reproducibilityindex.ai

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 [1].

International Conference on Machine Learning (ICML) - 2021

Website:

Venue	Year	Papers	Reproducibility Score Reproducibility Score based on Gundersen et al. (2025)	Documentation Score Global mean is the average score over the seven reproducibility variables for empirical research papers.	% 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