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) - 2021

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	2021	1183	0.52	3.38	92.98%	45.09%

Search Papers

	Pseudocode	Open Source Code	Open Datasets	Dataset Splits	Hardware Specification	Software Dependencies	Experiment Setup
"Hey, that’s not an ODE": Faster ODE Adjoints via Seminorms	❌	✅	✅	❌	❌	❌	✅	3
1-bit Adam: Communication Efficient Large-Scale Training with Adam’s Convergence Speed	✅	✅	✅	✅	✅	❌	✅	6
12-Lead ECG Reconstruction via Koopman Operators	❌	✅	✅	❌	❌	❌	✅	3
A Bit More Bayesian: Domain-Invariant Learning with Uncertainty	❌	✅	✅	✅	❌	❌	✅	4
A Collective Learning Framework to Boost GNN Expressiveness for Node Classification	❌	❌	✅	✅	❌	❌	✅	3
A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation	✅	✅	❌	❌	❌	❌	✅	3
A Differentiable Point Process with Its Application to Spiking Neural Networks	✅	✅	❌	✅	✅	❌	✅	5
A Discriminative Technique for Multiple-Source Adaptation	❌	❌	✅	✅	❌	❌	✅	3
A Distribution-dependent Analysis of Meta Learning	✅	✅	✅	❌	❌	❌	✅	4
A Framework for Private Matrix Analysis in Sliding Window Model	✅	❌	❌	❌	❌	❌	❌	1
A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration	✅	✅	✅	❌	✅	❌	✅	5
A Functional Perspective on Learning Symmetric Functions with Neural Networks	❌	❌	✅	❌	❌	❌	✅	2
A General Framework For Detecting Anomalous Inputs to DNN Classifiers	✅	✅	✅	✅	❌	❌	✅	5
A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization	✅	✅	✅	❌	❌	❌	✅	4
A Hybrid Variance-Reduced Method for Decentralized Stochastic Non-Convex Optimization	✅	❌	✅	❌	❌	❌	❌	2
A Language for Counterfactual Generative Models	❌	✅	✅	❌	❌	❌	❌	2
A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning	❌	❌	❌	❌	❌	❌	❌	0
A Modular Analysis of Provable Acceleration via Polyak’s Momentum: Training a Wide ReLU Network and a Deep Linear Network	✅	❌	❌	❌	❌	❌	❌	1
A New Formalism, Method and Open Issues for Zero-Shot Coordination	❌	✅	❌	❌	❌	❌	✅	2
A New Representation of Successor Features for Transfer across Dissimilar Environments	✅	❌	❌	✅	❌	❌	✅	3
A Novel Method to Solve Neural Knapsack Problems	✅	❌	✅	❌	✅	✅	✅	5
A Novel Sequential Coreset Method for Gradient Descent Algorithms	✅	❌	✅	❌	✅	❌	❌	3
A Nullspace Property for Subspace-Preserving Recovery	❌	❌	❌	❌	❌	❌	❌	0
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups	✅	✅	❌	❌	❌	❌	❌	2
A Precise Performance Analysis of Support Vector Regression	❌	❌	❌	❌	❌	❌	✅	1
A Probabilistic Approach to Neural Network Pruning	❌	❌	✅	❌	❌	❌	❌	1
A Proxy Variable View of Shared Confounding	❌	❌	❌	❌	❌	❌	✅	1
A Receptor Skeleton for Capsule Neural Networks	✅	❌	✅	❌	✅	❌	✅	4
A Regret Minimization Approach to Iterative Learning Control	✅	✅	❌	❌	❌	❌	❌	2
A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-Learning	✅	✅	✅	✅	❌	❌	✅	5
A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distance	✅	✅	❌	❌	✅	❌	✅	4
A Sampling-Based Method for Tensor Ring Decomposition	✅	✅	✅	✅	✅	✅	✅	7
A Scalable Deterministic Global Optimization Algorithm for Clustering Problems	✅	✅	✅	❌	✅	✅	✅	6
A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few Samples	✅	✅	❌	❌	❌	❌	✅	3
A Second look at Exponential and Cosine Step Sizes: Simplicity, Adaptivity, and Performance	❌	✅	✅	✅	❌	❌	✅	4
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play	✅	❌	❌	❌	❌	❌	❌	1
A Structured Observation Distribution for Generative Biological Sequence Prediction and Forecasting	❌	✅	✅	❌	❌	❌	❌	2
A Tale of Two Efficient and Informative Negative Sampling Distributions	✅	✅	✅	❌	✅	❌	✅	5
A Theory of Label Propagation for Subpopulation Shift	❌	❌	✅	❌	❌	❌	❌	1
A Unified Generative Adversarial Network Training via Self-Labeling and Self-Attention	✅	❌	✅	❌	✅	❌	✅	4
A Unified Lottery Ticket Hypothesis for Graph Neural Networks	✅	✅	✅	✅	❌	❌	✅	5
A Value-Function-based Interior-point Method for Non-convex Bi-level Optimization	✅	❌	✅	✅	❌	❌	❌	3
A Wasserstein Minimax Framework for Mixed Linear Regression	✅	✅	❌	❌	❌	❌	✅	3
A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimization	✅	✅	✅	❌	✅	✅	❌	5
A large-scale benchmark for few-shot program induction and synthesis	❌	❌	✅	✅	❌	❌	✅	3
A statistical perspective on distillation	❌	❌	✅	❌	❌	❌	✅	2
A theory of high dimensional regression with arbitrary correlations between input features and target functions: sample complexity, multiple descent curves and a hierarchy of phase transitions	❌	❌	❌	❌	❌	❌	✅	1
ACE: Explaining cluster from an adversarial perspective	❌	❌	✅	✅	❌	❌	✅	3
ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks	✅	❌	❌	❌	❌	❌	✅	2
AGENT: A Benchmark for Core Psychological Reasoning	❌	❌	✅	✅	❌	❌	❌	2
APS: Active Pretraining with Successor Features	✅	❌	✅	❌	❌	❌	✅	3
ARMS: Antithetic-REINFORCE-Multi-Sample Gradient for Binary Variables	✅	✅	✅	❌	✅	❌	✅	5
ASAM: Adaptive Sharpness-Aware Minimization for Scale-Invariant Learning of Deep Neural Networks	✅	❌	✅	✅	❌	❌	✅	4
Accelerate CNNs from Three Dimensions: A Comprehensive Pruning Framework	✅	❌	✅	❌	❌	❌	✅	3
Accelerated Algorithms for Smooth Convex-Concave Minimax Problems with O(1/k^2) Rate on Squared Gradient Norm	✅	❌	❌	❌	❌	❌	✅	2
Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving	✅	❌	✅	❌	✅	❌	✅	4
Accelerating Gossip SGD with Periodic Global Averaging	✅	❌	✅	✅	❌	❌	✅	4
Accelerating Safe Reinforcement Learning with Constraint-mismatched Baseline Policies	✅	✅	❌	❌	❌	❌	✅	3
Acceleration via Fractal Learning Rate Schedules	❌	✅	✅	❌	❌	❌	✅	3
Accumulated Decoupled Learning with Gradient Staleness Mitigation for Convolutional Neural Networks	✅	❌	✅	✅	✅	❌	✅	5
Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization	❌	❌	✅	❌	❌	❌	❌	1
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting	✅	✅	✅	❌	❌	❌	✅	4
Accurate Post Training Quantization With Small Calibration Sets	❌	✅	✅	✅	✅	❌	✅	5
Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously	✅	❌	❌	❌	❌	❌	❌	1
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training	✅	✅	✅	❌	✅	✅	✅	6
Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills	✅	❌	❌	❌	✅	❌	✅	3
Active Covering	✅	❌	✅	✅	✅	❌	✅	5
Active Deep Probabilistic Subsampling	✅	✅	✅	✅	❌	❌	✅	5
Active Feature Acquisition with Generative Surrogate Models	✅	✅	✅	✅	❌	✅	❌	5
Active Learning for Distributionally Robust Level-Set Estimation	✅	❌	❌	❌	❌	❌	✅	2
Active Learning of Continuous-time Bayesian Networks through Interventions	✅	❌	✅	❌	❌	❌	✅	3
Active Slices for Sliced Stein Discrepancy	✅	❌	✅	✅	❌	❌	✅	4
Active Testing: Sample-Efficient Model Evaluation	✅	✅	✅	❌	❌	❌	❌	3
AdaXpert: Adapting Neural Architecture for Growing Data	✅	✅	✅	✅	❌	❌	✅	5
Adapting to Delays and Data in Adversarial Multi-Armed Bandits	✅	❌	❌	❌	❌	❌	❌	1
Adapting to misspecification in contextual bandits with offline regression oracles	✅	❌	❌	❌	❌	❌	✅	2
Adaptive Newton Sketch: Linear-time Optimization with Quadratic Convergence and Effective Hessian Dimensionality	✅	❌	✅	✅	✅	❌	✅	5
Adaptive Sampling for Best Policy Identification in Markov Decision Processes	✅	❌	❌	❌	❌	❌	✅	2
Additive Error Guarantees for Weighted Low Rank Approximation	✅	✅	❌	❌	❌	❌	✅	3
Addressing Catastrophic Forgetting in Few-Shot Problems	✅	✅	✅	✅	❌	❌	✅	5
Adversarial Combinatorial Bandits with General Non-linear Reward Functions	❌	❌	❌	❌	❌	❌	❌	0
Adversarial Dueling Bandits	✅	❌	❌	❌	❌	❌	✅	2
Adversarial Multi Class Learning under Weak Supervision with Performance Guarantees	✅	✅	✅	❌	❌	❌	❌	3
Adversarial Option-Aware Hierarchical Imitation Learning	✅	❌	✅	❌	❌	❌	✅	3
Adversarial Policy Learning in Two-player Competitive Games	❌	✅	✅	❌	❌	❌	✅	3
Adversarial Purification with Score-based Generative Models	✅	❌	✅	❌	❌	❌	✅	3
Adversarial Robustness Guarantees for Random Deep Neural Networks	❌	✅	✅	❌	❌	❌	✅	3
Affine Invariant Analysis of Frank-Wolfe on Strongly Convex Sets	✅	❌	✅	❌	❌	❌	✅	3
Aggregating From Multiple Target-Shifted Sources	✅	❌	✅	✅	❌	❌	✅	4
Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins	❌	❌	❌	❌	❌	❌	❌	0
Align, then memorise: the dynamics of learning with feedback alignment	❌	✅	✅	❌	❌	❌	✅	3
Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits	✅	❌	❌	❌	❌	❌	✅	2
AlphaNet: Improved Training of Supernets with Alpha-Divergence	✅	✅	✅	✅	❌	❌	✅	5
Alternative Microfoundations for Strategic Classification	❌	❌	❌	❌	❌	❌	✅	1
Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation	✅	❌	✅	❌	✅	❌	❌	3
An Algorithm for Stochastic and Adversarial Bandits with Switching Costs	✅	❌	❌	❌	❌	❌	❌	1
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming	✅	✅	✅	❌	✅	❌	✅	5
An Identifiable Double VAE For Disentangled Representations	❌	❌	✅	❌	❌	❌	✅	2
An Information-Geometric Distance on the Space of Tasks	❌	❌	✅	❌	❌	❌	❌	1
An Integer Linear Programming Framework for Mining Constraints from Data	❌	✅	✅	✅	❌	✅	✅	5
An exact solver for the Weston-Watkins SVM subproblem	✅	✅	✅	❌	❌	❌	✅	4
Analysis of stochastic Lanczos quadrature for spectrum approximation	✅	❌	✅	❌	❌	❌	✅	3
Analyzing the tree-layer structure of Deep Forests	❌	✅	✅	✅	❌	❌	✅	4
Annealed Flow Transport Monte Carlo	✅	❌	✅	❌	❌	❌	✅	3
Approximate Group Fairness for Clustering	✅	❌	✅	❌	❌	❌	✅	3
Approximating a Distribution Using Weight Queries	✅	✅	✅	❌	❌	❌	✅	4
Approximation Theory Based Methods for RKHS Bandits	✅	❌	❌	❌	✅	❌	✅	3
Approximation Theory of Convolutional Architectures for Time Series Modelling	❌	❌	❌	❌	❌	❌	❌	0
Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections	❌	✅	✅	✅	❌	❌	✅	4
Asymmetric Loss Functions for Learning with Noisy Labels	❌	✅	✅	❌	❌	❌	❌	2
Asymptotic Normality and Confidence Intervals for Prediction Risk of the Min-Norm Least Squares Estimator	❌	❌	❌	❌	❌	❌	✅	1
Asymptotics of Ridge Regression in Convolutional Models	❌	❌	❌	❌	❌	❌	✅	1
Asynchronous Decentralized Optimization With Implicit Stochastic Variance Reduction	✅	✅	✅	❌	✅	✅	✅	6
Asynchronous Distributed Learning : Adapting to Gradient Delays without Prior Knowledge	✅	❌	✅	❌	❌	❌	❌	2
Attention is not all you need: pure attention loses rank doubly exponentially with depth	❌	✅	✅	❌	❌	❌	✅	3
Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment	✅	❌	✅	❌	❌	❌	✅	3
Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators	❌	✅	✅	✅	✅	❌	✅	5
AutoAttend: Automated Attention Representation Search	❌	✅	✅	✅	❌	❌	✅	4
AutoSampling: Search for Effective Data Sampling Schedules	✅	❌	✅	❌	✅	❌	✅	4
Autoencoder Image Interpolation by Shaping the Latent Space	❌	❌	✅	❌	✅	❌	✅	3
Autoencoding Under Normalization Constraints	✅	✅	✅	❌	❌	❌	✅	4
Automatic variational inference with cascading flows	✅	❌	❌	❌	❌	❌	✅	2
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting	✅	✅	✅	✅	✅	❌	✅	6
Average-Reward Off-Policy Policy Evaluation with Function Approximation	❌	✅	✅	❌	❌	❌	✅	3
BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining	✅	✅	✅	✅	✅	✅	✅	7
BASE Layers: Simplifying Training of Large, Sparse Models	✅	✅	✅	❌	✅	❌	✅	5
BASGD: Buffered Asynchronous SGD for Byzantine Learning	✅	❌	✅	❌	✅	✅	✅	5
BORE: Bayesian Optimization by Density-Ratio Estimation	✅	✅	✅	✅	❌	❌	✅	5
Backdoor Scanning for Deep Neural Networks through K-Arm Optimization	❌	✅	✅	❌	✅	❌	✅	4
Backpropagated Neighborhood Aggregation for Accurate Training of Spiking Neural Networks	✅	❌	✅	❌	✅	❌	✅	4
Barlow Twins: Self-Supervised Learning via Redundancy Reduction	✅	✅	✅	✅	✅	❌	✅	6
BasisDeVAE: Interpretable Simultaneous Dimensionality Reduction and Feature-Level Clustering with Derivative-Based Variational Autoencoders	❌	✅	✅	❌	✅	❌	✅	4
Batch Value-function Approximation with Only Realizability	✅	❌	❌	❌	❌	❌	❌	1
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information	✅	❌	✅	❌	❌	❌	❌	2
Bayesian Attention Belief Networks	❌	❌	✅	✅	❌	❌	✅	3
Bayesian Deep Learning via Subnetwork Inference	❌	❌	✅	✅	❌	❌	✅	3
Bayesian Optimistic Optimisation with Exponentially Decaying Regret	✅	❌	✅	✅	❌	❌	✅	4
Bayesian Optimization over Hybrid Spaces	✅	✅	✅	❌	✅	❌	✅	5
Bayesian Quadrature on Riemannian Data Manifolds	❌	✅	✅	❌	❌	❌	✅	3
Bayesian Structural Adaptation for Continual Learning	❌	✅	✅	❌	❌	❌	❌	2
Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement	✅	❌	✅	❌	❌	❌	✅	3
Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks	❌	❌	❌	❌	❌	❌	❌	0
Best Arm Identification in Graphical Bilinear Bandits	✅	❌	❌	❌	❌	❌	✅	2
Best Model Identification: A Rested Bandit Formulation	✅	❌	❌	❌	❌	❌	❌	1
Better Training using Weight-Constrained Stochastic Dynamics	✅	✅	✅	❌	❌	❌	✅	4
Beyond $log^2(T)$ regret for decentralized bandits in matching markets	✅	❌	❌	❌	❌	❌	✅	2
Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization	❌	❌	❌	❌	❌	❌	✅	1
Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design	❌	❌	❌	❌	❌	❌	✅	1
Bias-Free Scalable Gaussian Processes via Randomized Truncations	❌	✅	✅	✅	❌	❌	✅	4
Bias-Robust Bayesian Optimization via Dueling Bandits	✅	❌	❌	❌	❌	❌	✅	2
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning	✅	❌	✅	❌	❌	❌	✅	3
Bilevel Optimization: Convergence Analysis and Enhanced Design	✅	✅	✅	❌	❌	❌	✅	4
Bilinear Classes: A Structural Framework for Provable Generalization in RL	✅	❌	❌	❌	❌	❌	❌	1
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification	✅	✅	✅	❌	❌	❌	✅	4
Black-box density function estimation using recursive partitioning	✅	✅	✅	❌	❌	❌	✅	4
Blind Pareto Fairness and Subgroup Robustness	✅	✅	✅	✅	❌	❌	✅	5
Boosting for Online Convex Optimization	✅	✅	✅	❌	❌	❌	✅	4
Boosting the Throughput and Accelerator Utilization of Specialized CNN Inference Beyond Increasing Batch Size	❌	✅	✅	❌	✅	✅	✅	5
Bootstrapping Fitted Q-Evaluation for Off-Policy Inference	✅	❌	✅	❌	❌	❌	✅	3
Break-It-Fix-It: Unsupervised Learning for Program Repair	❌	✅	✅	✅	✅	❌	✅	5
Breaking the Deadly Triad with a Target Network	✅	✅	✅	❌	❌	❌	✅	4
Breaking the Limits of Message Passing Graph Neural Networks	✅	✅	✅	✅	❌	❌	✅	5
Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation	❌	✅	✅	✅	✅	❌	✅	5
Budgeted Heterogeneous Treatment Effect Estimation	✅	❌	✅	✅	❌	❌	✅	4
Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data	✅	❌	✅	❌	❌	❌	✅	3
CARTL: Cooperative Adversarially-Robust Transfer Learning	❌	✅	✅	❌	❌	❌	✅	3
CATE: Computation-aware Neural Architecture Encoding with Transformers	✅	✅	✅	✅	✅	❌	✅	6
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection	❌	❌	✅	❌	❌	❌	✅	2
CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients	❌	✅	✅	✅	❌	❌	✅	4
CRFL: Certifiably Robust Federated Learning against Backdoor Attacks	✅	✅	✅	❌	❌	❌	✅	4
CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee	✅	❌	✅	❌	❌	❌	✅	3
CURI: A Benchmark for Productive Concept Learning Under Uncertainty	❌	❌	❌	✅	❌	❌	❌	1
Calibrate Before Use: Improving Few-shot Performance of Language Models	❌	✅	✅	❌	❌	❌	✅	3
Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization?	✅	❌	✅	❌	❌	❌	❌	2
Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization	❌	❌	✅	✅	❌	❌	✅	3
Catformer: Designing Stable Transformers via Sensitivity Analysis	❌	❌	✅	❌	❌	❌	✅	2
Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning	✅	❌	❌	❌	❌	❌	❌	1
Causality-aware counterfactual confounding adjustment as an alternative to linear residualization in anticausal prediction tasks based on linear learners	❌	❌	✅	✅	❌	✅	✅	4
ChaCha for Online AutoML	✅	✅	✅	❌	❌	❌	✅	4
Characterizing Fairness Over the Set of Good Models Under Selective Labels	✅	❌	✅	✅	❌	❌	✅	4
Characterizing Structural Regularities of Labeled Data in Overparameterized Models	✅	✅	✅	✅	❌	❌	✅	5
Characterizing the Gap Between Actor-Critic and Policy Gradient	✅	❌	✅	❌	❌	❌	✅	3
Chebyshev Polynomial Codes: Task Entanglement-based Coding for Distributed Matrix Multiplication	✅	❌	❌	❌	✅	❌	✅	3
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels	✅	❌	✅	✅	❌	❌	❌	3
Classification with Rejection Based on Cost-sensitive Classification	❌	❌	✅	❌	✅	❌	✅	3
Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed	❌	✅	✅	❌	❌	❌	✅	3
Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels	✅	✅	✅	❌	❌	❌	✅	4
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning	✅	✅	✅	✅	❌	❌	✅	5
Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition	❌	❌	✅	❌	❌	❌	✅	2
Coded-InvNet for Resilient Prediction Serving Systems	❌	❌	✅	❌	✅	❌	✅	3
Collaborative Bayesian Optimization with Fair Regret	❌	❌	✅	❌	❌	❌	✅	2
CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints	✅	❌	✅	✅	❌	❌	❌	3
Combinatorial Blocking Bandits with Stochastic Delays	✅	❌	❌	❌	❌	❌	✅	2
Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning	❌	✅	❌	❌	❌	❌	✅	2
Communication-Efficient Distributed Optimization with Quantized Preconditioners	✅	❌	✅	❌	❌	❌	✅	3
Communication-Efficient Distributed SVD via Local Power Iterations	✅	❌	✅	❌	❌	❌	✅	3
Commutative Lie Group VAE for Disentanglement Learning	❌	✅	✅	❌	❌	❌	❌	2
Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved Generalization	❌	✅	✅	✅	❌	❌	✅	4
Composing Normalizing Flows for Inverse Problems	❌	❌	✅	❌	✅	❌	✅	3
Compositional Video Synthesis with Action Graphs	❌	✅	✅	✅	✅	❌	✅	5
Compressed Maximum Likelihood	❌	❌	❌	❌	❌	❌	❌	0
ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases	❌	✅	✅	✅	✅	❌	✅	5
Concentric mixtures of Mallows models for top-$k$ rankings: sampling and identifiability	✅	✅	✅	❌	❌	❌	✅	4
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression	✅	❌	✅	❌	❌	❌	❌	2
Conditional Temporal Neural Processes with Covariance Loss	❌	❌	✅	✅	✅	❌	❌	3
Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech	✅	✅	✅	✅	✅	❌	✅	6
Confidence Scores Make Instance-dependent Label-noise Learning Possible	✅	✅	✅	❌	✅	❌	✅	5
Confidence-Budget Matching for Sequential Budgeted Learning	✅	❌	❌	❌	❌	❌	❌	1
Conformal prediction interval for dynamic time-series	✅	❌	❌	✅	❌	❌	✅	3
Conjugate Energy-Based Models	❌	❌	✅	❌	❌	❌	✅	2
Connecting Interpretability and Robustness in Decision Trees through Separation	✅	✅	✅	✅	❌	❌	✅	5
Connecting Optimal Ex-Ante Collusion in Teams to Extensive-Form Correlation: Faster Algorithms and Positive Complexity Results	❌	❌	✅	❌	✅	✅	✅	4
Connecting Sphere Manifolds Hierarchically for Regularization	❌	❌	✅	❌	✅	❌	✅	3
Consensus Control for Decentralized Deep Learning	❌	❌	✅	❌	✅	❌	✅	3
Conservative Objective Models for Effective Offline Model-Based Optimization	✅	✅	✅	❌	❌	❌	✅	4
Consistent Nonparametric Methods for Network Assisted Covariate Estimation	✅	❌	✅	❌	✅	✅	✅	5
Consistent regression when oblivious outliers overwhelm	✅	❌	❌	❌	❌	❌	❌	1
Context-Aware Online Collective Inference for Templated Graphical Models	❌	✅	✅	❌	❌	❌	✅	3
Continual Learning in the Teacher-Student Setup: Impact of Task Similarity	❌	✅	❌	❌	❌	❌	✅	2
Continuous Coordination As a Realistic Scenario for Lifelong Learning	❌	✅	✅	✅	❌	❌	✅	4
Continuous-time Model-based Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Contrastive Learning Inverts the Data Generating Process	❌	✅	✅	❌	❌	❌	✅	3
Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks	❌	❌	✅	❌	❌	❌	❌	1
Convex Regularization in Monte-Carlo Tree Search	❌	❌	✅	❌	❌	❌	✅	2
ConvexVST: A Convex Optimization Approach to Variance-stabilizing Transformation	❌	✅	✅	❌	❌	❌	❌	2
Cooperative Exploration for Multi-Agent Deep Reinforcement Learning	✅	❌	✅	❌	❌	❌	❌	2
Correcting Exposure Bias for Link Recommendation	❌	✅	✅	✅	❌	❌	✅	4
Correlation Clustering in Constant Many Parallel Rounds	✅	❌	✅	❌	❌	❌	✅	3
CountSketches, Feature Hashing and the Median of Three	❌	❌	✅	❌	❌	❌	✅	2
Counterfactual Credit Assignment in Model-Free Reinforcement Learning	❌	❌	❌	❌	❌	❌	✅	1
Cross-Gradient Aggregation for Decentralized Learning from Non-IID Data	✅	✅	✅	❌	✅	❌	✅	5
Cross-domain Imitation from Observations	✅	✅	❌	❌	❌	❌	❌	2
Cross-model Back-translated Distillation for Unsupervised Machine Translation	✅	✅	✅	✅	✅	❌	✅	6
Crowdsourcing via Annotator Co-occurrence Imputation and Provable Symmetric Nonnegative Matrix Factorization	❌	❌	✅	❌	❌	❌	✅	2
Crystallization Learning with the Delaunay Triangulation	✅	❌	✅	✅	❌	❌	✅	4
Cumulants of Hawkes Processes are Robust to Observation Noise	❌	✅	✅	❌	❌	❌	✅	3
Cyclically Equivariant Neural Decoders for Cyclic Codes	❌	✅	❌	❌	❌	❌	✅	2
DAGs with No Curl: An Efficient DAG Structure Learning Approach	✅	✅	✅	❌	❌	❌	✅	4
DANCE: Enhancing saliency maps using decoys	❌	✅	✅	❌	❌	❌	❌	2
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning	❌	✅	✅	❌	❌	❌	❌	2
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs	✅	❌	✅	❌	❌	❌	✅	3
DORO: Distributional and Outlier Robust Optimization	✅	✅	✅	✅	❌	❌	✅	5
Dash: Semi-Supervised Learning with Dynamic Thresholding	✅	❌	✅	❌	❌	❌	✅	3
Data Augmentation for Meta-Learning	✅	✅	✅	✅	❌	❌	❌	4
Data augmentation for deep learning based accelerated MRI reconstruction with limited data	❌	✅	✅	✅	❌	❌	✅	4
Data-Free Knowledge Distillation for Heterogeneous Federated Learning	✅	✅	✅	❌	❌	❌	✅	4
Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps	✅	❌	❌	❌	❌	❌	❌	1
Data-efficient Hindsight Off-policy Option Learning	✅	❌	✅	❌	❌	❌	❌	2
Dataset Condensation with Differentiable Siamese Augmentation	✅	✅	✅	❌	❌	❌	✅	4
Dataset Dynamics via Gradient Flows in Probability Space	❌	❌	✅	✅	❌	❌	✅	3
Debiasing Model Updates for Improving Personalized Federated Training	✅	❌	✅	✅	❌	❌	✅	4
Debiasing a First-order Heuristic for Approximate Bi-level Optimization	✅	❌	✅	✅	❌	❌	✅	4
Decentralized Riemannian Gradient Descent on the Stiefel Manifold	✅	✅	✅	❌	✅	❌	✅	5
Decentralized Single-Timescale Actor-Critic on Zero-Sum Two-Player Stochastic Games	✅	❌	❌	❌	❌	❌	❌	1
Deciding What to Learn: A Rate-Distortion Approach	✅	❌	❌	❌	❌	❌	✅	2
Decision-Making Under Selective Labels: Optimal Finite-Domain Policies and Beyond	❌	❌	✅	❌	✅	❌	✅	3
Decomposable Submodular Function Minimization via Maximum Flow	✅	❌	❌	❌	❌	❌	❌	1
Decomposed Mutual Information Estimation for Contrastive Representation Learning	✅	❌	✅	❌	❌	❌	✅	3
Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices	✅	✅	✅	✅	❌	❌	✅	5
Decoupling Representation Learning from Reinforcement Learning	❌	✅	✅	❌	❌	❌	✅	3
Decoupling Value and Policy for Generalization in Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design	✅	✅	❌	❌	✅	❌	✅	4
Deep Coherent Exploration for Continuous Control	❌	✅	✅	❌	❌	❌	✅	3
Deep Continuous Networks	❌	✅	✅	✅	❌	❌	✅	4
Deep Generative Learning via Schrödinger Bridge	✅	✅	✅	❌	❌	❌	✅	4
Deep Latent Graph Matching	✅	❌	✅	✅	❌	❌	✅	4
Deep Learning for Functional Data Analysis with Adaptive Basis Layers	✅	✅	✅	✅	❌	❌	✅	5
Deep Reinforcement Learning amidst Continual Structured Non-Stationarity	✅	❌	✅	❌	❌	❌	✅	3
Deep kernel processes	✅	✅	✅	✅	❌	❌	✅	5
DeepReDuce: ReLU Reduction for Fast Private Inference	❌	❌	✅	❌	❌	❌	✅	2
DeepWalking Backwards: From Embeddings Back to Graphs	✅	✅	✅	❌	❌	❌	✅	4
Deeply-Debiased Off-Policy Interval Estimation	❌	✅	✅	✅	❌	❌	✅	4
Delving into Deep Imbalanced Regression	❌	✅	✅	✅	❌	❌	❌	3
Demonstration-Conditioned Reinforcement Learning for Few-Shot Imitation	✅	❌	✅	❌	✅	❌	❌	3
Demystifying Inductive Biases for (Beta-)VAE Based Architectures	❌	❌	✅	❌	❌	❌	❌	1
Dense for the Price of Sparse: Improved Performance of Sparsely Initialized Networks via a Subspace Offset	❌	✅	✅	❌	❌	❌	❌	2
Density Constrained Reinforcement Learning	✅	❌	✅	❌	❌	❌	❌	2
Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers	❌	✅	✅	✅	✅	✅	✅	6
Detecting Rewards Deterioration in Episodic Reinforcement Learning	✅	✅	✅	❌	✅	❌	✅	5
Detection of Signal in the Spiked Rectangular Models	✅	❌	✅	❌	❌	❌	❌	2
Dichotomous Optimistic Search to Quantify Human Perception	✅	❌	❌	❌	❌	❌	❌	1
Differentiable Dynamic Quantization with Mixed Precision and Adaptive Resolution	✅	❌	✅	✅	❌	❌	✅	4
Differentiable Particle Filtering via Entropy-Regularized Optimal Transport	✅	✅	✅	❌	❌	❌	✅	4
Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision	❌	✅	✅	❌	✅	❌	✅	4
Differentiable Spatial Planning using Transformers	❌	❌	✅	✅	❌	❌	✅	3
Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message	✅	❌	❌	❌	❌	❌	✅	2
Differentially Private Bayesian Inference for Generalized Linear Models	❌	❌	✅	❌	❌	❌	✅	2
Differentially Private Correlation Clustering	✅	❌	❌	❌	❌	❌	❌	1
Differentially Private Densest Subgraph Detection	✅	❌	✅	❌	❌	❌	❌	2
Differentially Private Quantiles	✅	✅	✅	❌	✅	❌	✅	5
Differentially Private Query Release Through Adaptive Projection	✅	✅	✅	❌	✅	✅	✅	6
Differentially Private Sliced Wasserstein Distance	✅	✅	✅	❌	❌	❌	✅	4
Differentially-Private Clustering of Easy Instances	✅	❌	❌	❌	✅	❌	✅	3
Diffusion Earth Mover’s Distance and Distribution Embeddings	✅	✅	✅	❌	❌	❌	❌	3
Diffusion Source Identification on Networks with Statistical Confidence	✅	✅	✅	❌	❌	❌	✅	4
Dimensionality Reduction for the Sum-of-Distances Metric	✅	✅	✅	❌	❌	❌	❌	3
Directed Graph Embeddings in Pseudo-Riemannian Manifolds	❌	❌	✅	✅	❌	❌	✅	3
Directional Bias Amplification	❌	✅	✅	✅	❌	❌	❌	3
Directional Graph Networks	❌	✅	✅	✅	❌	❌	✅	4
Disambiguation of Weak Supervision leading to Exponential Convergence rates	❌	✅	✅	❌	❌	✅	❌	3
Discovering symbolic policies with deep reinforcement learning	✅	✅	✅	❌	❌	❌	✅	4
Discrete-Valued Latent Preference Matrix Estimation with Graph Side Information	✅	❌	✅	✅	❌	❌	✅	4
Discretization Drift in Two-Player Games	❌	✅	✅	❌	❌	❌	✅	3
Discriminative Complementary-Label Learning with Weighted Loss	✅	✅	✅	✅	❌	❌	✅	5
Disentangling Sampling and Labeling Bias for Learning in Large-output Spaces	❌	❌	✅	❌	❌	❌	✅	2
Disentangling syntax and semantics in the brain with deep networks	❌	❌	✅	✅	❌	❌	✅	3
Dissecting Supervised Contrastive Learning	❌	✅	✅	❌	❌	❌	✅	3
Distributed Nyström Kernel Learning with Communications	✅	❌	❌	❌	❌	❌	✅	2
Distributed Second Order Methods with Fast Rates and Compressed Communication	✅	❌	✅	❌	❌	❌	✅	3
Distribution-Free Calibration Guarantees for Histogram Binning without Sample Splitting	✅	✅	✅	✅	❌	❌	✅	5
Distributionally Robust Optimization with Markovian Data	✅	✅	✅	❌	✅	❌	✅	5
Ditto: Fair and Robust Federated Learning Through Personalization	✅	✅	✅	✅	❌	❌	✅	5
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration	✅	✅	✅	❌	❌	❌	✅	4
Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training	❌	✅	✅	❌	❌	❌	✅	3
Domain Generalization using Causal Matching	✅	✅	✅	✅	❌	❌	✅	5
Don’t Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification	❌	❌	✅	❌	❌	❌	✅	2
DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning	✅	✅	❌	❌	✅	❌	✅	4
Double-Win Quant: Aggressively Winning Robustness of Quantized Deep Neural Networks via Random Precision Training and Inference	✅	✅	✅	✅	❌	❌	✅	5
Doubly Robust Off-Policy Actor-Critic: Convergence and Optimality	✅	❌	✅	❌	❌	❌	✅	3
DriftSurf: Stable-State / Reactive-State Learning under Concept Drift	✅	❌	✅	❌	❌	❌	❌	2
Dropout: Explicit Forms and Capacity Control	❌	❌	✅	❌	❌	❌	✅	2
Dual Principal Component Pursuit for Robust Subspace Learning: Theory and Algorithms for a Holistic Approach	✅	❌	❌	❌	✅	❌	✅	3
Dueling Convex Optimization	✅	❌	❌	❌	❌	❌	❌	1
Dynamic Balancing for Model Selection in Bandits and RL	✅	❌	❌	❌	❌	❌	✅	2
Dynamic Game Theoretic Neural Optimizer	✅	❌	✅	❌	❌	❌	✅	3
Dynamic Planning and Learning under Recovering Rewards	✅	❌	❌	❌	❌	❌	❌	1
E(n) Equivariant Graph Neural Networks	❌	❌	✅	✅	✅	❌	✅	4
EL-Attention: Memory Efficient Lossless Attention for Generation	✅	✅	✅	✅	✅	✅	✅	7
EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL	✅	❌	✅	✅	✅	❌	✅	5
Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games	✅	✅	✅	❌	✅	❌	❌	4
Efficient Differentiable Simulation of Articulated Bodies	❌	✅	✅	❌	✅	❌	✅	4
Efficient Generative Modelling of Protein Structure Fragments using a Deep Markov Model	❌	❌	✅	✅	✅	✅	✅	5
Efficient Iterative Amortized Inference for Learning Symmetric and Disentangled Multi-Object Representations	✅	❌	✅	❌	✅	❌	✅	4
Efficient Lottery Ticket Finding: Less Data is More	✅	✅	✅	❌	❌	❌	✅	4
Efficient Message Passing for 0–1 ILPs with Binary Decision Diagrams	✅	✅	✅	❌	❌	❌	✅	4
Efficient Online Learning for Dynamic k-Clustering	✅	❌	❌	❌	❌	❌	✅	2
Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations	✅	❌	❌	❌	❌	❌	✅	2
Efficient Statistical Tests: A Neural Tangent Kernel Approach	❌	✅	✅	❌	❌	❌	✅	3
Efficient Training of Robust Decision Trees Against Adversarial Examples	✅	✅	✅	✅	✅	❌	✅	6
EfficientNetV2: Smaller Models and Faster Training	✅	✅	✅	✅	✅	❌	✅	6
EfficientTTS: An Efficient and High-Quality Text-to-Speech Architecture	✅	❌	✅	❌	✅	❌	✅	4
Elastic Graph Neural Networks	✅	✅	✅	✅	❌	❌	✅	5
Elementary superexpressive activations	❌	❌	❌	❌	❌	❌	❌	0
Emergent Social Learning via Multi-agent Reinforcement Learning	❌	✅	✅	❌	✅	✅	✅	5
Emphatic Algorithms for Deep Reinforcement Learning	✅	❌	✅	❌	✅	❌	✅	4
End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series	❌	❌	✅	✅	❌	❌	❌	2
Enhancing Robustness of Neural Networks through Fourier Stabilization	❌	✅	✅	❌	❌	❌	✅	3
Ensemble Bootstrapping for Q-Learning	✅	❌	✅	❌	❌	❌	❌	2
Environment Inference for Invariant Learning	✅	✅	✅	✅	❌	❌	❌	4
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes	❌	❌	✅	❌	❌	❌	❌	1
Equivariant Networks for Pixelized Spheres	✅	✅	✅	✅	✅	❌	✅	6
Equivariant message passing for the prediction of tensorial properties and molecular spectra	❌	❌	✅	✅	✅	❌	✅	4
Estimating $α$-Rank from A Few Entries with Low Rank Matrix Completion	✅	✅	✅	❌	❌	❌	✅	4
Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning	✅	❌	❌	❌	❌	❌	✅	2
Estimation and Quantization of Expected Persistence Diagrams	✅	✅	✅	❌	❌	❌	✅	4
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?	❌	✅	✅	❌	❌	❌	❌	2
Evaluating the Implicit Midpoint Integrator for Riemannian Hamiltonian Monte Carlo	✅	✅	❌	❌	❌	❌	✅	3
Event Outlier Detection in Continuous Time	❌	✅	✅	✅	❌	❌	✅	4
Evolving Attention with Residual Convolutions	❌	✅	✅	✅	✅	❌	✅	5
Exact Gap between Generalization Error and Uniform Convergence in Random Feature Models	❌	❌	❌	❌	❌	❌	✅	1
Exact Optimization of Conformal Predictors via Incremental and Decremental Learning	✅	✅	✅	❌	❌	❌	✅	4
Examining and Combating Spurious Features under Distribution Shift	✅	✅	✅	✅	❌	❌	✅	5
Explainable Automated Graph Representation Learning with Hyperparameter Importance	✅	❌	✅	❌	❌	❌	✅	3
Explaining Time Series Predictions with Dynamic Masks	❌	✅	✅	❌	❌	❌	✅	3
Explanations for Monotonic Classifiers.	✅	✅	✅	❌	✅	❌	❌	4
Exploiting Shared Representations for Personalized Federated Learning	✅	❌	✅	❌	❌	❌	✅	3
Exploiting structured data for learning contagious diseases under incomplete testing	❌	✅	✅	✅	❌	❌	❌	3
Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning	✅	✅	❌	❌	❌	❌	❌	2
Explore Visual Concept Formation for Image Classification	✅	✅	✅	✅	✅	❌	✅	6
Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL	✅	❌	❌	❌	❌	❌	❌	1
Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics	✅	✅	✅	❌	❌	❌	✅	4
Exponentially Many Local Minima in Quantum Neural Networks	❌	❌	❌	❌	✅	❌	❌	1
Expressive 1-Lipschitz Neural Networks for Robust Multiple Graph Learning against Adversarial Attacks	❌	❌	✅	❌	❌	❌	✅	2
FILTRA: Rethinking Steerable CNN by Filter Transform	❌	✅	✅	❌	✅	❌	✅	4
FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis	✅	❌	✅	❌	✅	❌	✅	4
FOP: Factorizing Optimal Joint Policy of Maximum-Entropy Multi-Agent Reinforcement Learning	✅	❌	✅	❌	❌	❌	❌	2
Factor-analytic inverse regression for high-dimension, small-sample dimensionality reduction	❌	✅	✅	✅	❌	❌	✅	4
Fair Classification with Noisy Protected Attributes: A Framework with Provable Guarantees	❌	✅	✅	❌	❌	❌	✅	3
Fair Selective Classification Via Sufficiency	✅	❌	✅	✅	❌	❌	✅	4
Fairness and Bias in Online Selection	✅	✅	✅	❌	❌	❌	❌	3
Fairness for Image Generation with Uncertain Sensitive Attributes	❌	✅	✅	✅	❌	❌	✅	4
Fairness of Exposure in Stochastic Bandits	✅	❌	✅	✅	❌	❌	✅	4
Fast Algorithms for Stackelberg Prediction Game with Least Squares Loss	✅	✅	✅	✅	✅	✅	✅	7
Fast Projection Onto Convex Smooth Constraints	✅	❌	✅	❌	✅	❌	✅	4
Fast Sketching of Polynomial Kernels of Polynomial Degree	✅	❌	❌	❌	❌	❌	❌	1
Fast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction	✅	❌	✅	❌	❌	❌	✅	3
Fast active learning for pure exploration in reinforcement learning	✅	❌	❌	❌	❌	❌	❌	1
Fast margin maximization via dual acceleration	✅	❌	✅	❌	❌	❌	❌	2
Faster Kernel Matrix Algebra via Density Estimation	✅	❌	✅	❌	❌	❌	✅	3
Feature Clustering for Support Identification in Extreme Regions	✅	✅	✅	❌	❌	❌	❌	3
Federated Composite Optimization	✅	✅	✅	✅	❌	❌	✅	5
Federated Continual Learning with Weighted Inter-client Transfer	✅	✅	✅	✅	❌	❌	✅	5
Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity	✅	✅	✅	✅	❌	❌	✅	5
Federated Learning of User Verification Models Without Sharing Embeddings	✅	❌	✅	✅	❌	❌	✅	4
Federated Learning under Arbitrary Communication Patterns	✅	✅	✅	❌	❌	❌	✅	4
Few-Shot Conformal Prediction with Auxiliary Tasks	✅	✅	✅	✅	❌	❌	❌	4
Few-Shot Neural Architecture Search	❌	❌	✅	❌	❌	❌	❌	1
Few-shot Language Coordination by Modeling Theory of Mind	✅	✅	✅	✅	❌	❌	✅	5
Finding Relevant Information via a Discrete Fourier Expansion	✅	✅	✅	✅	❌	❌	❌	4
Finding k in Latent $k-$ polytope	✅	❌	❌	❌	❌	❌	❌	1
Finding the Stochastic Shortest Path with Low Regret: the Adversarial Cost and Unknown Transition Case	✅	❌	❌	❌	❌	❌	❌	1
Finite mixture models do not reliably learn the number of components	❌	❌	✅	❌	❌	❌	✅	2
Finite-Sample Analysis of Off-Policy Natural Actor-Critic Algorithm	✅	❌	❌	❌	❌	❌	❌	1
First-Order Methods for Wasserstein Distributionally Robust MDP	✅	❌	❌	❌	✅	✅	✅	4
Fixed-Parameter and Approximation Algorithms for PCA with Outliers	❌	❌	❌	❌	❌	❌	❌	0
Flow-based Attribution in Graphical Models: A Recursive Shapley Approach	✅	❌	❌	❌	❌	❌	❌	1
Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design	❌	✅	✅	✅	✅	❌	✅	5
Follow-the-Regularized-Leader Routes to Chaos in Routing Games	❌	❌	❌	❌	❌	❌	✅	1
From Local Structures to Size Generalization in Graph Neural Networks	❌	❌	✅	✅	✅	❌	✅	4
From Local to Global Norm Emergence: Dissolving Self-reinforcing Substructures with Incremental Social Instruments	✅	✅	✅	❌	❌	❌	✅	4
From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization	❌	❌	✅	❌	❌	❌	✅	2
Function Contrastive Learning of Transferable Meta-Representations	❌	❌	✅	✅	❌	❌	✅	3
Functional Space Analysis of Local GAN Convergence	❌	❌	✅	❌	❌	❌	✅	2
Fundamental Tradeoffs in Distributionally Adversarial Training	❌	❌	❌	❌	❌	❌	❌	0
Fused Acoustic and Text Encoding for Multimodal Bilingual Pretraining and Speech Translation	❌	❌	✅	✅	❌	❌	✅	3
GANMEX: One-vs-One Attributions using GAN-based Model Explainability	❌	❌	✅	❌	❌	❌	✅	2
GBHT: Gradient Boosting Histogram Transform for Density Estimation	✅	❌	❌	✅	❌	❌	✅	3
GLSearch: Maximum Common Subgraph Detection via Learning to Search	✅	❌	✅	❌	✅	❌	✅	4
GMAC: A Distributional Perspective on Actor-Critic Framework	✅	❌	✅	❌	❌	❌	✅	3
GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings	✅	✅	✅	❌	✅	❌	❌	4
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning	❌	✅	✅	✅	❌	❌	✅	4
GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training	✅	✅	✅	✅	✅	❌	✅	6
GRAND: Graph Neural Diffusion	❌	✅	✅	✅	✅	❌	✅	5
Gaussian Process-Based Real-Time Learning for Safety Critical Applications	✅	✅	✅	❌	❌	❌	✅	4
Generalised Lipschitz Regularisation Equals Distributional Robustness	✅	❌	✅	✅	❌	❌	✅	4
Generalizable Episodic Memory for Deep Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Generalization Bounds in the Presence of Outliers: a Median-of-Means Study	✅	❌	✅	❌	❌	❌	❌	2
Generalization Error Bound for Hyperbolic Ordinal Embedding	❌	❌	❌	❌	❌	❌	✅	1
Generalization Guarantees for Neural Architecture Search with Train-Validation Split	❌	❌	✅	✅	❌	❌	✅	3
Generalized Doubly Reparameterized Gradient Estimators	✅	✅	✅	❌	❌	❌	✅	4
Generating images with sparse representations	❌	❌	✅	❌	✅	❌	✅	3
Generative Adversarial Networks for Markovian Temporal Dynamics: Stochastic Continuous Data Generation	✅	❌	✅	❌	❌	❌	❌	2
Generative Adversarial Transformers	❌	✅	✅	❌	✅	❌	❌	3
Generative Causal Explanations for Graph Neural Networks	✅	✅	✅	✅	✅	❌	✅	6
Generative Particle Variational Inference via Estimation of Functional Gradients	✅	❌	✅	✅	❌	❌	✅	4
Generative Video Transformer: Can Objects be the Words?	❌	❌	✅	❌	❌	❌	✅	2
GeomCA: Geometric Evaluation of Data Representations	✅	✅	✅	❌	❌	❌	✅	4
Geometric convergence of elliptical slice sampling	✅	❌	❌	❌	❌	❌	✅	2
Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances	❌	❌	❌	❌	❌	❌	❌	0
Global Convergence of Policy Gradient for Linear-Quadratic Mean-Field Control/Game in Continuous Time	✅	❌	❌	❌	❌	❌	✅	2
Global Optimality Beyond Two Layers: Training Deep ReLU Networks via Convex Programs	❌	❌	✅	❌	❌	❌	✅	2
Global Prosody Style Transfer Without Text Transcriptions	❌	❌	✅	❌	❌	❌	✅	2
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes	✅	✅	✅	✅	❌	❌	✅	5
Globally-Robust Neural Networks	❌	✅	✅	❌	✅	❌	✅	4
Goal-Conditioned Reinforcement Learning with Imagined Subgoals	✅	✅	❌	❌	❌	❌	❌	2
Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech	❌	❌	✅	❌	✅	❌	✅	3
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix	✅	✅	✅	❌	✅	✅	✅	6
Graph Contrastive Learning Automated	✅	✅	✅	✅	❌	❌	✅	5
Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization	❌	❌	✅	❌	❌	❌	✅	2
Graph Cuts Always Find a Global Optimum for Potts Models (With a Catch)	✅	❌	✅	❌	❌	✅	✅	4
Graph Mixture Density Networks	❌	✅	✅	✅	❌	❌	✅	4
Graph Neural Networks Inspired by Classical Iterative Algorithms	❌	✅	✅	✅	❌	❌	❌	3
GraphDF: A Discrete Flow Model for Molecular Graph Generation	❌	✅	✅	❌	❌	❌	✅	3
GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training	❌	❌	✅	❌	❌	❌	✅	2
Grey-box Extraction of Natural Language Models	❌	❌	✅	✅	❌	❌	✅	3
Grid-Functioned Neural Networks	❌	❌	✅	✅	✅	❌	✅	4
Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning	❌	✅	✅	✅	❌	❌	❌	3
Group Fisher Pruning for Practical Network Compression	✅	✅	✅	✅	✅	❌	✅	6
Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings	❌	✅	✅	✅	❌	❌	✅	4
Guarantees for Tuning the Step Size using a Learning-to-Learn Approach	❌	✅	✅	✅	❌	❌	✅	4
Guided Exploration with Proximal Policy Optimization using a Single Demonstration	✅	✅	✅	❌	❌	❌	✅	4
HAWQ-V3: Dyadic Neural Network Quantization	❌	✅	✅	❌	✅	❌	✅	4
HEMET: A Homomorphic-Encryption-Friendly Privacy-Preserving Mobile Neural Network Architecture	✅	❌	✅	❌	✅	❌	✅	4
HardCoRe-NAS: Hard Constrained diffeRentiable Neural Architecture Search	✅	✅	✅	✅	✅	❌	✅	6
Heterogeneity for the Win: One-Shot Federated Clustering	✅	✅	✅	❌	❌	❌	❌	3
Heterogeneous Risk Minimization	❌	❌	✅	✅	❌	❌	✅	3
Hierarchical Agglomerative Graph Clustering in Nearly-Linear Time	✅	✅	✅	❌	✅	❌	✅	5
Hierarchical Clustering of Data Streams: Scalable Algorithms and Approximation Guarantees	✅	❌	✅	❌	❌	❌	✅	3
Hierarchical VAEs Know What They Don’t Know	❌	✅	✅	❌	❌	❌	✅	3
High Confidence Generalization for Reinforcement Learning	✅	❌	✅	✅	❌	❌	❌	3
High-Dimensional Gaussian Process Inference with Derivatives	✅	✅	❌	❌	✅	❌	✅	4
High-Performance Large-Scale Image Recognition Without Normalization	❌	✅	✅	✅	✅	❌	✅	5
High-dimensional Experimental Design and Kernel Bandits	✅	❌	❌	❌	❌	❌	❌	1
Homomorphic Sensing: Sparsity and Noise	✅	❌	❌	❌	✅	❌	✅	3
HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical Projections	❌	✅	✅	✅	❌	❌	❌	3
Householder Sketch for Accurate and Accelerated Least-Mean-Squares Solvers	✅	✅	✅	✅	✅	❌	✅	6
How Do Adam and Training Strategies Help BNNs Optimization	❌	✅	✅	❌	❌	❌	✅	3
How Does Loss Function Affect Generalization Performance of Deep Learning? Application to Human Age Estimation	❌	❌	✅	✅	❌	❌	✅	3
How Framelets Enhance Graph Neural Networks	❌	❌	✅	✅	✅	❌	✅	4
How Important is the Train-Validation Split in Meta-Learning?	❌	❌	✅	✅	❌	❌	✅	3
How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference	✅	✅	✅	❌	❌	❌	✅	4
How could Neural Networks understand Programs?	❌	✅	✅	❌	❌	❌	✅	3
How rotational invariance of common kernels prevents generalization in high dimensions	❌	❌	✅	✅	❌	❌	✅	3
How to Learn when Data Reacts to Your Model: Performative Gradient Descent	✅	❌	❌	❌	❌	❌	✅	2
HyperHyperNetwork for the Design of Antenna Arrays	❌	✅	✅	✅	❌	❌	✅	4
Hyperparameter Selection for Imitation Learning	❌	❌	✅	✅	❌	❌	✅	3
I-BERT: Integer-only BERT Quantization	✅	✅	✅	✅	✅	❌	✅	6
Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection	❌	❌	✅	✅	✅	✅	✅	5
Imitation by Predicting Observations	✅	❌	✅	❌	❌	❌	✅	3
Implicit Bias of Linear RNNs	❌	❌	✅	❌	❌	❌	✅	2
Implicit Regularization in Tensor Factorization	❌	❌	✅	✅	❌	❌	✅	3
Implicit rate-constrained optimization of non-decomposable objectives	✅	❌	✅	✅	❌	❌	✅	4
Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold	❌	✅	✅	❌	❌	❌	❌	2
Improved Algorithms for Agnostic Pool-based Active Classification	✅	✅	✅	❌	❌	❌	✅	4
Improved Confidence Bounds for the Linear Logistic Model and Applications to Bandits	✅	❌	✅	❌	❌	❌	✅	3
Improved Contrastive Divergence Training of Energy-Based Models	✅	✅	✅	❌	✅	❌	✅	5
Improved Corruption Robust Algorithms for Episodic Reinforcement Learning	✅	❌	❌	❌	❌	❌	❌	1
Improved Denoising Diffusion Probabilistic Models	❌	✅	✅	✅	❌	❌	✅	4
Improved OOD Generalization via Adversarial Training and Pretraing	✅	❌	✅	❌	❌	❌	✅	3
Improved Regret Bound and Experience Replay in Regularized Policy Iteration	✅	❌	✅	❌	✅	❌	✅	4
Improved Regret Bounds of Bilinear Bandits using Action Space Analysis	✅	❌	❌	❌	❌	❌	✅	2
Improved, Deterministic Smoothing for L_1 Certified Robustness	❌	✅	✅	❌	✅	❌	❌	3
Improving Breadth-Wise Backpropagation in Graph Neural Networks Helps Learning Long-Range Dependencies.	❌	✅	✅	✅	❌	❌	✅	4
Improving Generalization in Meta-learning via Task Augmentation	✅	❌	✅	✅	❌	❌	✅	4
Improving Gradient Regularization using Complex-Valued Neural Networks	❌	✅	✅	❌	❌	❌	✅	3
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding	✅	✅	✅	✅	✅	❌	✅	6
Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity	❌	✅	✅	✅	❌	❌	✅	4
Improving Predictors via Combination Across Diverse Task Categories	❌	❌	✅	✅	❌	❌	✅	3
Improving Ultrametrics Embeddings Through Coresets	✅	❌	✅	❌	✅	❌	✅	4
In-Database Regression in Input Sparsity Time	✅	✅	✅	❌	✅	❌	❌	4
Incentivized Bandit Learning with Self-Reinforcing User Preferences	✅	❌	❌	❌	❌	❌	✅	2
Incentivizing Compliance with Algorithmic Instruments	✅	✅	❌	❌	❌	❌	✅	3
Inference for Network Regression Models with Community Structure	✅	❌	✅	❌	❌	❌	✅	3
Inferring Latent Dynamics Underlying Neural Population Activity via Neural Differential Equations	❌	❌	❌	❌	❌	❌	✅	1
Inferring serial correlation with dynamic backgrounds	❌	❌	✅	✅	❌	❌	✅	3
Infinite-Dimensional Optimization for Zero-Sum Games via Variational Transport	✅	❌	❌	❌	❌	❌	✅	2
Information Obfuscation of Graph Neural Networks	✅	✅	✅	❌	❌	❌	✅	4
Instabilities of Offline RL with Pre-Trained Neural Representation	✅	❌	✅	❌	❌	❌	✅	3
Instance Specific Approximations for Submodular Maximization	✅	❌	✅	❌	❌	❌	✅	3
Instance-Optimal Compressed Sensing via Posterior Sampling	❌	✅	✅	❌	❌	❌	✅	3
Integer Programming for Causal Structure Learning in the Presence of Latent Variables	❌	❌	❌	❌	✅	✅	✅	3
Integrated Defense for Resilient Graph Matching	✅	❌	✅	❌	❌	❌	❌	2
Interaction-Grounded Learning	✅	❌	✅	❌	❌	❌	❌	2
Interactive Learning from Activity Description	✅	✅	✅	✅	❌	❌	❌	4
Intermediate Layer Optimization for Inverse Problems using Deep Generative Models	✅	❌	✅	❌	✅	❌	✅	4
Interpretable Stability Bounds for Spectral Graph Filters	❌	❌	✅	❌	❌	❌	✅	2
Interpretable Stein Goodness-of-fit Tests on Riemannian Manifold	✅	❌	✅	❌	❌	❌	✅	3
Interpreting and Disentangling Feature Components of Various Complexity from DNNs	❌	❌	✅	❌	❌	❌	✅	2
Inverse Constrained Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Inverse Decision Modeling: Learning Interpretable Representations of Behavior	❌	❌	✅	❌	❌	❌	✅	2
Is Pessimism Provably Efficient for Offline RL?	✅	❌	❌	❌	❌	❌	❌	1
Is Space-Time Attention All You Need for Video Understanding?	❌	✅	✅	✅	✅	❌	✅	5
Isometric Gaussian Process Latent Variable Model for Dissimilarity Data	❌	❌	✅	❌	❌	❌	✅	2
Joining datasets via data augmentation in the label space for neural networks	✅	❌	✅	❌	❌	❌	❌	2
Joint Online Learning and Decision-making via Dual Mirror Descent	✅	✅	✅	✅	❌	❌	✅	5
Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks	❌	✅	✅	❌	❌	❌	✅	3
Just Train Twice: Improving Group Robustness without Training Group Information	✅	✅	✅	✅	❌	❌	✅	5
K-shot NAS: Learnable Weight-Sharing for NAS with K-shot Supernets	✅	❌	✅	✅	✅	❌	✅	5
KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation	✅	✅	✅	❌	❌	❌	✅	4
KNAS: Green Neural Architecture Search	✅	✅	✅	❌	✅	❌	✅	5
KO codes: inventing nonlinear encoding and decoding for reliable wireless communication via deep-learning	❌	✅	❌	❌	❌	❌	❌	1
Kernel Continual Learning	❌	✅	✅	❌	❌	❌	✅	3
Kernel Stein Discrepancy Descent	✅	✅	❌	❌	❌	❌	✅	3
Kernel-Based Reinforcement Learning: A Finite-Time Analysis	✅	✅	❌	❌	❌	❌	✅	3
Keyframe-Focused Visual Imitation Learning	✅	✅	✅	❌	❌	❌	✅	4
Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks	❌	✅	✅	✅	❌	❌	✅	4
LAMDA: Label Matching Deep Domain Adaptation	✅	❌	✅	❌	✅	✅	✅	5
LARNet: Lie Algebra Residual Network for Face Recognition	❌	❌	✅	❌	❌	❌	✅	2
LEGO: Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs	✅	✅	✅	✅	❌	❌	❌	4
LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning	✅	✅	✅	✅	✅	❌	✅	6
LTL2Action: Generalizing LTL Instructions for Multi-Task RL	❌	✅	❌	❌	❌	❌	✅	2
Label Distribution Learning Machine	✅	❌	✅	✅	✅	❌	✅	5
Label Inference Attacks from Log-loss Scores	✅	✅	✅	❌	✅	❌	✅	5
Label-Only Membership Inference Attacks	❌	✅	✅	❌	❌	❌	✅	3
Large Scale Private Learning via Low-rank Reparametrization	✅	✅	✅	✅	✅	❌	✅	6
Large-Margin Contrastive Learning with Distance Polarization Regularizer	✅	❌	✅	✅	❌	❌	✅	4
Large-Scale Meta-Learning with Continual Trajectory Shifting	✅	✅	✅	❌	❌	❌	✅	4
Large-Scale Multi-Agent Deep FBSDEs	✅	❌	❌	❌	✅	❌	✅	3
Latent Programmer: Discrete Latent Codes for Program Synthesis	✅	❌	✅	❌	❌	❌	✅	3
Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classification	✅	✅	✅	❌	❌	❌	✅	4
Learn-to-Share: A Hardware-friendly Transfer Learning Framework Exploiting Computation and Parameter Sharing	✅	❌	✅	✅	✅	❌	✅	5
Learn2Hop: Learned Optimization on Rough Landscapes	❌	✅	❌	❌	✅	❌	✅	3
Learner-Private Convex Optimization	✅	❌	❌	❌	❌	❌	❌	1
Learning Binary Decision Trees by Argmin Differentiation	✅	✅	✅	✅	❌	❌	❌	4
Learning Bounds for Open-Set Learning	✅	✅	✅	✅	❌	❌	✅	5
Learning Curves for Analysis of Deep Networks	❌	✅	✅	✅	❌	❌	❌	3
Learning Deep Neural Networks under Agnostic Corrupted Supervision	✅	✅	✅	✅	❌	❌	❌	4
Learning Diverse-Structured Networks for Adversarial Robustness	✅	✅	✅	✅	✅	❌	✅	6
Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Learning Generalized Intersection Over Union for Dense Pixelwise Prediction	✅	❌	✅	❌	✅	❌	✅	4
Learning Gradient Fields for Molecular Conformation Generation	✅	✅	✅	❌	✅	❌	✅	5
Learning Interaction Kernels for Agent Systems on Riemannian Manifolds	✅	✅	❌	❌	❌	❌	✅	3
Learning Intra-Batch Connections for Deep Metric Learning	❌	✅	✅	✅	✅	❌	✅	5
Learning Neural Network Subspaces	✅	✅	✅	❌	❌	❌	✅	4
Learning Node Representations Using Stationary Flow Prediction on Large Payment and Cash Transaction Networks	❌	✅	✅	✅	✅	❌	✅	5
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization	❌	❌	✅	❌	✅	❌	✅	3
Learning Online Algorithms with Distributional Advice	✅	❌	❌	❌	❌	❌	❌	1
Learning Optimal Auctions with Correlated Valuations from Samples	✅	❌	❌	❌	❌	❌	❌	1
Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis	✅	✅	✅	✅	❌	❌	❌	4
Learning Randomly Perturbed Structured Predictors for Direct Loss Minimization	❌	✅	✅	❌	❌	❌	✅	3
Learning Representations by Humans, for Humans	✅	❌	✅	❌	❌	❌	✅	3
Learning Routines for Effective Off-Policy Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation	❌	❌	✅	✅	✅	❌	✅	4
Learning Stochastic Behaviour from Aggregate Data	✅	❌	✅	❌	❌	❌	✅	3
Learning Task Informed Abstractions	❌	✅	✅	❌	✅	❌	❌	3
Learning Transferable Visual Models From Natural Language Supervision	✅	✅	❌	❌	✅	✅	✅	5
Learning While Playing in Mean-Field Games: Convergence and Optimality	✅	❌	❌	❌	❌	❌	❌	1
Learning a Universal Template for Few-shot Dataset Generalization	❌	✅	✅	❌	❌	❌	✅	3
Learning and Planning in Average-Reward Markov Decision Processes	✅	❌	✅	❌	❌	❌	✅	3
Learning and Planning in Complex Action Spaces	❌	❌	✅	❌	❌	❌	✅	2
Learning by Turning: Neural Architecture Aware Optimisation	✅	✅	✅	❌	❌	❌	✅	4
Learning de-identified representations of prosody from raw audio	❌	❌	✅	✅	✅	❌	✅	4
Learning disentangled representations via product manifold projection	❌	❌	✅	❌	✅	❌	✅	3
Learning from Biased Data: A Semi-Parametric Approach	✅	❌	❌	❌	❌	❌	✅	2
Learning from History for Byzantine Robust Optimization	✅	✅	✅	❌	❌	❌	✅	4
Learning from Nested Data with Ornstein Auto-Encoders	✅	❌	✅	✅	❌	❌	✅	4
Learning from Noisy Labels with No Change to the Training Process	✅	❌	✅	❌	❌	❌	✅	3
Learning from Similarity-Confidence Data	❌	❌	✅	❌	✅	❌	✅	3
Learning in Nonzero-Sum Stochastic Games with Potentials	✅	❌	✅	❌	❌	❌	❌	2
Learning to Generate Noise for Multi-Attack Robustness	✅	✅	✅	✅	✅	❌	✅	6
Learning to Price Against a Moving Target	✅	❌	❌	❌	❌	❌	❌	1
Learning to Rehearse in Long Sequence Memorization	❌	❌	✅	✅	❌	❌	✅	3
Learning to Weight Imperfect Demonstrations	❌	❌	✅	❌	❌	❌	❌	1
Lenient Regret and Good-Action Identification in Gaussian Process Bandits	❌	✅	✅	✅	❌	❌	✅	4
Let’s Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework	❌	❌	❌	❌	❌	❌	❌	0
Leveraged Weighted Loss for Partial Label Learning	✅	❌	✅	✅	❌	❌	✅	4
Leveraging Good Representations in Linear Contextual Bandits	✅	❌	✅	❌	❌	❌	✅	3
Leveraging Language to Learn Program Abstractions and Search Heuristics	✅	✅	✅	❌	❌	❌	❌	3
Leveraging Non-uniformity in First-order Non-convex Optimization	✅	❌	❌	❌	❌	❌	✅	2
Leveraging Public Data for Practical Private Query Release	✅	❌	✅	❌	✅	❌	✅	4
Leveraging Sparse Linear Layers for Debuggable Deep Networks	❌	✅	✅	✅	❌	❌	✅	4
LieTransformer: Equivariant Self-Attention for Lie Groups	✅	✅	✅	❌	❌	❌	❌	3
Light RUMs	❌	❌	✅	✅	✅	❌	❌	3
Linear Transformers Are Secretly Fast Weight Programmers	❌	✅	✅	✅	✅	❌	✅	5
Link Prediction with Persistent Homology: An Interactive View	✅	✅	✅	✅	❌	❌	❌	4
Lipschitz normalization for self-attention layers with application to graph neural networks	❌	❌	✅	✅	❌	❌	✅	3
Local Algorithms for Finding Densely Connected Clusters	✅	✅	✅	❌	✅	❌	✅	5
Local Correlation Clustering with Asymmetric Classification Errors	✅	❌	❌	❌	❌	❌	❌	1
Locally Adaptive Label Smoothing Improves Predictive Churn	✅	❌	✅	❌	❌	❌	✅	3
Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards	✅	❌	✅	❌	❌	❌	✅	3
Locally Private k-Means in One Round	✅	❌	❌	❌	❌	❌	✅	2
LogME: Practical Assessment of Pre-trained Models for Transfer Learning	✅	✅	✅	✅	❌	❌	✅	5
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation	✅	❌	❌	❌	❌	❌	❌	1
Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling	❌	✅	✅	❌	❌	❌	✅	3
Lossless Compression of Efficient Private Local Randomizers	✅	❌	❌	❌	❌	❌	✅	2
Lottery Ticket Preserves Weight Correlation: Is It Desirable or Not?	❌	❌	✅	❌	❌	❌	✅	2
Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision	✅	✅	✅	❌	✅	❌	✅	5
Low-Rank Sinkhorn Factorization	✅	❌	❌	❌	❌	❌	✅	2
Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries	✅	✅	✅	❌	✅	❌	✅	5
Lower-Bounded Proper Losses for Weakly Supervised Classification	✅	✅	✅	❌	❌	❌	✅	4
MARINA: Faster Non-Convex Distributed Learning with Compression	✅	✅	✅	❌	❌	✅	✅	5
MC-LSTM: Mass-Conserving LSTM	❌	✅	✅	✅	❌	❌	✅	4
MOTS: Minimax Optimal Thompson Sampling	✅	❌	❌	❌	❌	❌	✅	2
MSA Transformer	❌	✅	✅	✅	✅	❌	✅	5
MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning	✅	✅	❌	❌	❌	❌	❌	2
Machine Unlearning for Random Forests	✅	❌	✅	✅	✅	❌	✅	5
Making Paper Reviewing Robust to Bid Manipulation Attacks	✅	✅	✅	❌	❌	❌	✅	4
Making transport more robust and interpretable by moving data through a small number of anchor points	✅	✅	✅	✅	❌	❌	✅	5
Mandoline: Model Evaluation under Distribution Shift	✅	✅	✅	✅	❌	❌	✅	5
Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data	❌	✅	✅	✅	❌	✅	✅	5
Marginalized Stochastic Natural Gradients for Black-Box Variational Inference	✅	❌	✅	❌	❌	❌	✅	3
Markpainting: Adversarial Machine Learning meets Inpainting	✅	✅	✅	❌	❌	❌	✅	4
Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling	✅	✅	✅	✅	✅	❌	✅	6
Matrix Completion with Model-free Weighting	❌	❌	✅	✅	❌	❌	❌	2
Matrix Sketching for Secure Collaborative Machine Learning	❌	✅	✅	❌	✅	❌	✅	4
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks	✅	✅	✅	❌	❌	❌	✅	4
Measuring Robustness in Deep Learning Based Compressive Sensing	❌	✅	✅	❌	❌	❌	❌	2
Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences	✅	✅	✅	✅	❌	❌	✅	5
Megaverse: Simulating Embodied Agents at One Million Experiences per Second	❌	✅	❌	❌	✅	❌	❌	2
Memory Efficient Online Meta Learning	✅	❌	✅	❌	❌	❌	❌	2
Memory-Efficient Pipeline-Parallel DNN Training	✅	❌	✅	❌	✅	❌	✅	4
Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning	✅	❌	✅	❌	❌	❌	✅	3
Meta Learning for Support Recovery in High-dimensional Precision Matrix Estimation	❌	❌	✅	❌	❌	❌	✅	2
Meta-Cal: Well-controlled Post-hoc Calibration by Ranking	✅	✅	✅	✅	❌	❌	✅	5
Meta-Learning Bidirectional Update Rules	❌	✅	✅	❌	❌	❌	✅	3
Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation	❌	❌	✅	❌	❌	❌	✅	2
Meta-Thompson Sampling	✅	❌	❌	❌	❌	❌	✅	2
Meta-learning Hyperparameter Performance Prediction with Neural Processes	✅	❌	✅	✅	❌	❌	✅	4
MetaCURE: Meta Reinforcement Learning with Empowerment-Driven Exploration	✅	✅	✅	❌	❌	❌	❌	3
Mind the Box: $l_1$-APGD for Sparse Adversarial Attacks on Image Classifiers	✅	✅	✅	❌	❌	❌	✅	4
Mixed Cross Entropy Loss for Neural Machine Translation	❌	✅	✅	✅	❌	❌	✅	4
Mixed Nash Equilibria in the Adversarial Examples Game	✅	❌	✅	❌	✅	❌	✅	4
Model Distillation for Revenue Optimization: Interpretable Personalized Pricing	❌	❌	✅	❌	❌	❌	✅	2
Model Fusion for Personalized Learning	❌	✅	✅	❌	❌	❌	❌	2
Model Performance Scaling with Multiple Data Sources	❌	❌	✅	❌	❌	❌	✅	2
Model-Based Reinforcement Learning via Latent-Space Collocation	✅	❌	✅	❌	❌	❌	✅	3
Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity	✅	❌	❌	❌	❌	❌	❌	1
Model-Free and Model-Based Policy Evaluation when Causality is Uncertain	❌	❌	✅	❌	❌	❌	✅	2
Model-Targeted Poisoning Attacks with Provable Convergence	✅	✅	✅	❌	❌	❌	✅	4
Model-based Reinforcement Learning for Continuous Control with Posterior Sampling	✅	❌	❌	❌	❌	❌	❌	1
Modeling Hierarchical Structures with Continuous Recursive Neural Networks	✅	✅	✅	✅	✅	❌	❌	5
Modelling Behavioural Diversity for Learning in Open-Ended Games	✅	❌	✅	❌	❌	❌	✅	3
Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment	❌	❌	❌	❌	❌	❌	✅	1
Momentum Residual Neural Networks	❌	✅	✅	❌	✅	❌	✅	4
Monotonic Robust Policy Optimization with Model Discrepancy	✅	❌	❌	❌	❌	❌	✅	2
Monte Carlo Variational Auto-Encoders	✅	✅	✅	❌	❌	❌	✅	4
More Powerful and General Selective Inference for Stepwise Feature Selection using Homotopy Method	✅	✅	✅	❌	❌	❌	✅	4
Moreau-Yosida $f$-divergences	✅	✅	✅	❌	❌	❌	✅	4
MorphVAE: Generating Neural Morphologies from 3D-Walks using a Variational Autoencoder with Spherical Latent Space	✅	✅	✅	✅	✅	❌	✅	6
Muesli: Combining Improvements in Policy Optimization	❌	❌	✅	❌	❌	❌	✅	2
Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers	✅	❌	✅	❌	❌	❌	✅	3
Multi-Dimensional Classification via Sparse Label Encoding	✅	❌	✅	✅	❌	❌	✅	4
Multi-Receiver Online Bayesian Persuasion	✅	❌	❌	❌	❌	❌	❌	1
Multi-Task Reinforcement Learning with Context-based Representations	✅	✅	✅	❌	❌	❌	✅	4
Multi-group Agnostic PAC Learnability	✅	❌	❌	❌	❌	❌	❌	1
Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning	✅	❌	✅	❌	❌	❌	✅	3
Multidimensional Scaling: Approximation and Complexity	✅	❌	✅	❌	✅	❌	✅	4
Multiplicative Noise and Heavy Tails in Stochastic Optimization	❌	❌	✅	❌	❌	❌	✅	2
Multiplying Matrices Without Multiplying	✅	✅	✅	❌	✅	❌	❌	4
Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference	✅	❌	✅	❌	❌	❌	❌	2
Narrow Margins: Classification, Margins and Fat Tails	❌	❌	❌	❌	❌	❌	❌	0
Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation	❌	❌	✅	✅	❌	❌	✅	3
NeRF-VAE: A Geometry Aware 3D Scene Generative Model	❌	❌	✅	❌	❌	❌	✅	2
Near Optimal Reward-Free Reinforcement Learning	✅	❌	❌	❌	❌	❌	❌	1
Near-Optimal Algorithms for Explainable k-Medians and k-Means	✅	❌	❌	❌	❌	❌	❌	1
Near-Optimal Confidence Sequences for Bounded Random Variables	✅	❌	❌	❌	❌	❌	✅	2
Near-Optimal Entrywise Anomaly Detection for Low-Rank Matrices with Sub-Exponential Noise	✅	❌	❌	❌	❌	❌	✅	2
Near-Optimal Linear Regression under Distribution Shift	✅	❌	✅	✅	❌	❌	✅	4
Near-Optimal Model-Free Reinforcement Learning in Non-Stationary Episodic MDPs	✅	❌	✅	❌	❌	❌	❌	2
Near-Optimal Representation Learning for Linear Bandits and Linear RL	✅	❌	❌	❌	❌	❌	❌	1
Necessary and sufficient conditions for causal feature selection in time series with latent common causes	✅	❌	✅	❌	❌	❌	✅	3
Neighborhood Contrastive Learning Applied to Online Patient Monitoring	❌	✅	✅	✅	❌	❌	✅	4
Network Inference and Influence Maximization from Samples	✅	❌	❌	❌	❌	❌	❌	1
Neural Architecture Search without Training	✅	✅	✅	✅	✅	❌	✅	6
Neural Feature Matching in Implicit 3D Representations	❌	❌	✅	❌	❌	❌	✅	2
Neural Pharmacodynamic State Space Modeling	❌	✅	✅	✅	❌	❌	✅	4
Neural Rough Differential Equations for Long Time Series	❌	✅	✅	❌	❌	❌	✅	3
Neural SDEs as Infinite-Dimensional GANs	❌	✅	✅	❌	❌	❌	✅	3
Neural Symbolic Regression that scales	✅	✅	✅	❌	❌	❌	✅	4
Neural Tangent Generalization Attacks	✅	✅	✅	✅	✅	❌	✅	6
Neural Transformation Learning for Deep Anomaly Detection Beyond Images	❌	❌	✅	✅	❌	❌	✅	3
Neural-Pull: Learning Signed Distance Function from Point clouds by Learning to Pull Space onto Surface	❌	✅	✅	❌	❌	❌	✅	3
Neuro-algorithmic Policies Enable Fast Combinatorial Generalization	✅	✅	✅	❌	❌	❌	❌	3
Newton Method over Networks is Fast up to the Statistical Precision	✅	❌	✅	❌	✅	✅	✅	5
No-regret Algorithms for Capturing Events in Poisson Point Processes	✅	❌	✅	❌	❌	❌	✅	3
Noise and Fluctuation of Finite Learning Rate Stochastic Gradient Descent	❌	❌	✅	❌	❌	❌	❌	1
Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction	❌	❌	✅	❌	✅	❌	✅	3
Non-Exponentially Weighted Aggregation: Regret Bounds for Unbounded Loss Functions	❌	❌	❌	❌	❌	❌	❌	0
Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation	✅	✅	✅	❌	❌	❌	✅	4
Nondeterminism and Instability in Neural Network Optimization	❌	✅	✅	✅	✅	❌	✅	5
Nonmyopic Multifidelity Acitve Search	✅	✅	✅	❌	❌	❌	✅	4
Nonparametric Decomposition of Sparse Tensors	❌	❌	✅	✅	❌	❌	✅	3
Nonparametric Hamiltonian Monte Carlo	✅	✅	✅	✅	❌	❌	✅	5
Not All Memories are Created Equal: Learning to Forget by Expiring	❌	✅	✅	❌	❌	❌	❌	2
Object Segmentation Without Labels with Large-Scale Generative Models	❌	✅	✅	❌	✅	❌	✅	4
Objective Bound Conditional Gaussian Process for Bayesian Optimization	❌	✅	✅	✅	❌	❌	✅	4
Oblivious Sketching for Logistic Regression	❌	✅	✅	❌	❌	❌	✅	3
Oblivious Sketching-based Central Path Method for Linear Programming	✅	❌	❌	❌	❌	❌	❌	1
Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap	✅	✅	✅	❌	❌	❌	❌	3
Off-Belief Learning	❌	✅	❌	❌	❌	❌	❌	1
Off-Policy Confidence Sequences	✅	✅	✅	❌	❌	❌	✅	4
Offline Contextual Bandits with Overparameterized Models	❌	✅	✅	❌	❌	❌	✅	3
Offline Meta-Reinforcement Learning with Advantage Weighting	✅	✅	✅	✅	❌	❌	✅	5
Offline Reinforcement Learning with Fisher Divergence Critic Regularization	✅	✅	✅	❌	✅	❌	✅	5
Offline Reinforcement Learning with Pseudometric Learning	✅	❌	✅	❌	❌	❌	✅	3
OmniNet: Omnidirectional Representations from Transformers	❌	❌	✅	✅	✅	❌	✅	4
On Characterizing GAN Convergence Through Proximal Duality Gap	❌	✅	✅	❌	❌	❌	✅	3
On Disentangled Representations Learned from Correlated Data	❌	✅	✅	❌	✅	❌	✅	4
On Energy-Based Models with Overparametrized Shallow Neural Networks	✅	✅	❌	✅	❌	❌	✅	4
On Estimation in Latent Variable Models	✅	❌	❌	❌	❌	❌	✅	2
On Explainability of Graph Neural Networks via Subgraph Explorations	✅	✅	✅	❌	✅	✅	✅	6
On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting	✅	❌	❌	❌	❌	❌	✅	2
On Limited-Memory Subsampling Strategies for Bandits	✅	✅	❌	❌	❌	❌	✅	3
On Linear Identifiability of Learned Representations	❌	✅	✅	❌	❌	❌	✅	3
On Lower Bounds for Standard and Robust Gaussian Process Bandit Optimization	❌	❌	❌	❌	❌	❌	❌	0
On Monotonic Linear Interpolation of Neural Network Parameters	❌	❌	✅	❌	❌	❌	✅	2
On Perceptual Lossy Compression: The Cost of Perceptual Reconstruction and An Optimal Training Framework	❌	✅	✅	❌	❌	❌	✅	3
On Proximal Policy Optimization’s Heavy-tailed Gradients	✅	❌	❌	❌	❌	❌	✅	2
On Recovering from Modeling Errors Using Testing Bayesian Networks	❌	❌	❌	❌	❌	❌	❌	0
On Reinforcement Learning with Adversarial Corruption and Its Application to Block MDP	✅	❌	❌	❌	❌	❌	❌	1
On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game	✅	❌	❌	❌	❌	❌	❌	1
On Robust Mean Estimation under Coordinate-level Corruption	✅	❌	✅	❌	❌	❌	❌	2
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes	❌	✅	✅	❌	❌	❌	✅	3
On Variational Inference in Biclustering Models	✅	❌	❌	❌	❌	❌	✅	2
On a Combination of Alternating Minimization and Nesterov’s Momentum	✅	✅	✅	❌	❌	❌	✅	4
On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients	✅	❌	✅	❌	❌	❌	✅	3
On the Explicit Role of Initialization on the Convergence and Implicit Bias of Overparametrized Linear Networks	❌	❌	❌	❌	❌	❌	✅	1
On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models	❌	❌	❌	❌	❌	❌	✅	1
On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent	❌	❌	❌	❌	❌	❌	❌	0
On the Inherent Regularization Effects of Noise Injection During Training	❌	❌	✅	❌	❌	❌	✅	2
On the Optimality of Batch Policy Optimization Algorithms	❌	❌	❌	❌	❌	❌	❌	0
On the Power of Localized Perceptron for Label-Optimal Learning of Halfspaces with Adversarial Noise	✅	❌	❌	❌	❌	❌	❌	1
On the Predictability of Pruning Across Scales	✅	❌	✅	❌	❌	❌	✅	3
On the Problem of Underranking in Group-Fair Ranking	✅	❌	✅	❌	✅	❌	✅	4
On the Proof of Global Convergence of Gradient Descent for Deep ReLU Networks with Linear Widths	❌	❌	❌	❌	❌	❌	❌	0
On the Random Conjugate Kernel and Neural Tangent Kernel	❌	❌	❌	❌	❌	❌	✅	1
On the difficulty of unbiased alpha divergence minimization	❌	❌	✅	❌	❌	❌	✅	2
On the price of explainability for some clustering problems	✅	✅	✅	❌	✅	❌	✅	5
On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification	✅	❌	✅	✅	❌	✅	✅	5
On-Policy Deep Reinforcement Learning for the Average-Reward Criterion	✅	❌	✅	❌	❌	❌	❌	2
On-the-fly Rectification for Robust Large-Vocabulary Topic Inference	✅	✅	✅	❌	❌	❌	✅	4
One Pass Late Fusion Multi-view Clustering	✅	❌	✅	❌	✅	❌	✅	4
One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning	✅	❌	✅	✅	❌	❌	✅	4
One-sided Frank-Wolfe algorithms for saddle problems	✅	❌	✅	❌	❌	❌	✅	3
Oneshot Differentially Private Top-k Selection	✅	❌	❌	❌	❌	❌	❌	1
Online A-Optimal Design and Active Linear Regression	✅	❌	❌	❌	❌	❌	❌	1
Online Graph Dictionary Learning	✅	✅	✅	✅	✅	❌	✅	6
Online Learning for Load Balancing of Unknown Monotone Resource Allocation Games	✅	❌	❌	❌	❌	❌	✅	2
Online Learning in Unknown Markov Games	✅	❌	❌	❌	❌	❌	❌	1
Online Learning with Optimism and Delay	❌	✅	✅	❌	❌	❌	✅	3
Online Limited Memory Neural-Linear Bandits with Likelihood Matching	✅	✅	✅	❌	❌	❌	✅	4
Online Optimization in Games via Control Theory: Connecting Regret, Passivity and Poincaré Recurrence	❌	❌	❌	❌	❌	❌	❌	0
Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with $\sqrt$T Regret	✅	❌	❌	❌	❌	❌	❌	1
Online Selection Problems against Constrained Adversary	✅	❌	❌	❌	❌	❌	❌	1
Online Submodular Resource Allocation with Applications to Rebalancing Shared Mobility Systems	✅	❌	✅	❌	❌	❌	✅	3
Online Unrelated Machine Load Balancing with Predictions Revisited	✅	❌	❌	❌	❌	❌	❌	1
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions	✅	✅	✅	❌	❌	❌	❌	3
Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics	❌	✅	✅	❌	❌	❌	✅	3
Operationalizing Complex Causes: A Pragmatic View of Mediation	✅	✅	✅	❌	❌	❌	✅	4
OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation	✅	❌	✅	❌	❌	❌	✅	3
Optimal Complexity in Decentralized Training	✅	❌	✅	❌	✅	✅	❌	4
Optimal Counterfactual Explanations in Tree Ensembles	❌	✅	✅	✅	✅	✅	✅	6
Optimal Estimation of High Dimensional Smooth Additive Function Based on Noisy Observations	❌	❌	❌	❌	❌	❌	✅	1
Optimal Non-Convex Exact Recovery in Stochastic Block Model via Projected Power Method	✅	✅	✅	❌	✅	✅	✅	6
Optimal Off-Policy Evaluation from Multiple Logging Policies	✅	❌	✅	✅	❌	❌	✅	4
Optimal Streaming Algorithms for Multi-Armed Bandits	✅	❌	❌	❌	❌	❌	❌	1
Optimal Thompson Sampling strategies for support-aware CVaR bandits	✅	❌	✅	❌	❌	❌	✅	3
Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search	✅	✅	✅	❌	❌	❌	✅	4
Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization	✅	❌	❌	❌	❌	❌	✅	2
Optimization Planning for 3D ConvNets	❌	❌	✅	✅	✅	❌	✅	4
Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth	❌	❌	✅	❌	❌	❌	✅	2
Optimizing Black-box Metrics with Iterative Example Weighting	✅	✅	✅	✅	❌	❌	✅	5
Optimizing persistent homology based functions	✅	✅	✅	✅	❌	❌	✅	5
Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation	✅	❌	✅	❌	❌	❌	✅	3
Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translation	✅	✅	✅	✅	❌	❌	✅	5
Out-of-Distribution Generalization via Risk Extrapolation (REx)	❌	❌	✅	❌	❌	❌	❌	1
Outlier-Robust Optimal Transport	✅	❌	✅	❌	❌	❌	✅	3
Outside the Echo Chamber: Optimizing the Performative Risk	✅	❌	✅	❌	❌	❌	✅	3
Overcoming Catastrophic Forgetting by Bayesian Generative Regularization	✅	❌	✅	✅	❌	❌	❌	3
PAC-Learning for Strategic Classification	❌	❌	❌	❌	❌	❌	❌	0
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees	✅	✅	✅	❌	❌	❌	✅	4
PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization	✅	❌	✅	❌	✅	❌	✅	4
PAPRIKA: Private Online False Discovery Rate Control	✅	✅	❌	❌	❌	❌	✅	3
PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration	✅	❌	✅	❌	❌	❌	✅	3
PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training	✅	✅	✅	❌	❌	❌	❌	3
PHEW : Constructing Sparse Networks that Learn Fast and Generalize Well without Training Data	❌	❌	✅	❌	❌	❌	❌	1
PID Accelerated Value Iteration Algorithm	✅	❌	✅	❌	❌	❌	✅	3
PODS: Policy Optimization via Differentiable Simulation	✅	❌	❌	❌	✅	❌	✅	3
Parallel Droplet Control in MEDA Biochips using Multi-Agent Reinforcement Learning	❌	✅	❌	❌	✅	❌	✅	3
Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics	✅	✅	✅	❌	✅	❌	❌	4
Parallel tempering on optimized paths	✅	✅	✅	❌	❌	❌	✅	4
Parallelizing Legendre Memory Unit Training	❌	❌	✅	✅	✅	❌	✅	4
Parameter-free Locally Accelerated Conditional Gradients	✅	✅	❌	❌	❌	❌	✅	3
Parameterless Transductive Feature Re-representation for Few-Shot Learning	❌	❌	✅	✅	❌	❌	✅	3
Parametric Graph for Unimodal Ranking Bandit	✅	✅	✅	❌	❌	❌	✅	4
Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions	❌	❌	✅	✅	❌	❌	✅	3
Partially Observed Exchangeable Modeling	❌	✅	✅	❌	❌	❌	❌	2
Path Planning using Neural A* Search	✅	✅	✅	✅	❌	❌	✅	5
Perceiver: General Perception with Iterative Attention	❌	❌	✅	✅	❌	❌	✅	3
Permutation Weighting	❌	❌	✅	✅	❌	❌	✅	3
Personalized Federated Learning using Hypernetworks	✅	✅	✅	✅	❌	❌	✅	5
Phase Transitions, Distance Functions, and Implicit Neural Representations	❌	❌	✅	❌	❌	❌	✅	2
Phasic Policy Gradient	✅	✅	✅	❌	❌	❌	✅	4
PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models	✅	✅	✅	❌	✅	✅	✅	6
PixelTransformer: Sample Conditioned Signal Generation	❌	❌	✅	✅	❌	❌	✅	3
Pointwise Binary Classification with Pairwise Confidence Comparisons	❌	✅	✅	❌	✅	❌	✅	4
Poisson-Randomised DirBN: Large Mutation is Needed in Dirichlet Belief Networks	❌	✅	✅	❌	❌	❌	✅	3
Policy Analysis using Synthetic Controls in Continuous-Time	❌	❌	✅	❌	❌	❌	❌	1
Policy Caches with Successor Features	✅	❌	✅	❌	❌	❌	✅	3
Policy Gradient Bayesian Robust Optimization for Imitation Learning	✅	✅	✅	❌	❌	❌	✅	4
Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Poolingformer: Long Document Modeling with Pooling Attention	❌	❌	✅	✅	✅	❌	✅	4
PopSkipJump: Decision-Based Attack for Probabilistic Classifiers	✅	✅	✅	❌	✅	❌	✅	5
Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization	✅	✅	✅	❌	❌	❌	✅	4
Post-selection inference with HSIC-Lasso	✅	✅	✅	✅	❌	❌	✅	5
Posterior Value Functions: Hindsight Baselines for Policy Gradient Methods	❌	❌	❌	❌	❌	❌	❌	0
Practical and Private (Deep) Learning Without Sampling or Shuffling	✅	✅	✅	❌	❌	❌	✅	4
Predict then Interpolate: A Simple Algorithm to Learn Stable Classifiers	❌	✅	✅	✅	❌	❌	✅	4
Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations	❌	✅	✅	❌	❌	❌	✅	3
Preferential Temporal Difference Learning	✅	✅	✅	❌	❌	❌	✅	4
Principal Bit Analysis: Autoencoding with Schur-Concave Loss	✅	✅	✅	❌	❌	❌	✅	4
Principal Component Hierarchy for Sparse Quadratic Programs	❌	✅	✅	❌	✅	✅	✅	5
Principled Exploration via Optimistic Bootstrapping and Backward Induction	✅	✅	✅	❌	✅	❌	✅	5
Principled Simplicial Neural Networks for Trajectory Prediction	✅	✅	✅	❌	❌	❌	✅	4
Prior Image-Constrained Reconstruction using Style-Based Generative Models	✅	✅	✅	❌	✅	✅	✅	6
Prioritized Level Replay	✅	✅	✅	❌	❌	❌	✅	4
Privacy-Preserving Feature Selection with Secure Multiparty Computation	✅	❌	✅	✅	✅	✅	✅	6
Privacy-Preserving Video Classification with Convolutional Neural Networks	✅	❌	✅	❌	✅	❌	✅	4
Private Adaptive Gradient Methods for Convex Optimization	✅	✅	✅	✅	❌	❌	✅	5
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates	✅	❌	✅	❌	❌	❌	✅	3
Private Stochastic Convex Optimization: Optimal Rates in L1 Geometry	✅	❌	❌	❌	❌	❌	❌	1
ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations	❌	✅	✅	✅	❌	❌	✅	4
Probabilistic Generating Circuits	❌	❌	✅	✅	❌	❌	✅	3
Probabilistic Programs with Stochastic Conditioning	❌	✅	✅	❌	❌	❌	✅	3
Probabilistic Sequential Shrinking: A Best Arm Identification Algorithm for Stochastic Bandits with Corruptions	✅	✅	❌	❌	❌	❌	✅	3
Problem Dependent View on Structured Thresholding Bandit Problems	✅	❌	❌	❌	❌	❌	❌	1
Progressive-Scale Boundary Blackbox Attack via Projective Gradient Estimation	✅	✅	✅	✅	❌	❌	✅	5
Projection Robust Wasserstein Barycenters	✅	✅	✅	❌	✅	❌	✅	5
Projection techniques to update the truncated SVD of evolving matrices with applications	✅	✅	✅	✅	✅	✅	✅	7
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise	❌	❌	❌	❌	❌	❌	✅	1
Provable Lipschitz Certification for Generative Models	❌	❌	✅	❌	❌	❌	✅	2
Provable Meta-Learning of Linear Representations	✅	✅	❌	❌	❌	❌	✅	3
Provable Robustness of Adversarial Training for Learning Halfspaces with Noise	✅	❌	❌	❌	❌	❌	❌	1
Provably Correct Optimization and Exploration with Non-linear Policies	✅	✅	✅	❌	❌	❌	✅	4
Provably Efficient Algorithms for Multi-Objective Competitive RL	✅	❌	❌	❌	❌	❌	❌	1
Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions	✅	❌	❌	❌	❌	❌	❌	1
Provably Efficient Learning of Transferable Rewards	✅	❌	❌	❌	❌	❌	✅	2
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping	✅	❌	❌	❌	❌	❌	❌	1
Provably End-to-end Label-noise Learning without Anchor Points	❌	❌	✅	✅	✅	❌	✅	4
Provably Strict Generalisation Benefit for Equivariant Models	❌	❌	❌	❌	❌	❌	❌	0
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction	❌	✅	✅	❌	❌	❌	✅	3
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning	✅	❌	✅	❌	❌	❌	❌	2
Pure Exploration and Regret Minimization in Matching Bandits	✅	✅	❌	❌	❌	❌	✅	3
Putting the “Learning" into Learning-Augmented Algorithms for Frequency Estimation	✅	✅	✅	✅	✅	❌	✅	6
Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability	❌	✅	✅	❌	❌	✅	✅	4
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding	❌	✅	✅	✅	❌	❌	❌	3
Quantifying and Reducing Bias in Maximum Likelihood Estimation of Structured Anomalies	❌	❌	✅	❌	❌	❌	✅	2
Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels	❌	❌	❌	❌	❌	❌	✅	1
Quantile Bandits for Best Arms Identification	✅	✅	❌	❌	❌	❌	✅	3
Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding	❌	❌	✅	✅	❌	❌	✅	3
Quantization Algorithms for Random Fourier Features	✅	❌	✅	❌	❌	❌	✅	3
Quantum algorithms for reinforcement learning with a generative model	✅	❌	❌	❌	❌	❌	❌	1
Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data	✅	✅	✅	❌	❌	❌	✅	4
Query Complexity of Adversarial Attacks	❌	❌	❌	❌	❌	❌	❌	0
RATT: Leveraging Unlabeled Data to Guarantee Generalization	❌	❌	✅	❌	❌	❌	✅	2
REPAINT: Knowledge Transfer in Deep Reinforcement Learning	✅	❌	✅	❌	❌	❌	✅	3
RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting	✅	❌	✅	✅	❌	❌	✅	4
RNNRepair: Automatic RNN Repair via Model-based Analysis	❌	✅	✅	❌	❌	❌	✅	3
RRL: Resnet as representation for Reinforcement Learning	✅	❌	✅	❌	❌	❌	✅	3
Randomized Algorithms for Submodular Function Maximization with a $k$-System Constraint	✅	❌	✅	❌	❌	❌	✅	3
Randomized Dimensionality Reduction for Facility Location and Single-Linkage Clustering	❌	❌	✅	❌	✅	❌	✅	3
Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning	❌	✅	✅	❌	❌	❌	❌	2
Randomized Exploration in Reinforcement Learning with General Value Function Approximation	✅	❌	✅	❌	❌	❌	✅	3
Rate-Distortion Analysis of Minimum Excess Risk in Bayesian Learning	❌	❌	❌	❌	❌	❌	❌	0
Re-understanding Finite-State Representations of Recurrent Policy Networks	✅	✅	✅	❌	❌	❌	❌	3
Reasoning Over Virtual Knowledge Bases With Open Predicate Relations	❌	❌	✅	✅	❌	❌	❌	2
Recomposing the Reinforcement Learning Building Blocks with Hypernetworks	❌	✅	✅	❌	❌	❌	✅	3
Recovering AES Keys with a Deep Cold Boot Attack	❌	❌	❌	❌	❌	❌	✅	1
Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach	✅	❌	❌	❌	❌	❌	❌	1
Regret and Cumulative Constraint Violation Analysis for Online Convex Optimization with Long Term Constraints	✅	❌	❌	❌	❌	❌	✅	2
Regularized Online Allocation Problems: Fairness and Beyond	✅	❌	✅	❌	❌	❌	✅	3
Regularized Submodular Maximization at Scale	✅	❌	✅	❌	❌	❌	✅	3
Regularizing towards Causal Invariance: Linear Models with Proxies	❌	✅	✅	✅	❌	❌	✅	4
Reinforcement Learning Under Moral Uncertainty	✅	✅	❌	❌	❌	❌	✅	3
Reinforcement Learning for Cost-Aware Markov Decision Processes	✅	❌	❌	❌	❌	❌	❌	1
Reinforcement Learning of Implicit and Explicit Control Flow Instructions	✅	✅	❌	❌	❌	❌	❌	2
Reinforcement Learning with Prototypical Representations	✅	✅	✅	❌	❌	✅	✅	5
Relative Deviation Margin Bounds	❌	❌	❌	❌	❌	❌	❌	0
Relative Positional Encoding for Transformers with Linear Complexity	✅	✅	✅	✅	❌	❌	✅	5
Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data	❌	✅	✅	✅	❌	❌	✅	4
Representation Matters: Offline Pretraining for Sequential Decision Making	❌	✅	✅	❌	❌	❌	✅	3
Representation Subspace Distance for Domain Adaptation Regression	❌	✅	✅	✅	✅	❌	✅	5
Representational aspects of depth and conditioning in normalizing flows	❌	❌	❌	❌	❌	❌	✅	1
Reserve Price Optimization for First Price Auctions in Display Advertising	✅	❌	❌	❌	❌	❌	✅	2
Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism	✅	❌	❌	❌	❌	❌	✅	2
Rethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives	❌	❌	✅	✅	❌	❌	✅	3
Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss	❌	✅	✅	✅	✅	❌	✅	5
Revealing the Structure of Deep Neural Networks via Convex Duality	❌	❌	✅	❌	❌	❌	✅	2
Revenue-Incentive Tradeoffs in Dynamic Reserve Pricing	❌	❌	❌	❌	❌	❌	✅	1
Revisiting Peng’s Q($λ$) for Modern Reinforcement Learning	❌	❌	❌	❌	❌	❌	✅	1
Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline	❌	✅	✅	✅	✅	✅	✅	6
Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research	❌	✅	✅	❌	❌	❌	✅	3
Reward Identification in Inverse Reinforcement Learning	✅	❌	❌	❌	❌	❌	❌	1
Riemannian Convex Potential Maps	❌	✅	❌	❌	❌	❌	✅	2
Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning	✅	❌	❌	❌	❌	❌	❌	1
Risk-Sensitive Reinforcement Learning with Function Approximation: A Debiasing Approach	✅	❌	❌	❌	❌	❌	❌	1
Rissanen Data Analysis: Examining Dataset Characteristics via Description Length	❌	✅	✅	✅	❌	❌	✅	4
Robust Asymmetric Learning in POMDPs	✅	✅	❌	❌	❌	❌	✅	3
Robust Density Estimation from Batches: The Best Things in Life are (Nearly) Free	✅	❌	❌	❌	✅	❌	✅	3
Robust Inference for High-Dimensional Linear Models via Residual Randomization	✅	✅	❌	❌	❌	❌	✅	3
Robust Learning for Data Poisoning Attacks	❌	✅	✅	✅	❌	❌	✅	4
Robust Learning-Augmented Caching: An Experimental Study	❌	✅	✅	✅	❌	❌	✅	4
Robust Policy Gradient against Strong Data Corruption	✅	❌	✅	❌	❌	❌	✅	3
Robust Pure Exploration in Linear Bandits with Limited Budget	✅	❌	❌	❌	❌	❌	✅	2
Robust Reinforcement Learning using Least Squares Policy Iteration with Provable Performance Guarantees	✅	❌	✅	❌	❌	❌	❌	2
Robust Representation Learning via Perceptual Similarity Metrics	❌	❌	✅	❌	❌	❌	✅	2
Robust Testing and Estimation under Manipulation Attacks	❌	❌	❌	❌	❌	❌	❌	0
Robust Unsupervised Learning via L-statistic Minimization	✅	❌	✅	❌	✅	❌	✅	4
Run-Sort-ReRun: Escaping Batch Size Limitations in Sliced Wasserstein Generative Models	✅	✅	✅	❌	❌	❌	✅	4
SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning	❌	❌	❌	❌	✅	❌	✅	2
SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II	❌	❌	✅	❌	❌	❌	✅	2
SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies	✅	✅	✅	❌	✅	❌	✅	5
SG-PALM: a Fast Physically Interpretable Tensor Graphical Model	✅	✅	✅	❌	✅	✅	✅	6
SGA: A Robust Algorithm for Partial Recovery of Tree-Structured Graphical Models with Noisy Samples	✅	✅	❌	❌	❌	❌	❌	2
SGLB: Stochastic Gradient Langevin Boosting	✅	✅	✅	✅	❌	❌	✅	5
SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes	❌	✅	✅	✅	✅	❌	✅	5
SMG: A Shuffling Gradient-Based Method with Momentum	✅	❌	✅	❌	❌	❌	✅	3
SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation	❌	✅	✅	❌	❌	❌	✅	3
SPECTRE: defending against backdoor attacks using robust statistics	✅	✅	✅	❌	❌	❌	✅	4
STRODE: Stochastic Boundary Ordinary Differential Equation	❌	❌	✅	✅	✅	❌	✅	4
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Safe Reinforcement Learning Using Advantage-Based Intervention	✅	✅	❌	❌	❌	❌	❌	2
Safe Reinforcement Learning with Linear Function Approximation	✅	❌	✅	❌	❌	❌	✅	3
SagaNet: A Small Sample Gated Network for Pediatric Cancer Diagnosis	✅	❌	✅	✅	✅	❌	✅	5
Sample Complexity of Robust Linear Classification on Separated Data	✅	❌	❌	❌	❌	❌	❌	1
Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity	✅	❌	❌	❌	❌	❌	❌	1
Sample-Optimal PAC Learning of Halfspaces with Malicious Noise	✅	❌	❌	❌	❌	❌	❌	1
Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network	✅	✅	✅	❌	✅	❌	✅	5
Scalable Certified Segmentation via Randomized Smoothing	✅	✅	✅	❌	✅	✅	✅	6
Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks	✅	✅	✅	❌	❌	❌	✅	4
Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot	❌	❌	❌	❌	❌	❌	✅	1
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning	✅	❌	✅	❌	❌	❌	✅	3
Scalable Normalizing Flows for Permutation Invariant Densities	❌	❌	✅	✅	✅	❌	✅	4
Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More	❌	✅	✅	❌	❌	❌	✅	3
Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition	❌	✅	✅	✅	✅	❌	✅	5
Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing	❌	✅	✅	❌	✅	❌	✅	4
Scaling Properties of Deep Residual Networks	❌	✅	✅	❌	❌	❌	✅	3
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision	❌	❌	✅	✅	✅	❌	✅	4
Segmenting Hybrid Trajectories using Latent ODEs	✅	✅	✅	✅	❌	❌	❌	4
Selecting Data Augmentation for Simulating Interventions	❌	✅	✅	✅	❌	❌	✅	4
Self Normalizing Flows	❌	✅	✅	✅	❌	❌	❌	3
Self-Damaging Contrastive Learning	❌	✅	✅	✅	✅	❌	✅	5
Self-Improved Retrosynthetic Planning	✅	❌	✅	✅	❌	❌	✅	4
Self-Paced Context Evaluation for Contextual Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Self-Tuning for Data-Efficient Deep Learning	❌	✅	✅	✅	❌	❌	✅	4
Self-supervised Graph-level Representation Learning with Local and Global Structure	✅	❌	✅	✅	❌	❌	✅	4
Self-supervised and Supervised Joint Training for Resource-rich Machine Translation	✅	❌	✅	✅	✅	❌	✅	5
Selfish Sparse RNN Training	✅	✅	✅	✅	❌	❌	✅	5
Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts	❌	✅	✅	❌	✅	✅	✅	5
Sharf: Shape-conditioned Radiance Fields from a Single View	❌	❌	✅	❌	❌	❌	✅	2
Sharing Less is More: Lifelong Learning in Deep Networks with Selective Layer Transfer	✅	✅	✅	✅	❌	❌	❌	4
Sharper Generalization Bounds for Clustering	❌	❌	❌	❌	❌	❌	❌	0
Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks	❌	❌	✅	❌	❌	❌	✅	2
SiameseXML: Siamese Networks meet Extreme Classifiers with 100M Labels	❌	✅	✅	✅	✅	❌	✅	5
SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data	✅	❌	✅	✅	❌	❌	✅	4
Signatured Deep Fictitious Play for Mean Field Games with Common Noise	✅	✅	❌	✅	✅	❌	✅	5
SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks	✅	✅	✅	✅	✅	❌	✅	6
Simple and Effective VAE Training with Calibrated Decoders	❌	✅	✅	❌	❌	❌	✅	3
Simultaneous Similarity-based Self-Distillation for Deep Metric Learning	✅	✅	✅	✅	❌	❌	✅	5
SinIR: Efficient General Image Manipulation with Single Image Reconstruction	❌	✅	✅	❌	✅	❌	✅	4
Single Pass Entrywise-Transformed Low Rank Approximation	✅	❌	✅	❌	✅	✅	✅	5
Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training	✅	✅	✅	❌	❌	❌	✅	4
Size-Invariant Graph Representations for Graph Classification Extrapolations	❌	✅	✅	✅	❌	❌	✅	4
SketchEmbedNet: Learning Novel Concepts by Imitating Drawings	❌	❌	✅	✅	❌	❌	✅	3
Skew Orthogonal Convolutions	✅	✅	✅	❌	✅	❌	✅	5
Skill Discovery for Exploration and Planning using Deep Skill Graphs	❌	✅	✅	❌	❌	❌	✅	3
Sliced Iterative Normalizing Flows	✅	❌	✅	✅	✅	❌	✅	5
Slot Machines: Discovering Winning Combinations of Random Weights in Neural Networks	❌	❌	✅	✅	❌	❌	✅	3
Smooth $p$-Wasserstein Distance: Structure, Empirical Approximation, and Statistical Applications	❌	✅	❌	❌	❌	❌	✅	2
Soft then Hard: Rethinking the Quantization in Neural Image Compression	❌	❌	✅	❌	✅	❌	✅	3
Solving Challenging Dexterous Manipulation Tasks With Trajectory Optimisation and Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Solving Inverse Problems with a Flow-based Noise Model	❌	❌	✅	❌	❌	❌	✅	2
Solving high-dimensional parabolic PDEs using the tensor train format	✅	❌	❌	❌	❌	❌	✅	2
SoundDet: Polyphonic Moving Sound Event Detection and Localization from Raw Waveform	❌	❌	✅	✅	✅	❌	✅	4
Sparse Bayesian Learning via Stepwise Regression	✅	✅	✅	✅	✅	❌	✅	6
Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient	✅	❌	❌	❌	❌	❌	✅	2
Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm	✅	✅	✅	✅	❌	❌	✅	5
Sparse within Sparse Gaussian Processes using Neighbor Information	✅	❌	✅	✅	❌	❌	✅	4
SparseBERT: Rethinking the Importance Analysis in Self-attention	✅	✅	✅	✅	✅	❌	✅	6
Sparsifying Networks via Subdifferential Inclusion	✅	✅	✅	❌	❌	❌	✅	4
Sparsity-Agnostic Lasso Bandit	✅	❌	❌	❌	❌	❌	❌	1
Spectral Normalisation for Deep Reinforcement Learning: An Optimisation Perspective	✅	✅	✅	❌	❌	❌	❌	3
Spectral Smoothing Unveils Phase Transitions in Hierarchical Variational Autoencoders	❌	❌	✅	❌	❌	❌	✅	2
Spectral vertex sparsifiers and pair-wise spanners over distributed graphs	✅	❌	❌	❌	✅	❌	✅	3
SpreadsheetCoder: Formula Prediction from Semi-structured Context	❌	✅	✅	✅	❌	❌	✅	4
Stability and Convergence of Stochastic Gradient Clipping: Beyond Lipschitz Continuity and Smoothness	❌	❌	✅	❌	❌	❌	✅	2
Stability and Generalization of Stochastic Gradient Methods for Minimax Problems	❌	✅	✅	❌	❌	❌	✅	3
Stabilizing Equilibrium Models by Jacobian Regularization	❌	✅	✅	✅	❌	❌	✅	4
State Entropy Maximization with Random Encoders for Efficient Exploration	✅	✅	✅	❌	❌	❌	✅	4
State Relevance for Off-Policy Evaluation	✅	✅	❌	❌	❌	❌	✅	3
Statistical Estimation from Dependent Data	❌	❌	✅	✅	❌	❌	❌	2
Stochastic Iterative Graph Matching	✅	❌	✅	✅	✅	❌	✅	5
Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions	✅	❌	❌	❌	❌	❌	✅	2
Stochastic Sign Descent Methods: New Algorithms and Better Theory	✅	❌	✅	❌	❌	❌	✅	3
Straight to the Gradient: Learning to Use Novel Tokens for Neural Text Generation	❌	✅	✅	✅	✅	✅	✅	6
Strategic Classification Made Practical	❌	✅	✅	✅	❌	❌	✅	4
Strategic Classification in the Dark	❌	✅	✅	✅	❌	❌	❌	3
Streaming Bayesian Deep Tensor Factorization	✅	❌	✅	❌	✅	❌	✅	4
Streaming and Distributed Algorithms for Robust Column Subset Selection	✅	✅	✅	❌	✅	❌	✅	5
Structured Convolutional Kernel Networks for Airline Crew Scheduling	✅	✅	✅	❌	✅	❌	❌	4
Structured World Belief for Reinforcement Learning in POMDP	✅	❌	✅	❌	❌	❌	✅	3
Submodular Maximization subject to a Knapsack Constraint: Combinatorial Algorithms with Near-optimal Adaptive Complexity	✅	❌	✅	❌	❌	❌	✅	3
Supervised Tree-Wasserstein Distance	✅	❌	✅	✅	✅	❌	✅	5
Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approach	✅	✅	✅	✅	❌	❌	✅	5
Synthesizer: Rethinking Self-Attention for Transformer Models	❌	✅	✅	❌	✅	❌	❌	3
Systematic Analysis of Cluster Similarity Indices: How to Validate Validation Measures	❌	❌	✅	❌	❌	❌	❌	1
T-SCI: A Two-Stage Conformal Inference Algorithm with Guaranteed Coverage for Cox-MLP	✅	❌	✅	✅	❌	❌	❌	3
TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer	✅	✅	✅	✅	✅	❌	✅	6
Targeted Data Acquisition for Evolving Negotiation Agents	❌	❌	✅	❌	❌	❌	❌	1
Task-Optimal Exploration in Linear Dynamical Systems	✅	❌	❌	❌	❌	❌	✅	2
Taylor Expansion of Discount Factors	✅	❌	✅	❌	❌	❌	❌	2
TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL	❌	✅	❌	❌	❌	❌	✅	2
TempoRL: Learning When to Act	✅	✅	✅	❌	✅	✅	✅	6
Temporal Difference Learning as Gradient Splitting	✅	❌	❌	❌	❌	❌	❌	1
Temporal Predictive Coding For Model-Based Planning In Latent Space	❌	❌	✅	❌	❌	❌	❌	1
Temporally Correlated Task Scheduling for Sequence Learning	✅	✅	✅	✅	❌	❌	✅	5
Tensor Programs IIb: Architectural Universality Of Neural Tangent Kernel Training Dynamics	❌	❌	❌	❌	❌	❌	❌	0
Tensor Programs IV: Feature Learning in Infinite-Width Neural Networks	❌	❌	✅	❌	❌	❌	✅	2
TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models	✅	✅	✅	❌	✅	❌	✅	5
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning	✅	❌	✅	❌	❌	❌	❌	2
Testing DNN-based Autonomous Driving Systems under Critical Environmental Conditions	❌	✅	✅	❌	❌	❌	✅	3
Testing Group Fairness via Optimal Transport Projections	✅	❌	✅	❌	❌	❌	❌	2
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation	✅	✅	✅	❌	❌	❌	✅	4
The Earth Mover’s Pinball Loss: Quantiles for Histogram-Valued Regression	❌	❌	✅	❌	❌	❌	✅	2
The Emergence of Individuality	❌	❌	❌	❌	❌	❌	❌	0
The Heavy-Tail Phenomenon in SGD	❌	✅	✅	❌	❌	❌	✅	3
The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning	❌	❌	✅	❌	❌	❌	❌	1
The Impact of Record Linkage on Learning from Feature Partitioned Data	❌	❌	✅	❌	❌	❌	✅	2
The Implicit Bias for Adaptive Optimization Algorithms on Homogeneous Neural Networks	❌	❌	✅	❌	❌	❌	✅	2
The Limits of Min-Max Optimization Algorithms: Convergence to Spurious Non-Critical Sets	❌	❌	❌	❌	❌	❌	❌	0
The Lipschitz Constant of Self-Attention	❌	❌	✅	✅	❌	❌	✅	3
The Logical Options Framework	✅	✅	✅	❌	❌	❌	❌	3
The Power of Adaptivity for Stochastic Submodular Cover	✅	❌	✅	❌	✅	✅	✅	5
The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy Optimization	❌	✅	✅	❌	❌	❌	❌	2
The Symmetry between Arms and Knapsacks: A Primal-Dual Approach for Bandits with Knapsacks	✅	❌	❌	❌	❌	❌	❌	1
Theory of Spectral Method for Union of Subspaces-Based Random Geometry Graph	✅	❌	❌	❌	❌	❌	✅	2
Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces	✅	✅	✅	❌	❌	❌	✅	4
Thinking Like Transformers	✅	✅	❌	❌	❌	❌	❌	2
Three Operator Splitting with a Nonconvex Loss Function	✅	✅	✅	❌	✅	✅	✅	6
Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks	❌	❌	❌	❌	❌	❌	❌	0
Tightening the Dependence on Horizon in the Sample Complexity of Q-Learning	✅	❌	❌	❌	❌	❌	❌	1
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients	❌	✅	✅	✅	✅	❌	✅	5
Tilting the playing field: Dynamical loss functions for machine learning	❌	✅	✅	❌	❌	❌	✅	3
To be Robust or to be Fair: Towards Fairness in Adversarial Training	✅	✅	✅	✅	❌	❌	✅	5
Top-k eXtreme Contextual Bandits with Arm Hierarchy	✅	✅	✅	❌	❌	❌	✅	4
Toward Better Generalization Bounds with Locally Elastic Stability	❌	❌	✅	❌	❌	❌	✅	2
Toward Understanding the Feature Learning Process of Self-supervised Contrastive Learning	❌	❌	✅	❌	❌	❌	✅	2
Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawing	❌	❌	✅	❌	❌	❌	❌	1
Towards Better Robust Generalization with Shift Consistency Regularization	✅	❌	✅	❌	❌	❌	✅	3
Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neurons	❌	✅	✅	❌	✅	❌	✅	4
Towards Defending against Adversarial Examples via Attack-Invariant Features	✅	✅	✅	✅	✅	❌	✅	6
Towards Distraction-Robust Active Visual Tracking	❌	✅	❌	❌	❌	❌	❌	1
Towards Domain-Agnostic Contrastive Learning	✅	❌	✅	✅	❌	❌	✅	4
Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning	✅	✅	❌	❌	❌	❌	✅	3
Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach	❌	✅	✅	✅	✅	❌	✅	5
Towards Practical Mean Bounds for Small Samples	✅	✅	❌	❌	❌	❌	✅	3
Towards Rigorous Interpretations: a Formalisation of Feature Attribution	❌	✅	✅	✅	❌	❌	✅	4
Towards Tight Bounds on the Sample Complexity of Average-reward MDPs	❌	❌	❌	❌	❌	❌	❌	0
Towards Understanding Learning in Neural Networks with Linear Teachers	❌	❌	✅	❌	❌	❌	✅	2
Towards Understanding and Mitigating Social Biases in Language Models	✅	✅	✅	❌	❌	❌	❌	3
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations	❌	❌	✅	✅	❌	❌	✅	3
Tractable structured natural-gradient descent using local parameterizations	❌	❌	✅	❌	❌	❌	✅	2
Train simultaneously, generalize better: Stability of gradient-based minimax learners	❌	❌	✅	❌	❌	❌	✅	2
Training Adversarially Robust Sparse Networks via Bayesian Connectivity Sampling	✅	✅	✅	❌	❌	❌	✅	4
Training Data Subset Selection for Regression with Controlled Generalization Error	✅	✅	✅	✅	❌	❌	✅	5
Training Graph Neural Networks with 1000 Layers	❌	✅	✅	✅	✅	❌	✅	5
Training Quantized Neural Networks to Global Optimality via Semidefinite Programming	✅	❌	✅	❌	✅	❌	✅	4
Training Recurrent Neural Networks via Forward Propagation Through Time	✅	✅	✅	✅	✅	❌	✅	6
Training data-efficient image transformers & distillation through attention	❌	✅	✅	✅	✅	❌	✅	5
Trajectory Diversity for Zero-Shot Coordination	✅	✅	✅	❌	❌	❌	✅	4
Transfer-Based Semantic Anomaly Detection	❌	✅	✅	❌	❌	❌	✅	3
Trees with Attention for Set Prediction Tasks	❌	✅	✅	✅	❌	❌	✅	4
Two Heads are Better Than One: Hypergraph-Enhanced Graph Reasoning for Visual Event Ratiocination	❌	❌	✅	✅	❌	❌	✅	3
Two-way kernel matrix puncturing: towards resource-efficient PCA and spectral clustering	❌	✅	✅	❌	❌	❌	✅	3
UCB Momentum Q-learning: Correcting the bias without forgetting	✅	✅	✅	❌	❌	❌	✅	4
UnICORNN: A recurrent model for learning very long time dependencies	❌	✅	✅	✅	✅	❌	✅	5
Unbalanced minibatch Optimal Transport; applications to Domain Adaptation	❌	✅	✅	✅	❌	❌	✅	4
Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies	✅	✅	✅	❌	✅	❌	✅	5
Uncertainty Principles of Encoding GANs	❌	❌	❌	❌	❌	❌	✅	1
Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability	❌	✅	✅	❌	❌	❌	✅	3
Understanding Failures in Out-of-Distribution Detection with Deep Generative Models	❌	❌	✅	❌	❌	❌	✅	2
Understanding Instance-Level Label Noise: Disparate Impacts and Treatments	❌	❌	✅	❌	❌	❌	❌	1
Understanding Invariance via Feedforward Inversion of Discriminatively Trained Classifiers	❌	❌	✅	❌	✅	❌	❌	2
Understanding Noise Injection in GANs	❌	❌	✅	❌	❌	❌	❌	1
Understanding and Mitigating Accuracy Disparity in Regression	❌	✅	✅	❌	❌	❌	✅	3
Understanding self-supervised learning dynamics without contrastive pairs	❌	✅	✅	❌	❌	❌	✅	3
Understanding the Dynamics of Gradient Flow in Overparameterized Linear models	❌	❌	❌	❌	❌	❌	✅	1
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning	❌	❌	✅	❌	❌	❌	✅	2
UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data	❌	✅	✅	✅	❌	✅	✅	5
Unified Robust Semi-Supervised Variational Autoencoder	❌	❌	✅	❌	✅	❌	✅	3
Uniform Convergence, Adversarial Spheres and a Simple Remedy	❌	❌	❌	❌	❌	❌	✅	1
Unifying Vision-and-Language Tasks via Text Generation	❌	✅	✅	✅	✅	✅	✅	6
Unitary Branching Programs: Learnability and Lower Bounds	✅	✅	❌	❌	✅	❌	✅	4
Unsupervised Co-part Segmentation through Assembly	❌	❌	✅	❌	❌	❌	❌	1
Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot Classification	✅	✅	✅	✅	❌	❌	✅	5
Unsupervised Learning of Visual 3D Keypoints for Control	✅	✅	✅	✅	❌	❌	✅	5
Unsupervised Part Representation by Flow Capsules	❌	❌	✅	✅	❌	❌	✅	3
Unsupervised Representation Learning via Neural Activation Coding	❌	✅	✅	❌	❌	❌	✅	3
Unsupervised Skill Discovery with Bottleneck Option Learning	✅	✅	✅	❌	❌	❌	✅	4
Valid Causal Inference with (Some) Invalid Instruments	✅	✅	❌	❌	❌	✅	❌	3
Value Alignment Verification	❌	✅	✅	❌	❌	❌	❌	2
Value Iteration in Continuous Actions, States and Time	✅	❌	✅	❌	✅	❌	✅	4
Value-at-Risk Optimization with Gaussian Processes	✅	❌	✅	❌	❌	❌	✅	3
Variance Reduced Training with Stratified Sampling for Forecasting Models	✅	❌	✅	❌	✅	❌	✅	4
Variance Reduction via Primal-Dual Accelerated Dual Averaging for Nonsmooth Convex Finite-Sums	✅	❌	✅	❌	❌	❌	✅	3
Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models	❌	✅	✅	❌	❌	❌	✅	3
Variational Auto-Regressive Gaussian Processes for Continual Learning	✅	✅	✅	✅	❌	❌	✅	5
Variational Data Assimilation with a Learned Inverse Observation Operator	❌	✅	❌	❌	✅	❌	✅	3
Variational Empowerment as Representation Learning for Goal-Conditioned Reinforcement Learning	❌	❌	✅	❌	❌	❌	✅	2
Vector Quantized Models for Planning	❌	❌	✅	✅	❌	❌	✅	3
Versatile Verification of Tree Ensembles	❌	✅	✅	❌	✅	✅	✅	5
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision	❌	✅	✅	✅	✅	❌	✅	5
Voice2Series: Reprogramming Acoustic Models for Time Series Classification	✅	✅	✅	✅	❌	✅	✅	6
WGAN with an Infinitely Wide Generator Has No Spurious Stationary Points	❌	✅	❌	❌	❌	❌	✅	2
WILDS: A Benchmark of in-the-Wild Distribution Shifts	❌	✅	✅	✅	❌	❌	✅	4
Wasserstein Distributional Normalization For Robust Distributional Certification of Noisy Labeled Data	✅	❌	✅	✅	❌	❌	✅	4
Watermarking Deep Neural Networks with Greedy Residuals	✅	✅	✅	❌	❌	❌	✅	4
Weight-covariance alignment for adversarially robust neural networks	❌	❌	✅	❌	❌	❌	✅	2
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks	❌	❌	✅	✅	❌	❌	✅	3
What Are Bayesian Neural Network Posteriors Really Like?	✅	✅	✅	❌	✅	❌	✅	5
What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?	❌	❌	✅	✅	❌	❌	❌	2
What Makes for End-to-End Object Detection?	❌	❌	✅	✅	❌	❌	❌	2
What does LIME really see in images?	❌	✅	✅	❌	❌	❌	✅	3
What’s in the Box? Exploring the Inner Life of Neural Networks with Robust Rules	✅	✅	✅	❌	❌	❌	❌	3
When All We Need is a Piece of the Pie: A Generic Framework for Optimizing Two-way Partial AUC	❌	❌	✅	❌	❌	❌	❌	1
When Does Data Augmentation Help With Membership Inference Attacks?	❌	✅	✅	✅	❌	❌	✅	4
Which transformer architecture fits my data? A vocabulary bottleneck in self-attention	❌	❌	✅	❌	❌	❌	✅	2
Whitening and Second Order Optimization Both Make Information in the Dataset Unusable During Training, and Can Reduce or Prevent Generalization	❌	❌	✅	✅	❌	❌	✅	3
Whitening for Self-Supervised Representation Learning	❌	✅	✅	❌	❌	❌	✅	3
Whittle Networks: A Deep Likelihood Model for Time Series	❌	✅	✅	❌	❌	❌	✅	3
Winograd Algorithm for AdderNet	❌	❌	✅	❌	✅	❌	✅	3
World Model as a Graph: Learning Latent Landmarks for Planning	✅	✅	✅	❌	❌	❌	✅	4
XOR-CD: Linearly Convergent Constrained Structure Generation	✅	❌	✅	✅	❌	❌	✅	4
You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling	✅	✅	✅	✅	✅	❌	✅	6
Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting	❌	✅	✅	✅	✅	❌	✅	5
Zero-Shot Knowledge Distillation from a Decision-Based Black-Box Model	❌	❌	✅	❌	❌	❌	✅	2
Zero-Shot Text-to-Image Generation	❌	✅	✅	✅	✅	❌	✅	5
Zeroth-Order Non-Convex Learning via Hierarchical Dual Averaging	❌	❌	❌	❌	❌	❌	❌	0
Zoo-Tuning: Adaptive Transfer from A Zoo of Models	❌	❌	✅	✅	❌	❌	✅	3
f-Domain Adversarial Learning: Theory and Algorithms	❌	❌	✅	✅	✅	❌	✅	4
iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients	✅	❌	✅	✅	❌	❌	✅	4