✅	4
Bayesian Optimization for Distributionally Robust Chance-constrained Problem	✅	❌	❌	❌	❌	❌	✅	2
Bayesian Optimization under Stochastic Delayed Feedback	✅	✅	✅	✅	❌	❌	✅	5
Be Like Water: Adaptive Floating Point for Machine Learning	❌	❌	✅	❌	❌	❌	✅	2
Being Properly Improper	✅	✅	✅	❌	✅	❌	✅	5
Benchmarking and Analyzing Point Cloud Classification under Corruptions	❌	✅	✅	❌	❌	❌	✅	3
Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint	❌	❌	✅	❌	❌	❌	✅	2
Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features	✅	✅	✅	❌	❌	❌	❌	3
Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity	✅	❌	✅	❌	❌	❌	❌	2
Biased Gradient Estimate with Drastic Variance Reduction for Meta Reinforcement Learning	✅	❌	❌	❌	❌	❌	✅	2
Biological Sequence Design with GFlowNets	✅	✅	✅	✅	❌	❌	✅	5
Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning	✅	❌	❌	❌	❌	❌	✅	2
Bit Prioritization in Variational Autoencoders via Progressive Coding	❌	❌	✅	❌	❌	❌	✅	2
Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization	✅	❌	✅	❌	❌	❌	✅	3
Black-Box Tuning for Language-Model-as-a-Service	❌	✅	✅	✅	✅	❌	✅	5
Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning	✅	❌	❌	❌	✅	❌	✅	3
Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness	✅	✅	✅	❌	✅	❌	✅	5
Boosting Graph Structure Learning with Dummy Nodes	✅	✅	✅	✅	✅	✅	✅	7
Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization	✅	✅	✅	❌	❌	❌	✅	4
Bounding Training Data Reconstruction in Private (Deep) Learning	✅	✅	✅	❌	❌	❌	✅	4
Bounding the Width of Neural Networks via Coupled Initialization A Worst Case Analysis	❌	❌	❌	❌	❌	❌	❌	0
Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding	❌	✅	✅	✅	✅	❌	✅	5
Branching Reinforcement Learning	✅	❌	❌	❌	❌	❌	✅	2
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities	❌	✅	✅	✅	✅	❌	✅	5
Breaking the $\sqrtT$ Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits	✅	❌	❌	❌	❌	❌	❌	1
Bregman Neural Networks	❌	✅	✅	✅	❌	❌	✅	4
Bregman Power k-Means for Clustering Exponential Family Data	✅	✅	✅	❌	❌	❌	✅	4
Bregman Proximal Langevin Monte Carlo via Bregman-Moreau Envelopes	✅	✅	✅	❌	❌	❌	✅	4
Building Robust Ensembles via Margin Boosting	✅	✅	✅	❌	✅	❌	✅	5
Burst-Dependent Plasticity and Dendritic Amplification Support Target-Based Learning and Hierarchical Imitation Learning	❌	✅	❌	❌	❌	❌	✅	2
ButterflyFlow: Building Invertible Layers with Butterfly Matrices	❌	❌	✅	❌	✅	❌	✅	3
Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums	✅	✅	✅	❌	❌	❌	✅	4
C-algebra Net: A New Approach Generalizing Neural Network Parameters to C-algebra	❌	✅	✅	❌	❌	❌	✅	3
C-MinHash: Improving Minwise Hashing with Circulant Permutation	✅	❌	✅	❌	❌	❌	✅	3
CITRIS: Causal Identifiability from Temporal Intervened Sequences	❌	✅	✅	❌	✅	❌	✅	4
COAT: Measuring Object Compositionality in Emergent Representations	❌	❌	✅	❌	❌	❌	✅	2
COLA: Consistent Learning with Opponent-Learning Awareness	❌	❌	❌	❌	❌	❌	✅	1
Calibrated Learning to Defer with One-vs-All Classifiers	❌	✅	✅	✅	❌	❌	✅	4
Calibrated and Sharp Uncertainties in Deep Learning via Density Estimation	✅	❌	✅	✅	❌	❌	✅	4
Cascaded Gaps: Towards Logarithmic Regret for Risk-Sensitive Reinforcement Learning	✅	❌	❌	❌	❌	❌	❌	1
Causal Conceptions of Fairness and their Consequences	✅	✅	❌	❌	❌	❌	✅	3
Causal Dynamics Learning for Task-Independent State Abstraction	❌	❌	❌	✅	❌	❌	✅	2
Causal Imitation Learning under Temporally Correlated Noise	✅	✅	✅	❌	❌	❌	✅	4
Causal Inference Through the Structural Causal Marginal Problem	❌	✅	❌	❌	❌	✅	✅	3
Causal Transformer for Estimating Counterfactual Outcomes	✅	✅	✅	✅	✅	❌	✅	6
Causal structure-based root cause analysis of outliers	❌	✅	✅	❌	❌	❌	✅	3
Centroid Approximation for Bootstrap: Improving Particle Quality at Inference	✅	✅	✅	✅	❌	❌	✅	5
CerDEQ: Certifiable Deep Equilibrium Model	❌	❌	✅	❌	✅	❌	✅	3
Certified Adversarial Robustness Under the Bounded Support Set	✅	❌	✅	❌	✅	❌	✅	4
Certified Neural Network Watermarks with Randomized Smoothing	✅	❌	✅	❌	❌	❌	✅	3
Certified Robustness Against Natural Language Attacks by Causal Intervention	✅	✅	✅	❌	✅	❌	✅	5
Certifying Out-of-Domain Generalization for Blackbox Functions	❌	✅	✅	❌	❌	❌	❌	2
Channel Importance Matters in Few-Shot Image Classification	❌	❌	✅	✅	❌	❌	✅	3
Characterizing and Overcoming the Greedy Nature of Learning in Multi-modal Deep Neural Networks	✅	✅	✅	✅	❌	❌	✅	5
Choosing Answers in Epsilon-Best-Answer Identification for Linear Bandits	✅	✅	❌	❌	❌	✅	✅	4
Class-Imbalanced Semi-Supervised Learning with Adaptive Thresholding	✅	✅	✅	❌	✅	❌	✅	5
Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments	❌	✅	✅	❌	❌	❌	✅	3
Closed-Form Diffeomorphic Transformations for Time Series Alignment	❌	✅	✅	✅	✅	❌	✅	5
Co-training Improves Prompt-based Learning for Large Language Models	✅	✅	✅	✅	✅	❌	✅	6
Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets	✅	✅	✅	✅	✅	✅	✅	7
Coin Flipping Neural Networks	❌	❌	✅	✅	❌	❌	✅	3
Collaboration of Experts: Achieving 80% Top-1 Accuracy on ImageNet with 100M FLOPs	❌	❌	✅	✅	✅	✅	✅	5
Combining Diverse Feature Priors	✅	✅	✅	✅	✅	❌	✅	6
Communicating via Markov Decision Processes	✅	✅	✅	❌	❌	✅	✅	5
Communication-Efficient Adaptive Federated Learning	✅	❌	✅	❌	❌	❌	✅	3
Communication-efficient Distributed Learning for Large Batch Optimization	✅	❌	✅	❌	✅	❌	✅	4
Composing Partial Differential Equations with Physics-Aware Neural Networks	❌	✅	❌	✅	✅	❌	✅	4
Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning	❌	✅	✅	✅	❌	❌	✅	4
Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data	✅	✅	✅	❌	✅	❌	✅	5
Conditional GANs with Auxiliary Discriminative Classifier	❌	✅	✅	❌	❌	❌	✅	3
Confidence Score for Source-Free Unsupervised Domain Adaptation	✅	✅	✅	❌	❌	❌	✅	4
Conformal Prediction Sets with Limited False Positives	✅	✅	✅	✅	❌	❌	✅	5
Congested Bandits: Optimal Routing via Short-term Resets	✅	❌	❌	❌	❌	❌	✅	2
Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation	❌	❌	✅	❌	❌	❌	✅	2
Consensus Multiplicative Weights Update: Learning to Learn using Projector-based Game Signatures	❌	❌	❌	❌	✅	❌	✅	2
Consistent Polyhedral Surrogates for Top-k Classification and Variants	❌	❌	❌	❌	❌	❌	✅	1
Constants Matter: The Performance Gains of Active Learning	✅	❌	❌	❌	❌	❌	✅	2
Constrained Discrete Black-Box Optimization using Mixed-Integer Programming	✅	❌	✅	✅	✅	✅	✅	6
Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks	✅	❌	✅	❌	✅	❌	✅	4
Constrained Offline Policy Optimization	✅	❌	❌	❌	❌	❌	❌	1
Constrained Optimization with Dynamic Bound-scaling for Effective NLP Backdoor Defense	✅	✅	✅	❌	✅	❌	✅	5
Constrained Variational Policy Optimization for Safe Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Constraint-based graph network simulator	✅	❌	✅	✅	❌	❌	✅	4
Content Addressable Memory Without Catastrophic Forgetting by Heteroassociation with a Fixed Scaffold	❌	✅	✅	❌	❌	❌	✅	3
ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers	❌	✅	✅	✅	❌	❌	✅	4
Context-Aware Drift Detection	✅	✅	✅	✅	❌	❌	✅	5
Contextual Bandits with Large Action Spaces: Made Practical	✅	✅	✅	❌	✅	❌	❌	4
Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces	✅	✅	✅	❌	❌	❌	❌	3
Contextual Information-Directed Sampling	❌	❌	❌	❌	❌	❌	✅	1
Continual Learning via Sequential Function-Space Variational Inference	❌	✅	✅	✅	❌	❌	✅	4
Continual Learning with Guarantees via Weight Interval Constraints	✅	✅	✅	✅	❌	❌	✅	5
Continual Repeated Annealed Flow Transport Monte Carlo	✅	✅	✅	✅	✅	❌	✅	6
Continuous Control with Action Quantization from Demonstrations	❌	✅	✅	❌	❌	❌	✅	3
Continuous-Time Analysis of Accelerated Gradient Methods via Conservation Laws in Dilated Coordinate Systems	❌	❌	❌	❌	❌	❌	❌	0
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations	❌	✅	✅	✅	✅	❌	✅	5
Contrastive Learning with Boosted Memorization	✅	✅	✅	✅	❌	❌	✅	5
Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness	❌	✅	✅	✅	✅	✅	✅	6
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Controlling Conditional Language Models without Catastrophic Forgetting	✅	✅	✅	❌	✅	❌	✅	5
Convergence Rates of Non-Convex Stochastic Gradient Descent Under a Generic Lojasiewicz Condition and Local Smoothness	✅	❌	✅	❌	❌	❌	✅	3
Convergence and Recovery Guarantees of the K-Subspaces Method for Subspace Clustering	✅	✅	✅	❌	❌	✅	✅	5
Convergence of Invariant Graph Networks	❌	❌	❌	❌	❌	❌	✅	1
Convergence of Policy Gradient for Entropy Regularized MDPs with Neural Network Approximation in the Mean-Field Regime	❌	❌	❌	❌	❌	❌	❌	0
Convergence of Uncertainty Sampling for Active Learning	✅	❌	✅	❌	❌	❌	❌	2
Convolutional and Residual Networks Provably Contain Lottery Tickets	✅	✅	✅	❌	✅	❌	✅	5
Cooperative Online Learning in Stochastic and Adversarial MDPs	✅	❌	❌	❌	❌	❌	❌	1
Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms	✅	❌	❌	❌	❌	❌	✅	2
Coordinated Double Machine Learning	✅	✅	✅	✅	❌	❌	✅	5
Correct-N-Contrast: a Contrastive Approach for Improving Robustness to Spurious Correlations	✅	✅	✅	✅	✅	❌	✅	6
Correlated Quantization for Distributed Mean Estimation and Optimization	✅	✅	✅	❌	❌	❌	✅	4
Correlation Clustering via Strong Triadic Closure Labeling: Fast Approximation Algorithms and Practical Lower Bounds	✅	✅	✅	❌	✅	❌	✅	5
Counterfactual Prediction for Outcome-Oriented Treatments	✅	❌	✅	❌	❌	❌	✅	3
Counterfactual Transportability: A Formal Approach	✅	❌	❌	❌	❌	❌	❌	1
Cross-Space Active Learning on Graph Convolutional Networks	✅	❌	❌	❌	❌	❌	❌	1
CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer	✅	✅	✅	❌	✅	❌	✅	5
Curriculum Reinforcement Learning via Constrained Optimal Transport	✅	✅	❌	❌	✅	✅	✅	5
Cycle Representation Learning for Inductive Relation Prediction	❌	✅	✅	✅	✅	❌	✅	5
DAVINZ: Data Valuation using Deep Neural Networks at Initialization	✅	❌	✅	✅	✅	❌	✅	5
DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning	✅	✅	✅	❌	❌	❌	✅	4
DNA: Domain Generalization with Diversified Neural Averaging	✅	✅	✅	✅	✅	✅	✅	7
DNNR: Differential Nearest Neighbors Regression	✅	✅	✅	✅	✅	❌	✅	6
DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning	✅	✅	✅	❌	✅	❌	✅	5
DRAGONN: Distributed Randomized Approximate Gradients of Neural Networks	✅	❌	✅	❌	✅	✅	✅	5
DRIBO: Robust Deep Reinforcement Learning via Multi-View Information Bottleneck	✅	✅	✅	✅	✅	✅	✅	7
DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting	❌	✅	✅	✅	✅	❌	✅	5
Data Augmentation as Feature Manipulation	❌	❌	✅	❌	❌	❌	✅	2
Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)	❌	❌	✅	❌	❌	❌	✅	2
Data Scaling Laws in NMT: The Effect of Noise and Architecture	❌	❌	✅	❌	✅	❌	✅	3
Data-Efficient Double-Win Lottery Tickets from Robust Pre-training	❌	✅	✅	❌	❌	❌	✅	3
Data-SUITE: Data-centric identification of in-distribution incongruous examples	✅	✅	✅	✅	✅	❌	✅	6
Datamodels: Understanding Predictions with Data and Data with Predictions	❌	❌	✅	✅	✅	❌	✅	4
Dataset Condensation via Efficient Synthetic-Data Parameterization	✅	✅	✅	❌	✅	❌	✅	5
Dataset Condensation with Contrastive Signals	✅	✅	✅	❌	❌	❌	✅	4
De novo mass spectrometry peptide sequencing with a transformer model	❌	✅	✅	✅	✅	❌	✅	5
Debiaser Beware: Pitfalls of Centering Regularized Transport Maps	❌	✅	✅	✅	❌	❌	✅	4
Decentralized Online Convex Optimization in Networked Systems	✅	❌	❌	❌	❌	❌	❌	1
Deciphering Lasso-based Classification Through a Large Dimensional Analysis of the Iterative Soft-Thresholding Algorithm	✅	✅	✅	✅	❌	❌	✅	5
Decision-Focused Learning: Through the Lens of Learning to Rank	✅	✅	✅	✅	❌	❌	✅	5
Decomposing Temporal High-Order Interactions via Latent ODEs	✅	❌	✅	❌	❌	❌	✅	3
Deconfounded Value Decomposition for Multi-Agent Reinforcement Learning	✅	❌	✅	❌	❌	❌	✅	3
Deduplicating Training Data Mitigates Privacy Risks in Language Models	❌	✅	✅	❌	❌	❌	❌	2
Deep Causal Metric Learning	✅	✅	✅	✅	❌	❌	✅	5
Deep Hierarchy in Bandits	✅	❌	✅	❌	❌	❌	✅	3
Deep Network Approximation in Terms of Intrinsic Parameters	❌	❌	✅	❌	❌	❌	✅	2
Deep Networks on Toroids: Removing Symmetries Reveals the Structure of Flat Regions in the Landscape Geometry	✅	✅	✅	❌	❌	✅	✅	5
Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning	✅	✅	✅	❌	✅	❌	✅	5
Deep Probability Estimation	✅	✅	✅	✅	❌	❌	❌	4
Deep Reference Priors: What is the best way to pretrain a model?	✅	✅	✅	❌	❌	❌	✅	4
Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm	✅	❌	✅	❌	❌	❌	✅	3
Deep Squared Euclidean Approximation to the Levenshtein Distance for DNA Storage	❌	❌	✅	❌	❌	❌	✅	2
Deep Variational Graph Convolutional Recurrent Network for Multivariate Time Series Anomaly Detection	✅	✅	✅	✅	✅	❌	✅	6
Deep and Flexible Graph Neural Architecture Search	✅	✅	✅	✅	✅	✅	✅	7
Deep equilibrium networks are sensitive to initialization statistics	❌	❌	✅	❌	✅	❌	✅	3
Deep symbolic regression for recurrence prediction	❌	❌	✅	✅	✅	❌	✅	4
DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale	❌	✅	✅	✅	✅	❌	✅	5
Delay-Adaptive Step-sizes for Asynchronous Learning	✅	❌	✅	❌	❌	❌	✅	3
Delayed Reinforcement Learning by Imitation	✅	❌	✅	✅	❌	❌	✅	4
Deletion Robust Submodular Maximization over Matroids	✅	❌	✅	❌	❌	❌	✅	3
Demystifying the Adversarial Robustness of Random Transformation Defenses	✅	✅	✅	✅	✅	❌	✅	6
Denoised MDPs: Learning World Models Better Than the World Itself	✅	✅	✅	✅	✅	❌	✅	6
Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations	✅	✅	✅	✅	❌	❌	✅	5
DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks	❌	✅	✅	❌	✅	❌	✅	4
Describing Differences between Text Distributions with Natural Language	❌	✅	✅	❌	❌	❌	✅	3
Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization	❌	✅	✅	✅	✅	❌	✅	5
Detached Error Feedback for Distributed SGD with Random Sparsification	✅	❌	✅	✅	✅	❌	✅	5
Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them	❌	❌	✅	❌	❌	❌	❌	1
Detecting Corrupted Labels Without Training a Model to Predict	✅	✅	✅	❌	❌	❌	✅	4
Dialog Inpainting: Turning Documents into Dialogs	❌	❌	✅	✅	✅	❌	✅	4
Difference Advantage Estimation for Multi-Agent Policy Gradients	✅	❌	✅	❌	❌	❌	✅	3
Differentiable Top-k Classification Learning	❌	✅	✅	❌	❌	❌	✅	3
Differentially Private Approximate Quantiles	✅	✅	✅	❌	✅	❌	✅	5
Differentially Private Community Detection for Stochastic Block Models	✅	❌	✅	❌	❌	✅	✅	4
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization	✅	✅	✅	❌	✅	❌	✅	5
Differentially Private Maximal Information Coefficients	❌	✅	✅	❌	❌	❌	✅	3
Diffusion Models for Adversarial Purification	❌	❌	✅	✅	✅	❌	✅	4
Diffusion bridges vector quantized variational autoencoders	✅	❌	✅	❌	✅	❌	✅	4
Dimension-free Complexity Bounds for High-order Nonconvex Finite-sum Optimization	✅	❌	❌	❌	❌	❌	❌	1
Direct Behavior Specification via Constrained Reinforcement Learning	✅	✅	❌	❌	❌	❌	✅	3
Directed Acyclic Transformer for Non-Autoregressive Machine Translation	✅	✅	✅	✅	✅	❌	✅	6
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training	✅	✅	✅	❌	❌	❌	✅	4
Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning	✅	✅	❌	❌	❌	❌	✅	3
Discrete Probabilistic Inverse Optimal Transport	✅	❌	✅	❌	❌	✅	✅	4
Discrete Tree Flows via Tree-Structured Permutations	✅	❌	✅	✅	✅	❌	✅	5
Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations	✅	✅	✅	✅	✅	❌	✅	6
Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring	✅	❌	✅	❌	❌	❌	✅	3
Disentangling Disease-related Representation from Obscure for Disease Prediction	❌	❌	✅	✅	❌	❌	✅	3
Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning	✅	❌	✅	❌	✅	✅	✅	5
Distinguishing rule and exemplar-based generalization in learning systems	❌	✅	✅	✅	❌	❌	✅	4
Distribution Regression with Sliced Wasserstein Kernels	✅	✅	✅	✅	❌	❌	✅	5
Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning	✅	❌	✅	❌	❌	❌	✅	3
Distributionally Robust $Q$-Learning	✅	❌	❌	❌	❌	❌	✅	2
Distributionally-Aware Kernelized Bandit Problems for Risk Aversion	✅	✅	❌	✅	✅	❌	✅	5
Divergence-Regularized Multi-Agent Actor-Critic	✅	❌	✅	❌	✅	❌	✅	4
Diversified Adversarial Attacks based on Conjugate Gradient Method	✅	✅	✅	✅	✅	❌	✅	6
Do Differentiable Simulators Give Better Policy Gradients?	❌	❌	❌	❌	❌	❌	✅	1
Do More Negative Samples Necessarily Hurt In Contrastive Learning?	❌	❌	✅	❌	❌	❌	✅	2
Does the Data Induce Capacity Control in Deep Learning?	❌	✅	✅	✅	❌	❌	✅	4
Domain Adaptation for Time Series Forecasting via Attention Sharing	✅	❌	✅	✅	❌	❌	✅	4
Double Sampling Randomized Smoothing	✅	✅	✅	❌	❌	❌	✅	4
Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning	✅	✅	❌	✅	❌	❌	✅	4
DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations	❌	✅	✅	❌	✅	❌	✅	4
Dual Decomposition of Convex Optimization Layers for Consistent Attention in Medical Images	❌	✅	✅	✅	✅	❌	✅	5
Dual Perspective of Label-Specific Feature Learning for Multi-Label Classification	❌	✅	✅	✅	❌	❌	✅	4
DynaMixer: A Vision MLP Architecture with Dynamic Mixing	✅	✅	✅	✅	✅	❌	✅	6
Dynamic Regret of Online Markov Decision Processes	✅	❌	❌	❌	❌	❌	❌	1
Dynamic Topic Models for Temporal Document Networks	✅	❌	✅	✅	✅	❌	✅	5
EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning	✅	❌	✅	❌	❌	❌	✅	3
EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning	❌	✅	✅	❌	✅	✅	✅	5
Easy Variational Inference for Categorical Models via an Independent Binary Approximation	✅	✅	✅	❌	❌	❌	✅	4
Efficient Approximate Inference for Stationary Kernel on Frequency Domain	✅	✅	✅	✅	✅	✅	✅	7
Efficient Computation of Higher-Order Subgraph Attribution via Message Passing	✅	✅	✅	❌	✅	❌	✅	5
Efficient Distributionally Robust Bayesian Optimization with Worst-case Sensitivity	✅	✅	✅	❌	❌	❌	✅	4
Efficient Learning for AlphaZero via Path Consistency	✅	✅	✅	❌	✅	❌	✅	5
Efficient Learning of CNNs using Patch Based Features	✅	✅	✅	❌	❌	❌	✅	4
Efficient Low Rank Convex Bounds for Pairwise Discrete Graphical Models	❌	✅	✅	❌	✅	✅	✅	5
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation	✅	❌	❌	❌	❌	❌	✅	2
Efficient Online ML API Selection for Multi-Label Classification Tasks	✅	✅	✅	✅	✅	❌	✅	6
Efficient PAC Learning from the Crowd with Pairwise Comparisons	✅	❌	❌	❌	❌	❌	❌	1
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach	✅	✅	❌	❌	❌	❌	✅	3
Efficient Representation Learning via Adaptive Context Pooling	❌	❌	✅	✅	✅	❌	✅	4
Efficient Test-Time Model Adaptation without Forgetting	✅	✅	✅	✅	✅	❌	✅	6
Efficient Variance Reduction for Meta-learning	✅	❌	✅	❌	❌	❌	✅	3
Efficiently Learning the Topology and Behavior of a Networked Dynamical System Via Active Queries	✅	✅	✅	❌	✅	❌	✅	5
End-to-End Balancing for Causal Continuous Treatment-Effect Estimation	✅	❌	✅	✅	✅	❌	✅	5
Entropic Causal Inference: Graph Identifiability	✅	❌	✅	❌	❌	❌	✅	3
Entropic Gromov-Wasserstein between Gaussian Distributions	❌	✅	❌	❌	❌	❌	✅	2
EqR: Equivariant Representations for Data-Efficient Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction	❌	✅	✅	✅	✅	❌	✅	5
Equivalence Analysis between Counterfactual Regret Minimization and Online Mirror Descent	✅	❌	✅	❌	❌	❌	✅	3
Equivariance versus Augmentation for Spherical Images	❌	✅	✅	✅	✅	❌	✅	5
Equivariant Diffusion for Molecule Generation in 3D	✅	❌	✅	✅	✅	❌	✅	5
Equivariant Priors for compressed sensing with unknown orientation	✅	❌	✅	✅	❌	❌	✅	4
Equivariant Quantum Graph Circuits	❌	❌	❌	❌	❌	❌	✅	1
Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass	✅	✅	✅	✅	❌	❌	✅	5
Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network	✅	❌	✅	✅	✅	✅	✅	6
Estimating and Penalizing Induced Preference Shifts in Recommender Systems	✅	❌	❌	✅	✅	✅	✅	5
Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models	✅	✅	✅	❌	✅	❌	✅	5
Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing	❌	❌	❌	❌	❌	❌	✅	1
Evaluating the Adversarial Robustness of Adaptive Test-time Defenses	❌	✅	✅	❌	✅	❌	✅	4
Evolving Curricula with Regret-Based Environment Design	✅	✅	✅	✅	✅	❌	✅	6
Exact Learning of Preference Structure: Single-peaked Preferences and Beyond	❌	❌	❌	❌	❌	❌	✅	1
Exact Optimal Accelerated Complexity for Fixed-Point Iterations	❌	❌	❌	❌	❌	❌	✅	1
Examining Scaling and Transfer of Language Model Architectures for Machine Translation	❌	❌	✅	✅	❌	❌	✅	3
Exploiting Independent Instruments: Identification and Distribution Generalization	✅	✅	✅	❌	❌	❌	✅	4
Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups	❌	✅	✅	✅	✅	❌	✅	5
Exploring and Exploiting Hubness Priors for High-Quality GAN Latent Sampling	✅	✅	✅	❌	✅	❌	✅	5
Exploring the Gap between Collapsed & Whitened Features in Self-Supervised Learning	✅	❌	✅	✅	❌	❌	✅	4
Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control	✅	✅	✅	❌	❌	❌	✅	4
Extended Unconstrained Features Model for Exploring Deep Neural Collapse	❌	❌	✅	❌	❌	❌	✅	2
Extracting Latent State Representations with Linear Dynamics from Rich Observations	❌	❌	✅	❌	❌	❌	✅	2
FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting	❌	✅	✅	✅	✅	✅	✅	6
FITNESS: (Fine Tune on New and Similar Samples) to detect anomalies in streams with drift and outliers	✅	❌	✅	❌	❌	❌	✅	3
FOCUS: Familiar Objects in Common and Uncommon Settings	❌	✅	✅	❌	❌	❌	✅	3
Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data	❌	❌	✅	❌	❌	❌	✅	2
Fair Generalized Linear Models with a Convex Penalty	✅	✅	✅	❌	❌	❌	✅	4
Fair Representation Learning through Implicit Path Alignment	✅	❌	✅	✅	❌	❌	✅	4
Fair and Fast k-Center Clustering for Data Summarization	✅	❌	✅	❌	✅	✅	✅	5
Fairness Interventions as (Dis)Incentives for Strategic Manipulation	❌	❌	✅	❌	❌	❌	✅	2
Fairness with Adaptive Weights	✅	❌	✅	✅	❌	❌	❌	3
Fast Aquatic Swimmer Optimization with Differentiable Projective Dynamics and Neural Network Hydrodynamic Models	❌	❌	❌	❌	❌	❌	✅	1
Fast Composite Optimization and Statistical Recovery in Federated Learning	✅	❌	✅	❌	❌	❌	✅	3
Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions	✅	✅	✅	✅	✅	✅	✅	7
Fast Finite Width Neural Tangent Kernel	❌	✅	✅	❌	✅	✅	✅	5
Fast Lossless Neural Compression with Integer-Only Discrete Flows	✅	✅	✅	❌	✅	✅	✅	6
Fast Population-Based Reinforcement Learning on a Single Machine	✅	✅	✅	❌	✅	❌	✅	5
Fast Provably Robust Decision Trees and Boosting	✅	❌	✅	✅	✅	❌	✅	5
Fast Relative Entropy Coding with A* coding	✅	✅	✅	❌	❌	❌	✅	4
Fast and Provable Nonconvex Tensor RPCA	✅	❌	✅	❌	✅	✅	✅	5
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack	✅	✅	✅	❌	✅	✅	✅	6
Fast rates for noisy interpolation require rethinking the effect of inductive bias	❌	✅	✅	❌	❌	❌	✅	3
Fast-Rate PAC-Bayesian Generalization Bounds for Meta-Learning	✅	❌	✅	❌	❌	❌	✅	3
Faster Algorithms for Learning Convex Functions	✅	✅	✅	✅	✅	✅	✅	7
Faster Fundamental Graph Algorithms via Learned Predictions	✅	❌	✅	❌	❌	❌	✅	3
Faster Privacy Accounting via Evolving Discretization	✅	❌	❌	❌	❌	❌	✅	2
Fat–Tailed Variational Inference with Anisotropic Tail Adaptive Flows	❌	✅	✅	❌	✅	✅	✅	5
Feature Learning and Signal Propagation in Deep Neural Networks	✅	❌	✅	✅	❌	❌	✅	4
Feature Space Particle Inference for Neural Network Ensembles	✅	✅	✅	❌	✅	❌	✅	5
Feature and Parameter Selection in Stochastic Linear Bandits	✅	❌	✅	❌	❌	❌	✅	3
Feature selection using e-values	✅	✅	✅	✅	✅	❌	✅	6
FedNL: Making Newton-Type Methods Applicable to Federated Learning	✅	❌	✅	❌	❌	❌	✅	3
FedNest: Federated Bilevel, Minimax, and Compositional Optimization	✅	✅	✅	✅	❌	❌	✅	5
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning	✅	✅	✅	❌	❌	❌	✅	4
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale	✅	✅	✅	✅	✅	✅	✅	7
Federated Learning with Label Distribution Skew via Logits Calibration	✅	❌	✅	❌	✅	❌	✅	4
Federated Learning with Partial Model Personalization	✅	✅	✅	✅	❌	❌	✅	5
Federated Learning with Positive and Unlabeled Data	✅	❌	✅	❌	❌	❌	✅	3
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms	✅	❌	✅	❌	✅	✅	✅	5
Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling	✅	❌	❌	❌	❌	❌	❌	1
Fenrir: Physics-Enhanced Regression for Initial Value Problems	❌	✅	✅	❌	❌	❌	✅	3
Fictitious Play and Best-Response Dynamics in Identical Interest and Zero-Sum Stochastic Games	✅	❌	❌	❌	❌	❌	❌	1
Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming	❌	❌	✅	✅	❌	❌	✅	3
Finding Global Homophily in Graph Neural Networks When Meeting Heterophily	✅	✅	✅	✅	✅	❌	✅	6
Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks	✅	✅	✅	✅	✅	✅	✅	7
Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications	✅	❌	✅	✅	✅	❌	✅	5
First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach	✅	❌	❌	❌	❌	❌	❌	1
Fisher SAM: Information Geometry and Sharpness Aware Minimisation	✅	❌	✅	❌	✅	❌	✅	4
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification	✅	✅	✅	✅	✅	❌	✅	6
Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization	✅	✅	✅	✅	✅	❌	✅	6
Flashlight: Enabling Innovation in Tools for Machine Learning	✅	✅	✅	✅	✅	✅	✅	7
Flow-Guided Sparse Transformer for Video Deblurring	❌	✅	✅	❌	✅	❌	✅	4
Flow-based Recurrent Belief State Learning for POMDPs	✅	❌	✅	❌	❌	❌	✅	3
Flowformer: Linearizing Transformers with Conservation Flows	✅	✅	✅	❌	✅	❌	✅	5
Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension	❌	❌	❌	❌	❌	❌	✅	1
For Learning in Symmetric Teams, Local Optima are Global Nash Equilibria	❌	✅	✅	❌	✅	❌	✅	4
Forget-free Continual Learning with Winning Subnetworks	✅	✅	✅	✅	✅	❌	✅	6
Forward Operator Estimation in Generative Models with Kernel Transfer Operators	✅	✅	✅	❌	✅	❌	✅	5
Fourier Learning with Cyclical Data	✅	✅	✅	❌	❌	❌	✅	4
Framework for Evaluating Faithfulness of Local Explanations	✅	❌	✅	✅	❌	❌	✅	4
FriendlyCore: Practical Differentially Private Aggregation	✅	❌	✅	❌	✅	❌	✅	4
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses	✅	❌	✅	❌	❌	❌	✅	3
From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model	✅	✅	✅	✅	❌	❌	✅	5
From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers	✅	✅	✅	✅	✅	❌	✅	6
From data to functa: Your data point is a function and you can treat it like one	✅	❌	✅	❌	❌	❌	✅	3
Frustratingly Easy Transferability Estimation	✅	✅	✅	❌	✅	❌	✅	5
Fully-Connected Network on Noncompact Symmetric Space and Ridgelet Transform based on Helgason-Fourier Analysis	❌	❌	❌	❌	❌	❌	❌	0
Function-space Inference with Sparse Implicit Processes	❌	✅	✅	✅	✅	❌	✅	5
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions	✅	✅	❌	✅	❌	❌	✅	4
Functional Output Regression with Infimal Convolution: Exploring the Huber and $ε$-insensitive Losses	✅	✅	✅	❌	❌	❌	✅	4
G$^2$CN: Graph Gaussian Convolution Networks with Concentrated Graph Filters	❌	❌	✅	✅	✅	❌	✅	4
G-Mixup: Graph Data Augmentation for Graph Classification	✅	✅	✅	✅	❌	❌	✅	5
GACT: Activation Compressed Training for Generic Network Architectures	✅	❌	✅	❌	✅	✅	✅	5
GALAXY: Graph-based Active Learning at the Extreme	✅	✅	✅	❌	❌	❌	✅	4
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models	❌	✅	✅	✅	✅	❌	✅	5
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts	❌	❌	✅	✅	✅	❌	✅	4
GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks	✅	✅	✅	❌	✅	✅	✅	6
GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing	❌	❌	✅	❌	✅	❌	✅	3
Gating Dropout: Communication-efficient Regularization for Sparsely Activated Transformers	❌	✅	✅	✅	✅	❌	✅	5
Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification	❌	✅	✅	✅	✅	❌	✅	5
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications	❌	✅	✅	❌	✅	❌	✅	4
GenLabel: Mixup Relabeling using Generative Models	✅	✅	✅	✅	✅	❌	✅	6
General-purpose, long-context autoregressive modeling with Perceiver AR	❌	✅	✅	✅	✅	❌	✅	5
Generalised Policy Improvement with Geometric Policy Composition	✅	❌	❌	❌	❌	❌	✅	2
Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers	✅	❌	✅	❌	✅	❌	✅	4
Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling	✅	❌	❌	❌	❌	❌	✅	2
Generalization and Robustness Implications in Object-Centric Learning	❌	✅	✅	✅	✅	❌	✅	5
Generalized Beliefs for Cooperative AI	❌	✅	✅	❌	✅	❌	✅	4
Generalized Data Distribution Iteration	✅	❌	✅	❌	✅	❌	✅	4
Generalized Federated Learning via Sharpness Aware Minimization	✅	❌	✅	✅	✅	❌	✅	5
Generalized Leverage Scores: Geometric Interpretation and Applications	✅	❌	✅	❌	❌	❌	✅	3
Generalized Results for the Existence and Consistency of the MLE in the Bradley-Terry-Luce Model	❌	✅	❌	❌	❌	❌	✅	2
Generalized Strategic Classification and the Case of Aligned Incentives	✅	✅	✅	❌	❌	❌	✅	4
Generalizing Gaussian Smoothing for Random Search	❌	❌	✅	❌	✅	❌	✅	3
Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder	✅	❌	✅	✅	❌	❌	✅	4
Generalizing to New Physical Systems via Context-Informed Dynamics Model	✅	✅	✅	❌	✅	✅	✅	6
Generating 3D Molecules for Target Protein Binding	❌	✅	✅	❌	❌	❌	❌	2
Generating Distributional Adversarial Examples to Evade Statistical Detectors	❌	❌	✅	❌	❌	❌	✅	2
Generative Coarse-Graining of Molecular Conformations	❌	✅	✅	✅	❌	❌	✅	4
Generative Cooperative Networks for Natural Language Generation	✅	❌	✅	❌	✅	❌	✅	4
Generative Flow Networks for Discrete Probabilistic Modeling	✅	✅	✅	✅	❌	❌	✅	5
Generative Modeling for Multi-task Visual Learning	✅	✅	✅	✅	❌	❌	✅	5
Generative Trees: Adversarial and Copycat	✅	❌	✅	✅	✅	✅	✅	6
Generic Coreset for Scalable Learning of Monotonic Kernels: Logistic Regression, Sigmoid and more	✅	✅	✅	❌	❌	❌	✅	4
Geometric Multimodal Contrastive Representation Learning	❌	✅	✅	❌	❌	❌	✅	3
Global Optimization Networks	❌	✅	✅	✅	❌	❌	✅	4
Global Optimization of K-Center Clustering	✅	✅	✅	❌	❌	✅	✅	5
Goal Misgeneralization in Deep Reinforcement Learning	❌	✅	✅	❌	✅	❌	✅	4
Going Deeper into Permutation-Sensitive Graph Neural Networks	❌	✅	✅	✅	✅	✅	✅	6
Gradient Based Clustering	❌	❌	✅	❌	❌	❌	✅	2
Gradient Descent on Neurons and its Link to Approximate Second-order Optimization	✅	✅	✅	❌	❌	❌	✅	4
Gradient-Free Method for Heavily Constrained Nonconvex Optimization	✅	❌	❌	✅	✅	❌	✅	4
Graph Neural Architecture Search Under Distribution Shifts	✅	❌	✅	✅	✅	❌	✅	5
Graph-Coupled Oscillator Networks	❌	✅	✅	✅	✅	❌	✅	5
GraphFM: Improving Large-Scale GNN Training via Feature Momentum	✅	✅	✅	✅	✅	❌	✅	6
Greedy based Value Representation for Optimal Coordination in Multi-agent Reinforcement Learning	✅	❌	✅	❌	❌	❌	✅	3
Greedy when Sure and Conservative when Uncertain about the Opponents	✅	✅	✅	❌	❌	❌	✅	4
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation	✅	❌	❌	❌	❌	❌	❌	1
Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance	✅	❌	✅	✅	✅	❌	✅	5
H-Consistency Bounds for Surrogate Loss Minimizers	❌	❌	❌	❌	❌	❌	❌	0
Hardness and Algorithms for Robust and Sparse Optimization	✅	❌	❌	❌	❌	❌	❌	1
Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning	❌	✅	✅	✅	❌	❌	✅	4
Hermite Polynomial Features for Private Data Generation	❌	✅	✅	❌	✅	✅	✅	5
Hessian-Free High-Resolution Nesterov Acceleration For Sampling	✅	❌	✅	❌	❌	❌	✅	3
Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models.	❌	✅	✅	✅	❌	❌	✅	4
High Probability Guarantees for Nonconvex Stochastic Gradient Descent with Heavy Tails	✅	❌	❌	❌	❌	❌	❌	1
Hindering Adversarial Attacks with Implicit Neural Representations	✅	❌	✅	❌	❌	❌	✅	3
History Compression via Language Models in Reinforcement Learning	✅	✅	✅	✅	❌	❌	✅	5
HousE: Knowledge Graph Embedding with Householder Parameterization	✅	✅	✅	✅	❌	❌	✅	5
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models	❌	✅	✅	✅	❌	❌	✅	4
How Powerful are Spectral Graph Neural Networks	❌	✅	✅	✅	❌	❌	✅	4
How Tempering Fixes Data Augmentation in Bayesian Neural Networks	❌	❌	✅	❌	✅	❌	✅	3
How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity	✅	❌	✅	❌	✅	❌	✅	4
How to Leverage Unlabeled Data in Offline Reinforcement Learning	❌	❌	✅	❌	✅	❌	✅	3
How to Stay Curious while avoiding Noisy TVs using Aleatoric Uncertainty Estimation	❌	✅	✅	❌	✅	❌	✅	4
How to Steer Your Adversary: Targeted and Efficient Model Stealing Defenses with Gradient Redirection	✅	✅	✅	❌	✅	❌	✅	5
How to Train Your Wide Neural Network Without Backprop: An Input-Weight Alignment Perspective	✅	✅	✅	✅	✅	❌	✅	6
Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation	✅	❌	❌	❌	❌	❌	❌	1
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection	✅	✅	✅	❌	✅	❌	✅	5
HyperPrompt: Prompt-based Task-Conditioning of Transformers	❌	❌	✅	✅	❌	❌	✅	3
HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning	❌	✅	✅	❌	❌	❌	✅	3
IDYNO: Learning Nonparametric DAGs from Interventional Dynamic Data	❌	❌	✅	✅	✅	❌	✅	4
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages	❌	✅	✅	✅	✅	❌	✅	5
Identifiability Conditions for Domain Adaptation	✅	❌	✅	✅	❌	❌	✅	4
Identification of Linear Non-Gaussian Latent Hierarchical Structure	✅	✅	✅	❌	❌	❌	❌	3
Identity-Disentangled Adversarial Augmentation for Self-supervised Learning	❌	✅	✅	✅	✅	✅	✅	6
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging	✅	✅	✅	✅	❌	❌	✅	5
Imitation Learning by Estimating Expertise of Demonstrators	❌	✅	✅	❌	✅	❌	✅	4
Implicit Bias of Linear Equivariant Networks	❌	✅	✅	❌	❌	❌	✅	3
Implicit Bias of the Step Size in Linear Diagonal Neural Networks	❌	❌	❌	❌	❌	❌	✅	1
Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks	❌	✅	✅	✅	✅	❌	✅	5
Implicit Regularization with Polynomial Growth in Deep Tensor Factorization	❌	❌	✅	✅	❌	❌	✅	3
Importance Weighted Kernel Bayes’ Rule	✅	❌	✅	✅	❌	❌	✅	4
Improve Single-Point Zeroth-Order Optimization Using High-Pass and Low-Pass Filters	❌	❌	❌	❌	❌	❌	✅	1
Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation	❌	❌	✅	❌	❌	❌	✅	2
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning	✅	❌	❌	❌	❌	❌	❌	1
Improved No-Regret Algorithms for Stochastic Shortest Path with Linear MDP	✅	❌	❌	❌	❌	❌	❌	1
Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data	✅	❌	❌	❌	❌	❌	❌	1
Improved Regret for Differentially Private Exploration in Linear MDP	✅	❌	❌	❌	❌	❌	❌	1
Improved StyleGAN-v2 based Inversion for Out-of-Distribution Images	✅	✅	✅	❌	❌	❌	✅	4
Improving Adversarial Robustness via Mutual Information Estimation	✅	✅	✅	✅	❌	❌	✅	5
Improving Ensemble Distillation With Weight Averaging and Diversifying Perturbation	✅	✅	✅	✅	✅	✅	✅	7
Improving Language Models by Retrieving from Trillions of Tokens	✅	❌	✅	✅	❌	❌	✅	4
Improving Mini-batch Optimal Transport via Partial Transportation	✅	✅	✅	✅	✅	❌	✅	6
Improving Out-of-Distribution Robustness via Selective Augmentation	✅	✅	✅	✅	❌	❌	✅	5
Improving Policy Optimization with Generalist-Specialist Learning	✅	❌	✅	❌	❌	❌	✅	3
Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification	✅	❌	✅	❌	❌	❌	✅	3
Improving Screening Processes via Calibrated Subset Selection	✅	✅	✅	❌	❌	❌	✅	4
Improving Task-free Continual Learning by Distributionally Robust Memory Evolution	✅	❌	✅	✅	❌	❌	✅	4
Improving Transformers with Probabilistic Attention Keys	❌	✅	✅	✅	✅	❌	✅	5
In defense of dual-encoders for neural ranking	❌	❌	✅	✅	❌	❌	✅	3
Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence	✅	❌	✅	❌	❌	❌	✅	3
Individual Preference Stability for Clustering	✅	✅	✅	❌	❌	❌	✅	4
Individual Reward Assisted Multi-Agent Reinforcement Learning	✅	✅	❌	❌	❌	❌	✅	3
Inducing Causal Structure for Interpretable Neural Networks	✅	✅	✅	✅	✅	❌	✅	6
Inductive Biases and Variable Creation in Self-Attention Mechanisms	❌	❌	❌	✅	✅	❌	✅	3
Inductive Matrix Completion: No Bad Local Minima and a Fast Algorithm	✅	✅	❌	❌	❌	✅	✅	4
Inferring Cause and Effect in the Presence of Heteroscedastic Noise	✅	✅	✅	❌	✅	❌	✅	5
Influence-Augmented Local Simulators: a Scalable Solution for Fast Deep RL in Large Networked Systems	✅	❌	❌	❌	✅	❌	❌	2
Information Discrepancy in Strategic Learning	✅	✅	✅	❌	❌	❌	✅	4
Informed Learning by Wide Neural Networks: Convergence, Generalization and Sampling Complexity	✅	❌	❌	❌	✅	❌	✅	3
Injecting Logical Constraints into Neural Networks via Straight-Through Estimators	❌	✅	✅	❌	✅	❌	✅	4
Input Dependent Sparse Gaussian Processes	✅	✅	✅	❌	✅	✅	✅	6
Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing	❌	❌	✅	❌	✅	✅	✅	4
Instance Dependent Regret Analysis of Kernelized Bandits	✅	❌	❌	❌	❌	❌	❌	1
Instrumental Variable Regression with Confounder Balancing	✅	✅	✅	✅	✅	✅	✅	7
Interactive Correlation Clustering with Existential Cluster Constraints	✅	❌	✅	✅	❌	✅	✅	5
Interactive Inverse Reinforcement Learning for Cooperative Games	✅	✅	❌	❌	✅	✅	✅	5
Interactively Learning Preference Constraints in Linear Bandits	✅	✅	✅	❌	❌	❌	✅	4
Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings	✅	✅	✅	❌	✅	✅	✅	6
Interpretable Off-Policy Learning via Hyperbox Search	✅	✅	✅	✅	✅	✅	✅	7
Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism	❌	✅	✅	✅	✅	❌	✅	5
Interventional Contrastive Learning with Meta Semantic Regularizer	✅	❌	✅	✅	❌	❌	✅	4
Intriguing Properties of Input-Dependent Randomized Smoothing	✅	❌	✅	❌	❌	❌	✅	3
Invariant Ancestry Search	✅	✅	✅	❌	❌	❌	✅	4
Inverse Contextual Bandits: Learning How Behavior Evolves over Time	✅	✅	✅	❌	❌	❌	✅	4
Investigating Generalization by Controlling Normalized Margin	✅	✅	✅	❌	❌	❌	✅	4
Investigating Why Contrastive Learning Benefits Robustness against Label Noise	❌	❌	✅	❌	✅	❌	✅	3
Iterative Double Sketching for Faster Least-Squares Optimization	✅	❌	❌	❌	❌	✅	✅	3
Iterative Hard Thresholding with Adaptive Regularization: Sparser Solutions Without Sacrificing Runtime	✅	✅	✅	❌	✅	✅	✅	6
It’s Raw! Audio Generation with State-Space Models	❌	❌	✅	✅	✅	❌	✅	4
Kernel Methods for Radial Transformed Compositional Data with Many Zeros	❌	❌	✅	❌	❌	❌	✅	2
Kernelized Multiplicative Weights for 0/1-Polyhedral Games: Bridging the Gap Between Learning in Extensive-Form and Normal-Form Games	✅	❌	✅	❌	❌	❌	✅	3
Kill a Bird with Two Stones: Closing the Convergence Gaps in Non-Strongly Convex Optimization by Directly Accelerated SVRG with Double Compensation and Snapshots	✅	❌	✅	❌	✅	❌	✅	4
Knowledge Base Question Answering by Case-based Reasoning over Subgraphs	❌	✅	✅	✅	❌	❌	✅	4
Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations	❌	✅	✅	✅	✅	❌	✅	5
Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics	❌	❌	✅	✅	✅	✅	✅	5
LCANets: Lateral Competition Improves Robustness Against Corruption and Attack	❌	❌	✅	✅	✅	✅	✅	5
LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood	✅	✅	✅	❌	❌	❌	✅	4
LIMO: Latent Inceptionism for Targeted Molecule Generation	✅	✅	✅	✅	✅	✅	✅	7
LSB: Local Self-Balancing MCMC in Discrete Spaces	✅	✅	✅	❌	❌	❌	✅	4
Label Ranking through Nonparametric Regression	✅	✅	✅	✅	❌	❌	❌	4
Label-Descriptive Patterns and Their Application to Characterizing Classification Errors	✅	✅	✅	✅	✅	✅	✅	7
Label-Free Explainability for Unsupervised Models	✅	✅	✅	✅	✅	✅	✅	7
Lagrangian Method for Q-Function Learning (with Applications to Machine Translation)	✅	✅	✅	❌	✅	❌	✅	5
Langevin Monte Carlo for Contextual Bandits	✅	✅	✅	❌	✅	❌	✅	5
Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents	✅	✅	✅	❌	❌	❌	✅	4
Large Batch Experience Replay	✅	✅	✅	❌	✅	❌	✅	5
Large-Scale Graph Neural Architecture Search	✅	✅	✅	✅	✅	✅	✅	7
Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence	✅	✅	✅	✅	✅	❌	✅	6
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression	✅	❌	❌	❌	❌	❌	✅	2
Latent Diffusion Energy-Based Model for Interpretable Text Modelling	✅	✅	✅	❌	❌	❌	✅	4
Latent Outlier Exposure for Anomaly Detection with Contaminated Data	✅	✅	✅	✅	❌	❌	✅	5
Lazy Estimation of Variable Importance for Large Neural Networks	✅	✅	✅	✅	❌	❌	✅	5
LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation	✅	✅	✅	❌	❌	❌	✅	4
Learning Augmented Binary Search Trees	❌	❌	✅	✅	❌	❌	❌	2
Learning Bellman Complete Representations for Offline Policy Evaluation	✅	✅	✅	❌	❌	❌	✅	4
Learning Domain Adaptive Object Detection with Probabilistic Teacher	✅	✅	✅	✅	❌	❌	✅	5
Learning Dynamics and Generalization in Deep Reinforcement Learning	❌	❌	✅	❌	❌	❌	✅	2
Learning Efficient and Robust Ordinary Differential Equations via Invertible Neural Networks	✅	✅	✅	❌	✅	❌	✅	5
Learning General Halfspaces with Adversarial Label Noise via Online Gradient Descent	✅	❌	❌	❌	❌	❌	❌	1
Learning Infinite-horizon Average-reward Markov Decision Process with Constraints	✅	❌	❌	❌	❌	❌	✅	2
Learning Iterative Reasoning through Energy Minimization	✅	✅	❌	❌	✅	❌	✅	4
Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits	✅	❌	❌	❌	❌	❌	❌	1
Learning Mixtures of Linear Dynamical Systems	✅	❌	✅	❌	❌	❌	✅	3
Learning Multiscale Transformer Models for Sequence Generation	❌	✅	✅	✅	❌	❌	✅	4
Learning Pseudometric-based Action Representations for Offline Reinforcement Learning	✅	❌	❌	❌	❌	❌	✅	2
Learning Stable Classifiers by Transferring Unstable Features	❌	✅	✅	✅	✅	❌	✅	5
Learning Stochastic Shortest Path with Linear Function Approximation	✅	❌	❌	❌	❌	❌	✅	2
Learning Symmetric Embeddings for Equivariant World Models	❌	✅	✅	❌	✅	❌	✅	4
Learning fair representation with a parametric integral probability metric	✅	✅	✅	✅	❌	❌	✅	5
Learning from Counterfactual Links for Link Prediction	✅	✅	✅	✅	✅	✅	✅	7
Learning from Demonstration: Provably Efficient Adversarial Policy Imitation with Linear Function Approximation	✅	✅	❌	❌	❌	❌	✅	3
Learning from a Learning User for Optimal Recommendations	✅	❌	❌	❌	❌	❌	✅	2
Learning inverse folding from millions of predicted structures	❌	✅	✅	✅	✅	❌	✅	5
Learning of Cluster-based Feature Importance for Electronic Health Record Time-series	❌	✅	✅	✅	✅	❌	✅	5
Learning to Cut by Looking Ahead: Cutting Plane Selection via Imitation Learning	❌	❌	✅	✅	✅	✅	✅	5
Learning to Estimate and Refine Fluid Motion with Physical Dynamics	❌	✅	✅	❌	✅	❌	✅	4
Learning to Hash Robustly, Guaranteed	✅	❌	✅	❌	✅	✅	✅	5
Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization	❌	❌	✅	❌	❌	❌	✅	2
Learning to Infer Structures of Network Games	❌	❌	✅	✅	✅	❌	✅	4
Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters	✅	✅	✅	✅	❌	❌	✅	5
Learning to Separate Voices by Spatial Regions	✅	❌	✅	✅	✅	❌	✅	5
Learning to Solve PDE-constrained Inverse Problems with Graph Networks	❌	❌	❌	✅	✅	❌	✅	3
Learning-based Optimisation of Particle Accelerators Under Partial Observability Without Real-World Training	❌	❌	❌	❌	✅	❌	✅	2
Least Squares Estimation using Sketched Data with Heteroskedastic Errors	❌	✅	✅	❌	❌	❌	✅	3
Let Invariant Rationale Discovery Inspire Graph Contrastive Learning	✅	✅	✅	❌	✅	❌	✅	5
Leverage Score Sampling for Tensor Product Matrices in Input Sparsity Time	✅	❌	✅	❌	❌	❌	✅	3
Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity	✅	✅	❌	❌	❌	❌	✅	3
Lie Point Symmetry Data Augmentation for Neural PDE Solvers	❌	✅	❌	❌	✅	❌	✅	3
Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent	✅	❌	❌	❌	✅	❌	❌	2
Linear Adversarial Concept Erasure	✅	✅	✅	✅	❌	❌	✅	5
Linear Bandit Algorithms with Sublinear Time Complexity	✅	❌	✅	❌	❌	❌	✅	3
Linear Complexity Randomized Self-attention Mechanism	✅	❌	✅	✅	❌	❌	✅	4
Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs	✅	✅	✅	❌	✅	❌	✅	5
Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness	❌	✅	✅	❌	❌	❌	✅	3
Local Augmentation for Graph Neural Networks	✅	✅	✅	✅	✅	✅	✅	7
Local Linear Convergence of Douglas-Rachford for Linear Programming: a Probabilistic Analysis	✅	❌	❌	❌	❌	❌	✅	2
Locally Sparse Neural Networks for Tabular Biomedical Data	✅	❌	✅	✅	✅	❌	✅	5
Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets	✅	✅	✅	✅	❌	❌	✅	5
Loss Function Learning for Domain Generalization by Implicit Gradient	✅	❌	✅	✅	✅	❌	✅	5
Low-Complexity Deep Convolutional Neural Networks on Fully Homomorphic Encryption Using Multiplexed Parallel Convolutions	✅	✅	✅	❌	✅	✅	✅	6
Low-Precision Stochastic Gradient Langevin Dynamics	✅	❌	✅	❌	❌	❌	✅	3
LyaNet: A Lyapunov Framework for Training Neural ODEs	✅	✅	✅	✅	✅	❌	✅	6
Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control	✅	❌	✅	❌	❌	❌	✅	3
MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection	✅	✅	✅	✅	✅	❌	✅	6
MAML and ANIL Provably Learn Representations	❌	❌	❌	❌	❌	❌	✅	1
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer	❌	✅	✅	❌	✅	❌	✅	4
ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases	❌	❌	✅	❌	✅	✅	✅	4
Making Linear MDPs Practical via Contrastive Representation Learning	✅	❌	✅	❌	❌	❌	✅	3
Marginal Distribution Adaptation for Discrete Sets via Module-Oriented Divergence Minimization	❌	❌	✅	✅	✅	❌	✅	4
Marginal Tail-Adaptive Normalizing Flows	✅	✅	✅	✅	❌	❌	✅	5
Markov Chain Monte Carlo for Continuous-Time Switching Dynamical Systems	✅	✅	❌	❌	❌	❌	✅	3
Maslow’s Hammer in Catastrophic Forgetting: Node Re-Use vs. Node Activation	❌	✅	✅	❌	❌	❌	✅	3
Massively Parallel $k$-Means Clustering for Perturbation Resilient Instances	✅	❌	✅	❌	✅	❌	✅	4
Matching Learned Causal Effects of Neural Networks with Domain Priors	✅	✅	✅	✅	✅	❌	✅	6
Matching Normalizing Flows and Probability Paths on Manifolds	❌	❌	✅	❌	❌	❌	✅	2
Matching Structure for Dual Learning	❌	✅	✅	✅	❌	❌	✅	4
Maximum Likelihood Training for Score-based Diffusion ODEs by High Order Denoising Score Matching	✅	✅	✅	❌	✅	❌	✅	5
Meaningfully debugging model mistakes using conceptual counterfactual explanations	✅	✅	✅	✅	❌	❌	✅	5
Measure Estimation in the Barycentric Coding Model	✅	✅	✅	❌	✅	❌	✅	5
Measuring Representational Robustness of Neural Networks Through Shared Invariances	❌	✅	✅	✅	❌	❌	✅	4
Measuring dissimilarity with diffeomorphism invariance	❌	✅	✅	❌	❌	✅	✅	4
Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments	✅	✅	✅	✅	✅	❌	✅	6
MemSR: Training Memory-efficient Lightweight Model for Image Super-Resolution	✅	❌	✅	✅	✅	✅	✅	6
Memory-Based Model Editing at Scale	❌	❌	✅	✅	❌	❌	✅	3
MetAug: Contrastive Learning via Meta Feature Augmentation	✅	❌	✅	❌	❌	❌	✅	3
Meta-Learning Hypothesis Spaces for Sequential Decision-making	❌	❌	✅	✅	❌	❌	✅	3
Metric-Fair Active Learning	✅	❌	❌	❌	❌	❌	✅	2
Metric-Fair Classifier Derandomization	❌	❌	❌	❌	❌	❌	❌	0
Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees	✅	✅	✅	✅	❌	❌	✅	5
Minimax M-estimation under Adversarial Contamination	✅	❌	❌	❌	❌	❌	✅	2
Minimizing Control for Credit Assignment with Strong Feedback	✅	✅	✅	✅	❌	❌	✅	5
Minimum Cost Intervention Design for Causal Effect Identification	✅	✅	✅	❌	❌	❌	✅	4
Mirror Learning: A Unifying Framework of Policy Optimisation	❌	✅	❌	❌	❌	❌	❌	1
Mitigating Gender Bias in Face Recognition using the von Mises-Fisher Mixture Model	❌	❌	✅	❌	✅	✅	✅	4
Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization	✅	❌	✅	✅	❌	❌	✅	4
Mitigating Neural Network Overconfidence with Logit Normalization	❌	✅	✅	✅	✅	❌	✅	5
ModLaNets: Learning Generalisable Dynamics via Modularity and Physical Inductive Bias	✅	❌	❌	❌	✅	❌	✅	3
Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably)	❌	❌	✅	✅	✅	❌	✅	4
Model Agnostic Sample Reweighting for Out-of-Distribution Learning	✅	✅	✅	✅	❌	❌	✅	5
Model Selection in Batch Policy Optimization	✅	❌	❌	❌	❌	❌	✅	2
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time	✅	✅	✅	✅	❌	❌	✅	5
Model-Free Opponent Shaping	✅	❌	✅	❌	❌	❌	✅	3
Model-Value Inconsistency as a Signal for Epistemic Uncertainty	❌	❌	✅	✅	❌	❌	✅	3
Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models and Amortized Policy Search	✅	✅	✅	✅	✅	❌	✅	6
Modeling Adversarial Noise for Adversarial Training	✅	✅	✅	✅	❌	❌	✅	5
Modeling Irregular Time Series with Continuous Recurrent Units	✅	✅	✅	✅	✅	❌	✅	6
Modeling Strong and Human-Like Gameplay with KL-Regularized Search	✅	❌	✅	✅	❌	❌	✅	4
Modeling Structure with Undirected Neural Networks	❌	✅	✅	✅	❌	❌	✅	4
Modular Conformal Calibration	✅	❌	✅	✅	❌	❌	✅	4
Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks	✅	✅	✅	✅	❌	❌	✅	5
Monarch: Expressive Structured Matrices for Efficient and Accurate Training	✅	✅	✅	✅	✅	❌	✅	6
More Efficient Sampling for Tensor Decomposition With Worst-Case Guarantees	✅	✅	✅	✅	✅	✅	✅	7
More Than a Toy: Random Matrix Models Predict How Real-World Neural Representations Generalize	❌	✅	✅	❌	✅	❌	✅	4
Multi Resolution Analysis (MRA) for Approximate Self-Attention	✅	✅	✅	❌	✅	❌	✅	5
Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts	❌	✅	✅	✅	✅	❌	✅	5
Multi-Level Branched Regularization for Federated Learning	✅	✅	✅	❌	❌	❌	✅	4
Multi-Task Learning as a Bargaining Game	✅	✅	✅	✅	❌	❌	✅	5
Multi-scale Feature Learning Dynamics: Insights for Double Descent	❌	✅	✅	❌	❌	❌	✅	3
Multi-slots Online Matching with High Entropy	✅	❌	❌	❌	❌	❌	✅	2
Multiclass learning with margin: exponential rates with no bias-variance trade-off	❌	❌	❌	❌	❌	❌	❌	0
Multicoated Supermasks Enhance Hidden Networks	❌	✅	✅	✅	❌	❌	✅	4
Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms	✅	❌	❌	❌	❌	❌	✅	2
Multirate Training of Neural Networks	✅	✅	✅	✅	✅	❌	✅	6
N-Penetrate: Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations	✅	❌	✅	✅	✅	❌	✅	5
NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning	✅	✅	✅	✅	✅	✅	✅	7
NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks	✅	✅	✅	✅	❌	❌	✅	5
NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework	❌	✅	✅	✅	❌	❌	✅	4
NOMU: Neural Optimization-based Model Uncertainty	✅	✅	✅	✅	❌	❌	✅	5
NP-Match: When Neural Processes meet Semi-Supervised Learning	❌	✅	✅	✅	✅	❌	✅	5
Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning	❌	✅	✅	✅	❌	❌	✅	4
Near-Optimal Algorithms for Autonomous Exploration and Multi-Goal Stochastic Shortest Path	✅	❌	❌	❌	❌	❌	❌	1
Near-Optimal Learning of Extensive-Form Games with Imperfect Information	✅	❌	❌	❌	❌	❌	❌	1
Near-optimal rate of consistency for linear models with missing values	❌	✅	✅	❌	❌	❌	✅	3
Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation	✅	❌	❌	❌	❌	❌	❌	1
Nearly Optimal Catoni’s M-estimator for Infinite Variance	✅	❌	❌	❌	❌	❌	✅	2
Nearly Optimal Policy Optimization with Stable at Any Time Guarantee	✅	❌	❌	❌	❌	❌	❌	1
Nested Bandits	✅	✅	❌	❌	✅	❌	✅	4
Nesterov Accelerated Shuffling Gradient Method for Convex Optimization	✅	✅	✅	✅	❌	✅	✅	6
Neural Fisher Discriminant Analysis: Optimal Neural Network Embeddings in Polynomial Time	✅	❌	✅	❌	❌	❌	✅	3
Neural Implicit Dictionary Learning via Mixture-of-Expert Training	❌	✅	✅	❌	❌	❌	✅	3
Neural Inverse Kinematic	❌	❌	❌	❌	❌	❌	✅	1
Neural Inverse Transform Sampler	✅	✅	✅	❌	✅	❌	✅	5
Neural Language Models are not Born Equal to Fit Brain Data, but Training Helps	❌	❌	✅	✅	❌	✅	✅	4
Neural Laplace: Learning diverse classes of differential equations in the Laplace domain	✅	✅	❌	✅	✅	❌	✅	5
Neural Network Poisson Models for Behavioural and Neural Spike Train Data	❌	✅	✅	❌	❌	❌	✅	3
Neural Network Pruning Denoises the Features and Makes Local Connectivity Emerge in Visual Tasks	❌	✅	✅	✅	❌	✅	✅	5
Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective	❌	✅	✅	❌	❌	❌	✅	3
Neural Tangent Kernel Analysis of Deep Narrow Neural Networks	❌	✅	✅	❌	❌	❌	✅	3
Neural Tangent Kernel Beyond the Infinite-Width Limit: Effects of Depth and Initialization	❌	✅	✅	❌	❌	❌	✅	3
Neural Tangent Kernel Empowered Federated Learning	❌	✅	✅	❌	❌	❌	✅	3
Neural-Symbolic Models for Logical Queries on Knowledge Graphs	✅	✅	✅	✅	✅	❌	✅	6
NeuralEF: Deconstructing Kernels by Deep Neural Networks	✅	✅	✅	❌	❌	❌	✅	4
Neuro-Symbolic Hierarchical Rule Induction	❌	✅	✅	✅	❌	❌	✅	4
Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval	❌	✅	✅	✅	✅	❌	✅	5
NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural Radiance Fields	❌	✅	❌	❌	❌	❌	✅	2
Neurocoder: General-Purpose Computation Using Stored Neural Programs	❌	❌	✅	❌	✅	❌	✅	3
Neuron Dependency Graphs: A Causal Abstraction of Neural Networks	✅	✅	✅	✅	❌	❌	✅	5
Neurotoxin: Durable Backdoors in Federated Learning	✅	✅	✅	❌	❌	❌	✅	4
No-Regret Learning in Partially-Informed Auctions	✅	❌	❌	❌	❌	❌	❌	1
No-Regret Learning in Time-Varying Zero-Sum Games	✅	❌	❌	❌	❌	❌	✅	2
Non-Vacuous Generalisation Bounds for Shallow Neural Networks	❌	✅	✅	✅	❌	❌	✅	4
Nonlinear Feature Diffusion on Hypergraphs	✅	✅	✅	✅	❌	❌	✅	5
Nonparametric Embeddings of Sparse High-Order Interaction Events	❌	❌	✅	❌	❌	❌	✅	2
Nonparametric Factor Trajectory Learning for Dynamic Tensor Decomposition	❌	❌	✅	✅	❌	❌	✅	3
Nonparametric Involutive Markov Chain Monte Carlo	✅	✅	❌	❌	❌	❌	✅	3
Nonparametric Sparse Tensor Factorization with Hierarchical Gamma Processes	❌	❌	✅	✅	❌	❌	✅	3
Not All Poisons are Created Equal: Robust Training against Data Poisoning	✅	✅	✅	❌	✅	❌	✅	5
NysADMM: faster composite convex optimization via low-rank approximation	✅	❌	✅	❌	✅	❌	✅	4
Nyström Kernel Mean Embeddings	❌	❌	✅	❌	❌	❌	✅	2
OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework	❌	✅	✅	✅	❌	❌	✅	4
Object Permanence Emerges in a Random Walk along Memory	✅	✅	✅	✅	❌	❌	✅	5
Off-Policy Evaluation for Large Action Spaces via Embeddings	✅	✅	✅	❌	❌	❌	✅	4
Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory	✅	❌	❌	❌	❌	❌	❌	1
Off-Policy Reinforcement Learning with Delayed Rewards	✅	❌	✅	❌	✅	❌	✅	4
Offline Meta-Reinforcement Learning with Online Self-Supervision	✅	❌	✅	❌	❌	❌	✅	3
Offline RL Policies Should Be Trained to be Adaptive	✅	❌	✅	❌	❌	❌	✅	3
Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning	✅	❌	✅	✅	❌	❌	✅	4
On Collective Robustness of Bagging Against Data Poisoning	✅	✅	✅	✅	✅	✅	✅	7
On Convergence of Gradient Descent Ascent: A Tight Local Analysis	❌	❌	✅	❌	❌	❌	✅	2
On Distribution Shift in Learning-based Bug Detectors	❌	✅	✅	✅	✅	❌	✅	5
On Finite-Sample Identifiability of Contrastive Learning-Based Nonlinear Independent Component Analysis	❌	❌	✅	❌	❌	❌	✅	2
On Implicit Bias in Overparameterized Bilevel Optimization	✅	❌	✅	❌	✅	✅	✅	5
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning	✅	❌	✅	❌	❌	❌	✅	3
On Last-Iterate Convergence Beyond Zero-Sum Games	❌	❌	✅	❌	❌	❌	✅	2
On Learning Mixture of Linear Regressions in the Non-Realizable Setting	✅	❌	✅	✅	❌	❌	✅	4
On Measuring Causal Contributions via do-interventions	✅	❌	✅	✅	❌	❌	✅	4
On Non-local Convergence Analysis of Deep Linear Networks	❌	❌	❌	❌	❌	❌	✅	1
On Numerical Integration in Neural Ordinary Differential Equations	❌	✅	❌	❌	❌	❌	✅	2
On Transportation of Mini-batches: A Hierarchical Approach	✅	✅	✅	❌	✅	❌	✅	5
On Well-posedness and Minimax Optimal Rates of Nonparametric Q-function Estimation in Off-policy Evaluation	❌	❌	❌	❌	❌	❌	❌	0
On the Adversarial Robustness of Causal Algorithmic Recourse	✅	✅	✅	❌	❌	❌	✅	4
On the Convergence of Inexact Predictor-Corrector Methods for Linear Programming	✅	❌	❌	❌	❌	❌	✅	2
On the Convergence of Local Stochastic Compositional Gradient Descent with Momentum	✅	❌	✅	✅	✅	❌	✅	5
On the Convergence of the Shapley Value in Parametric Bayesian Learning Games	❌	✅	✅	❌	❌	❌	✅	3
On the Difficulty of Defending Self-Supervised Learning against Model Extraction	✅	❌	✅	❌	✅	❌	✅	4
On the Effects of Artificial Data Modification	❌	✅	✅	❌	✅	❌	✅	4
On the Equivalence Between Temporal and Static Equivariant Graph Representations	❌	✅	✅	✅	✅	❌	✅	5
On the Finite-Time Complexity and Practical Computation of Approximate Stationarity Concepts of Lipschitz Functions	✅	❌	❌	❌	❌	❌	❌	1
On the Finite-Time Performance of the Knowledge Gradient Algorithm	✅	❌	❌	❌	❌	❌	✅	2
On the Generalization Analysis of Adversarial Learning	❌	❌	❌	❌	❌	❌	❌	0
On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces	✅	❌	❌	❌	❌	❌	✅	2
On the Impossibility of Learning to Cooperate with Adaptive Partner Strategies in Repeated Games	❌	❌	❌	❌	❌	❌	❌	0
On the Learning of Non-Autoregressive Transformers	❌	❌	✅	❌	✅	❌	✅	3
On the Optimization Landscape of Neural Collapse under MSE Loss: Global Optimality with Unconstrained Features	❌	✅	✅	✅	❌	❌	✅	4
On the Practicality of Deterministic Epistemic Uncertainty	❌	❌	✅	✅	✅	✅	✅	5
On the Robustness of CountSketch to Adaptive Inputs	✅	✅	❌	❌	❌	❌	✅	3
On the Role of Discount Factor in Offline Reinforcement Learning	✅	❌	✅	❌	❌	❌	✅	3
On the Sample Complexity of Learning Infinite-horizon Discounted Linear Kernel MDPs	✅	❌	❌	❌	❌	❌	❌	1
On the Statistical Benefits of Curriculum Learning	✅	❌	❌	❌	❌	❌	✅	2
On the Surrogate Gap between Contrastive and Supervised Losses	❌	✅	✅	✅	✅	✅	✅	6
One-Pass Algorithms for MAP Inference of Nonsymmetric Determinantal Point Processes	✅	❌	✅	❌	✅	❌	✅	4
One-Pass Diversified Sampling with Application to Terabyte-Scale Genomic Sequence Streams	✅	✅	✅	❌	✅	❌	✅	5
Online Active Regression	✅	❌	✅	❌	✅	❌	✅	4
Online Algorithms with Multiple Predictions	✅	❌	❌	❌	❌	❌	❌	1
Online Balanced Experimental Design	✅	❌	❌	❌	✅	❌	✅	3
Online Continual Learning through Mutual Information Maximization	✅	✅	✅	❌	❌	❌	✅	4
Online Decision Transformer	✅	❌	✅	❌	❌	❌	✅	3
Online Learning and Pricing with Reusable Resources: Linear Bandits with Sub-Exponential Rewards	✅	❌	❌	❌	❌	❌	✅	2
Online Learning for Min Sum Set Cover and Pandora’s Box	✅	❌	❌	❌	❌	❌	❌	1
Online Learning with Knapsacks: the Best of Both Worlds	✅	❌	❌	❌	❌	❌	❌	1
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback	✅	❌	❌	❌	✅	❌	✅	3
Online and Consistent Correlation Clustering	✅	✅	✅	❌	✅	❌	✅	5
Only tails matter: Average-Case Universality and Robustness in the Convex Regime	✅	❌	✅	❌	❌	❌	✅	3
Open-Sampling: Exploring Out-of-Distribution data for Re-balancing Long-tailed datasets	✅	✅	✅	✅	✅	❌	✅	6
Optimal Algorithms for Mean Estimation under Local Differential Privacy	✅	❌	❌	❌	❌	❌	❌	1
Optimal Algorithms for Stochastic Multi-Level Compositional Optimization	✅	❌	✅	❌	❌	❌	✅	3
Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training	❌	❌	✅	❌	✅	❌	✅	3
Optimal Clustering with Noisy Queries via Multi-Armed Bandit	✅	❌	❌	❌	❌	❌	❌	1
Optimal Estimation of Policy Gradient via Double Fitted Iteration	✅	❌	❌	❌	❌	❌	❌	1
Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits	✅	❌	❌	❌	❌	❌	✅	2
Optimally Controllable Perceptual Lossy Compression	❌	✅	✅	❌	❌	❌	✅	3
Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer	✅	✅	✅	❌	❌	❌	✅	4
Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training	✅	❌	✅	❌	✅	❌	✅	4
Optimization-Induced Graph Implicit Nonlinear Diffusion	❌	✅	✅	✅	❌	❌	✅	4
Optimizing Sequential Experimental Design with Deep Reinforcement Learning	❌	✅	❌	❌	✅	❌	✅	3
Optimizing Tensor Network Contraction Using Reinforcement Learning	❌	✅	✅	❌	✅	❌	✅	4
Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering	✅	✅	✅	✅	✅	❌	✅	6
Order Constraints in Optimal Transport	✅	✅	✅	✅	✅	❌	✅	6
Out-of-Distribution Detection with Deep Nearest Neighbors	✅	✅	✅	✅	❌	❌	✅	5
Overcoming Oscillations in Quantization-Aware Training	✅	✅	✅	✅	❌	❌	✅	5
PAC-Bayesian Bounds on Rate-Efficient Classifiers	❌	✅	✅	✅	❌	❌	✅	4
PAC-Net: A Model Pruning Approach to Inductive Transfer Learning	✅	❌	✅	❌	❌	❌	✅	3
PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs	✅	✅	✅	✅	✅	❌	✅	6
PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation	✅	✅	✅	❌	❌	❌	✅	4
PDE-Based Optimal Strategy for Unconstrained Online Learning	✅	✅	✅	❌	❌	❌	✅	4
PDO-s3DCNNs: Partial Differential Operator Based Steerable 3D CNNs	❌	❌	✅	✅	✅	❌	✅	4
PINs: Progressive Implicit Networks for Multi-Scale Neural Representations	❌	❌	✅	✅	❌	❌	✅	3
PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information	✅	✅	✅	✅	✅	❌	✅	6
PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance	✅	✅	✅	✅	✅	❌	✅	6
PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration	✅	❌	✅	❌	✅	❌	✅	4
POEM: Out-of-Distribution Detection with Posterior Sampling	✅	✅	✅	❌	✅	✅	✅	6
POET: Training Neural Networks on Tiny Devices with Integrated Rematerialization and Paging	✅	✅	✅	❌	✅	❌	✅	5
Pairwise Conditional Gradients without Swap Steps and Sparser Kernel Herding	✅	✅	✅	❌	❌	❌	✅	4
Parametric Visual Program Induction with Function Modularization	✅	❌	✅	❌	✅	❌	❌	3
Parsimonious Learning-Augmented Caching	✅	❌	✅	❌	❌	❌	✅	3
Partial Counterfactual Identification from Observational and Experimental Data	✅	❌	✅	❌	❌	❌	✅	3
Partial Label Learning via Label Influence Function	✅	❌	✅	✅	❌	❌	✅	4
Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition	✅	✅	✅	✅	❌	❌	✅	5
Partial disentanglement for domain adaptation	✅	❌	✅	❌	❌	❌	✅	3
Particle Transformer for Jet Tagging	❌	✅	✅	✅	❌	❌	✅	4
Path-Aware and Structure-Preserving Generation of Synthetically Accessible Molecules	❌	❌	✅	✅	❌	❌	✅	3
Path-Gradient Estimators for Continuous Normalizing Flows	✅	✅	✅	❌	✅	❌	✅	5
Penalizing Gradient Norm for Efficiently Improving Generalization in Deep Learning	✅	✅	✅	❌	✅	❌	✅	5
Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning	❌	✅	✅	❌	✅	❌	✅	4
Permutation Search of Tensor Network Structures via Local Sampling	✅	✅	✅	❌	❌	❌	✅	4
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning	✅	✅	✅	❌	❌	❌	✅	4
Personalized Federated Learning through Local Memorization	✅	✅	✅	✅	❌	❌	✅	5
Personalized Federated Learning via Variational Bayesian Inference	✅	❌	✅	✅	✅	❌	✅	5
Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning	✅	❌	❌	❌	❌	❌	❌	1
Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets	✅	❌	❌	❌	❌	❌	❌	1
Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity	✅	❌	❌	❌	❌	❌	❌	1
Phasic Self-Imitative Reduction for Sparse-Reward Goal-Conditioned Reinforcement Learning	✅	✅	✅	❌	✅	❌	✅	5
Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification	✅	✅	✅	❌	❌	❌	✅	4
Plan Your Target and Learn Your Skills: Transferable State-Only Imitation Learning via Decoupled Policy Optimization	✅	✅	✅	✅	❌	❌	✅	5
Planning with Diffusion for Flexible Behavior Synthesis	✅	✅	✅	❌	❌	✅	✅	5
Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks	❌	✅	✅	❌	✅	✅	✅	5
Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations	✅	✅	✅	❌	❌	❌	✅	4
PoF: Post-Training of Feature Extractor for Improving Generalization	✅	❌	✅	✅	✅	❌	✅	5
Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets	❌	✅	✅	✅	✅	✅	✅	6
Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL	❌	❌	✅	❌	❌	❌	✅	2
Policy Gradient Method For Robust Reinforcement Learning	✅	❌	✅	❌	❌	❌	✅	3
Popular decision tree algorithms are provably noise tolerant	✅	❌	❌	❌	❌	❌	❌	1
Position Prediction as an Effective Pretraining Strategy	✅	❌	✅	✅	✅	❌	✅	5
Power-Law Escape Rate of SGD	❌	❌	✅	❌	❌	❌	✅	2
Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering	✅	✅	✅	❌	✅	✅	✅	6
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization	✅	✅	✅	✅	✅	❌	✅	6
Predicting Out-of-Distribution Error with the Projection Norm	✅	✅	✅	✅	❌	❌	✅	5
Principal Component Flows	✅	✅	✅	❌	✅	❌	✅	5
Principled Knowledge Extrapolation with GANs	✅	❌	✅	❌	✅	❌	✅	4
Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt	✅	✅	✅	✅	❌	❌	✅	5
Privacy for Free: How does Dataset Condensation Help Privacy?	❌	❌	✅	❌	❌	✅	✅	3
Private Adaptive Optimization with Side information	✅	✅	✅	✅	❌	❌	✅	5
Private Streaming SCO in $\ell_p$ geometry with Applications in High Dimensional Online Decision Making	✅	✅	❌	❌	❌	❌	✅	3
Private frequency estimation via projective geometry	❌	✅	❌	❌	✅	✅	✅	4
Private optimization in the interpolation regime: faster rates and hardness results	✅	❌	❌	❌	❌	❌	❌	1
ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning	✅	✅	✅	✅	✅	❌	✅	6
Probabilistic Bilevel Coreset Selection	✅	❌	✅	✅	❌	❌	✅	4
Probabilistic ODE Solutions in Millions of Dimensions	❌	✅	✅	❌	❌	❌	✅	3
Probabilistically Robust Learning: Balancing Average and Worst-case Performance	✅	✅	✅	✅	✅	❌	✅	6
ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training	✅	✅	✅	❌	❌	❌	✅	4
Prompting Decision Transformer for Few-Shot Policy Generalization	✅	❌	✅	❌	❌	❌	✅	3
Prototype Based Classification from Hierarchy to Fairness	❌	✅	✅	❌	❌	❌	✅	3
Prototype-Anchored Learning for Learning with Imperfect Annotations	✅	❌	✅	✅	❌	❌	✅	4
Provable Acceleration of Heavy Ball beyond Quadratics for a Class of Polyak-Lojasiewicz Functions when the Non-Convexity is Averaged-Out	✅	❌	❌	❌	❌	❌	✅	2
Provable Domain Generalization via Invariant-Feature Subspace Recovery	✅	✅	✅	✅	❌	❌	✅	5
Provable Reinforcement Learning with a Short-Term Memory	✅	❌	❌	❌	❌	❌	❌	1
Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance	✅	✅	✅	✅	✅	❌	✅	6
Provably Adversarially Robust Nearest Prototype Classifiers	✅	✅	✅	❌	❌	❌	❌	3
Provably Efficient Offline Reinforcement Learning for Partially Observable Markov Decision Processes	✅	❌	❌	❌	❌	❌	❌	1
Proving Theorems using Incremental Learning and Hindsight Experience Replay	✅	❌	✅	❌	✅	✅	✅	5
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!	✅	✅	✅	❌	✅	❌	✅	5
Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularization	✅	✅	✅	❌	❌	❌	✅	4
Proximal Exploration for Model-guided Protein Sequence Design	✅	✅	✅	❌	❌	❌	✅	4
Proximal and Federated Random Reshuffling	✅	✅	✅	❌	❌	❌	✅	4
Public Data-Assisted Mirror Descent for Private Model Training	✅	✅	✅	✅	❌	❌	✅	5
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images	✅	✅	✅	✅	❌	❌	✅	5
QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning	✅	✅	✅	❌	✅	❌	✅	5
Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features	✅	✅	✅	✅	✅	✅	✅	7
Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding	❌	✅	✅	❌	❌	❌	✅	3
Quantifying and Learning Linear Symmetry-Based Disentanglement	❌	✅	✅	✅	✅	❌	✅	5
Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra	✅	❌	✅	❌	✅	❌	✅	4
Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization	✅	✅	✅	❌	❌	❌	✅	4
RECAPP: Crafting a More Efficient Catalyst for Convex Optimization	✅	✅	✅	❌	❌	❌	✅	4
REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer	✅	✅	✅	❌	❌	❌	✅	4
ROCK: Causal Inference Principles for Reasoning about Commonsense Causality	❌	✅	✅	✅	❌	❌	✅	4
RUMs from Head-to-Head Contests	✅	❌	✅	✅	✅	❌	✅	5
Random Forest Density Estimation	✅	❌	✅	✅	❌	❌	✅	4
Random Gegenbauer Features for Scalable Kernel Methods	❌	❌	✅	✅	❌	❌	✅	3
RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression	❌	✅	✅	✅	✅	❌	✅	5
Re-evaluating Word Mover’s Distance	❌	✅	✅	✅	✅	❌	✅	5
Reachability Constrained Reinforcement Learning	✅	❌	✅	❌	❌	❌	✅	3
Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series	❌	✅	✅	✅	✅	❌	✅	5
Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs	❌	✅	✅	❌	✅	❌	✅	4
Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks	✅	✅	✅	❌	❌	❌	✅	4
Refined Convergence Rates for Maximum Likelihood Estimation under Finite Mixture Models	✅	✅	❌	❌	❌	✅	✅	4
Region-Based Semantic Factorization in GANs	❌	✅	✅	❌	✅	❌	✅	4
Regret Bounds for Stochastic Shortest Path Problems with Linear Function Approximation	✅	❌	❌	❌	❌	❌	❌	1
Regret Minimization with Performative Feedback	✅	❌	❌	❌	❌	❌	❌	1
Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency	✅	❌	❌	❌	❌	❌	❌	1
Reinforcement Learning with Action-Free Pre-Training from Videos	❌	✅	✅	❌	✅	❌	✅	4
Removing Batch Normalization Boosts Adversarial Training	❌	✅	✅	✅	✅	❌	✅	5
Representation Topology Divergence: A Method for Comparing Neural Network Representations.	✅	✅	✅	❌	❌	❌	✅	4
Residual-Based Sampling for Online Outlier-Robust PCA	✅	❌	❌	❌	❌	❌	✅	2
Resilient and Communication Efficient Learning for Heterogeneous Federated Systems	✅	❌	✅	❌	❌	❌	✅	3
Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the $O(ε^-7/4)$ Complexity	✅	❌	✅	❌	❌	❌	✅	3
Rethinking Attention-Model Explainability through Faithfulness Violation Test	❌	✅	✅	❌	❌	❌	❌	2
Rethinking Fano’s Inequality in Ensemble Learning	❌	✅	✅	✅	✅	✅	✅	6
Rethinking Graph Neural Networks for Anomaly Detection	❌	✅	✅	✅	✅	❌	✅	5
Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems	✅	✅	✅	❌	✅	❌	✅	5
Retrieval-Augmented Reinforcement Learning	✅	❌	✅	❌	❌	❌	✅	3
RetrievalGuard: Provably Robust 1-Nearest Neighbor Image Retrieval	✅	❌	✅	❌	✅	❌	✅	4
Retroformer: Pushing the Limits of End-to-end Retrosynthesis Transformer	✅	✅	✅	✅	✅	❌	✅	6
Reverse Engineering $\ell_p$ attacks: A block-sparse optimization approach with recovery guarantees	✅	❌	✅	❌	❌	❌	✅	3
Reverse Engineering the Neural Tangent Kernel	❌	✅	✅	✅	❌	❌	✅	4
Revisiting Consistency Regularization for Deep Partial Label Learning	✅	❌	✅	❌	✅	❌	✅	4
Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework	❌	❌	✅	❌	✅	❌	✅	3
Revisiting End-to-End Speech-to-Text Translation From Scratch	❌	✅	✅	✅	❌	❌	✅	4
Revisiting Label Smoothing and Knowledge Distillation Compatibility: What was Missing?	✅	✅	✅	✅	❌	✅	✅	6
Revisiting Online Submodular Minimization: Gap-Dependent Regret Bounds, Best of Both Worlds and Adversarial Robustness	✅	❌	❌	❌	❌	❌	❌	1
Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning	❌	❌	✅	❌	❌	❌	✅	2
Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization	✅	✅	✅	✅	✅	❌	✅	6
Revisiting the Effects of Stochasticity for Hamiltonian Samplers	❌	❌	✅	❌	✅	❌	✅	3
Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes	✅	❌	❌	❌	❌	❌	❌	1
Rich Feature Construction for the Optimization-Generalization Dilemma	✅	✅	✅	✅	❌	❌	✅	5
RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests	❌	✅	✅	❌	❌	❌	✅	3
Ripple Attention for Visual Perception with Sub-quadratic Complexity	✅	❌	✅	✅	✅	❌	✅	5
Risk-Averse No-Regret Learning in Online Convex Games	✅	❌	❌	❌	❌	❌	✅	2
Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data	❌	✅	✅	✅	❌	❌	✅	4
Robust Counterfactual Explanations for Tree-Based Ensembles	✅	❌	✅	❌	❌	❌	✅	3
Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum	✅	✅	✅	❌	✅	❌	✅	5
Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees	✅	❌	✅	✅	❌	❌	✅	4
Robust Group Synchronization via Quadratic Programming	✅	✅	✅	❌	❌	❌	✅	4
Robust Imitation Learning against Variations in Environment Dynamics	✅	✅	✅	✅	❌	❌	✅	5
Robust Kernel Density Estimation with Median-of-Means principle	❌	✅	✅	❌	❌	❌	✅	3
Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile	✅	✅	✅	✅	✅	❌	✅	6
Robust Models Are More Interpretable Because Attributions Look Normal	❌	✅	✅	❌	✅	✅	✅	5
Robust Multi-Objective Bayesian Optimization Under Input Noise	❌	✅	✅	❌	✅	✅	✅	5
Robust Policy Learning over Multiple Uncertainty Sets	✅	✅	✅	❌	❌	❌	✅	4
Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning	✅	✅	❌	❌	❌	❌	✅	3
Robust Task Representations for Offline Meta-Reinforcement Learning via Contrastive Learning	✅	✅	✅	❌	❌	❌	✅	4
Robust Training of Neural Networks Using Scale Invariant Architectures	✅	❌	✅	❌	❌	❌	✅	3
Robust Training under Label Noise by Over-parameterization	✅	✅	✅	✅	✅	✅	✅	7
Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation	❌	❌	✅	✅	❌	❌	✅	3
Robustness Implies Generalization via Data-Dependent Generalization Bounds	❌	❌	✅	✅	❌	❌	✅	3
Robustness Verification for Contrastive Learning	✅	✅	✅	❌	✅	❌	✅	5
Robustness and Accuracy Could Be Reconcilable by (Proper) Definition	❌	✅	✅	✅	❌	❌	✅	4
Robustness in Multi-Objective Submodular Optimization: a Quantile Approach	✅	❌	✅	❌	❌	❌	❌	2
Role-based Multiplex Network Embedding	✅	❌	✅	❌	❌	❌	✅	3
Rotting Infinitely Many-Armed Bandits	✅	✅	❌	❌	❌	❌	✅	3
SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation	✅	✅	✅	✅	❌	❌	✅	5
SDQ: Stochastic Differentiable Quantization with Mixed Precision	✅	❌	✅	✅	✅	❌	✅	5
SE(3) Equivariant Graph Neural Networks with Complete Local Frames	✅	❌	✅	✅	✅	❌	✅	5
SPDY: Accurate Pruning with Speedup Guarantees	✅	✅	✅	✅	✅	✅	✅	7
SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators	❌	✅	✅	✅	✅	❌	✅	5
SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization	✅	✅	✅	✅	❌	❌	✅	5
Safe Exploration for Efficient Policy Evaluation and Comparison	✅	❌	✅	❌	❌	❌	✅	3
Safe Learning in Tree-Form Sequential Decision Making: Handling Hard and Soft Constraints	✅	❌	✅	❌	❌	❌	✅	3
Sample Efficient Learning of Predictors that Complement Humans	✅	✅	✅	✅	❌	❌	✅	5
Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis	✅	❌	❌	❌	❌	❌	✅	2
Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost	✅	❌	❌	❌	❌	❌	❌	1
Sanity Simulations for Saliency Methods	❌	✅	✅	✅	✅	❌	✅	5
Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation	✅	✅	✅	❌	❌	✅	✅	5
Scalable Computation of Causal Bounds	✅	❌	❌	❌	❌	❌	❌	1
Scalable Deep Gaussian Markov Random Fields for General Graphs	❌	✅	✅	✅	❌	❌	✅	4
Scalable Deep Reinforcement Learning Algorithms for Mean Field Games	✅	✅	❌	❌	❌	❌	✅	3
Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation	✅	✅	✅	❌	❌	❌	✅	4
Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes	✅	✅	✅	✅	❌	❌	✅	5
Scalable Spike-and-Slab	✅	✅	✅	✅	✅	❌	✅	6
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times	✅	❌	✅	❌	❌	❌	✅	3
Scaling Out-of-Distribution Detection for Real-World Settings	❌	✅	✅	✅	❌	❌	✅	4
Scaling Structured Inference with Randomization	✅	✅	✅	❌	✅	❌	✅	5
Scaling-up Diverse Orthogonal Convolutional Networks by a Paraunitary Framework	✅	✅	✅	❌	✅	❌	✅	5
Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models	✅	✅	✅	❌	❌	❌	✅	4
Score-Guided Intermediate Level Optimization: Fast Langevin Mixing for Inverse Problems	✅	✅	✅	❌	✅	❌	✅	5
Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations	✅	✅	✅	✅	✅	✅	✅	7
Searching for BurgerFormer with Micro-Meso-Macro Space Design	❌	✅	✅	✅	✅	❌	✅	5
Secure Distributed Training at Scale	✅	✅	✅	❌	✅	❌	✅	5
Secure Quantized Training for Deep Learning	✅	✅	✅	✅	✅	❌	✅	6
Selective Network Linearization for Efficient Private Inference	✅	✅	✅	✅	❌	❌	✅	5
Selective Regression under Fairness Criteria	✅	✅	✅	❌	❌	❌	✅	4
Self-Organized Polynomial-Time Coordination Graphs	✅	✅	✅	❌	✅	❌	✅	5
Self-Supervised Models of Audio Effectively Explain Human Cortical Responses to Speech	❌	❌	✅	✅	❌	✅	✅	4
Self-Supervised Representation Learning via Latent Graph Prediction	✅	✅	✅	✅	✅	✅	✅	7
Self-conditioning Pre-Trained Language Models	✅	✅	✅	❌	✅	❌	✅	5
Self-supervised Models are Good Teaching Assistants for Vision Transformers	❌	✅	✅	✅	❌	❌	✅	4
Self-supervised learning with random-projection quantizer for speech recognition	❌	❌	✅	❌	❌	❌	✅	2
Selling Data To a Machine Learner: Pricing via Costly Signaling	❌	❌	❌	❌	❌	❌	✅	1
Sequential Covariate Shift Detection Using Classifier Two-Sample Tests	✅	✅	✅	❌	❌	❌	✅	4
Sequential and Parallel Constrained Max-value Entropy Search via Information Lower Bound	✅	❌	✅	❌	❌	❌	✅	3
Set Based Stochastic Subsampling	✅	❌	✅	❌	❌	❌	✅	3
Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets	❌	✅	✅	❌	✅	❌	✅	4
Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning	✅	✅	✅	❌	❌	❌	✅	4
Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood	✅	❌	❌	❌	❌	❌	✅	2
ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks	❌	✅	✅	✅	✅	❌	✅	5
Short-Term Plasticity Neurons Learning to Learn and Forget	✅	✅	✅	✅	❌	❌	✅	5
Showing Your Offline Reinforcement Learning Work: Online Evaluation Budget Matters	❌	✅	✅	✅	✅	❌	✅	5
Shuffle Private Linear Contextual Bandits	✅	✅	❌	❌	❌	❌	✅	3
Simple and near-optimal algorithms for hidden stratification and multi-group learning	✅	❌	❌	❌	❌	❌	❌	1
Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games	✅	❌	✅	❌	✅	❌	✅	4
Simultaneous Graph Signal Clustering and Graph Learning	✅	✅	✅	❌	❌	❌	✅	4
Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback	✅	❌	❌	❌	❌	❌	❌	1
Sketching Algorithms and Lower Bounds for Ridge Regression	✅	❌	❌	❌	❌	❌	✅	2
SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks	❌	✅	✅	✅	✅	❌	✅	5
Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification	❌	✅	✅	✅	✅	❌	✅	5
Smoothed Adaptive Weighting for Imbalanced Semi-Supervised Learning: Improve Reliability Against Unknown Distribution Data	✅	✅	✅	❌	✅	✅	✅	6
Smoothed Adversarial Linear Contextual Bandits with Knapsacks	✅	❌	❌	❌	❌	❌	❌	1
SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac Signals	✅	✅	✅	✅	❌	❌	✅	5
Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation	❌	✅	✅	❌	✅	❌	✅	4
Solving Stackelberg Prediction Game with Least Squares Loss via Spherically Constrained Least Squares Reformulation	✅	❌	✅	❌	✅	✅	✅	5
SpaceMAP: Visualizing High-Dimensional Data by Space Expansion	✅	❌	✅	✅	✅	❌	✅	5
Sparse Double Descent: Where Network Pruning Aggravates Overfitting	❌	✅	✅	❌	❌	❌	✅	3
Sparse Invariant Risk Minimization	✅	❌	✅	❌	❌	❌	✅	3
Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxation	❌	❌	❌	❌	❌	❌	✅	1
Sparsity in Partially Controllable Linear Systems	✅	❌	❌	❌	❌	❌	✅	2
Spatial-Channel Token Distillation for Vision MLPs	✅	❌	✅	❌	✅	❌	❌	3
Spectral Representation of Robustness Measures for Optimization Under Input Uncertainty	✅	✅	✅	❌	❌	❌	✅	4
SpeqNets: Sparsity-aware permutation-equivariant graph networks	✅	✅	✅	✅	✅	✅	✅	7
Stability Based Generalization Bounds for Exponential Family Langevin Dynamics	❌	❌	✅	❌	✅	❌	✅	3
Stabilizing Off-Policy Deep Reinforcement Learning from Pixels	❌	✅	✅	✅	❌	❌	✅	4
Stabilizing Q-learning with Linear Architectures for Provable Efficient Learning	✅	❌	❌	❌	❌	❌	❌	1
Stable Conformal Prediction Sets	✅	✅	✅	❌	❌	❌	✅	4
Staged Training for Transformer Language Models	✅	✅	✅	❌	❌	❌	✅	4
State Transition of Dendritic Spines Improves Learning of Sparse Spiking Neural Networks	❌	❌	✅	✅	✅	❌	✅	4
Statistical inference with implicit SGD: proximal Robbins-Monro vs. Polyak-Ruppert	❌	❌	❌	❌	❌	❌	✅	1
Steerable 3D Spherical Neurons	❌	✅	✅	✅	❌	❌	✅	4
Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models	✅	❌	❌	❌	✅	❌	✅	3
Stochastic Continuous Submodular Maximization: Boosting via Non-oblivious Function	✅	❌	✅	❌	❌	❌	✅	3
Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning	✅	✅	✅	❌	❌	❌	✅	4
Stochastic Reweighted Gradient Descent	✅	❌	✅	❌	❌	❌	✅	3
Stochastic Rising Bandits	✅	✅	✅	❌	❌	❌	✅	4
Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification	❌	✅	✅	✅	❌	❌	✅	4
Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses	❌	❌	✅	❌	✅	❌	✅	3
Strategic Representation	✅	❌	❌	❌	❌	❌	❌	1
Strategies for Safe Multi-Armed Bandits with Logarithmic Regret and Risk	✅	❌	✅	❌	❌	❌	✅	3
Streaming Algorithm for Monotone k-Submodular Maximization with Cardinality Constraints	✅	❌	✅	❌	❌	❌	✅	3
Streaming Algorithms for High-Dimensional Robust Statistics	✅	❌	❌	❌	❌	❌	❌	1
Streaming Algorithms for Support-Aware Histograms	✅	❌	✅	❌	❌	❌	✅	3
Streaming Inference for Infinite Feature Models	❌	❌	✅	❌	❌	❌	❌	1
StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models	❌	❌	✅	✅	✅	❌	✅	4
Structural Entropy Guided Graph Hierarchical Pooling	✅	✅	✅	✅	❌	❌	✅	5
Structure Preserving Neural Networks: A Case Study in the Entropy Closure of the Boltzmann Equation	✅	✅	❌	✅	✅	✅	✅	6
Structure-Aware Transformer for Graph Representation Learning	❌	✅	✅	✅	✅	❌	✅	5
Structure-preserving GANs	❌	❌	✅	✅	❌	✅	✅	4
Structured Stochastic Gradient MCMC	✅	✅	✅	✅	❌	❌	✅	5
Style Equalization: Unsupervised Learning of Controllable Generative Sequence Models	❌	✅	✅	✅	✅	❌	✅	5
Sublinear-Time Clustering Oracle for Signed Graphs	✅	✅	✅	❌	✅	❌	✅	5
Subspace Learning for Effective Meta-Learning	✅	❌	✅	✅	❌	❌	✅	4
Supervised Learning with General Risk Functionals	❌	❌	✅	✅	❌	❌	✅	3
Supervised Off-Policy Ranking	✅	✅	✅	✅	✅	❌	✅	6
Surrogate Likelihoods for Variational Annealed Importance Sampling	✅	❌	✅	❌	✅	❌	✅	4
Symmetric Machine Theory of Mind	✅	✅	❌	❌	✅	❌	✅	4
Synergy and Symmetry in Deep Learning: Interactions between the Data, Model, and Inference Algorithm	❌	❌	✅	✅	❌	❌	✅	3
TACTiS: Transformer-Attentional Copulas for Time Series	❌	✅	✅	✅	✅	❌	✅	5
TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification	✅	❌	✅	✅	❌	❌	✅	4
TPC: Transformation-Specific Smoothing for Point Cloud Models	❌	✅	✅	❌	❌	❌	❌	2
TSPipe: Learn from Teacher Faster with Pipelines	❌	✅	✅	❌	✅	❌	✅	4
TURF: Two-Factor, Universal, Robust, Fast Distribution Learning Algorithm	✅	❌	❌	❌	❌	❌	✅	2
Tackling Data Heterogeneity: A New Unified Framework for Decentralized SGD with Sample-induced Topology	❌	❌	✅	✅	✅	❌	✅	4
Tackling covariate shift with node-based Bayesian neural networks	❌	✅	✅	❌	✅	❌	✅	4
Task-aware Privacy Preservation for Multi-dimensional Data	✅	✅	✅	❌	✅	❌	✅	5
Tell me why! Explanations support learning relational and causal structure	❌	❌	❌	❌	✅	✅	✅	3
Temporal Difference Learning for Model Predictive Control	✅	✅	✅	❌	✅	❌	✅	5
Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing	❌	✅	✅	✅	❌	❌	✅	4
The Algebraic Path Problem for Graph Metrics	❌	❌	❌	❌	❌	❌	❌	0
The CLRS Algorithmic Reasoning Benchmark	✅	✅	✅	✅	✅	❌	✅	6
The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks	✅	✅	✅	❌	❌	❌	✅	4
The Complexity of k-Means Clustering when Little is Known	❌	❌	❌	❌	❌	❌	❌	0
The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention	❌	✅	✅	✅	❌	❌	✅	4
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning	✅	✅	✅	✅	❌	❌	✅	5
The Geometry of Robust Value Functions	❌	❌	❌	❌	❌	❌	❌	0
The Importance of Non-Markovianity in Maximum State Entropy Exploration	❌	❌	✅	❌	❌	❌	✅	2
The Infinite Contextual Graph Markov Model	✅	✅	✅	✅	❌	❌	✅	5
The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks	✅	✅	✅	❌	❌	❌	✅	4
The Neural Race Reduction: Dynamics of Abstraction in Gated Networks	❌	✅	✅	✅	❌	❌	✅	4
The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation	✅	❌	❌	❌	❌	❌	✅	2
The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces	✅	❌	❌	❌	❌	❌	❌	1
The Primacy Bias in Deep Reinforcement Learning	❌	✅	✅	❌	❌	❌	✅	3
The Role of Deconfounding in Meta-learning	✅	❌	✅	✅	❌	❌	✅	4
The State of Sparse Training in Deep Reinforcement Learning	❌	✅	✅	❌	❌	❌	✅	3
The Teaching Dimension of Regularized Kernel Learners	❌	✅	✅	❌	❌	❌	✅	3
The Unsurprising Effectiveness of Pre-Trained Vision Models for Control	❌	✅	✅	❌	✅	❌	✅	4
The dynamics of representation learning in shallow, non-linear autoencoders	❌	✅	✅	❌	❌	❌	✅	3
The power of first-order smooth optimization for black-box non-smooth problems	❌	✅	✅	❌	✅	✅	✅	5
Thompson Sampling for (Combinatorial) Pure Exploration	✅	❌	❌	❌	❌	❌	✅	2
Thompson Sampling for Robust Transfer in Multi-Task Bandits	✅	✅	❌	❌	❌	❌	✅	3
Three-stage Evolution and Fast Equilibrium for SGD with Non-degerate Critical Points	❌	❌	✅	❌	❌	❌	✅	2
Thresholded Lasso Bandit	✅	✅	✅	❌	❌	❌	✅	4
Tight and Robust Private Mean Estimation with Few Users	✅	❌	❌	❌	❌	❌	❌	1
Time Is MattEr: Temporal Self-supervision for Video Transformers	❌	✅	✅	✅	❌	❌	✅	4
To Smooth or Not? When Label Smoothing Meets Noisy Labels	❌	✅	✅	❌	❌	❌	✅	3
Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning	❌	✅	✅	✅	✅	✅	✅	6
Topology-aware Generalization of Decentralized SGD	❌	✅	✅	✅	✅	❌	✅	5
Toward Compositional Generalization in Object-Oriented World Modeling	❌	✅	❌	❌	✅	❌	✅	3
Towards Coherent and Consistent Use of Entities in Narrative Generation	❌	❌	✅	✅	✅	❌	✅	4
Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods	✅	✅	✅	✅	❌	❌	✅	5
Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent	❌	✅	✅	❌	❌	❌	✅	3
Towards Scaling Difference Target Propagation by Learning Backprop Targets	✅	✅	✅	❌	❌	❌	✅	4
Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs	❌	✅	✅	❌	✅	✅	✅	5
Towards Understanding Sharpness-Aware Minimization	❌	✅	✅	❌	✅	❌	✅	4
Towards Uniformly Superhuman Autonomy via Subdominance Minimization	✅	❌	❌	❌	❌	❌	❌	1
Towards understanding how momentum improves generalization in deep learning	❌	❌	✅	❌	❌	❌	✅	2
Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems	❌	✅	✅	❌	❌	❌	✅	3
Tractable Uncertainty for Structure Learning	❌	✅	❌	❌	❌	❌	✅	2
Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four	❌	❌	✅	❌	❌	❌	✅	2
Training Discrete Deep Generative Models via Gapped Straight-Through Estimator	✅	✅	✅	✅	✅	❌	✅	6
Training OOD Detectors in their Natural Habitats	✅	✅	✅	✅	✅	❌	✅	6
Training Your Sparse Neural Network Better with Any Mask	❌	✅	✅	❌	❌	❌	✅	3
Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval	❌	❌	✅	✅	✅	❌	✅	4
Transfer Learning In Differential Privacy’s Hybrid-Model	✅	❌	❌	❌	❌	❌	✅	2
Transfer and Marginalize: Explaining Away Label Noise with Privileged Information	❌	❌	✅	✅	❌	❌	✅	3
Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling	❌	✅	✅	❌	✅	❌	✅	4
Transformer Quality in Linear Time	✅	❌	✅	❌	✅	❌	✅	4
Transformers are Meta-Reinforcement Learners	✅	✅	✅	❌	❌	❌	✅	4
Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots	✅	❌	✅	❌	❌	❌	✅	3
Translatotron 2: High-quality direct speech-to-speech translation with voice preservation	❌	✅	✅	✅	❌	❌	✅	4
UAST: Uncertainty-Aware Siamese Tracking	✅	❌	✅	✅	✅	✅	✅	6
UNIREX: A Unified Learning Framework for Language Model Rationale Extraction	❌	❌	✅	✅	❌	❌	✅	3
Unaligned Supervision for Automatic Music Transcription in The Wild	✅	✅	✅	✅	✅	❌	✅	6
Uncertainty Modeling in Generative Compressed Sensing	✅	✅	✅	❌	✅	❌	✅	5
UnderGrad: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees	✅	✅	❌	❌	❌	❌	✅	3
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy	✅	❌	✅	✅	❌	❌	✅	4
Understanding Contrastive Learning Requires Incorporating Inductive Biases	❌	❌	✅	✅	❌	❌	✅	3
Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information	✅	✅	✅	❌	❌	❌	✅	4
Understanding Doubly Stochastic Clustering	❌	❌	❌	❌	❌	❌	✅	1
Understanding Gradient Descent on the Edge of Stability in Deep Learning	✅	❌	✅	❌	❌	❌	✅	3
Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond	❌	✅	✅	✅	❌	❌	✅	4
Understanding Instance-Level Impact of Fairness Constraints	❌	✅	✅	❌	✅	❌	✅	4
Understanding Policy Gradient Algorithms: A Sensitivity-Based Approach	❌	❌	❌	❌	❌	❌	✅	1
Understanding Robust Generalization in Learning Regular Languages	❌	❌	❌	✅	❌	❌	✅	2
Understanding Robust Overfitting of Adversarial Training and Beyond	✅	✅	✅	❌	❌	❌	✅	4
Understanding The Robustness in Vision Transformers	❌	✅	✅	✅	❌	❌	✅	4
Understanding and Improving Knowledge Graph Embedding for Entity Alignment	✅	✅	✅	✅	✅	❌	❌	5
Understanding the unstable convergence of gradient descent	❌	❌	✅	❌	❌	❌	✅	2
UniRank: Unimodal Bandit Algorithms for Online Ranking	✅	❌	✅	❌	❌	❌	❌	2
Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces	❌	❌	✅	❌	❌	❌	✅	2
Unified Scaling Laws for Routed Language Models	❌	✅	❌	❌	✅	✅	✅	4
Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models	❌	✅	✅	❌	❌	❌	✅	3
Universal Joint Approximation of Manifolds and Densities by Simple Injective Flows	❌	❌	❌	❌	❌	❌	❌	0
Universal and data-adaptive algorithms for model selection in linear contextual bandits	✅	❌	❌	❌	❌	❌	❌	1
Universality of Winning Tickets: A Renormalization Group Perspective	❌	❌	✅	❌	❌	❌	✅	2
Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers	❌	❌	✅	❌	✅	❌	✅	3
Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology	❌	✅	✅	✅	✅	❌	✅	5
Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration	✅	✅	✅	✅	✅	❌	✅	6
Unsupervised Ground Metric Learning Using Wasserstein Singular Vectors	❌	✅	✅	❌	✅	❌	✅	4
Unsupervised Image Representation Learning with Deep Latent Particles	✅	✅	✅	✅	❌	❌	✅	5
Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion	❌	❌	✅	✅	✅	✅	✅	5
Utility Theory for Sequential Decision Making	❌	❌	❌	❌	❌	❌	❌	0
Utilizing Expert Features for Contrastive Learning of Time-Series Representations	❌	✅	✅	✅	❌	❌	✅	4
VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix	✅	✅	✅	❌	✅	❌	✅	5
VLUE: A Multi-Task Multi-Dimension Benchmark for Evaluating Vision-Language Pre-training	❌	✅	✅	✅	✅	❌	✅	5
Validating Causal Inference Methods	❌	❌	✅	❌	❌	❌	✅	2
Value Function based Difference-of-Convex Algorithm for Bilevel Hyperparameter Selection Problems	✅	✅	✅	✅	✅	❌	✅	6
VarScene: A Deep Generative Model for Realistic Scene Graph Synthesis	❌	✅	✅	✅	✅	❌	✅	5
VariGrow: Variational Architecture Growing for Task-Agnostic Continual Learning based on Bayesian Novelty	❌	❌	✅	✅	❌	❌	✅	3
Variational Feature Pyramid Networks	❌	❌	✅	✅	✅	❌	✅	4
Variational Inference for Infinitely Deep Neural Networks	✅	✅	✅	✅	❌	✅	✅	6
Variational Inference with Locally Enhanced Bounds for Hierarchical Models	❌	❌	✅	❌	❌	❌	✅	2
Variational Mixtures of ODEs for Inferring Cellular Gene Expression Dynamics	❌	✅	✅	❌	✅	❌	✅	4
Variational On-the-Fly Personalization	✅	❌	✅	✅	✅	❌	✅	5
Variational Sparse Coding with Learned Thresholding	✅	✅	✅	✅	✅	✅	✅	7
Variational Wasserstein gradient flow	✅	✅	✅	❌	✅	❌	✅	5
Variational nearest neighbor Gaussian process	❌	✅	✅	✅	✅	❌	✅	5
Versatile Dueling Bandits: Best-of-both World Analyses for Learning from Relative Preferences	✅	❌	✅	❌	❌	❌	✅	3
Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy Matching	✅	✅	✅	❌	❌	❌	✅	4
ViT-NeT: Interpretable Vision Transformers with Neural Tree Decoder	✅	✅	✅	❌	✅	❌	✅	5
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning	✅	✅	✅	❌	❌	❌	✅	4
Visual Attention Emerges from Recurrent Sparse Reconstruction	❌	❌	✅	✅	❌	❌	✅	3
Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes	❌	✅	✅	❌	✅	❌	✅	4
Weisfeiler-Lehman Meets Gromov-Wasserstein	✅	✅	✅	❌	❌	❌	✅	4
Welfare Maximization in Competitive Equilibrium: Reinforcement Learning for Markov Exchange Economy	✅	✅	❌	❌	❌	❌	✅	3
What Can Linear Interpolation of Neural Network Loss Landscapes Tell Us?	❌	❌	✅	❌	✅	❌	✅	3
What Dense Graph Do You Need for Self-Attention?	✅	✅	✅	❌	✅	❌	✅	5
What Language Model Architecture and Pretraining Objective Works Best for Zero-Shot Generalization?	❌	✅	✅	❌	✅	❌	✅	4
When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee	✅	✅	✅	✅	❌	❌	✅	5
When Are Linear Stochastic Bandits Attackable?	✅	❌	❌	❌	❌	❌	✅	2
When and How Mixup Improves Calibration	✅	❌	✅	❌	✅	❌	✅	4
Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error	❌	❌	❌	❌	❌	✅	✅	2
Why the Rich Get Richer? On the Balancedness of Random Partition Models	❌	✅	✅	❌	✅	❌	✅	4
Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling	❌	✅	✅	❌	❌	❌	✅	3
Wide Neural Networks Forget Less Catastrophically	❌	❌	✅	✅	❌	❌	✅	3
Winning the Lottery Ahead of Time: Efficient Early Network Pruning	✅	✅	✅	❌	✅	❌	✅	5
XAI for Transformers: Better Explanations through Conservative Propagation	❌	✅	✅	❌	❌	❌	✅	3
You Only Cut Once: Boosting Data Augmentation with a Single Cut	❌	✅	✅	❌	❌	❌	✅	3
YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for Everyone	❌	✅	✅	✅	✅	❌	✅	5
Zero-Shot Reward Specification via Grounded Natural Language	❌	❌	❌	❌	❌	❌	✅	1
Zero-shot AutoML with Pretrained Models	❌	✅	✅	✅	✅	❌	✅	5
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language	❌	✅	✅	✅	❌	❌	✅	4
pathGCN: Learning General Graph Spatial Operators from Paths	❌	❌	✅	✅	✅	❌	✅	4