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].

Conference on Neural Information Processing Systems (NeurIPS) - 2020

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
NeurIPS	2020	1898	0.56	3.38	89.88%	43.26%

Search Papers

	Pseudocode	Open Source Code	Open Datasets	Dataset Splits	Hardware Specification	Software Dependencies	Experiment Setup
(De)Randomized Smoothing for Certifiable Defense against Patch Attacks	❌	❌	✅	✅	✅	❌	✅	4
3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data	❌	❌	✅	❌	❌	❌	✅	2
3D Self-Supervised Methods for Medical Imaging	❌	✅	✅	✅	❌	❌	❌	3
3D Shape Reconstruction from Vision and Touch	❌	✅	❌	❌	❌	❌	❌	1
A Bandit Learning Algorithm and Applications to Auction Design	✅	❌	❌	❌	❌	❌	❌	1
A Bayesian Nonparametrics View into Deep Representations	❌	❌	✅	❌	❌	❌	❌	1
A Bayesian Perspective on Training Speed and Model Selection	✅	❌	✅	❌	✅	❌	✅	4
A Benchmark for Systematic Generalization in Grounded Language Understanding	❌	✅	✅	✅	❌	❌	✅	4
A Biologically Plausible Neural Network for Slow Feature Analysis	✅	✅	✅	❌	❌	❌	❌	3
A Boolean Task Algebra for Reinforcement Learning	✅	❌	❌	❌	❌	❌	❌	1
A Catalyst Framework for Minimax Optimization	✅	❌	❌	❌	❌	❌	✅	2
A Causal View on Robustness of Neural Networks	❌	❌	✅	❌	❌	❌	❌	1
A Class of Algorithms for General Instrumental Variable Models	✅	✅	✅	❌	❌	❌	✅	4
A Closer Look at Accuracy vs. Robustness	❌	✅	✅	❌	❌	❌	✅	3
A Closer Look at the Training Strategy for Modern Meta-Learning	❌	✅	✅	✅	❌	❌	✅	4
A Combinatorial Perspective on Transfer Learning	❌	✅	✅	✅	❌	❌	✅	4
A Computational Separation between Private Learning and Online Learning	✅	❌	❌	❌	❌	❌	❌	1
A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval	❌	❌	❌	❌	❌	❌	✅	1
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions	✅	❌	✅	❌	❌	❌	✅	3
A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction	❌	✅	✅	✅	✅	❌	✅	5
A Decentralized Parallel Algorithm for Training Generative Adversarial Nets	✅	❌	✅	❌	❌	❌	✅	3
A Dictionary Approach to Domain-Invariant Learning in Deep Networks	❌	❌	✅	✅	❌	❌	❌	2
A Discrete Variational Recurrent Topic Model without the Reparametrization Trick	❌	✅	✅	✅	❌	❌	✅	4
A Dynamical Central Limit Theorem for Shallow Neural Networks	❌	❌	❌	❌	❌	❌	✅	1
A Fair Classifier Using Kernel Density Estimation	❌	❌	✅	❌	✅	❌	✅	3
A Feasible Level Proximal Point Method for Nonconvex Sparse Constrained Optimization	✅	❌	✅	✅	❌	❌	✅	4
A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods	✅	❌	❌	❌	❌	❌	❌	1
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding	✅	✅	✅	✅	❌	❌	✅	5
A Game Theoretic Analysis of Additive Adversarial Attacks and Defenses	❌	❌	✅	❌	❌	❌	❌	1
A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling	❌	❌	❌	❌	❌	❌	❌	0
A General Large Neighborhood Search Framework for Solving Integer Linear Programs	✅	❌	✅	✅	❌	✅	✅	5
A General Method for Robust Learning from Batches	❌	❌	❌	❌	❌	❌	❌	0
A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks	✅	❌	❌	❌	❌	❌	❌	1
A Group-Theoretic Framework for Data Augmentation	❌	❌	✅	❌	❌	❌	✅	2
A Limitation of the PAC-Bayes Framework	❌	❌	❌	❌	❌	❌	❌	0
A Local Temporal Difference Code for Distributional Reinforcement Learning	❌	❌	❌	❌	❌	❌	❌	0
A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model	❌	✅	✅	✅	✅	❌	✅	5
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices	✅	❌	✅	❌	❌	❌	✅	3
A Maximum-Entropy Approach to Off-Policy Evaluation in Average-Reward MDPs	❌	❌	✅	❌	❌	❌	✅	2
A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings	❌	❌	❌	❌	❌	❌	✅	1
A Non-Asymptotic Analysis for Stein Variational Gradient Descent	❌	❌	❌	❌	❌	❌	❌	0
A Novel Approach for Constrained Optimization in Graphical Models	✅	❌	✅	❌	❌	✅	✅	4
A Novel Automated Curriculum Strategy to Solve Hard Sokoban Planning Instances	✅	❌	✅	❌	❌	❌	✅	3
A Randomized Algorithm to Reduce the Support of Discrete Measures	✅	✅	✅	❌	✅	❌	❌	4
A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection	✅	✅	✅	✅	✅	❌	✅	6
A Robust Functional EM Algorithm for Incomplete Panel Count Data	✅	❌	✅	❌	❌	❌	✅	3
A Scalable Approach for Privacy-Preserving Collaborative Machine Learning	✅	❌	✅	❌	✅	❌	✅	4
A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees	✅	❌	✅	✅	❌	✅	✅	5
A Self-Tuning Actor-Critic Algorithm	✅	❌	✅	❌	❌	❌	✅	3
A Simple Language Model for Task-Oriented Dialogue	❌	✅	✅	✅	❌	❌	❌	3
A Simple and Efficient Smoothing Method for Faster Optimization and Local Exploration	✅	❌	❌	❌	❌	❌	✅	2
A Single Recipe for Online Submodular Maximization with Adversarial or Stochastic Constraints	✅	❌	✅	❌	❌	❌	✅	3
A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problems	✅	❌	✅	❌	❌	❌	❌	2
A Spectral Energy Distance for Parallel Speech Synthesis	❌	✅	❌	✅	✅	❌	✅	4
A Statistical Framework for Low-bitwidth Training of Deep Neural Networks	❌	✅	✅	✅	✅	❌	✅	5
A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning	❌	❌	❌	❌	❌	❌	✅	1
A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm	✅	❌	✅	❌	❌	❌	✅	3
A Study on Encodings for Neural Architecture Search	❌	✅	✅	✅	✅	❌	✅	5
A Theoretical Framework for Target Propagation	❌	✅	✅	✅	❌	❌	✅	4
A Tight Lower Bound and Efficient Reduction for Swap Regret	✅	❌	❌	❌	❌	❌	❌	1
A Topological Filter for Learning with Label Noise	✅	✅	✅	✅	✅	❌	✅	6
A Unified Switching System Perspective and Convergence Analysis of Q-Learning Algorithms	❌	❌	❌	❌	❌	❌	✅	1
A Unified View of Label Shift Estimation	❌	❌	✅	✅	❌	❌	✅	3
A Unifying View of Optimism in Episodic Reinforcement Learning	❌	❌	❌	❌	❌	❌	❌	0
A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions	❌	❌	❌	❌	❌	❌	❌	0
A Variational Approach for Learning from Positive and Unlabeled Data	✅	✅	✅	✅	❌	✅	✅	6
A causal view of compositional zero-shot recognition	❌	✅	✅	✅	✅	❌	✅	5
A convex optimization formulation for multivariate regression	❌	❌	❌	✅	❌	❌	✅	2
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning	❌	✅	❌	❌	❌	❌	✅	2
A graph similarity for deep learning	✅	✅	✅	✅	❌	❌	✅	5
A kernel test for quasi-independence	❌	❌	✅	❌	❌	❌	❌	1
A mathematical model for automatic differentiation in machine learning	✅	❌	❌	❌	❌	❌	❌	1
A mathematical theory of cooperative communication	❌	❌	✅	❌	❌	❌	✅	2
A mean-field analysis of two-player zero-sum games	✅	✅	✅	❌	❌	❌	✅	4
A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network	❌	❌	❌	❌	❌	❌	✅	1
A new convergent variant of Q-learning with linear function approximation	❌	❌	✅	❌	❌	❌	✅	2
A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons	❌	✅	❌	❌	❌	❌	✅	2
A novel variational form of the Schatten-$p$ quasi-norm	❌	❌	✅	❌	✅	✅	✅	4
A polynomial-time algorithm for learning nonparametric causal graphs	✅	✅	❌	❌	❌	❌	❌	2
A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent	❌	❌	✅	❌	❌	❌	✅	2
A shooting formulation of deep learning	❌	✅	✅	✅	❌	❌	✅	4
A simple normative network approximates local non-Hebbian learning in the cortex	✅	❌	✅	❌	❌	❌	❌	2
A/B Testing in Dense Large-Scale Networks: Design and Inference	✅	❌	❌	❌	❌	❌	❌	1
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity	❌	✅	✅	✅	❌	❌	✅	4
AOT: Appearance Optimal Transport Based Identity Swapping for Forgery Detection	❌	❌	✅	❌	❌	❌	❌	1
ARMA Nets: Expanding Receptive Field for Dense Prediction	❌	✅	✅	✅	❌	❌	✅	4
AViD Dataset: Anonymized Videos from Diverse Countries	❌	✅	✅	❌	✅	❌	✅	4
Accelerating Reinforcement Learning through GPU Atari Emulation	❌	✅	✅	❌	✅	❌	✅	4
Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping	✅	✅	✅	✅	✅	❌	✅	6
Acceleration with a Ball Optimization Oracle	✅	❌	❌	❌	❌	❌	❌	1
Achieving Equalized Odds by Resampling Sensitive Attributes	✅	✅	✅	✅	❌	❌	❌	4
Active Invariant Causal Prediction: Experiment Selection through Stability	✅	✅	❌	❌	❌	❌	✅	3
Active Structure Learning of Causal DAGs via Directed Clique Trees	✅	✅	❌	❌	❌	❌	✅	3
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients	✅	✅	✅	✅	❌	❌	✅	5
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning	❌	❌	✅	❌	❌	❌	✅	2
AdaTune: Adaptive Tensor Program Compilation Made Efficient	✅	❌	✅	❌	✅	❌	✅	4
Adam with Bandit Sampling for Deep Learning	✅	❌	✅	❌	❌	❌	✅	3
Adaptation Properties Allow Identification of Optimized Neural Codes	❌	❌	❌	❌	❌	❌	❌	0
Adapting Neural Architectures Between Domains	❌	❌	✅	✅	❌	❌	✅	3
Adapting to Misspecification in Contextual Bandits	✅	❌	❌	❌	❌	❌	❌	1
Adaptive Discretization for Model-Based Reinforcement Learning	✅	✅	❌	❌	❌	❌	✅	3
Adaptive Experimental Design with Temporal Interference: A Maximum Likelihood Approach	❌	❌	❌	❌	❌	❌	❌	0
Adaptive Gradient Quantization for Data-Parallel SGD	✅	✅	✅	✅	❌	❌	✅	5
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting	❌	✅	✅	✅	✅	✅	✅	6
Adaptive Importance Sampling for Finite-Sum Optimization and Sampling with Decreasing Step-Sizes	❌	❌	✅	❌	❌	❌	✅	2
Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier with Application to Real-Time Information Filtering on the Web	❌	✅	✅	❌	❌	❌	✅	3
Adaptive Learning of Rank-One Models for Efficient Pairwise Sequence Alignment	✅	✅	✅	❌	❌	❌	❌	3
Adaptive Online Estimation of Piecewise Polynomial Trends	✅	❌	❌	❌	❌	❌	✅	2
Adaptive Probing Policies for Shortest Path Routing	✅	❌	✅	❌	❌	❌	✅	3
Adaptive Reduced Rank Regression	✅	❌	❌	❌	❌	❌	❌	1
Adaptive Sampling for Stochastic Risk-Averse Learning	✅	✅	✅	❌	❌	❌	✅	4
Adaptive Shrinkage Estimation for Streaming Graphs	✅	❌	✅	❌	✅	❌	✅	4
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows	✅	✅	✅	❌	❌	❌	✅	4
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization	❌	❌	✅	❌	❌	❌	✅	2
Adversarial Attacks on Deep Graph Matching	✅	❌	✅	❌	❌	❌	✅	3
Adversarial Attacks on Linear Contextual Bandits	✅	❌	✅	❌	❌	❌	✅	3
Adversarial Bandits with Corruptions: Regret Lower Bound and No-regret Algorithm	✅	❌	❌	❌	❌	❌	❌	1
Adversarial Blocking Bandits	✅	❌	❌	❌	❌	❌	❌	1
Adversarial Counterfactual Learning and Evaluation for Recommender System	✅	❌	✅	✅	❌	❌	❌	3
Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion	✅	✅	✅	❌	❌	❌	❌	3
Adversarial Distributional Training for Robust Deep Learning	✅	✅	✅	❌	❌	❌	✅	4
Adversarial Example Games	❌	✅	✅	❌	❌	❌	✅	3
Adversarial Learning for Robust Deep Clustering	✅	✅	✅	✅	❌	❌	✅	5
Adversarial Robustness of Supervised Sparse Coding	❌	✅	✅	✅	❌	❌	✅	4
Adversarial Self-Supervised Contrastive Learning	✅	✅	✅	❌	❌	❌	✅	4
Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization	✅	❌	❌	❌	❌	❌	✅	2
Adversarial Sparse Transformer for Time Series Forecasting	✅	❌	✅	❌	❌	❌	✅	3
Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation	✅	✅	✅	❌	❌	❌	❌	3
Adversarial Training is a Form of Data-dependent Operator Norm Regularization	❌	❌	✅	❌	✅	❌	❌	2
Adversarial Weight Perturbation Helps Robust Generalization	✅	✅	✅	❌	❌	❌	✅	4
Adversarial robustness via robust low rank representations	✅	❌	✅	❌	❌	❌	✅	3
Adversarially Robust Few-Shot Learning: A Meta-Learning Approach	✅	✅	✅	❌	❌	❌	✅	4
Adversarially Robust Streaming Algorithms via Differential Privacy	✅	❌	❌	❌	❌	❌	❌	1
Adversarially-learned Inference via an Ensemble of Discrete Undirected Graphical Models	❌	❌	✅	❌	❌	❌	✅	2
Agnostic $Q$-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity	✅	❌	❌	❌	❌	❌	❌	1
Agnostic Learning of a Single Neuron with Gradient Descent	❌	❌	❌	❌	❌	❌	❌	0
Agnostic Learning with Multiple Objectives	✅	❌	✅	✅	❌	❌	✅	4
Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient Space	❌	✅	✅	✅	❌	❌	✅	4
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach	❌	✅	✅	❌	❌	❌	✅	3
All Word Embeddings from One Embedding	❌	✅	✅	✅	❌	❌	✅	4
All your loss are belong to Bayes	❌	✅	✅	✅	❌	❌	✅	4
All-or-nothing statistical and computational phase transitions in sparse spiked matrix estimation	❌	❌	❌	❌	❌	❌	❌	0
Almost Optimal Model-Free Reinforcement Learningvia Reference-Advantage Decomposition	✅	❌	❌	❌	❌	❌	❌	1
Almost Surely Stable Deep Dynamics	❌	✅	❌	❌	❌	❌	✅	2
An Analysis of SVD for Deep Rotation Estimation	❌	❌	✅	❌	❌	❌	✅	2
An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits	✅	❌	❌	❌	❌	❌	✅	2
An Efficient Adversarial Attack for Tree Ensembles	✅	✅	✅	❌	❌	❌	✅	4
An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search	✅	✅	✅	❌	✅	❌	✅	5
An Efficient Framework for Clustered Federated Learning	✅	✅	✅	❌	❌	❌	✅	4
An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits	✅	❌	❌	❌	❌	❌	✅	2
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay	❌	✅	✅	❌	❌	❌	❌	2
An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch	✅	❌	✅	❌	❌	❌	✅	3
An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods	✅	✅	❌	❌	❌	❌	✅	3
An Improved Analysis of Stochastic Gradient Descent with Momentum	✅	✅	✅	❌	❌	❌	✅	4
An Optimal Elimination Algorithm for Learning a Best Arm	✅	❌	❌	❌	❌	❌	❌	1
An Unbiased Risk Estimator for Learning with Augmented Classes	❌	❌	✅	✅	❌	❌	❌	2
An Unsupervised Information-Theoretic Perceptual Quality Metric	❌	✅	✅	❌	❌	❌	❌	2
An analytic theory of shallow networks dynamics for hinge loss classification	❌	❌	✅	❌	❌	❌	✅	2
An efficient nonconvex reformulation of stagewise convex optimization problems	✅	❌	✅	❌	❌	❌	✅	3
An implicit function learning approach for parametric modal regression	❌	❌	✅	❌	❌	❌	✅	2
An operator view of policy gradient methods	❌	❌	❌	❌	❌	❌	✅	1
Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring	✅	❌	❌	❌	❌	❌	✅	2
Analytic Characterization of the Hessian in Shallow ReLU Models: A Tale of Symmetry	❌	❌	❌	❌	❌	❌	✅	1
Analytical Probability Distributions and Exact Expectation-Maximization for Deep Generative Networks	✅	✅	✅	❌	❌	❌	✅	4
Applications of Common Entropy for Causal Inference	✅	❌	✅	❌	❌	❌	✅	3
Approximate Cross-Validation for Structured Models	✅	❌	✅	✅	✅	❌	❌	4
Approximate Cross-Validation with Low-Rank Data in High Dimensions	❌	❌	✅	✅	❌	❌	✅	3
Approximate Heavily-Constrained Learning with Lagrange Multiplier Models	❌	✅	✅	❌	❌	❌	✅	3
Approximation Based Variance Reduction for Reparameterization Gradients	✅	❌	✅	❌	✅	✅	✅	5
Assessing SATNet's Ability to Solve the Symbol Grounding Problem	❌	❌	✅	❌	❌	❌	✅	2
Assisted Learning: A Framework for Multi-Organization Learning	✅	❌	✅	✅	❌	❌	❌	3
Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability	❌	❌	✅	❌	❌	❌	❌	1
Asymptotic Guarantees for Generative Modeling Based on the Smooth Wasserstein Distance	❌	❌	❌	❌	❌	❌	✅	1
Asymptotic normality and confidence intervals for derivatives of 2-layers neural network in the random features model	❌	✅	❌	❌	❌	❌	✅	2
Asymptotically Optimal Exact Minibatch Metropolis-Hastings	✅	✅	✅	❌	❌	❌	✅	4
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning	❌	✅	✅	❌	❌	❌	✅	3
AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control	❌	❌	✅	✅	❌	❌	✅	3
Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagation	❌	✅	✅	❌	❌	❌	✅	3
Attribute Prototype Network for Zero-Shot Learning	❌	❌	✅	✅	❌	❌	✅	3
Attribution Preservation in Network Compression for Reliable Network Interpretation	❌	❌	✅	❌	❌	❌	✅	2
Audeo: Audio Generation for a Silent Performance Video	❌	✅	✅	❌	✅	❌	✅	4
Auditing Differentially Private Machine Learning: How Private is Private SGD?	✅	❌	✅	❌	❌	❌	✅	3
Auto Learning Attention	❌	✅	✅	✅	❌	❌	✅	4
Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation	✅	✅	✅	✅	✅	❌	✅	6
AutoBSS: An Efficient Algorithm for Block Stacking Style Search	❌	❌	✅	✅	❌	❌	✅	3
AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference	❌	❌	✅	❌	✅	❌	✅	3
AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning	❌	✅	✅	❌	✅	❌	❌	3
Autoencoders that don't overfit towards the Identity	❌	✅	✅	❌	❌	❌	✅	3
Autofocused oracles for model-based design	✅	✅	✅	❌	❌	❌	✅	4
Automatic Curriculum Learning through Value Disagreement	✅	✅	✅	❌	✅	❌	✅	5
Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond	✅	✅	✅	❌	✅	❌	✅	5
Automatically Learning Compact Quality-aware Surrogates for Optimization Problems	❌	✅	✅	✅	❌	✅	✅	5
Autoregressive Score Matching	❌	❌	✅	❌	✅	❌	❌	2
Auxiliary Task Reweighting for Minimum-data Learning	✅	✅	✅	❌	❌	❌	❌	3
AvE: Assistance via Empowerment	✅	✅	✅	❌	❌	❌	✅	4
Avoiding Side Effects By Considering Future Tasks	✅	✅	❌	❌	❌	❌	✅	3
Avoiding Side Effects in Complex Environments	✅	✅	✅	✅	❌	❌	✅	5
Axioms for Learning from Pairwise Comparisons	❌	❌	❌	❌	❌	❌	❌	0
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning	✅	✅	✅	✅	❌	❌	✅	5
BERT Loses Patience: Fast and Robust Inference with Early Exit	❌	✅	✅	✅	✅	❌	✅	5
BOSS: Bayesian Optimization over String Spaces	❌	✅	✅	❌	✅	❌	✅	4
BRP-NAS: Prediction-based NAS using GCNs	❌	✅	✅	✅	✅	❌	✅	5
Backpropagating Linearly Improves Transferability of Adversarial Examples	❌	✅	✅	❌	✅	❌	✅	4
Bad Global Minima Exist and SGD Can Reach Them	✅	✅	✅	❌	❌	✅	✅	5
Balanced Meta-Softmax for Long-Tailed Visual Recognition	❌	✅	✅	✅	❌	❌	❌	3
Bandit Linear Control	✅	❌	❌	❌	❌	❌	❌	1
Bandit Samplers for Training Graph Neural Networks	✅	✅	✅	✅	✅	❌	✅	6
BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits	✅	✅	✅	❌	❌	❌	✅	4
Barking up the right tree: an approach to search over molecule synthesis DAGs	✅	✅	✅	❌	❌	❌	❌	3
Batch Normalization Biases Residual Blocks Towards the Identity Function in Deep Networks	❌	✅	✅	✅	❌	❌	✅	4
Batch normalization provably avoids ranks collapse for randomly initialised deep networks	❌	❌	✅	❌	❌	❌	✅	2
Batched Coarse Ranking in Multi-Armed Bandits	✅	❌	✅	✅	❌	❌	✅	4
Baxter Permutation Process	✅	✅	✅	❌	❌	❌	❌	3
BayReL: Bayesian Relational Learning for Multi-omics Data Integration	❌	✅	✅	✅	❌	❌	✅	4
Bayes Consistency vs. H-Consistency: The Interplay between Surrogate Loss Functions and the Scoring Function Class	❌	❌	✅	✅	❌	❌	✅	3
Bayesian Attention Modules	✅	✅	✅	✅	✅	❌	✅	6
Bayesian Bits: Unifying Quantization and Pruning	❌	❌	✅	❌	✅	❌	✅	3
Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks	✅	❌	✅	❌	✅	❌	✅	4
Bayesian Deep Ensembles via the Neural Tangent Kernel	✅	✅	✅	❌	❌	❌	✅	4
Bayesian Deep Learning and a Probabilistic Perspective of Generalization	❌	✅	✅	✅	❌	❌	✅	4
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels	✅	✅	✅	✅	❌	❌	✅	5
Bayesian Multi-type Mean Field Multi-agent Imitation Learning	✅	❌	❌	❌	❌	❌	❌	1
Bayesian Optimization for Iterative Learning	✅	✅	✅	❌	✅	❌	✅	5
Bayesian Optimization of Risk Measures	❌	✅	❌	❌	❌	❌	✅	2
Bayesian Probabilistic Numerical Integration with Tree-Based Models	✅	❌	✅	❌	❌	❌	✅	3
Bayesian Pseudocoresets	✅	✅	❌	❌	❌	❌	✅	3
Bayesian Robust Optimization for Imitation Learning	❌	✅	❌	❌	❌	❌	✅	2
Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods	✅	❌	✅	❌	❌	❌	✅	3
Belief Propagation Neural Networks	❌	❌	✅	✅	❌	❌	✅	3
Belief-Dependent Macro-Action Discovery in POMDPs using the Value of Information	✅	❌	❌	❌	❌	❌	✅	2
Benchmarking Deep Inverse Models over time, and the Neural-Adjoint method	❌	✅	✅	✅	❌	❌	❌	3
Benchmarking Deep Learning Interpretability in Time Series Predictions	✅	✅	✅	❌	❌	❌	❌	3
Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs	❌	✅	✅	✅	❌	❌	✅	4
Beta R-CNN: Looking into Pedestrian Detection from Another Perspective	❌	❌	✅	✅	✅	❌	✅	4
Better Full-Matrix Regret via Parameter-Free Online Learning	✅	❌	❌	❌	❌	❌	❌	1
Better Set Representations For Relational Reasoning	✅	✅	✅	✅	❌	❌	✅	5
Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs	✅	✅	✅	✅	✅	❌	✅	6
Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses	✅	❌	✅	❌	❌	❌	✅	3
Beyond Lazy Training for Over-parameterized Tensor Decomposition	✅	❌	❌	❌	❌	❌	❌	1
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples	✅	❌	✅	❌	❌	❌	❌	2
Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency	❌	✅	✅	❌	❌	✅	✅	4
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties	✅	✅	✅	✅	❌	❌	✅	5
Bi-level Score Matching for Learning Energy-based Latent Variable Models	✅	✅	✅	✅	❌	❌	✅	5
Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs	✅	❌	❌	❌	❌	❌	❌	1
Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning	✅	❌	❌	❌	❌	❌	✅	2
Bidirectional Convolutional Poisson Gamma Dynamical Systems	❌	✅	✅	✅	✅	❌	✅	5
Big Bird: Transformers for Longer Sequences	❌	✅	✅	❌	❌	❌	✅	3
Big Self-Supervised Models are Strong Semi-Supervised Learners	❌	✅	✅	✅	✅	❌	✅	5
Biological credit assignment through dynamic inversion of feedforward networks	❌	❌	✅	❌	❌	❌	✅	2
Biologically Inspired Mechanisms for Adversarial Robustness	❌	❌	✅	❌	❌	❌	✅	2
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework	❌	❌	✅	❌	❌	❌	❌	1
Black-Box Optimization with Local Generative Surrogates	✅	❌	✅	❌	❌	❌	✅	3
Black-Box Ripper: Copying black-box models using generative evolutionary algorithms	✅	✅	✅	❌	❌	❌	✅	4
Blind Video Temporal Consistency via Deep Video Prior	❌	✅	✅	✅	✅	❌	✅	5
BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images	❌	✅	✅	❌	❌	❌	❌	2
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization	✅	✅	✅	❌	❌	❌	❌	3
Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning	❌	✅	✅	✅	❌	❌	❌	3
Boosting Adversarial Training with Hypersphere Embedding	❌	✅	✅	✅	❌	❌	✅	4
Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst-Case Rates	✅	❌	✅	❌	❌	❌	✅	3
Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning	✅	✅	✅	✅	✅	❌	✅	6
Bootstrapping neural processes	❌	❌	✅	✅	❌	❌	✅	3
Boundary thickness and robustness in learning models	❌	✅	✅	❌	❌	❌	✅	3
BoxE: A Box Embedding Model for Knowledge Base Completion	❌	❌	✅	✅	❌	❌	❌	2
Breaking Reversibility Accelerates Langevin Dynamics for Non-Convex Optimization	❌	❌	✅	❌	❌	❌	❌	1
Breaking the Communication-Privacy-Accuracy Trilemma	❌	❌	❌	❌	❌	❌	❌	0
Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model	❌	❌	❌	❌	❌	❌	✅	1
Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning	✅	❌	✅	❌	✅	❌	✅	4
Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS	✅	✅	✅	✅	✅	❌	✅	6
Building powerful and equivariant graph neural networks with structural message-passing	❌	✅	✅	✅	❌	❌	✅	4
Byzantine Resilient Distributed Multi-Task Learning	❌	✅	✅	❌	❌	❌	✅	3
CASTLE: Regularization via Auxiliary Causal Graph Discovery	✅	✅	✅	✅	❌	❌	✅	5
CHIP: A Hawkes Process Model for Continuous-time Networks with Scalable and Consistent Estimation	✅	✅	✅	❌	✅	❌	✅	5
CLEARER: Multi-Scale Neural Architecture Search for Image Restoration	❌	✅	✅	✅	✅	❌	✅	5
CO-Optimal Transport	✅	❌	✅	❌	✅	❌	✅	4
COBE: Contextualized Object Embeddings from Narrated Instructional Video	❌	❌	✅	❌	❌	❌	❌	1
COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning	❌	✅	✅	✅	✅	❌	✅	5
COPT: Coordinated Optimal Transport on Graphs	✅	❌	✅	✅	✅	❌	✅	5
COT-GAN: Generating Sequential Data via Causal Optimal Transport	✅	✅	✅	❌	❌	❌	✅	4
CSER: Communication-efficient SGD with Error Reset	✅	❌	✅	❌	✅	❌	✅	4
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances	❌	✅	✅	❌	❌	❌	✅	3
CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations	❌	❌	✅	❌	❌	❌	❌	1
Calibrated Reliable Regression using Maximum Mean Discrepancy	✅	❌	✅	❌	✅	❌	✅	4
Calibrating CNNs for Lifelong Learning	❌	❌	✅	✅	❌	❌	✅	3
Calibrating Deep Neural Networks using Focal Loss	❌	✅	✅	✅	❌	❌	✅	4
Calibration of Shared Equilibria in General Sum Partially Observable Markov Games	✅	❌	❌	❌	❌	❌	✅	2
Can Graph Neural Networks Count Substructures?	❌	✅	✅	✅	✅	❌	✅	5
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference	❌	❌	✅	❌	❌	❌	✅	2
Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study	❌	❌	❌	❌	❌	❌	✅	1
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?	❌	✅	✅	❌	❌	❌	❌	2
Can Temporal-Diﬀerence and Q-Learning Learn Representation? A Mean-Field Theory	✅	❌	❌	❌	❌	❌	❌	1
Can the Brain Do Backpropagation? --- Exact Implementation of Backpropagation in Predictive Coding Networks	✅	❌	✅	❌	❌	❌	✅	3
Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction	❌	❌	✅	❌	❌	❌	❌	1
Cascaded Text Generation with Markov Transformers	✅	✅	✅	✅	✅	❌	✅	6
Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning	✅	❌	❌	❌	❌	❌	❌	1
Causal Discovery in Physical Systems from Videos	❌	❌	❌	❌	❌	❌	❌	0
Causal Estimation with Functional Confounders	✅	❌	✅	✅	❌	❌	✅	4
Causal Imitation Learning With Unobserved Confounders	✅	❌	✅	❌	❌	❌	❌	2
Causal Intervention for Weakly-Supervised Semantic Segmentation	❌	✅	✅	✅	❌	❌	❌	3
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models	❌	❌	✅	❌	❌	❌	✅	2
Causal analysis of Covid-19 Spread in Germany	❌	❌	✅	❌	❌	❌	✅	2
Certifiably Adversarially Robust Detection of Out-of-Distribution Data	❌	✅	✅	❌	❌	❌	✅	3
Certified Defense to Image Transformations via Randomized Smoothing	❌	✅	✅	❌	✅	❌	✅	4
Certified Monotonic Neural Networks	✅	✅	✅	✅	✅	✅	✅	7
Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks	✅	✅	✅	✅	❌	❌	✅	5
Certifying Confidence via Randomized Smoothing	❌	✅	✅	❌	❌	❌	✅	3
Certifying Strategyproof Auction Networks	❌	❌	❌	✅	❌	❌	✅	2
Chaos, Extremism and Optimism: Volume Analysis of Learning in Games	❌	❌	❌	❌	❌	❌	❌	0
Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe	✅	❌	❌	❌	❌	❌	❌	1
Characterizing emergent representations in a space of candidate learning rules for deep networks	❌	❌	❌	❌	❌	❌	✅	1
Choice Bandits	✅	❌	✅	❌	❌	❌	✅	3
CircleGAN: Generative Adversarial Learning across Spherical Circles	✅	❌	✅	❌	❌	❌	✅	3
Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability	✅	✅	✅	✅	✅	❌	✅	6
Classification with Valid and Adaptive Coverage	✅	✅	✅	✅	❌	❌	✅	5
Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow	❌	✅	✅	❌	❌	❌	❌	2
Co-Tuning for Transfer Learning	✅	✅	✅	✅	❌	❌	✅	5
Co-exposure Maximization in Online Social Networks	✅	✅	✅	❌	✅	❌	✅	5
CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection	❌	✅	✅	❌	✅	❌	✅	4
CoMIR: Contrastive Multimodal Image Representation for Registration	❌	✅	✅	✅	❌	❌	✅	4
CoSE: Compositional Stroke Embeddings	❌	✅	✅	❌	❌	❌	❌	2
CodeCMR: Cross-Modal Retrieval For Function-Level Binary Source Code Matching	❌	❌	✅	✅	❌	❌	✅	3
Coded Sequential Matrix Multiplication For Straggler Mitigation	✅	❌	✅	❌	✅	❌	✅	4
CogLTX: Applying BERT to Long Texts	✅	✅	✅	✅	✅	❌	✅	6
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models	❌	❌	✅	✅	❌	❌	✅	3
Coherent Hierarchical Multi-Label Classification Networks	❌	✅	✅	✅	❌	❌	✅	4
CoinDICE: Off-Policy Confidence Interval Estimation	❌	✅	✅	❌	❌	❌	✅	3
CoinPress: Practical Private Mean and Covariance Estimation	✅	✅	✅	❌	✅	❌	✅	5
ColdGANs: Taming Language GANs with Cautious Sampling Strategies	❌	❌	✅	✅	❌	❌	✅	3
Collapsing Bandits and Their Application to Public Health Intervention	✅	✅	✅	❌	❌	❌	✅	4
Collegial Ensembles	✅	❌	✅	❌	❌	❌	✅	3
Color Visual Illusions: A Statistics-based Computational Model	❌	✅	✅	❌	❌	❌	❌	2
Combining Deep Reinforcement Learning and Search for Imperfect-Information Games	✅	✅	✅	❌	❌	❌	✅	4
Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian	✅	✅	✅	❌	✅	❌	❌	4
Community detection using fast low-cardinality semidefinite programming	✅	✅	✅	❌	❌	❌	✅	4
CompRess: Self-Supervised Learning by Compressing Representations	❌	✅	✅	✅	✅	❌	✅	5
Compact task representations as a normative model for higher-order brain activity	❌	✅	✅	❌	❌	❌	❌	2
Comparator-Adaptive Convex Bandits	✅	❌	❌	❌	❌	❌	❌	1
Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval	❌	❌	❌	❌	❌	❌	✅	1
Compositional Explanations of Neurons	❌	✅	✅	✅	❌	❌	✅	4
Compositional Generalization by Learning Analytical Expressions	❌	✅	✅	❌	✅	❌	✅	4
Compositional Generalization via Neural-Symbolic Stack Machines	❌	❌	✅	✅	❌	❌	❌	2
Compositional Visual Generation with Energy Based Models	❌	✅	✅	❌	❌	❌	❌	2
Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition	✅	❌	✅	✅	✅	❌	✅	5
Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection	❌	✅	✅	✅	❌	❌	✅	4
Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding	✅	✅	✅	❌	❌	❌	✅	4
Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programming	✅	✅	✅	❌	❌	❌	✅	4
Conditioning and Processing: Techniques to Improve Information-Theoretic Generalization Bounds	❌	❌	❌	❌	❌	❌	❌	0
Confidence sequences for sampling without replacement	❌	✅	❌	❌	❌	❌	❌	1
Conformal Symplectic and Relativistic Optimization	✅	✅	✅	❌	❌	❌	✅	4
Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning	✅	❌	❌	❌	❌	✅	✅	3
Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices	✅	✅	✅	❌	❌	❌	✅	4
Consequences of Misaligned AI	❌	❌	❌	❌	❌	❌	❌	0
Conservative Q-Learning for Offline Reinforcement Learning	✅	❌	✅	❌	❌	❌	✅	3
Consistency Regularization for Certified Robustness of Smoothed Classifiers	❌	✅	✅	❌	✅	❌	✅	4
Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations	✅	❌	✅	✅	❌	❌	✅	4
Consistent Plug-in Classifiers for Complex Objectives and Constraints	✅	✅	✅	❌	❌	❌	✅	4
Consistent Structural Relation Learning for Zero-Shot Segmentation	❌	❌	✅	✅	❌	❌	❌	2
Consistent feature selection for analytic deep neural networks	❌	✅	✅	❌	❌	❌	✅	3
Constant-Expansion Suffices for Compressed Sensing with Generative Priors	❌	❌	❌	❌	❌	❌	❌	0
Constrained episodic reinforcement learning in concave-convex and knapsack settings	❌	✅	✅	❌	❌	❌	✅	3
Constraining Variational Inference with Geometric Jensen-Shannon Divergence	❌	✅	✅	❌	❌	❌	✅	3
Content Provider Dynamics and Coordination in Recommendation Ecosystems	✅	❌	❌	❌	❌	❌	❌	1
Contextual Games: Multi-Agent Learning with Side Information	✅	❌	✅	❌	❌	❌	✅	3
Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming	❌	✅	✅	✅	❌	✅	✅	5
Continual Deep Learning by Functional Regularisation of Memorable Past	✅	✅	✅	❌	❌	❌	✅	4
Continual Learning in Low-rank Orthogonal Subspaces	✅	✅	✅	❌	❌	❌	✅	4
Continual Learning of Control Primitives : Skill Discovery via Reset-Games	✅	✅	✅	❌	❌	❌	❌	3
Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks	❌	✅	✅	✅	❌	❌	✅	4
Continual Learning with Node-Importance based Adaptive Group Sparse Regularization	✅	❌	✅	❌	❌	❌	✅	3
Continuous Meta-Learning without Tasks	✅	✅	✅	❌	❌	❌	❌	3
Continuous Object Representation Networks: Novel View Synthesis without Target View Supervision	❌	✅	✅	❌	❌	❌	❌	2
Continuous Regularized Wasserstein Barycenters	✅	✅	✅	❌	✅	✅	✅	6
Continuous Submodular Maximization: Beyond DR-Submodularity	✅	❌	❌	❌	❌	❌	❌	1
Continuous Surface Embeddings	❌	❌	✅	✅	❌	❌	✅	3
ContraGAN: Contrastive Learning for Conditional Image Generation	✅	✅	✅	✅	❌	❌	✅	5
Contrastive Learning with Adversarial Examples	✅	❌	✅	❌	❌	❌	✅	3
Contrastive learning of global and local features for medical image segmentation with limited annotations	❌	✅	✅	✅	❌	❌	✅	4
ConvBERT: Improving BERT with Span-based Dynamic Convolution	❌	✅	✅	✅	❌	❌	✅	4
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs	❌	✅	❌	❌	❌	❌	❌	1
Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters	✅	❌	✅	❌	❌	❌	✅	3
Convex optimization based on global lower second-order models	✅	✅	✅	❌	✅	❌	❌	4
Convolutional Generation of Textured 3D Meshes	❌	✅	✅	❌	✅	❌	✅	4
Convolutional Tensor-Train LSTM for Spatio-Temporal Learning	✅	✅	✅	❌	❌	❌	✅	4
Cooperative Heterogeneous Deep Reinforcement Learning	✅	❌	✅	❌	❌	❌	✅	3
Cooperative Multi-player Bandit Optimization	✅	❌	❌	❌	❌	❌	✅	2
Coresets for Near-Convex Functions	✅	✅	✅	❌	✅	✅	✅	6
Coresets for Regressions with Panel Data	✅	✅	❌	❌	✅	❌	✅	4
Coresets for Robust Training of Deep Neural Networks against Noisy Labels	✅	✅	✅	❌	✅	❌	✅	5
Coresets via Bilevel Optimization for Continual Learning and Streaming	✅	✅	✅	❌	✅	❌	✅	5
Correlation Robust Influence Maximization	❌	✅	✅	❌	✅	❌	❌	3
Correspondence learning via linearly-invariant embedding	❌	✅	✅	❌	❌	❌	✅	3
Counterexample-Guided Learning of Monotonic Neural Networks	❌	✅	✅	❌	❌	❌	✅	3
Counterfactual Contrastive Learning for Weakly-Supervised Vision-Language Grounding	❌	❌	✅	❌	❌	❌	✅	2
Counterfactual Data Augmentation using Locally Factored Dynamics	✅	✅	✅	❌	❌	❌	✅	4
Counterfactual Prediction for Bundle Treatment	❌	❌	✅	❌	❌	❌	✅	2
Counterfactual Predictions under Runtime Confounding	✅	❌	❌	✅	❌	❌	✅	3
Counterfactual Vision-and-Language Navigation: Unravelling the Unseen	✅	❌	✅	✅	❌	❌	✅	4
Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators	❌	❌	❌	❌	❌	❌	❌	0
Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search	✅	✅	✅	✅	✅	❌	✅	6
Critic Regularized Regression	✅	❌	✅	❌	❌	❌	✅	3
Cross-Scale Internal Graph Neural Network for Image Super-Resolution	❌	✅	✅	❌	✅	❌	✅	4
Cross-lingual Retrieval for Iterative Self-Supervised Training	✅	✅	✅	✅	❌	❌	✅	5
Cross-validation Confidence Intervals for Test Error	❌	✅	✅	✅	❌	❌	✅	4
CrossTransformers: spatially-aware few-shot transfer	❌	✅	✅	✅	❌	❌	✅	4
Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality	✅	❌	❌	❌	❌	❌	❌	1
CryptoNAS: Private Inference on a ReLU Budget	✅	❌	✅	❌	✅	❌	✅	4
Curriculum By Smoothing	✅	❌	✅	❌	✅	❌	✅	4
Curriculum Learning by Dynamic Instance Hardness	✅	✅	✅	❌	❌	❌	✅	4
Curriculum learning for multilevel budgeted combinatorial problems	✅	✅	✅	❌	✅	✅	❌	5
Curvature Regularization to Prevent Distortion in Graph Embedding	✅	❌	✅	✅	❌	❌	✅	4
Cycle-Contrast for Self-Supervised Video Representation Learning	❌	❌	✅	✅	❌	❌	✅	3
DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks	✅	❌	❌	❌	❌	❌	❌	1
DISK: Learning local features with policy gradient	❌	✅	✅	✅	❌	❌	✅	4
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles	✅	✅	✅	❌	❌	❌	✅	4
Dark Experience for General Continual Learning: a Strong, Simple Baseline	✅	✅	✅	✅	✅	❌	✅	6
Data Diversification: A Simple Strategy For Neural Machine Translation	✅	✅	✅	✅	❌	❌	✅	5
De-Anonymizing Text by Fingerprinting Language Generation	✅	❌	✅	❌	✅	✅	✅	5
Debiased Contrastive Learning	✅	✅	✅	✅	❌	❌	✅	5
Debiasing Averaged Stochastic Gradient Descent to handle missing values	✅	✅	❌	❌	❌	❌	✅	3
Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization	❌	❌	✅	❌	❌	❌	✅	2
Debugging Tests for Model Explanations	❌	✅	✅	❌	❌	❌	✅	3
Decentralized Accelerated Proximal Gradient Descent	✅	❌	❌	❌	❌	❌	✅	2
Decentralized Langevin Dynamics for Bayesian Learning	✅	❌	✅	❌	❌	❌	❌	2
Decentralized TD Tracking with Linear Function Approximation and its Finite-Time Analysis	✅	❌	❌	❌	❌	❌	✅	2
Decision trees as partitioning machines to characterize their generalization properties	✅	✅	✅	✅	❌	❌	✅	5
Decision-Making with Auto-Encoding Variational Bayes	❌	✅	✅	❌	✅	❌	✅	4
Decisions, Counterfactual Explanations and Strategic Behavior	✅	✅	✅	❌	❌	❌	✅	4
Deep Archimedean Copulas	❌	✅	✅	✅	✅	✅	✅	6
Deep Automodulators	❌	✅	✅	❌	❌	❌	✅	3
Deep Diffusion-Invariant Wasserstein Distributional Classification	✅	❌	✅	❌	✅	✅	✅	5
Deep Direct Likelihood Knockoffs	✅	❌	✅	✅	✅	❌	✅	5
Deep Energy-based Modeling of Discrete-Time Physics	✅	✅	✅	❌	❌	❌	✅	4
Deep Evidential Regression	❌	❌	✅	❌	✅	❌	✅	3
Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking	❌	✅	✅	❌	❌	❌	✅	3
Deep Imitation Learning for Bimanual Robotic Manipulation	❌	✅	❌	❌	❌	❌	✅	2
Deep Inverse Q-learning with Constraints	✅	❌	✅	❌	✅	❌	✅	4
Deep Metric Learning with Spherical Embedding	❌	❌	✅	✅	❌	❌	✅	3
Deep Multimodal Fusion by Channel Exchanging	❌	✅	✅	✅	❌	❌	✅	4
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting	✅	❌	✅	❌	❌	❌	✅	3
Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games	❌	✅	✅	❌	❌	❌	✅	3
Deep Reinforcement and InfoMax Learning	✅	✅	✅	❌	❌	❌	✅	4
Deep Relational Topic Modeling via Graph Poisson Gamma Belief Network	✅	✅	✅	✅	❌	❌	✅	5
Deep Shells: Unsupervised Shape Correspondence with Optimal Transport	❌	❌	✅	❌	❌	❌	✅	2
Deep Smoothing of the Implied Volatility Surface	❌	❌	❌	✅	❌	❌	✅	2
Deep Statistical Solvers	❌	✅	✅	✅	✅	✅	✅	6
Deep Structural Causal Models for Tractable Counterfactual Inference	❌	✅	✅	✅	✅	✅	✅	6
Deep Subspace Clustering with Data Augmentation	❌	✅	✅	❌	❌	❌	✅	3
Deep Transformation-Invariant Clustering	✅	✅	✅	❌	✅	❌	✅	5
Deep Transformers with Latent Depth	✅	❌	✅	✅	❌	❌	✅	4
Deep Variational Instance Segmentation	❌	✅	✅	✅	✅	❌	✅	5
Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring	❌	✅	✅	❌	❌	❌	✅	3
Deep active inference agents using Monte-Carlo methods	❌	✅	✅	❌	❌	❌	❌	2
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel	❌	❌	✅	❌	❌	✅	✅	3
Deep reconstruction of strange attractors from time series	❌	✅	✅	✅	❌	❌	✅	4
DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs	❌	✅	✅	❌	❌	❌	✅	3
DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation	❌	✅	✅	❌	✅	❌	✅	4
Deeply Learned Spectral Total Variation Decomposition	❌	✅	✅	❌	✅	❌	✅	4
Delay and Cooperation in Nonstochastic Linear Bandits	✅	❌	❌	❌	❌	❌	❌	1
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians	✅	❌	✅	❌	❌	❌	✅	3
Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation	❌	❌	✅	✅	✅	❌	✅	4
Demixed shared component analysis of neural population data from multiple brain areas	❌	✅	✅	❌	❌	❌	❌	2
Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases	❌	❌	✅	❌	❌	❌	❌	1
Demystifying Orthogonal Monte Carlo and Beyond	✅	✅	❌	❌	❌	❌	❌	2
Denoised Smoothing: A Provable Defense for Pretrained Classifiers	❌	✅	✅	✅	❌	❌	✅	4
Denoising Diffusion Probabilistic Models	✅	✅	✅	❌	❌	❌	✅	4
Dense Correspondences between Human Bodies via Learning Transformation Synchronization on Graphs	❌	✅	✅	✅	❌	❌	✅	4
Depth Uncertainty in Neural Networks	❌	✅	✅	✅	✅	❌	✅	5
Design Space for Graph Neural Networks	❌	✅	✅	✅	❌	❌	✅	4
Detecting Hands and Recognizing Physical Contact in the Wild	❌	✅	✅	✅	❌	❌	✅	4
Detecting Interactions from Neural Networks via Topological Analysis	✅	❌	✅	✅	❌	❌	✅	4
Detection as Regression: Certified Object Detection with Median Smoothing	✅	❌	✅	❌	❌	❌	✅	3
Deterministic Approximation for Submodular Maximization over a Matroid in Nearly Linear Time	✅	❌	✅	❌	❌	❌	✅	3
Dialog without Dialog Data: Learning Visual Dialog Agents from VQA Data	✅	✅	✅	✅	❌	❌	✅	5
DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling	❌	❌	✅	✅	✅	❌	✅	4
Differentiable Augmentation for Data-Efficient GAN Training	❌	✅	✅	✅	✅	❌	✅	5
Differentiable Causal Discovery from Interventional Data	❌	✅	✅	❌	❌	❌	✅	3
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization	❌	✅	✅	❌	✅	❌	✅	4
Differentiable Meta-Learning of Bandit Policies	✅	❌	❌	❌	✅	❌	✅	3
Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement	✅	❌	✅	✅	❌	❌	✅	4
Differentiable Top-k with Optimal Transport	✅	✅	✅	❌	❌	❌	✅	4
Differentially Private Clustering: Tight Approximation Ratios	✅	❌	❌	❌	❌	❌	❌	1
Differentially-Private Federated Linear Bandits	✅	❌	❌	❌	❌	❌	✅	2
Digraph Inception Convolutional Networks	❌	✅	✅	✅	✅	❌	✅	5
Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures	❌	✅	✅	❌	❌	❌	✅	3
Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces	✅	❌	✅	❌	❌	❌	✅	3
Directional Pruning of Deep Neural Networks	✅	✅	✅	❌	❌	❌	✅	4
Directional convergence and alignment in deep learning	❌	❌	✅	❌	❌	❌	✅	2
Dirichlet Graph Variational Autoencoder	❌	❌	✅	❌	❌	❌	✅	2
DisARM: An Antithetic Gradient Estimator for Binary Latent Variables	✅	✅	✅	✅	✅	❌	✅	6
DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction	✅	❌	✅	✅	❌	❌	✅	4
Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation	❌	❌	✅	❌	❌	❌	✅	2
Discovering Reinforcement Learning Algorithms	✅	❌	❌	❌	❌	❌	✅	2
Discovering Symbolic Models from Deep Learning with Inductive Biases	❌	✅	✅	❌	❌	❌	✅	3
Discovering conflicting groups in signed networks	✅	✅	✅	❌	✅	❌	❌	4
Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching	❌	✅	✅	❌	❌	❌	✅	3
Disentangling Human Error from Ground Truth in Segmentation of Medical Images	❌	✅	✅	✅	❌	❌	❌	3
Disentangling by Subspace Diffusion	✅	❌	✅	❌	❌	❌	✅	3
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation	❌	✅	✅	✅	✅	❌	✅	5
Dissecting Neural ODEs	❌	✅	✅	❌	❌	✅	❌	3
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning	✅	✅	✅	✅	❌	❌	❌	4
Distributed Distillation for On-Device Learning	✅	❌	✅	❌	❌	❌	✅	3
Distributed Newton Can Communicate Less and Resist Byzantine Workers	✅	❌	✅	❌	❌	❌	✅	3
Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms	✅	❌	✅	❌	❌	❌	✅	3
Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning	✅	❌	✅	❌	❌	❌	✅	3
Distribution Matching for Crowd Counting	❌	✅	✅	❌	❌	❌	✅	3
Distribution-free binary classification: prediction sets, confidence intervals and calibration	❌	❌	❌	❌	❌	❌	❌	0
Distributional Robustness with IPMs and links to Regularization and GANs	❌	❌	❌	❌	❌	❌	❌	0
Distributionally Robust Federated Averaging	✅	✅	✅	❌	✅	❌	✅	5
Distributionally Robust Local Non-parametric Conditional Estimation	❌	✅	✅	✅	❌	❌	✅	4
Distributionally Robust Parametric Maximum Likelihood Estimation	❌	✅	✅	✅	✅	✅	✅	6
Diverse Image Captioning with Context-Object Split Latent Spaces	❌	✅	✅	✅	❌	❌	❌	3
Diversity can be Transferred: Output Diversification for White- and Black-box Attacks	✅	❌	✅	❌	❌	❌	✅	3
Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations	✅	✅	✅	❌	❌	❌	✅	4
Do Adversarially Robust ImageNet Models Transfer Better?	❌	✅	✅	❌	❌	❌	✅	3
Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?	❌	✅	✅	✅	❌	❌	✅	4
Domain Adaptation as a Problem of Inference on Graphical Models	❌	✅	✅	❌	❌	❌	✅	3
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift	✅	✅	✅	❌	❌	❌	✅	4
Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization	❌	✅	✅	✅	❌	❌	❌	3
Domain Generalization via Entropy Regularization	✅	✅	✅	✅	❌	❌	✅	5
Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies	✅	❌	❌	❌	❌	❌	✅	2
Dual Instrumental Variable Regression	✅	✅	✅	✅	❌	❌	✅	5
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks	❌	❌	✅	❌	❌	❌	✅	2
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning	✅	❌	✅	✅	❌	❌	✅	4
Dual-Free Stochastic Decentralized Optimization with Variance Reduction	✅	✅	✅	❌	❌	❌	✅	4
Dual-Resolution Correspondence Networks	❌	✅	✅	✅	❌	❌	✅	4
Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion	❌	✅	✅	✅	❌	❌	✅	4
DynaBERT: Dynamic BERT with Adaptive Width and Depth	✅	✅	✅	✅	✅	❌	✅	6
Dynamic Fusion of Eye Movement Data and Verbal Narrations in Knowledge-rich Domains	✅	❌	✅	❌	❌	✅	❌	3
Dynamic Regret of Convex and Smooth Functions	✅	❌	❌	❌	❌	❌	❌	1
Dynamic Regret of Policy Optimization in Non-Stationary Environments	✅	❌	❌	❌	❌	❌	❌	1
Dynamic Submodular Maximization	✅	❌	❌	❌	❌	❌	❌	1
Dynamic allocation of limited memory resources in reinforcement learning	✅	✅	✅	❌	❌	❌	✅	4
Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification	❌	❌	❌	❌	❌	❌	✅	1
Early-Learning Regularization Prevents Memorization of Noisy Labels	❌	✅	✅	✅	❌	❌	✅	4
EcoLight: Intersection Control in Developing Regions Under Extreme Budget and Network Constraints	✅	❌	✅	❌	✅	❌	✅	4
Effective Dimension Adaptive Sketching Methods for Faster Regularized Least-Squares Optimization	✅	✅	✅	❌	❌	❌	✅	4
Effective Diversity in Population Based Reinforcement Learning	❌	✅	✅	❌	✅	❌	✅	4
Efficient Algorithms for Device Placement of DNN Graph Operators	❌	✅	❌	❌	✅	❌	✅	3
Efficient Clustering Based On A Unified View Of $K$-means And Ratio-cut	✅	✅	✅	❌	✅	❌	✅	5
Efficient Clustering for Stretched Mixtures: Landscape and Optimality	✅	❌	✅	❌	❌	❌	✅	3
Efficient Contextual Bandits with Continuous Actions	✅	❌	✅	❌	❌	❌	✅	3
Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning	✅	❌	❌	❌	❌	❌	❌	1
Efficient Exact Verification of Binarized Neural Networks	❌	✅	✅	✅	❌	❌	✅	4
Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization	✅	❌	✅	❌	❌	❌	❌	2
Efficient Generation of Structured Objects with Constrained Adversarial Networks	❌	✅	✅	❌	✅	❌	✅	4
Efficient Learning of Discrete Graphical Models	✅	❌	❌	❌	❌	❌	✅	2
Efficient Learning of Generative Models via Finite-Difference Score Matching	✅	✅	✅	❌	❌	❌	✅	4
Efficient Low Rank Gaussian Variational Inference for Neural Networks	❌	✅	✅	❌	❌	❌	✅	3
Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity	❌	✅	✅	❌	❌	❌	✅	3
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning	✅	✅	❌	❌	❌	❌	✅	3
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees	✅	✅	❌	❌	❌	❌	✅	3
Efficient Online Learning of Optimal Rankings: Dimensionality Reduction via Gradient Descent	✅	✅	❌	❌	❌	❌	✅	3
Efficient Planning in Large MDPs with Weak Linear Function Approximation	✅	❌	❌	❌	❌	❌	❌	1
Efficient Projection-free Algorithms for Saddle Point Problems	✅	❌	✅	❌	❌	❌	✅	3
Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee	✅	❌	✅	❌	❌	❌	✅	3
Efficient active learning of sparse halfspaces with arbitrary bounded noise	✅	❌	❌	❌	❌	❌	❌	1
Efficient estimation of neural tuning during naturalistic behavior	❌	✅	✅	❌	❌	❌	✅	3
Efficient semidefinite-programming-based inference for binary and multi-class MRFs	✅	✅	❌	❌	❌	❌	✅	3
Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced Data	❌	✅	✅	✅	❌	❌	❌	3
Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks	✅	❌	✅	❌	✅	❌	✅	4
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design	✅	✅	✅	❌	❌	❌	✅	4
Emergent Reciprocity and Team Formation from Randomized Uncertain Social Preferences	❌	✅	❌	❌	❌	❌	✅	2
Empirical Likelihood for Contextual Bandits	❌	✅	✅	✅	❌	❌	✅	4
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming	✅	✅	✅	❌	✅	❌	✅	5
End-to-End Learning and Intervention in Games	❌	❌	❌	❌	❌	❌	✅	1
Energy-based Out-of-distribution Detection	❌	✅	✅	✅	✅	❌	✅	5
Ensemble Distillation for Robust Model Fusion in Federated Learning	✅	❌	✅	✅	❌	❌	✅	4
Ensembling geophysical models with Bayesian Neural Networks	✅	✅	✅	✅	❌	❌	❌	4
Ensuring Fairness Beyond the Training Data	✅	✅	✅	✅	❌	❌	✅	5
Entropic Causal Inference: Identifiability and Finite Sample Results	❌	❌	✅	❌	❌	❌	❌	1
Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form	❌	❌	❌	❌	❌	❌	✅	1
Entrywise convergence of iterative methods for eigenproblems	✅	❌	✅	❌	❌	❌	❌	2
Equivariant Networks for Hierarchical Structures	❌	✅	✅	❌	❌	❌	✅	3
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs	❌	✅	✅	✅	❌	✅	❌	4
Error Bounds of Imitating Policies and Environments	✅	❌	✅	❌	❌	❌	✅	3
Escaping Saddle-Point Faster under Interpolation-like Conditions	✅	❌	❌	❌	❌	❌	❌	1
Escaping the Gravitational Pull of Softmax	❌	❌	✅	✅	❌	❌	✅	3
Estimating Fluctuations in Neural Representations of Uncertain Environments	❌	❌	❌	❌	❌	❌	❌	0
Estimating Rank-One Spikes from Heavy-Tailed Noise via Self-Avoiding Walks	✅	❌	❌	❌	❌	❌	❌	1
Estimating Training Data Influence by Tracing Gradient Descent	❌	✅	✅	❌	❌	❌	✅	3
Estimating decision tree learnability with polylogarithmic sample complexity	✅	❌	❌	❌	❌	❌	❌	1
Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks	✅	✅	✅	❌	❌	❌	❌	3
Estimating weighted areas under the ROC curve	❌	❌	❌	❌	❌	❌	❌	0
Estimation and Imputation in Probabilistic Principal Component Analysis with Missing Not At Random Data	✅	✅	✅	❌	❌	❌	❌	3
Estimation of Skill Distribution from a Tournament	✅	❌	✅	❌	❌	❌	✅	3
Evaluating Attribution for Graph Neural Networks	❌	✅	✅	❌	❌	❌	✅	3
Evaluating and Rewarding Teamwork Using Cooperative Game Abstractions	❌	❌	✅	✅	❌	❌	✅	3
Every View Counts: Cross-View Consistency in 3D Object Detection with Hybrid-Cylindrical-Spherical Voxelization	❌	❌	✅	✅	✅	❌	✅	4
Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders	❌	✅	✅	❌	✅	❌	✅	4
EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning	❌	❌	✅	❌	❌	❌	❌	1
Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation	❌	✅	✅	✅	❌	❌	✅	4
Evolving Normalization-Activation Layers	✅	✅	✅	✅	❌	❌	✅	5
Exact Recovery of Mangled Clusters with Same-Cluster Queries	✅	❌	❌	❌	❌	❌	✅	2
Exact expressions for double descent and implicit regularization via surrogate random design	❌	❌	❌	❌	❌	❌	✅	1
Exactly Computing the Local Lipschitz Constant of ReLU Networks	❌	❌	✅	❌	❌	❌	❌	1
Exchangeable Neural ODE for Set Modeling	❌	✅	✅	❌	✅	❌	❌	3
Exemplar Guided Active Learning	✅	❌	❌	❌	❌	❌	❌	1
Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation	❌	✅	✅	✅	❌	❌	✅	4
ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks	❌	✅	✅	✅	✅	❌	✅	5
Experimental design for MRI by greedy policy search	❌	✅	✅	✅	❌	❌	✅	4
Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation	✅	✅	✅	✅	❌	❌	✅	5
Explainable Voting	❌	❌	❌	❌	❌	❌	❌	0
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay	✅	✅	✅	❌	✅	❌	❌	4
Explicit Regularisation in Gaussian Noise Injections	❌	❌	✅	❌	❌	❌	✅	2
Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits	✅	❌	❌	❌	❌	❌	❌	1
Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning	❌	❌	✅	✅	❌	❌	✅	3
Exploiting the Surrogate Gap in Online Multiclass Classification	✅	❌	❌	❌	❌	❌	❌	1
Exploiting weakly supervised visual patterns to learn from partial annotations	❌	❌	✅	✅	✅	❌	✅	4
Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling	❌	❌	❌	❌	❌	❌	❌	0
Exponential ergodicity of mirror-Langevin diffusions	✅	❌	❌	❌	❌	❌	✅	2
Extrapolation Towards Imaginary 0-Nearest Neighbour and Its Improved Convergence Rate	❌	❌	✅	✅	❌	❌	✅	3
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs	✅	❌	❌	❌	❌	❌	❌	1
Factor Graph Grammars	❌	❌	❌	❌	❌	❌	❌	0
Factor Graph Neural Networks	✅	❌	✅	✅	❌	❌	✅	4
Factorizable Graph Convolutional Networks	❌	✅	✅	✅	❌	❌	✅	4
Factorized Neural Processes for Neural Processes: K-Shot Prediction of Neural Responses	❌	✅	✅	✅	✅	❌	❌	4
Fair Hierarchical Clustering	✅	✅	✅	❌	❌	❌	✅	4
Fair Multiple Decision Making Through Soft Interventions	❌	✅	✅	✅	✅	❌	✅	5
Fair Performance Metric Elicitation	✅	❌	❌	❌	❌	❌	✅	2
Fair regression via plug-in estimator and recalibration with statistical guarantees	✅	✅	✅	✅	❌	❌	✅	5
Fair regression with Wasserstein barycenters	✅	✅	✅	✅	❌	❌	✅	5
Fairness constraints can help exact inference in structured prediction	❌	❌	❌	❌	❌	❌	❌	0
Fairness in Streaming Submodular Maximization: Algorithms and Hardness	✅	✅	✅	❌	❌	❌	❌	3
Fairness with Overlapping Groups; a Probabilistic Perspective	✅	✅	✅	❌	❌	❌	❌	3
Fairness without Demographics through Adversarially Reweighted Learning	❌	❌	✅	✅	❌	❌	✅	3
Faithful Embeddings for Knowledge Base Queries	❌	✅	✅	✅	❌	❌	✅	4
Falcon: Fast Spectral Inference on Encrypted Data	✅	❌	✅	❌	✅	✅	✅	5
Fast Adaptive Non-Monotone Submodular Maximization Subject to a Knapsack Constraint	✅	❌	✅	❌	❌	❌	✅	3
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms	❌	✅	✅	❌	❌	❌	✅	3
Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolev	❌	❌	❌	❌	❌	❌	❌	0
Fast Epigraphical Projection-based Incremental Algorithms for Wasserstein Distributionally Robust Support Vector Machine	❌	✅	✅	❌	✅	✅	❌	4
Fast Fourier Convolution	✅	❌	✅	✅	❌	❌	✅	4
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization	✅	✅	✅	❌	✅	❌	✅	5
Fast Transformers with Clustered Attention	❌	✅	✅	❌	✅	❌	✅	4
Fast Unbalanced Optimal Transport on a Tree	✅	✅	✅	✅	✅	❌	✅	6
Fast and Accurate $k$-means++ via Rejection Sampling	✅	❌	✅	❌	❌	❌	❌	2
Fast and Flexible Temporal Point Processes with Triangular Maps	❌	✅	✅	✅	✅	❌	✅	5
Fast geometric learning with symbolic matrices	✅	✅	✅	❌	✅	❌	✅	5
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation	❌	❌	✅	✅	❌	❌	❌	2
Faster DBSCAN via subsampled similarity queries	✅	❌	✅	❌	❌	❌	✅	3
Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC	❌	❌	❌	❌	❌	❌	❌	0
Faster Randomized Infeasible Interior Point Methods for Tall/Wide Linear Programs	✅	❌	✅	❌	✅	❌	✅	4
Faster Wasserstein Distance Estimation with the Sinkhorn Divergence	❌	✅	❌	❌	❌	❌	✅	2
Feature Importance Ranking for Deep Learning	✅	✅	✅	✅	❌	❌	❌	4
Feature Shift Detection: Localizing Which Features Have Shifted via Conditional Distribution Tests	❌	✅	✅	❌	❌	❌	✅	3
FedSplit: an algorithmic framework for fast federated optimization	✅	❌	✅	❌	❌	❌	✅	3
Federated Accelerated Stochastic Gradient Descent	✅	✅	✅	❌	❌	❌	✅	4
Federated Bayesian Optimization via Thompson Sampling	✅	❌	✅	❌	❌	❌	✅	3
Federated Principal Component Analysis	✅	✅	✅	❌	✅	✅	✅	6
Few-Cost Salient Object Detection with Adversarial-Paced Learning	❌	✅	✅	❌	✅	❌	✅	4
Few-shot Image Generation with Elastic Weight Consolidation	❌	❌	✅	❌	❌	❌	❌	1
Few-shot Visual Reasoning with Meta-Analogical Contrastive Learning	❌	❌	✅	✅	✅	❌	✅	4
Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies	❌	✅	✅	❌	✅	❌	✅	4
Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications	✅	❌	❌	❌	❌	❌	✅	2
Field-wise Learning for Multi-field Categorical Data	❌	✅	✅	✅	✅	❌	✅	5
Fighting Copycat Agents in Behavioral Cloning from Observation Histories	✅	❌	✅	❌	❌	❌	✅	3
Finding All $\epsilon$-Good Arms in Stochastic Bandits	✅	✅	❌	❌	❌	❌	✅	3
Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems	✅	❌	❌	❌	❌	❌	✅	2
Finding the Homology of Decision Boundaries with Active Learning	❌	✅	✅	❌	❌	❌	❌	2
Fine-Grained Dynamic Head for Object Detection	❌	✅	✅	✅	❌	❌	✅	4
Finer Metagenomic Reconstruction via Biodiversity Optimization	❌	✅	✅	❌	❌	✅	✅	4
Finite Continuum-Armed Bandits	✅	❌	❌	❌	❌	❌	❌	1
Finite Versus Infinite Neural Networks: an Empirical Study	❌	❌	✅	❌	❌	❌	✅	2
Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes	❌	❌	❌	❌	❌	❌	❌	0
Finite-Time Analysis for Double Q-learning	✅	❌	❌	❌	❌	❌	❌	1
Finite-Time Analysis of Round-Robin Kullback-Leibler Upper Confidence Bounds for Optimal Adaptive Allocation with Multiple Plays and Markovian Rewards	✅	❌	❌	❌	✅	❌	✅	3
Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks	✅	❌	✅	✅	❌	❌	✅	4
First Order Constrained Optimization in Policy Space	✅	❌	✅	❌	❌	❌	✅	3
First-Order Methods for Large-Scale Market Equilibrium Computation	✅	✅	❌	❌	❌	❌	✅	3
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence	✅	✅	✅	✅	❌	❌	✅	5
Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm	✅	❌	✅	❌	❌	❌	✅	3
FleXOR: Trainable Fractional Quantization	✅	❌	✅	❌	❌	❌	✅	3
Flexible mean field variational inference using mixtures of non-overlapping exponential families	❌	✅	❌	❌	✅	❌	✅	3
Flows for simultaneous manifold learning and density estimation	❌	✅	✅	❌	❌	✅	✅	4
Focus of Attention Improves Information Transfer in Visual Features	❌	✅	❌	❌	❌	❌	✅	2
Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games	✅	❌	❌	❌	❌	❌	❌	1
Forethought and Hindsight in Credit Assignment	✅	❌	❌	❌	❌	❌	❌	1
Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumes	❌	❌	✅	❌	✅	❌	✅	3
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains	❌	❌	✅	❌	❌	❌	✅	2
Fourier Sparse Leverage Scores and Approximate Kernel Learning	❌	❌	✅	✅	❌	❌	❌	2
Fourier Spectrum Discrepancies in Deep Network Generated Images	❌	❌	✅	✅	❌	❌	✅	3
Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics	❌	❌	✅	✅	❌	❌	✅	3
FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training	✅	✅	✅	✅	✅	❌	✅	6
From Boltzmann Machines to Neural Networks and Back Again	✅	❌	✅	❌	✅	❌	✅	4
From Finite to Countable-Armed Bandits	✅	❌	❌	❌	❌	❌	✅	2
From Predictions to Decisions: Using Lookahead Regularization	❌	✅	✅	❌	❌	❌	✅	3
From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering	✅	✅	✅	✅	✅	❌	✅	6
FrugalML: How to use ML Prediction APIs more accurately and cheaply	✅	✅	✅	❌	❌	❌	❌	3
Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels	❌	❌	✅	✅	❌	❌	✅	3
Fully Dynamic Algorithm for Constrained Submodular Optimization	✅	✅	✅	❌	❌	❌	❌	3
Functional Regularization for Representation Learning: A Unified Theoretical Perspective	❌	❌	❌	❌	❌	❌	✅	1
Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing	❌	✅	✅	✅	❌	❌	✅	4
Further Analysis of Outlier Detection with Deep Generative Models	❌	✅	✅	❌	❌	❌	❌	2
GAIT-prop: A biologically plausible learning rule derived from backpropagation of error	✅	✅	✅	❌	❌	❌	❌	3
GAN Memory with No Forgetting	❌	✅	✅	❌	✅	❌	❌	3
GANSpace: Discovering Interpretable GAN Controls	❌	✅	✅	❌	❌	❌	❌	2
GCN meets GPU: Decoupling “When to Sample” from “How to Sample”	✅	❌	✅	✅	✅	❌	✅	5
GCOMB: Learning Budget-constrained Combinatorial Algorithms over Billion-sized Graphs	✅	✅	✅	✅	✅	✅	✅	7
GNNGuard: Defending Graph Neural Networks against Adversarial Attacks	✅	✅	✅	❌	❌	❌	✅	4
GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network	❌	✅	✅	❌	✅	❌	✅	4
GPS-Net: Graph-based Photometric Stereo Network	❌	❌	✅	✅	✅	❌	✅	4
GPU-Accelerated Primal Learning for Extremely Fast Large-Scale Classification	✅	❌	✅	✅	✅	✅	✅	6
GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis	❌	✅	✅	❌	❌	❌	✅	3
GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators	❌	❌	✅	❌	❌	❌	✅	2
Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction	✅	❌	✅	❌	❌	✅	✅	4
Gaussian Gated Linear Networks	✅	✅	✅	✅	❌	❌	✅	5
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective	✅	✅	✅	❌	❌	❌	✅	4
General Control Functions for Causal Effect Estimation from IVs	❌	❌	✅	✅	❌	❌	✅	3
General Transportability of Soft Interventions: Completeness Results	✅	❌	❌	❌	❌	❌	❌	1
Generalised Bayesian Filtering via Sequential Monte Carlo	✅	✅	❌	❌	❌	❌	❌	2
Generalization Bound of Gradient Descent for Non-Convex Metric Learning	❌	✅	✅	✅	❌	❌	✅	4
Generalization bound of globally optimal non-convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics	❌	❌	❌	❌	❌	❌	❌	0
Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization	❌	❌	❌	❌	❌	❌	❌	0
Generalized Boosting	✅	❌	✅	✅	❌	❌	✅	4
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection	❌	❌	✅	✅	✅	✅	✅	5
Generalized Hindsight for Reinforcement Learning	✅	❌	✅	❌	❌	❌	✅	3
Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs	✅	✅	✅	❌	❌	❌	✅	4
Generalized Leverage Score Sampling for Neural Networks	❌	❌	❌	❌	❌	❌	❌	0
Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning	❌	✅	✅	❌	❌	❌	✅	3
Generating Correct Answers for Progressive Matrices Intelligence Tests	❌	❌	✅	❌	❌	❌	✅	2
Generative 3D Part Assembly via Dynamic Graph Learning	❌	❌	✅	✅	❌	❌	✅	3
Generative Neurosymbolic Machines	❌	❌	✅	❌	❌	❌	✅	2
Generative View Synthesis: From Single-view Semantics to Novel-view Images	❌	✅	✅	❌	✅	❌	✅	4
Generative causal explanations of black-box classifiers	✅	✅	✅	✅	❌	❌	✅	5
Geo-PIFu: Geometry and Pixel Aligned Implicit Functions for Single-view Human Reconstruction	❌	❌	✅	❌	✅	❌	✅	3
Geometric All-way Boolean Tensor Decomposition	✅	✅	✅	❌	❌	❌	✅	4
Geometric Dataset Distances via Optimal Transport	❌	❌	✅	❌	❌	❌	✅	2
Geometric Exploration for Online Control	✅	❌	❌	❌	❌	❌	❌	1
Gibbs Sampling with People	❌	✅	✅	❌	❌	❌	✅	3
Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification	❌	✅	✅	✅	✅	❌	❌	4
Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems	✅	❌	✅	❌	❌	❌	✅	3
Global Convergence of Deep Networks with One Wide Layer Followed by Pyramidal Topology	❌	❌	❌	❌	❌	❌	❌	0
Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search	✅	✅	✅	✅	✅	❌	✅	6
Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data	❌	❌	✅	❌	✅	❌	✅	3
Goal-directed Generation of Discrete Structures with Conditional Generative Models	✅	❌	✅	✅	❌	❌	✅	4
GradAug: A New Regularization Method for Deep Neural Networks	✅	✅	✅	✅	✅	❌	✅	6
Gradient Boosted Normalizing Flows	❌	❌	✅	❌	❌	❌	✅	2
Gradient Estimation with Stochastic Softmax Tricks	❌	✅	✅	❌	❌	❌	✅	3
Gradient Regularized V-Learning for Dynamic Treatment Regimes	❌	✅	✅	❌	❌	❌	❌	2
Gradient Surgery for Multi-Task Learning	✅	✅	✅	❌	❌	❌	✅	4
Gradient-EM Bayesian Meta-Learning	✅	❌	✅	✅	❌	❌	✅	4
Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning	✅	✅	✅	✅	✅	❌	✅	6
GramGAN: Deep 3D Texture Synthesis From 2D Exemplars	❌	❌	❌	❌	✅	❌	✅	2
Graph Contrastive Learning with Augmentations	✅	✅	✅	❌	❌	❌	✅	4
Graph Cross Networks with Vertex Infomax Pooling	❌	✅	✅	✅	✅	✅	✅	6
Graph Geometry Interaction Learning	❌	✅	✅	✅	❌	❌	✅	4
Graph Information Bottleneck	✅	✅	✅	✅	❌	❌	✅	5
Graph Meta Learning via Local Subgraphs	✅	✅	✅	✅	❌	❌	✅	5
Graph Policy Network for Transferable Active Learning on Graphs	✅	✅	✅	✅	❌	❌	✅	5
Graph Random Neural Networks for Semi-Supervised Learning on Graphs	✅	✅	✅	✅	❌	✅	✅	6
Graph Stochastic Neural Networks for Semi-supervised Learning	✅	✅	✅	✅	❌	❌	✅	5
Graphon Neural Networks and the Transferability of Graph Neural Networks	❌	✅	✅	✅	❌	✅	✅	5
Grasp Proposal Networks: An End-to-End Solution for Visual Learning of Robotic Grasps	❌	✅	✅	❌	❌	❌	✅	3
Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough	✅	❌	✅	✅	❌	❌	✅	4
Greedy inference with structure-exploiting lazy maps	✅	✅	✅	❌	❌	❌	✅	4
GreedyFool: Distortion-Aware Sparse Adversarial Attack	✅	✅	✅	✅	❌	❌	✅	5
Group Contextual Encoding for 3D Point Clouds	❌	✅	✅	❌	❌	❌	✅	3
Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge	✅	✅	✅	✅	✅	❌	✅	6
Group-Fair Online Allocation in Continuous Time	✅	❌	❌	❌	❌	❌	✅	2
Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses	✅	✅	✅	❌	❌	❌	✅	4
Guiding Deep Molecular Optimization with Genetic Exploration	✅	✅	✅	❌	✅	❌	✅	5
H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks	❌	✅	✅	✅	❌	❌	✅	4
HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks	❌	❌	✅	❌	✅	❌	✅	3
HM-ANN: Efficient Billion-Point Nearest Neighbor Search on Heterogeneous Memory	✅	❌	✅	❌	✅	❌	✅	4
HOI Analysis: Integrating and Decomposing Human-Object Interaction	❌	✅	✅	✅	✅	❌	✅	5
HRN: A Holistic Approach to One Class Learning	❌	✅	✅	✅	❌	❌	✅	4
HYDRA: Pruning Adversarially Robust Neural Networks	✅	✅	✅	❌	❌	❌	✅	4
Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond	✅	✅	✅	❌	❌	❌	✅	4
Handling Missing Data with Graph Representation Learning	✅	✅	✅	✅	❌	❌	✅	5
Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning	❌	✅	✅	✅	❌	❌	✅	4
Hard Negative Mixing for Contrastive Learning	❌	✅	✅	✅	❌	❌	✅	4
Hard Shape-Constrained Kernel Machines	❌	✅	✅	✅	✅	❌	✅	5
Hardness of Learning Neural Networks with Natural Weights	❌	❌	❌	❌	❌	❌	❌	0
Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks	❌	✅	✅	❌	❌	❌	✅	3
Heavy-tailed Representations, Text Polarity Classification & Data Augmentation	✅	❌	✅	❌	❌	❌	✅	3
Hedging in games: Faster convergence of external and swap regrets	❌	❌	❌	❌	❌	❌	❌	0
Heuristic Domain Adaptation	❌	✅	✅	✅	❌	❌	✅	4
HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis	❌	✅	✅	❌	✅	❌	✅	4
HiPPO: Recurrent Memory with Optimal Polynomial Projections	❌	✅	✅	❌	❌	❌	❌	2
Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights	❌	❌	✅	❌	❌	❌	✅	2
Hierarchical Granularity Transfer Learning	❌	❌	✅	✅	❌	❌	✅	3
Hierarchical Neural Architecture Search for Deep Stereo Matching	❌	✅	✅	✅	✅	❌	✅	5
Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample	❌	✅	✅	✅	❌	❌	✅	4
Hierarchical Poset Decoding for Compositional Generalization in Language	✅	❌	✅	✅	❌	❌	✅	4
Hierarchical Quantized Autoencoders	✅	✅	✅	❌	❌	❌	✅	4
Hierarchical nucleation in deep neural networks	❌	✅	✅	❌	❌	❌	✅	3
Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic Systems	✅	✅	❌	❌	❌	❌	✅	3
High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds	✅	✅	✅	❌	❌	❌	✅	4
High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization	❌	✅	❌	❌	❌	❌	❌	1
High-Dimensional Sparse Linear Bandits	✅	❌	❌	❌	❌	❌	✅	2
High-Fidelity Generative Image Compression	❌	❌	✅	✅	❌	❌	✅	3
High-Throughput Synchronous Deep RL	❌	✅	✅	❌	❌	❌	✅	3
High-contrast “gaudy” images improve the training of deep neural network models of visual cortex	❌	✅	✅	❌	✅	❌	✅	4
High-recall causal discovery for autocorrelated time series with latent confounders	✅	✅	✅	❌	✅	❌	✅	5
Higher-Order Certification For Randomized Smoothing	❌	❌	✅	❌	❌	❌	✅	2
Higher-Order Spectral Clustering of Directed Graphs	❌	❌	✅	❌	✅	❌	✅	3
Hitting the High Notes: Subset Selection for Maximizing Expected Order Statistics	✅	❌	✅	❌	❌	❌	✅	3
Hold me tight! Influence of discriminative features on deep network boundaries	❌	✅	✅	❌	❌	❌	❌	2
How Can I Explain This to You? An Empirical Study of Deep Neural Network Explanation Methods	❌	✅	✅	❌	❌	❌	✅	3
How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19?	❌	✅	✅	❌	❌	❌	✅	3
How do fair decisions fare in long-term qualification?	❌	❌	✅	❌	❌	❌	❌	1
How does This Interaction Affect Me? Interpretable Attribution for Feature Interactions	❌	✅	✅	❌	✅	❌	❌	3
How does Weight Correlation Affect Generalisation Ability of Deep Neural Networks?	❌	❌	✅	❌	❌	❌	✅	2
How hard is to distinguish graphs with graph neural networks?	❌	❌	❌	✅	❌	❌	✅	2
How many samples is a good initial point worth in Low-rank Matrix Recovery?	❌	❌	❌	❌	❌	❌	❌	0
How to Characterize The Landscape of Overparameterized Convolutional Neural Networks	✅	❌	✅	❌	❌	❌	✅	3
How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization	✅	✅	✅	❌	❌	❌	✅	4
Human Parsing Based Texture Transfer from Single Image to 3D Human via Cross-View Consistency	❌	✅	✅	❌	❌	❌	✅	3
HyNet: Learning Local Descriptor with Hybrid Similarity Measure and Triplet Loss	❌	✅	✅	✅	❌	❌	✅	4
Hybrid Models for Learning to Branch	❌	✅	❌	✅	❌	❌	✅	3
Hybrid Variance-Reduced SGD Algorithms For Minimax Problems with Nonconvex-Linear Function	✅	❌	✅	❌	✅	✅	✅	5
Hyperparameter Ensembles for Robustness and Uncertainty Quantification	✅	✅	✅	✅	❌	❌	✅	5
Hypersolvers: Toward Fast Continuous-Depth Models	❌	✅	✅	❌	✅	❌	✅	4
ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping	❌	✅	✅	❌	❌	❌	❌	2
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA	❌	✅	✅	❌	❌	❌	❌	2
ICNet: Intra-saliency Correlation Network for Co-Saliency Detection	❌	✅	✅	❌	✅	❌	✅	4
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method	✅	❌	✅	❌	❌	❌	✅	3
ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding	✅	✅	✅	❌	✅	❌	✅	5
Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models	❌	✅	✅	✅	❌	❌	✅	4
Identifying Learning Rules From Neural Network Observables	❌	✅	✅	✅	✅	❌	✅	5
Identifying Mislabeled Data using the Area Under the Margin Ranking	✅	✅	✅	❌	❌	❌	✅	4
Identifying signal and noise structure in neural population activity with Gaussian process factor models	❌	✅	✅	✅	❌	❌	✅	4
ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite Pool	✅	❌	✅	❌	❌	❌	❌	2
Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy	❌	❌	❌	❌	❌	❌	✅	1
Implicit Distributional Reinforcement Learning	✅	✅	✅	❌	✅	❌	✅	5
Implicit Graph Neural Networks	❌	✅	✅	✅	❌	❌	✅	4
Implicit Neural Representations with Periodic Activation Functions	❌	✅	✅	❌	❌	❌	❌	2
Implicit Rank-Minimizing Autoencoder	❌	❌	✅	❌	❌	❌	✅	2
Implicit Regularization and Convergence for Weight Normalization	✅	❌	❌	❌	❌	❌	❌	1
Implicit Regularization in Deep Learning May Not Be Explainable by Norms	❌	❌	❌	❌	❌	❌	✅	1
Impossibility Results for Grammar-Compressed Linear Algebra	❌	❌	✅	❌	❌	❌	❌	1
Improved Algorithms for Convex-Concave Minimax Optimization	✅	❌	❌	❌	❌	❌	❌	1
Improved Algorithms for Online Submodular Maximization via First-order Regret Bounds	✅	❌	❌	❌	❌	❌	❌	1
Improved Analysis of Clipping Algorithms for Non-convex Optimization	✅	✅	✅	✅	❌	❌	✅	5
Improved Guarantees for k-means++ and k-means++ Parallel	✅	❌	✅	❌	❌	❌	❌	2
Improved Sample Complexity for Incremental Autonomous Exploration in MDPs	✅	❌	❌	❌	❌	❌	❌	1
Improved Schemes for Episodic Memory-based Lifelong Learning	✅	✅	✅	✅	✅	❌	✅	6
Improved Techniques for Training Score-Based Generative Models	✅	❌	✅	❌	❌	❌	✅	3
Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows	✅	✅	✅	❌	❌	❌	✅	4
Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method	❌	❌	✅	❌	❌	❌	✅	2
Improving Auto-Augment via Augmentation-Wise Weight Sharing	✅	❌	✅	✅	✅	❌	✅	5
Improving GAN Training with Probability Ratio Clipping and Sample Reweighting	✅	✅	✅	❌	❌	❌	✅	4
Improving Generalization in Reinforcement Learning with Mixture Regularization	❌	✅	✅	❌	❌	❌	❌	2
Improving Inference for Neural Image Compression	✅	❌	✅	❌	❌	✅	✅	4
Improving Local Identifiability in Probabilistic Box Embeddings	✅	✅	✅	❌	❌	❌	❌	3
Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention	❌	✅	✅	✅	❌	❌	✅	4
Improving Neural Network Training in Low Dimensional Random Bases	✅	✅	✅	❌	✅	❌	✅	5
Improving Online Rent-or-Buy Algorithms with Sequential Decision Making and ML Predictions	✅	❌	❌	❌	❌	❌	✅	2
Improving Policy-Constrained Kidney Exchange via Pre-Screening	✅	✅	❌	❌	❌	❌	✅	3
Improving Sample Complexity Bounds for (Natural) Actor-Critic Algorithms	✅	❌	❌	❌	❌	❌	❌	1
Improving Sparse Vector Technique with Renyi Differential Privacy	✅	❌	✅	❌	❌	❌	✅	3
Improving model calibration with accuracy versus uncertainty optimization	✅	✅	✅	❌	❌	❌	✅	4
Improving robustness against common corruptions by covariate shift adaptation	❌	✅	✅	✅	❌	❌	✅	4
In search of robust measures of generalization	❌	❌	✅	❌	❌	❌	✅	2
Incorporating BERT into Parallel Sequence Decoding with Adapters	❌	✅	✅	✅	✅	❌	✅	5
Incorporating Interpretable Output Constraints in Bayesian Neural Networks	❌	✅	✅	❌	❌	❌	✅	3
Incorporating Pragmatic Reasoning Communication into Emergent Language	❌	✅	❌	❌	✅	❌	✅	3
Independent Policy Gradient Methods for Competitive Reinforcement Learning	✅	❌	❌	❌	❌	❌	✅	2
Inductive Quantum Embedding	✅	✅	✅	✅	❌	❌	✅	5
Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation	✅	❌	✅	❌	✅	❌	✅	4
Inference for Batched Bandits	❌	❌	❌	❌	❌	❌	✅	1
Inferring learning rules from animal decision-making	❌	✅	✅	❌	❌	❌	✅	3
Influence-Augmented Online Planning for Complex Environments	✅	✅	❌	❌	❌	❌	✅	3
Information Maximization for Few-Shot Learning	❌	✅	✅	✅	✅	❌	✅	5
Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback	✅	✅	✅	✅	❌	❌	✅	5
Information Theoretic Regret Bounds for Online Nonlinear Control	✅	✅	✅	❌	❌	❌	❌	3
Information theoretic limits of learning a sparse rule	❌	❌	❌	❌	❌	❌	❌	0
Information-theoretic Task Selection for Meta-Reinforcement Learning	✅	✅	✅	✅	✅	❌	✅	6
Input-Aware Dynamic Backdoor Attack	✅	✅	✅	❌	❌	❌	✅	4
Instance Based Approximations to Profile Maximum Likelihood	✅	❌	❌	❌	❌	❌	❌	1
Instance Selection for GANs	❌	✅	✅	✅	✅	❌	✅	5
Instance-based Generalization in Reinforcement Learning	✅	✅	✅	❌	✅	✅	✅	6
Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms	✅	❌	❌	❌	❌	❌	❌	1
Instance-wise Feature Grouping	❌	✅	✅	✅	✅	❌	✅	5
Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients	❌	✅	✅	❌	✅	❌	✅	4
Interferobot: aligning an optical interferometer by a reinforcement learning agent	❌	✅	❌	❌	✅	❌	✅	3
Interior Point Solving for LP-based prediction+optimisation	✅	✅	✅	✅	✅	✅	✅	7
Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs	❌	✅	✅	❌	✅	❌	✅	4
Interpretable Sequence Learning for Covid-19 Forecasting	✅	❌	✅	✅	❌	❌	✅	4
Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations	✅	✅	✅	❌	❌	❌	❌	3
Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech	❌	❌	✅	✅	❌	❌	✅	3
Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding	✅	✅	✅	❌	✅	❌	❌	4
Interventional Few-Shot Learning	❌	✅	✅	✅	❌	❌	✅	4
Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks	❌	❌	✅	✅	❌	❌	❌	2
Intra-Processing Methods for Debiasing Neural Networks	✅	✅	✅	✅	❌	❌	✅	5
Introducing Routing Uncertainty in Capsule Networks	✅	✅	✅	✅	❌	❌	✅	5
Inverse Learning of Symmetries	❌	✅	✅	❌	❌	❌	❌	2
Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics	✅	❌	❌	❌	❌	❌	✅	2
Inverse Reinforcement Learning from a Gradient-based Learner	✅	❌	❌	❌	❌	❌	✅	2
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax	❌	✅	✅	✅	❌	❌	✅	4
Inverting Gradients - How easy is it to break privacy in federated learning?	❌	✅	✅	✅	❌	❌	✅	4
Investigating Gender Bias in Language Models Using Causal Mediation Analysis	❌	✅	✅	❌	❌	❌	❌	2
Is Long Horizon RL More Difficult Than Short Horizon RL?	✅	❌	❌	❌	❌	❌	❌	1
Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning?	✅	❌	❌	❌	❌	❌	❌	1
Is normalization indispensable for training deep neural network?	❌	✅	✅	✅	❌	❌	✅	4
Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings	✅	✅	✅	✅	❌	❌	❌	4
JAX MD: A Framework for Differentiable Physics	❌	✅	✅	✅	✅	❌	✅	5
Joint Contrastive Learning with Infinite Possibilities	✅	✅	✅	✅	✅	❌	✅	6
Joint Policy Search for Multi-agent Collaboration with Imperfect Information	✅	✅	✅	❌	❌	❌	✅	4
Joints in Random Forests	✅	❌	✅	✅	❌	❌	✅	4
Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout	✅	❌	✅	❌	✅	❌	✅	4
KFC: A Scalable Approximation Algorithm for $k$−center Fair Clustering	✅	✅	✅	❌	✅	❌	✅	5
Kalman Filtering Attention for User Behavior Modeling in CTR Prediction	❌	❌	✅	❌	❌	❌	✅	2
Kernel Alignment Risk Estimator: Risk Prediction from Training Data	❌	❌	✅	❌	❌	❌	✅	2
Kernel Based Progressive Distillation for Adder Neural Networks	✅	❌	✅	❌	✅	❌	✅	4
Kernel Methods Through the Roof: Handling Billions of Points Efficiently	✅	✅	✅	✅	✅	❌	✅	6
Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks	❌	✅	✅	✅	❌	❌	✅	4
Knowledge Augmented Deep Neural Networks for Joint Facial Expression and Action Unit Recognition	✅	❌	✅	✅	❌	❌	✅	4
Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher	❌	❌	❌	❌	❌	❌	✅	1
Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control	✅	❌	✅	❌	✅	❌	✅	4
LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-resolution and Beyond	❌	❌	✅	❌	✅	❌	✅	3
Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity	❌	✅	✅	❌	❌	❌	❌	2
Labelling unlabelled videos from scratch with multi-modal self-supervision	❌	❌	✅	❌	❌	❌	✅	2
Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks	❌	❌	❌	❌	❌	❌	✅	1
Language Models are Few-Shot Learners	❌	❌	✅	✅	✅	❌	✅	4
Language Through a Prism: A Spectral Approach for Multiscale Language Representations	✅	❌	✅	❌	❌	❌	✅	3
Language and Visual Entity Relationship Graph for Agent Navigation	❌	✅	✅	✅	❌	❌	❌	3
Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration	❌	✅	✅	❌	❌	❌	❌	2
Language-Conditioned Imitation Learning for Robot Manipulation Tasks	❌	✅	✅	✅	❌	❌	✅	4
Large-Scale Adversarial Training for Vision-and-Language Representation Learning	✅	✅	✅	✅	❌	❌	✅	5
Large-Scale Methods for Distributionally Robust Optimization	❌	✅	✅	❌	❌	❌	✅	3
Latent Bandits Revisited	✅	❌	✅	❌	❌	❌	✅	3
Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings	❌	✅	✅	✅	✅	❌	✅	5
Latent Template Induction with Gumbel-CRFs	✅	✅	✅	✅	✅	❌	✅	6
Latent World Models For Intrinsically Motivated Exploration	❌	✅	✅	❌	✅	❌	✅	4
Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge	❌	✅	✅	✅	❌	❌	❌	3
Learnability with Indirect Supervision Signals	❌	❌	❌	❌	❌	❌	❌	0
Learning About Objects by Learning to Interact with Them	❌	❌	✅	❌	❌	❌	✅	2
Learning Affordance Landscapes for Interaction Exploration in 3D Environments	❌	✅	✅	✅	❌	❌	✅	4
Learning Agent Representations for Ice Hockey	❌	✅	❌	✅	❌	❌	❌	2
Learning Augmented Energy Minimization via Speed Scaling	✅	✅	✅	❌	❌	❌	✅	4
Learning Black-Box Attackers with Transferable Priors and Query Feedback	✅	✅	✅	❌	❌	✅	✅	5
Learning Bounds for Risk-sensitive Learning	❌	✅	✅	❌	❌	❌	✅	3
Learning Causal Effects via Weighted Empirical Risk Minimization	✅	❌	❌	❌	❌	❌	❌	1
Learning Certified Individually Fair Representations	❌	✅	✅	❌	✅	❌	✅	4
Learning Composable Energy Surrogates for PDE Order Reduction	❌	❌	❌	✅	✅	❌	✅	3
Learning Compositional Rules via Neural Program Synthesis	❌	✅	✅	❌	❌	❌	✅	3
Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations	❌	✅	✅	❌	❌	❌	❌	2
Learning Deep Attribution Priors Based On Prior Knowledge	❌	❌	✅	✅	❌	❌	✅	3
Learning Deformable Tetrahedral Meshes for 3D Reconstruction	❌	✅	✅	❌	✅	❌	❌	3
Learning Differentiable Programs with Admissible Neural Heuristics	✅	✅	✅	✅	❌	❌	✅	5
Learning Differential Equations that are Easy to Solve	❌	✅	✅	❌	❌	❌	✅	3
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration	✅	✅	✅	❌	❌	❌	✅	4
Learning Disentangled Representations and Group Structure of Dynamical Environments	✅	✅	❌	❌	❌	❌	❌	2
Learning Disentangled Representations of Videos with Missing Data	❌	✅	✅	❌	❌	❌	✅	3
Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction	❌	✅	✅	❌	❌	❌	✅	3
Learning Dynamic Belief Graphs to Generalize on Text-Based Games	❌	✅	✅	✅	❌	❌	❌	3
Learning Feature Sparse Principal Subspace	✅	❌	✅	❌	✅	✅	✅	5
Learning Global Transparent Models consistent with Local Contrastive Explanations	✅	❌	✅	✅	❌	❌	✅	4
Learning Graph Structure With A Finite-State Automaton Layer	❌	✅	✅	❌	❌	❌	✅	3
Learning Guidance Rewards with Trajectory-space Smoothing	✅	✅	✅	❌	❌	❌	✅	4
Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning	❌	✅	✅	❌	❌	✅	✅	4
Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence	❌	✅	✅	❌	✅	❌	✅	4
Learning Individually Inferred Communication for Multi-Agent Cooperation	❌	❌	❌	❌	❌	❌	❌	0
Learning Invariances in Neural Networks from Training Data	✅	✅	✅	❌	❌	❌	✅	4
Learning Invariants through Soft Unification	✅	✅	✅	✅	✅	❌	✅	6
Learning Kernel Tests Without Data Splitting	✅	✅	✅	❌	❌	❌	✅	4
Learning Latent Space Energy-Based Prior Model	✅	✅	✅	❌	✅	❌	✅	5
Learning Linear Programs from Optimal Decisions	❌	✅	✅	✅	✅	✅	✅	6
Learning Loss for Test-Time Augmentation	❌	❌	✅	✅	❌	❌	❌	2
Learning Manifold Implicitly via Explicit Heat-Kernel Learning	✅	❌	✅	❌	❌	❌	✅	3
Learning Multi-Agent Communication through Structured Attentive Reasoning	❌	✅	✅	❌	❌	❌	✅	3
Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks	✅	✅	❌	❌	❌	❌	✅	3
Learning Mutational Semantics	❌	✅	✅	✅	❌	❌	✅	4
Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views	✅	✅	✅	❌	❌	❌	✅	4
Learning Optimal Representations with the Decodable Information Bottleneck	✅	❌	✅	❌	❌	❌	✅	3
Learning Parities with Neural Networks	❌	❌	✅	❌	❌	❌	✅	2
Learning Physical Constraints with Neural Projections	✅	❌	❌	❌	✅	❌	❌	2
Learning Physical Graph Representations from Visual Scenes	❌	❌	✅	❌	✅	✅	✅	4
Learning Representations from Audio-Visual Spatial Alignment	❌	✅	✅	❌	❌	❌	✅	3
Learning Restricted Boltzmann Machines with Sparse Latent Variables	✅	❌	❌	❌	❌	❌	❌	1
Learning Retrospective Knowledge with Reverse Reinforcement Learning	❌	✅	✅	❌	❌	❌	✅	3
Learning Rich Rankings	❌	✅	✅	✅	❌	❌	✅	4
Learning Robust Decision Policies from Observational Data	✅	✅	✅	❌	❌	❌	✅	4
Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search	❌	✅	✅	❌	❌	❌	✅	3
Learning Semantic-aware Normalization for Generative Adversarial Networks	❌	✅	✅	❌	✅	❌	✅	4
Learning Some Popular Gaussian Graphical Models without Condition Number Bounds	❌	❌	✅	❌	❌	❌	✅	2
Learning Sparse Prototypes for Text Generation	❌	✅	✅	✅	✅	❌	✅	5
Learning Strategic Network Emergence Games	✅	❌	✅	❌	❌	❌	❌	2
Learning Strategy-Aware Linear Classifiers	✅	✅	❌	❌	❌	❌	✅	3
Learning Structured Distributions From Untrusted Batches: Faster and Simpler	✅	✅	❌	❌	✅	❌	✅	4
Learning Utilities and Equilibria in Non-Truthful Auctions	❌	❌	❌	❌	❌	❌	❌	0
Learning abstract structure for drawing by efficient motor program induction	❌	❌	❌	❌	❌	❌	❌	0
Learning by Minimizing the Sum of Ranked Range	✅	✅	✅	✅	❌	❌	✅	5
Learning compositional functions via multiplicative weight updates	✅	✅	✅	❌	❌	❌	✅	4
Learning discrete distributions with infinite support	❌	❌	❌	❌	❌	❌	❌	0
Learning discrete distributions: user vs item-level privacy	✅	❌	❌	❌	❌	❌	❌	1
Learning efficient task-dependent representations with synaptic plasticity	❌	✅	❌	❌	❌	❌	✅	2
Learning from Aggregate Observations	❌	❌	✅	✅	❌	❌	✅	3
Learning from Failure: De-biasing Classifier from Biased Classifier	✅	❌	✅	❌	❌	❌	❌	2
Learning from Label Proportions: A Mutual Contamination Framework	✅	✅	✅	✅	❌	❌	✅	5
Learning from Mixtures of Private and Public Populations	✅	❌	❌	❌	❌	❌	❌	1
Learning from Positive and Unlabeled Data with Arbitrary Positive Shift	✅	✅	✅	✅	❌	❌	✅	5
Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE	❌	❌	✅	✅	❌	❌	✅	3
Learning of Discrete Graphical Models with Neural Networks	❌	✅	❌	❌	❌	❌	✅	2
Learning outside the Black-Box: The pursuit of interpretable models	✅	✅	✅	❌	❌	❌	❌	3
Learning sparse codes from compressed representations with biologically plausible local wiring constraints	❌	✅	✅	✅	❌	❌	✅	4
Learning the Geometry of Wave-Based Imaging	❌	✅	✅	❌	❌	❌	✅	3
Learning the Linear Quadratic Regulator from Nonlinear Observations	✅	❌	❌	❌	❌	❌	❌	1
Learning to Adapt to Evolving Domains	✅	✅	✅	✅	❌	❌	✅	5
Learning to Approximate a Bregman Divergence	❌	✅	✅	✅	❌	❌	✅	4
Learning to Decode: Reinforcement Learning for Decoding of Sparse Graph-Based Channel Codes	✅	❌	❌	❌	❌	❌	✅	2
Learning to Detect Objects with a 1 Megapixel Event Camera	❌	✅	✅	✅	✅	❌	✅	5
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning	❌	✅	✅	✅	✅	❌	✅	5
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks	❌	❌	❌	✅	❌	❌	✅	2
Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction	✅	✅	✅	✅	❌	❌	✅	5
Learning to Incentivize Other Learning Agents	✅	✅	✅	❌	❌	❌	✅	4
Learning to Learn Variational Semantic Memory	❌	❌	✅	❌	❌	❌	❌	1
Learning to Learn with Feedback and Local Plasticity	❌	✅	✅	✅	❌	❌	✅	4
Learning to Mutate with Hypergradient Guided Population	✅	❌	✅	✅	❌	❌	✅	4
Learning to Orient Surfaces by Self-supervised Spherical CNNs	❌	✅	✅	✅	❌	❌	✅	4
Learning to Play No-Press Diplomacy with Best Response Policy Iteration	✅	✅	✅	✅	❌	❌	✅	5
Learning to Play Sequential Games versus Unknown Opponents	✅	❌	✅	❌	❌	❌	✅	3
Learning to Prove Theorems by Learning to Generate Theorems	✅	✅	✅	✅	❌	❌	✅	5
Learning to Select Best Forecast Tasks for Clinical Outcome Prediction	✅	✅	✅	✅	❌	❌	✅	5
Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping	❌	❌	✅	❌	❌	❌	✅	2
Learning to search efficiently for causally near-optimal treatments	❌	✅	✅	✅	❌	❌	✅	4
Learning to solve TV regularised problems with unrolled algorithms	❌	❌	✅	❌	❌	❌	✅	2
Learning to summarize with human feedback	❌	✅	✅	✅	❌	❌	✅	4
Learning under Model Misspecification: Applications to Variational and Ensemble methods	❌	✅	✅	❌	❌	❌	❌	2
Learning with Differentiable Pertubed Optimizers	❌	✅	✅	✅	❌	❌	✅	4
Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces	✅	✅	✅	✅	❌	✅	✅	6
Learning with Optimized Random Features: Exponential Speedup by Quantum Machine Learning without Sparsity and Low-Rank Assumptions	✅	❌	❌	❌	❌	❌	❌	1
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms	✅	❌	❌	❌	❌	❌	✅	2
Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning	❌	❌	✅	❌	❌	❌	❌	1
Leveraging Predictions in Smoothed Online Convex Optimization via Gradient-based Algorithms	✅	❌	❌	❌	❌	❌	❌	1
Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations	❌	❌	✅	❌	❌	❌	✅	2
Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting	✅	✅	✅	❌	❌	❌	✅	4
Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation	❌	✅	✅	✅	✅	❌	✅	5
Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder	✅	❌	✅	❌	❌	❌	✅	3
Limits on Testing Structural Changes in Ising Models	❌	❌	❌	❌	❌	✅	✅	2
Limits to Depth Efficiencies of Self-Attention	❌	❌	✅	❌	✅	❌	❌	2
Linear Disentangled Representations and Unsupervised Action Estimation	❌	✅	✅	✅	❌	❌	✅	4
Linear Dynamical Systems as a Core Computational Primitive	✅	❌	✅	❌	❌	❌	✅	3
Linear Time Sinkhorn Divergences using Positive Features	✅	✅	✅	❌	✅	❌	✅	5
Linear-Sample Learning of Low-Rank Distributions	✅	❌	❌	❌	❌	❌	❌	1
Linearly Converging Error Compensated SGD	✅	✅	✅	❌	✅	❌	✅	5
Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing	❌	✅	✅	❌	❌	❌	❌	2
Lipschitz-Certifiable Training with a Tight Outer Bound	✅	✅	✅	❌	✅	❌	✅	5
List-Decodable Mean Estimation via Iterative Multi-Filtering	✅	❌	❌	❌	❌	❌	❌	1
Listening to Sounds of Silence for Speech Denoising	❌	❌	✅	❌	❌	❌	✅	2
LoCo: Local Contrastive Representation Learning	❌	❌	✅	✅	❌	❌	✅	3
Locally Differentially Private (Contextual) Bandits Learning	✅	❌	❌	❌	❌	❌	❌	1
Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms	❌	❌	❌	❌	❌	❌	❌	0
Locally-Adaptive Nonparametric Online Learning	✅	❌	❌	❌	❌	❌	❌	1
Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment	❌	✅	✅	❌	❌	❌	❌	2
Logarithmic Pruning is All You Need	❌	❌	❌	❌	❌	❌	❌	0
Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems	✅	❌	❌	❌	❌	❌	❌	1
Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors	✅	✅	✅	❌	✅	❌	❌	4
Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect	❌	✅	✅	✅	❌	❌	✅	4
Look-ahead Meta Learning for Continual Learning	✅	✅	✅	✅	❌	❌	✅	5
LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration	❌	✅	✅	❌	❌	❌	❌	2
Low Distortion Block-Resampling with Spatially Stochastic Networks	❌	❌	✅	❌	✅	❌	✅	3
Lower Bounds and Optimal Algorithms for Personalized Federated Learning	✅	❌	✅	❌	❌	❌	❌	2
MATE: Plugging in Model Awareness to Task Embedding for Meta Learning	❌	✅	✅	✅	❌	❌	✅	4
MCUNet: Tiny Deep Learning on IoT Devices	❌	❌	✅	✅	❌	❌	❌	2
MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning	✅	✅	✅	❌	❌	❌	❌	3
MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler	✅	✅	✅	✅	❌	❌	✅	5
MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal Angles	❌	✅	✅	✅	✅	❌	✅	5
MOPO: Model-based Offline Policy Optimization	✅	✅	✅	❌	❌	❌	✅	4
MOReL: Model-Based Offline Reinforcement Learning	✅	❌	✅	❌	❌	❌	❌	2
MPNet: Masked and Permuted Pre-training for Language Understanding	❌	✅	✅	✅	✅	❌	✅	5
MRI Banding Removal via Adversarial Training	❌	✅	✅	✅	❌	❌	✅	4
Make One-Shot Video Object Segmentation Efficient Again	✅	✅	✅	✅	✅	❌	✅	6
Making Non-Stochastic Control (Almost) as Easy as Stochastic	✅	❌	❌	❌	❌	❌	❌	1
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data	❌	❌	✅	✅	❌	❌	✅	3
Manifold structure in graph embeddings	❌	❌	✅	❌	❌	❌	✅	2
Marginal Utility for Planning in Continuous or Large Discrete Action Spaces	❌	❌	❌	❌	✅	❌	✅	2
Margins are Insufficient for Explaining Gradient Boosting	❌	❌	✅	✅	❌	❌	✅	3
Markovian Score Climbing: Variational Inference with KL(p\|\|q)	✅	✅	✅	✅	❌	❌	✅	5
Matrix Completion with Hierarchical Graph Side Information	✅	❌	✅	❌	❌	❌	✅	3
Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula	✅	❌	✅	✅	✅	❌	✅	5
Matrix Inference and Estimation in Multi-Layer Models	✅	✅	❌	❌	❌	❌	✅	3
Matérn Gaussian Processes on Riemannian Manifolds	❌	✅	✅	❌	❌	❌	✅	3
Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness	✅	✅	✅	✅	❌	❌	✅	5
Measuring Robustness to Natural Distribution Shifts in Image Classification	❌	✅	✅	❌	❌	❌	❌	2
Measuring Systematic Generalization in Neural Proof Generation with Transformers	✅	✅	✅	❌	❌	❌	✅	4
Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards	✅	❌	✅	❌	❌	❌	✅	3
Memory-Efficient Learning of Stable Linear Dynamical Systems for Prediction and Control	✅	✅	✅	❌	✅	✅	✅	6
MeshSDF: Differentiable Iso-Surface Extraction	✅	❌	✅	✅	❌	❌	✅	4
Meta-Consolidation for Continual Learning	✅	✅	✅	❌	✅	❌	✅	5
Meta-Gradient Reinforcement Learning with an Objective Discovered Online	✅	❌	✅	❌	❌	❌	✅	3
Meta-Learning Requires Meta-Augmentation	❌	✅	✅	❌	❌	❌	❌	2
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes	✅	✅	✅	❌	❌	❌	❌	3
Meta-Learning through Hebbian Plasticity in Random Networks	❌	✅	✅	❌	❌	✅	✅	4
Meta-Learning with Adaptive Hyperparameters	✅	✅	✅	✅	❌	❌	✅	5
Meta-Neighborhoods	✅	✅	✅	✅	❌	❌	✅	5
Meta-learning from Tasks with Heterogeneous Attribute Spaces	✅	❌	✅	✅	❌	❌	✅	4
Meta-trained agents implement Bayes-optimal agents	❌	❌	❌	❌	❌	❌	✅	1
MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures	✅	❌	✅	✅	❌	❌	✅	4
MetaPoison: Practical General-purpose Clean-label Data Poisoning	✅	✅	✅	❌	❌	❌	✅	4
MetaSDF: Meta-Learning Signed Distance Functions	✅	✅	✅	✅	❌	❌	✅	5
Metric-Free Individual Fairness in Online Learning	✅	❌	❌	❌	❌	❌	❌	1
MinMax Methods for Optimal Transport and Beyond: Regularization, Approximation and Numerics	❌	✅	❌	❌	❌	❌	✅	2
MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers	❌	✅	✅	❌	❌	❌	❌	2
Minibatch Stochastic Approximate Proximal Point Methods	❌	✅	❌	❌	❌	❌	✅	2
Minibatch vs Local SGD for Heterogeneous Distributed Learning	✅	❌	✅	❌	❌	❌	✅	3
Minimax Bounds for Generalized Linear Models	❌	❌	❌	❌	❌	❌	❌	0
Minimax Classification with 0-1 Loss and Performance Guarantees	✅	✅	✅	✅	❌	❌	✅	5
Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory Neurons	❌	❌	✅	❌	❌	✅	✅	3
Minimax Estimation of Conditional Moment Models	✅	✅	✅	❌	❌	❌	✅	4
Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks	❌	✅	✅	✅	❌	❌	✅	4
Minimax Optimal Nonparametric Estimation of Heterogeneous Treatment Effects	✅	✅	❌	❌	❌	❌	❌	2
Minimax Regret of Switching-Constrained Online Convex Optimization: No Phase Transition	❌	❌	❌	❌	❌	❌	❌	0
Minimax Value Interval for Off-Policy Evaluation and Policy Optimization	❌	❌	✅	❌	❌	❌	✅	2
Mitigating Forgetting in Online Continual Learning via Instance-Aware Parameterization	❌	❌	✅	❌	❌	❌	✅	2
Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments	❌	✅	✅	❌	✅	✅	✅	5
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions	✅	✅	✅	✅	❌	❌	✅	5
Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables	✅	✅	✅	❌	❌	❌	✅	4
Model Agnostic Multilevel Explanations	✅	❌	✅	✅	❌	❌	✅	4
Model Class Reliance for Random Forests	❌	✅	✅	❌	❌	❌	❌	2
Model Fusion via Optimal Transport	✅	✅	✅	❌	✅	❌	✅	5
Model Interpretability through the lens of Computational Complexity	❌	❌	❌	❌	❌	❌	❌	0
Model Inversion Networks for Model-Based Optimization	✅	❌	✅	❌	❌	❌	✅	3
Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNets	❌	✅	✅	✅	✅	❌	✅	5
Model Selection for Production System via Automated Online Experiments	✅	❌	✅	❌	❌	❌	✅	3
Model Selection in Contextual Stochastic Bandit Problems	✅	❌	❌	❌	❌	❌	✅	2
Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity	❌	❌	❌	❌	❌	❌	❌	0
Model-based Adversarial Meta-Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Model-based Policy Optimization with Unsupervised Model Adaptation	✅	✅	✅	❌	❌	❌	✅	4
Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs	✅	❌	✅	❌	❌	❌	❌	2
Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows	❌	✅	✅	❌	❌	❌	✅	3
Modeling Noisy Annotations for Crowd Counting	❌	❌	✅	✅	❌	❌	✅	3
Modeling Shared responses in Neuroimaging Studies through MultiView ICA	✅	✅	✅	✅	❌	❌	✅	5
Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction	❌	✅	✅	✅	❌	❌	❌	3
Modeling and Optimization Trade-off in Meta-learning	❌	❌	✅	✅	❌	❌	✅	3
Modern Hopfield Networks and Attention for Immune Repertoire Classification	❌	✅	✅	✅	❌	❌	✅	4
Modular Meta-Learning with Shrinkage	✅	❌	✅	✅	❌	❌	✅	4
MomentumRNN: Integrating Momentum into Recurrent Neural Networks	❌	❌	✅	✅	❌	❌	✅	3
Monotone operator equilibrium networks	✅	✅	✅	❌	✅	❌	✅	5
Most ReLU Networks Suffer from $\ell^2$ Adversarial Perturbations	❌	✅	✅	❌	❌	❌	✅	3
Movement Pruning: Adaptive Sparsity by Fine-Tuning	❌	❌	✅	❌	❌	❌	✅	2
MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models	❌	❌	✅	✅	❌	❌	✅	3
Multi-Fidelity Bayesian Optimization via Deep Neural Networks	✅	❌	✅	✅	✅	❌	✅	5
Multi-Plane Program Induction with 3D Box Priors	❌	❌	❌	❌	❌	❌	✅	1
Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates	❌	✅	❌	❌	✅	❌	✅	3
Multi-Stage Influence Function	✅	✅	✅	❌	✅	❌	✅	5
Multi-Task Reinforcement Learning with Soft Modularization	❌	✅	✅	❌	❌	❌	✅	3
Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement	❌	✅	✅	✅	✅	✅	✅	6
Multi-agent Trajectory Prediction with Fuzzy Query Attention	❌	✅	✅	✅	❌	❌	✅	4
Multi-agent active perception with prediction rewards	✅	✅	❌	❌	❌	❌	✅	3
Multi-label Contrastive Predictive Coding	✅	✅	✅	❌	✅	❌	✅	5
Multi-label classification: do Hamming loss and subset accuracy really conflict with each other?	❌	❌	✅	✅	❌	❌	✅	3
Multi-task Additive Models for Robust Estimation and Automatic Structure Discovery	✅	❌	✅	✅	✅	✅	✅	6
Multi-task Batch Reinforcement Learning with Metric Learning	✅	✅	✅	❌	❌	❌	✅	4
Multi-task Causal Learning with Gaussian Processes	❌	✅	✅	❌	❌	❌	❌	2
MultiON: Benchmarking Semantic Map Memory using Multi-Object Navigation	❌	✅	✅	✅	❌	❌	✅	4
Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning	❌	❌	✅	❌	❌	❌	✅	2
Multilabel Classification by Hierarchical Partitioning and Data-dependent Grouping	✅	✅	✅	❌	❌	❌	✅	4
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence	❌	❌	✅	❌	❌	❌	❌	1
Multimodal Graph Networks for Compositional Generalization in Visual Question Answering	❌	✅	✅	✅	❌	❌	✅	4
Multiparameter Persistence Image for Topological Machine Learning	❌	✅	✅	✅	✅	❌	✅	5
Multipole Graph Neural Operator for Parametric Partial Differential Equations	❌	✅	❌	❌	❌	❌	✅	2
Multiscale Deep Equilibrium Models	❌	✅	✅	✅	✅	❌	✅	5
Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance	❌	✅	✅	❌	❌	❌	✅	3
Munchausen Reinforcement Learning	❌	❌	✅	❌	❌	❌	✅	2
Mutual exclusivity as a challenge for deep neural networks	❌	❌	✅	❌	❌	❌	✅	2
Myersonian Regression	❌	❌	❌	❌	❌	❌	❌	0
NVAE: A Deep Hierarchical Variational Autoencoder	❌	✅	✅	❌	✅	❌	✅	4
NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity	❌	❌	✅	❌	✅	❌	✅	3
Natural Graph Networks	❌	❌	✅	✅	❌	❌	✅	3
Natural Policy Gradient Primal-Dual Method for Constrained Markov Decision Processes	✅	❌	❌	❌	❌	❌	✅	2
Near-Optimal Comparison Based Clustering	✅	✅	✅	❌	❌	❌	✅	4
Near-Optimal Reinforcement Learning with Self-Play	✅	❌	❌	❌	❌	❌	❌	1
Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals	❌	❌	❌	❌	❌	❌	❌	0
Network Diffusions via Neural Mean-Field Dynamics	✅	✅	✅	❌	❌	❌	✅	4
Network size and size of the weights in memorization with two-layers neural networks	❌	❌	❌	❌	❌	❌	❌	0
Network-to-Network Translation with Conditional Invertible Neural Networks	❌	✅	✅	❌	✅	❌	❌	3
NeuMiss networks: differentiable programming for supervised learning with missing values.	❌	✅	❌	❌	❌	❌	✅	2
Neural Anisotropy Directions	❌	✅	✅	❌	❌	❌	❌	2
Neural Architecture Generator Optimization	✅	✅	✅	❌	✅	❌	✅	5
Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems	✅	❌	✅	❌	❌	❌	❌	2
Neural Complexity Measures	✅	❌	✅	✅	❌	❌	✅	4
Neural Controlled Differential Equations for Irregular Time Series	❌	✅	✅	✅	❌	❌	✅	4
Neural Dynamic Policies for End-to-End Sensorimotor Learning	✅	✅	✅	❌	❌	❌	✅	4
Neural Execution Engines: Learning to Execute Subroutines	✅	✅	✅	❌	❌	❌	❌	3
Neural FFTs for Universal Texture Image Synthesis	❌	❌	✅	✅	✅	❌	✅	4
Neural Manifold Ordinary Differential Equations	✅	❌	✅	❌	❌	❌	✅	3
Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flows	❌	✅	✅	✅	✅	❌	✅	5
Neural Message Passing for Multi-Relational Ordered and Recursive Hypergraphs	❌	✅	✅	❌	❌	❌	✅	3
Neural Methods for Point-wise Dependency Estimation	❌	✅	✅	❌	❌	❌	❌	2
Neural Networks Fail to Learn Periodic Functions and How to Fix It	❌	✅	✅	✅	❌	❌	✅	4
Neural Networks Learning and Memorization with (almost) no Over-Parameterization	✅	❌	❌	❌	❌	❌	✅	2
Neural Networks with Recurrent Generative Feedback	✅	❌	✅	❌	❌	❌	✅	3
Neural Networks with Small Weights and Depth-Separation Barriers	❌	❌	❌	❌	❌	❌	❌	0
Neural Non-Rigid Tracking	✅	✅	✅	✅	✅	❌	✅	6
Neural Path Features and Neural Path Kernel : Understanding the role of gates in deep learning	❌	❌	✅	❌	❌	❌	✅	2
Neural Power Units	❌	✅	❌	❌	❌	❌	✅	2
Neural Sparse Representation for Image Restoration	❌	✅	✅	✅	✅	❌	✅	5
Neural Sparse Voxel Fields	✅	✅	✅	❌	✅	❌	✅	5
Neural Star Domain as Primitive Representation	❌	❌	✅	✅	✅	❌	✅	4
Neural Topographic Factor Analysis for fMRI Data	✅	❌	✅	❌	❌	❌	✅	3
Neural Unsigned Distance Fields for Implicit Function Learning	✅	✅	✅	✅	✅	❌	✅	6
Neural encoding with visual attention	❌	✅	✅	✅	❌	❌	✅	4
Neuron Merging: Compensating for Pruned Neurons	✅	✅	✅	✅	❌	❌	✅	5
Neuron Shapley: Discovering the Responsible Neurons	✅	✅	✅	✅	❌	❌	✅	5
Neuron-level Structured Pruning using Polarization Regularizer	✅	✅	✅	❌	❌	❌	✅	4
Neuronal Gaussian Process Regression	❌	✅	✅	✅	❌	❌	✅	4
Neurosymbolic Reinforcement Learning with Formally Verified Exploration	✅	✅	❌	❌	❌	❌	✅	3
Neurosymbolic Transformers for Multi-Agent Communication	❌	✅	❌	❌	❌	❌	✅	2
Neutralizing Self-Selection Bias in Sampling for Sortition	❌	✅	✅	❌	❌	❌	❌	2
Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning	✅	✅	✅	❌	✅	✅	✅	6
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems	✅	✅	✅	❌	❌	❌	❌	3
No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium	✅	❌	✅	❌	✅	❌	✅	4
No-Regret Learning and Mixed Nash Equilibria: They Do Not Mix	❌	❌	❌	❌	❌	❌	❌	0
No-regret Learning in Price Competitions under Consumer Reference Effects	✅	❌	❌	❌	❌	❌	✅	2
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding	❌	❌	✅	✅	❌	❌	✅	3
Node Embeddings and Exact Low-Rank Representations of Complex Networks	❌	✅	✅	❌	❌	❌	✅	3
Noise-Contrastive Estimation for Multivariate Point Processes	✅	✅	✅	✅	✅	❌	✅	6
Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising	❌	❌	✅	❌	❌	❌	✅	2
Non-Convex SGD Learns Halfspaces with Adversarial Label Noise	✅	❌	❌	❌	❌	❌	❌	1
Non-Crossing Quantile Regression for Distributional Reinforcement Learning	❌	❌	✅	❌	❌	❌	✅	2
Non-Euclidean Universal Approximation	❌	✅	✅	❌	❌	❌	❌	2
Non-Stochastic Control with Bandit Feedback	✅	✅	❌	❌	❌	❌	✅	3
Non-parametric Models for Non-negative Functions	❌	✅	❌	❌	❌	❌	✅	2
Non-reversible Gaussian processes for identifying latent dynamical structure in neural data	❌	❌	✅	❌	❌	❌	✅	2
Nonasymptotic Guarantees for Spiked Matrix Recovery with Generative Priors	✅	❌	❌	❌	❌	❌	✅	2
Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model	✅	✅	✅	❌	✅	❌	✅	5
Normalizing Kalman Filters for Multivariate Time Series Analysis	❌	❌	✅	✅	❌	❌	✅	3
Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning	✅	❌	✅	✅	✅	❌	✅	5
Novelty Search in Representational Space for Sample Efficient Exploration	✅	✅	❌	❌	❌	❌	✅	3
Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning	❌	✅	❌	❌	✅	❌	✅	3
O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers	❌	❌	✅	✅	❌	❌	✅	3
OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification	❌	✅	✅	❌	❌	❌	❌	2
OTLDA: A Geometry-aware Optimal Transport Approach for Topic Modeling	✅	❌	✅	✅	❌	❌	✅	4
Object Goal Navigation using Goal-Oriented Semantic Exploration	❌	✅	✅	✅	❌	❌	✅	4
Object-Centric Learning with Slot Attention	✅	✅	✅	✅	✅	❌	✅	6
Ode to an ODE	❌	❌	✅	❌	❌	❌	✅	2
Off-Policy Evaluation and Learning for External Validity under a Covariate Shift	✅	❌	✅	✅	❌	❌	❌	3
Off-Policy Evaluation via the Regularized Lagrangian	❌	❌	✅	❌	❌	❌	✅	2
Off-Policy Imitation Learning from Observations	✅	❌	✅	❌	❌	❌	❌	2
Off-Policy Interval Estimation with Lipschitz Value Iteration	✅	❌	❌	❌	❌	❌	✅	2
Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding	❌	✅	✅	❌	❌	❌	✅	3
Offline Imitation Learning with a Misspecified Simulator	✅	❌	✅	❌	❌	❌	✅	3
On 1/n neural representation and robustness	❌	✅	✅	❌	❌	❌	✅	3
On Adaptive Attacks to Adversarial Example Defenses	❌	✅	✅	❌	❌	❌	✅	3
On Adaptive Distance Estimation	✅	❌	❌	❌	❌	❌	✅	2
On Completeness-aware Concept-Based Explanations in Deep Neural Networks	❌	✅	✅	✅	✅	❌	✅	5
On Convergence and Generalization of Dropout Training	✅	❌	✅	❌	❌	❌	✅	3
On Convergence of Nearest Neighbor Classifiers over Feature Transformations	❌	✅	✅	✅	❌	❌	✅	4
On Correctness of Automatic Differentiation for Non-Differentiable Functions	❌	❌	❌	❌	❌	❌	❌	0
On Efficiency in Hierarchical Reinforcement Learning	✅	❌	❌	❌	❌	❌	❌	1
On Infinite-Width Hypernetworks	❌	❌	✅	❌	❌	❌	✅	2
On Learning Ising Models under Huber's Contamination Model	✅	❌	❌	❌	❌	❌	✅	2
On Numerosity of Deep Neural Networks	❌	❌	✅	❌	❌	❌	❌	1
On Power Laws in Deep Ensembles	❌	✅	✅	✅	❌	❌	✅	4
On Regret with Multiple Best Arms	✅	❌	✅	❌	❌	❌	✅	3
On Reward-Free Reinforcement Learning with Linear Function Approximation	✅	❌	❌	❌	❌	❌	❌	1
On Second Order Behaviour in Augmented Neural ODEs	❌	✅	✅	❌	❌	❌	✅	3
On Testing of Samplers	✅	✅	✅	❌	✅	❌	❌	4
On Uniform Convergence and Low-Norm Interpolation Learning	❌	❌	❌	❌	❌	❌	❌	0
On Warm-Starting Neural Network Training	❌	❌	✅	✅	✅	❌	✅	4
On ranking via sorting by estimated expected utility	❌	❌	❌	❌	❌	❌	✅	1
On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems	❌	❌	✅	❌	❌	❌	✅	2
On the Convergence of Smooth Regularized Approximate Value Iteration Schemes	❌	❌	❌	❌	❌	❌	❌	0
On the Equivalence between Online and Private Learnability beyond Binary Classification	✅	❌	❌	❌	❌	❌	❌	1
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method	❌	❌	❌	❌	❌	❌	❌	0
On the Error Resistance of Hinge-Loss Minimization	❌	❌	❌	❌	❌	❌	❌	0
On the Expressiveness of Approximate Inference in Bayesian Neural Networks	❌	✅	✅	❌	❌	❌	✅	3
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them	❌	✅	✅	❌	❌	❌	✅	3
On the Modularity of Hypernetworks	❌	❌	✅	❌	❌	❌	✅	2
On the Optimal Weighted $\ell_2$ Regularization in Overparameterized Linear Regression	❌	❌	❌	❌	❌	❌	✅	1
On the Power of Louvain in the Stochastic Block Model	❌	❌	❌	❌	❌	❌	✅	1
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs	❌	❌	✅	✅	✅	❌	✅	4
On the Similarity between the Laplace and Neural Tangent Kernels	❌	❌	✅	✅	❌	❌	❌	2
On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems	✅	❌	❌	❌	❌	❌	✅	2
On the Theory of Transfer Learning: The Importance of Task Diversity	❌	❌	❌	❌	❌	❌	❌	0
On the Tightness of Semidefinite Relaxations for Certifying Robustness to Adversarial Examples	❌	❌	✅	✅	✅	❌	✅	4
On the Trade-off between Adversarial and Backdoor Robustness	❌	✅	✅	❌	✅	❌	✅	4
On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law	❌	❌	✅	✅	❌	❌	✅	3
On the distance between two neural networks and the stability of learning	✅	✅	✅	❌	❌	❌	✅	4
On the equivalence of molecular graph convolution and molecular wave function with poor basis set	❌	✅	✅	✅	✅	❌	❌	4
On the linearity of large non-linear models: when and why the tangent kernel is constant	❌	❌	❌	❌	❌	❌	❌	0
On the training dynamics of deep networks with $L_2$ regularization	❌	❌	✅	❌	❌	❌	✅	2
On the universality of deep learning	❌	❌	❌	❌	❌	❌	❌	0
Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free	✅	✅	✅	❌	❌	❌	✅	4
One Ring to Rule Them All: Certifiably Robust Geometric Perception with Outliers	❌	✅	✅	❌	❌	❌	✅	3
One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL	✅	❌	✅	❌	❌	❌	✅	3
One-bit Supervision for Image Classification	❌	❌	✅	❌	✅	❌	✅	3
One-sample Guided Object Representation Disassembling	❌	❌	✅	❌	❌	❌	✅	2
Online Adaptation for Consistent Mesh Reconstruction in the Wild	❌	❌	✅	❌	❌	❌	✅	2
Online Agnostic Boosting via Regret Minimization	✅	❌	❌	❌	❌	❌	❌	1
Online Algorithm for Unsupervised Sequential Selection with Contextual Information	✅	❌	✅	❌	❌	❌	❌	2
Online Algorithms for Multi-shop Ski Rental with Machine Learned Advice	✅	✅	✅	❌	❌	❌	✅	4
Online Bayesian Goal Inference for Boundedly Rational Planning Agents	✅	✅	✅	❌	❌	❌	✅	4
Online Bayesian Persuasion	✅	❌	❌	❌	❌	❌	❌	1
Online Convex Optimization Over Erdos-Renyi Random Networks	✅	❌	✅	❌	❌	❌	✅	3
Online Decision Based Visual Tracking via Reinforcement Learning	✅	❌	✅	❌	✅	❌	✅	4
Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning	✅	✅	✅	✅	❌	❌	✅	5
Online Influence Maximization under Linear Threshold Model	✅	❌	❌	❌	❌	❌	❌	1
Online Learning in Contextual Bandits using Gated Linear Networks	✅	✅	✅	❌	❌	❌	✅	4
Online Learning with Primary and Secondary Losses	✅	❌	❌	❌	❌	❌	❌	1
Online Linear Optimization with Many Hints	✅	❌	❌	❌	❌	❌	❌	1
Online MAP Inference of Determinantal Point Processes	✅	❌	✅	❌	❌	❌	✅	3
Online Matrix Completion with Side Information	✅	❌	❌	❌	❌	❌	✅	2
Online Meta-Critic Learning for Off-Policy Actor-Critic Methods	✅	✅	✅	❌	❌	❌	✅	4
Online Multitask Learning with Long-Term Memory	✅	❌	❌	❌	❌	❌	❌	1
Online Neural Connectivity Estimation with Noisy Group Testing	✅	✅	❌	❌	❌	✅	✅	4
Online Non-Convex Optimization with Imperfect Feedback	✅	❌	❌	❌	❌	❌	❌	1
Online Optimization with Memory and Competitive Control	✅	❌	❌	❌	❌	❌	❌	1
Online Planning with Lookahead Policies	✅	❌	❌	❌	❌	❌	❌	1
Online Robust Regression via SGD on the l1 loss	❌	❌	❌	❌	❌	❌	✅	1
Online Sinkhorn: Optimal Transport distances from sample streams	✅	❌	✅	❌	❌	❌	✅	3
Online Structured Meta-learning	✅	❌	✅	❌	❌	❌	✅	3
Online learning with dynamics: A minimax perspective	❌	❌	❌	❌	❌	❌	❌	0
Open Graph Benchmark: Datasets for Machine Learning on Graphs	❌	✅	✅	✅	✅	❌	✅	5
Optimal Adaptive Electrode Selection to Maximize Simultaneously Recorded Neuron Yield	❌	✅	✅	✅	❌	❌	✅	4
Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards	✅	❌	❌	❌	❌	❌	✅	2
Optimal Approximation - Smoothness Tradeoffs for Soft-Max Functions	❌	❌	❌	❌	❌	❌	❌	0
Optimal Best-arm Identification in Linear Bandits	✅	❌	❌	❌	❌	❌	✅	2
Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization	✅	❌	❌	❌	❌	❌	❌	1
Optimal Iterative Sketching Methods with the Subsampled Randomized Hadamard Transform	❌	❌	✅	❌	❌	❌	✅	2
Optimal Learning from Verified Training Data	✅	❌	✅	✅	❌	✅	✅	5
Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient	❌	❌	✅	❌	✅	❌	✅	3
Optimal Prediction of the Number of Unseen Species with Multiplicity	❌	❌	❌	❌	❌	❌	✅	1
Optimal Private Median Estimation under Minimal Distributional Assumptions	✅	❌	✅	❌	❌	❌	✅	3
Optimal Query Complexity of Secure Stochastic Convex Optimization	✅	❌	❌	❌	❌	❌	❌	1
Optimal Robustness-Consistency Trade-offs for Learning-Augmented Online Algorithms	✅	❌	❌	❌	❌	❌	❌	1
Optimal Variance Control of the Score-Function Gradient Estimator for Importance-Weighted Bounds	❌	✅	✅	✅	❌	❌	✅	4
Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization	✅	❌	✅	❌	❌	❌	✅	3
Optimal visual search based on a model of target detectability in natural images	❌	✅	✅	✅	✅	❌	✅	5
Optimally Deceiving a Learning Leader in Stackelberg Games	❌	❌	❌	❌	❌	❌	❌	0
Optimistic Dual Extrapolation for Coherent Non-monotone Variational Inequalities	✅	❌	❌	❌	❌	❌	❌	1
Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks	✅	✅	✅	❌	❌	❌	❌	3
Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions	❌	❌	❌	❌	❌	❌	✅	1
Optimizing Mode Connectivity via Neuron Alignment	✅	✅	✅	✅	✅	❌	✅	6
Optimizing Neural Networks via Koopman Operator Theory	✅	❌	✅	❌	❌	❌	✅	3
OrganITE: Optimal transplant donor organ offering using an individual treatment effect	✅	❌	❌	✅	❌	❌	❌	2
Organizing recurrent network dynamics by task-computation to enable continual learning	❌	❌	✅	❌	❌	❌	✅	2
Outlier Robust Mean Estimation with Subgaussian Rates via Stability	❌	❌	❌	❌	❌	❌	❌	0
Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality	✅	❌	✅	❌	❌	❌	❌	2
Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree	❌	✅	❌	❌	❌	❌	❌	1
PAC-Bayes Analysis Beyond the Usual Bounds	❌	❌	❌	❌	❌	❌	❌	0
PAC-Bayes Learning Bounds for Sample-Dependent Priors	❌	❌	❌	❌	❌	❌	❌	0
PAC-Bayesian Bound for the Conditional Value at Risk	❌	❌	❌	❌	❌	❌	❌	0
PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning	✅	❌	✅	❌	❌	❌	❌	2
PEP: Parameter Ensembling by Perturbation	❌	❌	✅	✅	❌	❌	✅	3
PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks	✅	✅	✅	✅	❌	❌	✅	5
PIE-NET: Parametric Inference of Point Cloud Edges	❌	❌	✅	❌	✅	❌	✅	3
PLANS: Neuro-Symbolic Program Learning from Videos	❌	✅	✅	✅	✅	❌	❌	4
PLLay: Efficient Topological Layer based on Persistent Landscapes	✅	✅	✅	❌	❌	✅	✅	5
POLY-HOOT: Monte-Carlo Planning in Continuous Space MDPs with Non-Asymptotic Analysis	✅	❌	✅	❌	✅	❌	❌	3
POMDPs in Continuous Time and Discrete Spaces	❌	✅	❌	❌	❌	❌	❌	1
POMO: Policy Optimization with Multiple Optima for Reinforcement Learning	✅	✅	✅	✅	✅	❌	✅	6
PRANK: motion Prediction based on RANKing	❌	❌	✅	✅	✅	❌	✅	4
Parabolic Approximation Line Search for DNNs	✅	❌	✅	❌	✅	✅	✅	5
Parameterized Explainer for Graph Neural Network	✅	✅	✅	❌	❌	❌	❌	3
Parametric Instance Classification for Unsupervised Visual Feature learning	❌	✅	✅	✅	✅	❌	✅	5
Part-dependent Label Noise: Towards Instance-dependent Label Noise	✅	✅	✅	✅	✅	❌	✅	6
Partial Optimal Tranport with applications on Positive-Unlabeled Learning	✅	✅	✅	❌	❌	❌	✅	4
Partially View-aligned Clustering	✅	❌	✅	✅	✅	❌	✅	5
Passport-aware Normalization for Deep Model Protection	❌	❌	✅	❌	❌	❌	✅	2
Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning	✅	❌	✅	✅	❌	❌	✅	4
Path Integral Based Convolution and Pooling for Graph Neural Networks	❌	✅	✅	✅	✅	❌	✅	5
Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks	✅	✅	✅	❌	❌	❌	✅	4
Penalized Langevin dynamics with vanishing penalty for smooth and log-concave targets	❌	❌	❌	❌	❌	❌	❌	0
Permute-and-Flip: A new mechanism for differentially private selection	✅	❌	✅	❌	❌	❌	❌	2
Personalized Federated Learning with Moreau Envelopes	✅	✅	✅	✅	❌	✅	✅	6
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach	✅	❌	✅	❌	❌	❌	✅	3
Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability	❌	❌	✅	✅	❌	❌	✅	3
Phase retrieval in high dimensions: Statistical and computational phase transitions	❌	❌	❌	❌	❌	❌	❌	0
Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games	✅	✅	❌	❌	❌	❌	✅	3
Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation	❌	✅	✅	✅	❌	❌	✅	4
PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals	✅	❌	✅	❌	❌	❌	✅	3
Planning in Markov Decision Processes with Gap-Dependent Sample Complexity	✅	✅	❌	❌	❌	❌	✅	3
Planning with General Objective Functions: Going Beyond Total Rewards	✅	❌	❌	❌	❌	❌	❌	1
Point process models for sequence detection in high-dimensional neural spike trains	❌	✅	✅	✅	✅	❌	✅	5
Pointer Graph Networks	✅	✅	❌	✅	❌	❌	✅	4
Policy Improvement via Imitation of Multiple Oracles	✅	✅	✅	❌	❌	❌	✅	4
Polynomial-Time Computation of Optimal Correlated Equilibria in Two-Player Extensive-Form Games with Public Chance Moves and Beyond	✅	❌	✅	❌	✅	✅	✅	5
Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework	✅	✅	❌	❌	❌	❌	✅	3
Position-based Scaled Gradient for Model Quantization and Pruning	❌	✅	✅	❌	❌	❌	✅	3
Post-training Iterative Hierarchical Data Augmentation for Deep Networks	✅	❌	✅	✅	❌	❌	✅	4
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts	❌	✅	✅	✅	❌	❌	✅	4
Posterior Re-calibration for Imbalanced Datasets	✅	✅	✅	✅	❌	❌	❌	4
Practical Low-Rank Communication Compression in Decentralized Deep Learning	✅	✅	✅	❌	❌	❌	✅	4
Practical No-box Adversarial Attacks against DNNs	❌	✅	✅	❌	✅	❌	✅	4
Practical Quasi-Newton Methods for Training Deep Neural Networks	✅	✅	✅	❌	✅	❌	✅	5
Pre-training via Paraphrasing	❌	❌	✅	❌	❌	❌	✅	2
Precise expressions for random projections: Low-rank approximation and randomized Newton	❌	❌	✅	❌	❌	❌	❌	1
Predicting Training Time Without Training	❌	❌	✅	❌	❌	❌	✅	2
Prediction with Corrupted Expert Advice	❌	❌	❌	❌	❌	❌	✅	1
Predictive Information Accelerates Learning in RL	✅	✅	✅	❌	❌	❌	✅	4
Predictive coding in balanced neural networks with noise, chaos and delays	❌	❌	❌	❌	❌	❌	✅	1
Predictive inference is free with the jackknife+-after-bootstrap	✅	✅	✅	❌	❌	❌	✅	4
Preference learning along multiple criteria: A game-theoretic perspective	❌	❌	❌	❌	❌	❌	✅	1
Preference-based Reinforcement Learning with Finite-Time Guarantees	✅	❌	❌	❌	❌	❌	✅	2
Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm	❌	❌	❌	❌	❌	❌	❌	0
Primal-Dual Mesh Convolutional Neural Networks	❌	✅	✅	❌	❌	❌	✅	3
Principal Neighbourhood Aggregation for Graph Nets	❌	✅	✅	✅	❌	❌	✅	4
Privacy Amplification via Random Check-Ins	✅	❌	❌	❌	❌	❌	❌	1
Private Identity Testing for High-Dimensional Distributions	✅	❌	❌	❌	❌	❌	❌	1
Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity	✅	❌	❌	❌	❌	❌	❌	1
Probabilistic Active Meta-Learning	✅	❌	❌	❌	❌	❌	✅	2
Probabilistic Circuits for Variational Inference in Discrete Graphical Models	✅	✅	✅	❌	❌	❌	❌	3
Probabilistic Fair Clustering	✅	❌	✅	❌	✅	✅	✅	5
Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations	✅	❌	❌	❌	❌	❌	✅	2
Probabilistic Linear Solvers for Machine Learning	✅	✅	✅	❌	❌	❌	✅	4
Probabilistic Orientation Estimation with Matrix Fisher Distributions	❌	✅	✅	✅	❌	❌	✅	4
Probabilistic Time Series Forecasting with Shape and Temporal Diversity	✅	✅	✅	✅	❌	❌	❌	4
Probably Approximately Correct Constrained Learning	✅	❌	✅	❌	❌	❌	✅	3
Profile Entropy: A Fundamental Measure for the Learnability and Compressibility of Distributions	✅	❌	❌	❌	❌	❌	❌	1
Program Synthesis with Pragmatic Communication	❌	✅	❌	❌	❌	❌	✅	2
Projected Stein Variational Gradient Descent	✅	✅	✅	❌	✅	❌	✅	5
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method	✅	✅	✅	❌	❌	❌	❌	3
Projection Robust Wasserstein Distance and Riemannian Optimization	✅	❌	✅	❌	❌	❌	✅	3
Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning	✅	❌	✅	❌	❌	❌	✅	3
Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method	✅	❌	✅	❌	✅	❌	✅	4
Prophet Attention: Predicting Attention with Future Attention	❌	❌	✅	✅	✅	❌	✅	4
Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning	✅	✅	❌	❌	❌	❌	✅	3
Provable Overlapping Community Detection in Weighted Graphs	✅	❌	✅	❌	❌	❌	✅	3
Provably Consistent Partial-Label Learning	✅	❌	✅	✅	❌	❌	✅	4
Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning	✅	✅	❌	❌	❌	❌	❌	2
Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach	✅	❌	❌	❌	❌	❌	❌	1
Provably Efficient Neural GTD for Off-Policy Learning	✅	❌	❌	❌	❌	❌	✅	2
Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits	✅	✅	✅	❌	❌	❌	✅	4
Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations	✅	❌	❌	❌	❌	❌	❌	1
Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration	✅	❌	❌	❌	❌	❌	❌	1
Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration	✅	❌	✅	❌	❌	❌	✅	3
Provably Robust Metric Learning	✅	❌	✅	❌	✅	❌	❌	3
Provably adaptive reinforcement learning in metric spaces	✅	❌	❌	❌	❌	❌	❌	1
Proximal Mapping for Deep Regularization	❌	✅	✅	❌	✅	❌	✅	4
Proximity Operator of the Matrix Perspective Function and its Applications	✅	❌	❌	❌	✅	✅	❌	3
Pruning Filter in Filter	❌	✅	✅	❌	❌	❌	✅	3
Pruning neural networks without any data by iteratively conserving synaptic flow	✅	✅	✅	❌	❌	❌	❌	3
Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point	❌	❌	✅	❌	✅	❌	✅	3
PyGlove: Symbolic Programming for Automated Machine Learning	❌	✅	✅	✅	✅	❌	✅	5
Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning	❌	❌	✅	❌	❌	❌	✅	2
Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality	❌	❌	❌	❌	✅	❌	✅	2
Quantile Propagation for Wasserstein-Approximate Gaussian Processes	✅	✅	✅	✅	❌	❌	✅	5
Quantitative Propagation of Chaos for SGD in Wide Neural Networks	❌	❌	✅	❌	❌	❌	✅	2
Quantized Variational Inference	✅	✅	✅	❌	❌	✅	✅	5
R-learning in actor-critic model offers a biologically relevant mechanism for sequential decision-making	❌	❌	❌	❌	❌	❌	❌	0
RANet: Region Attention Network for Semantic Segmentation	❌	✅	✅	✅	✅	❌	✅	5
RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning	❌	✅	✅	✅	✅	❌	✅	5
RD$^2$: Reward Decomposition with Representation Decomposition	✅	❌	✅	❌	❌	❌	❌	2
RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces	❌	✅	✅	❌	❌	❌	✅	3
RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning	❌	✅	✅	✅	❌	❌	❌	3
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference	✅	✅	✅	✅	✅	❌	❌	5
RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor	❌	✅	✅	✅	✅	❌	✅	5
RandAugment: Practical Automated Data Augmentation with a Reduced Search Space	✅	✅	✅	✅	❌	❌	✅	5
Random Reshuffling is Not Always Better	✅	❌	❌	❌	❌	❌	✅	2
Random Reshuffling: Simple Analysis with Vast Improvements	✅	✅	✅	❌	❌	❌	✅	4
Random Walk Graph Neural Networks	❌	❌	✅	✅	❌	❌	✅	3
Randomized tests for high-dimensional regression: A more efficient and powerful solution	✅	❌	❌	❌	❌	❌	✅	2
Rankmax: An Adaptive Projection Alternative to the Softmax Function	✅	❌	✅	✅	❌	❌	✅	4
Ratio Trace Formulation of Wasserstein Discriminant Analysis	✅	❌	✅	❌	❌	❌	✅	3
Rational neural networks	❌	✅	✅	❌	❌	❌	✅	3
Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization	✅	✅	✅	❌	❌	❌	❌	3
Real World Games Look Like Spinning Tops	❌	❌	❌	❌	❌	❌	✅	1
Reasoning about Uncertainties in Discrete-Time Dynamical Systems using Polynomial Forms.	❌	❌	✅	❌	❌	❌	✅	2
Reciprocal Adversarial Learning via Characteristic Functions	✅	✅	✅	❌	❌	❌	✅	4
Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate	❌	❌	✅	❌	❌	❌	✅	2
Reconsidering Generative Objectives For Counterfactual Reasoning	✅	✅	✅	✅	❌	❌	✅	5
Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN	❌	❌	✅	✅	❌	❌	✅	3
Recovery of sparse linear classifiers from mixture of responses	✅	❌	✅	❌	❌	❌	❌	2
Recurrent Quantum Neural Networks	❌	❌	✅	✅	❌	❌	✅	3
Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations	❌	✅	❌	✅	❌	❌	✅	3
Recursive Inference for Variational Autoencoders	✅	✅	✅	✅	✅	❌	✅	6
Reducing Adversarially Robust Learning to Non-Robust PAC Learning	✅	❌	❌	❌	❌	❌	❌	1
Refactoring Policy for Compositional Generalizability using Self-Supervised Object Proposals	❌	❌	✅	❌	❌	❌	✅	2
Regression with reject option and application to kNN	❌	✅	✅	✅	❌	❌	✅	4
Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses	✅	❌	❌	❌	❌	❌	❌	1
Regret in Online Recommendation Systems	✅	❌	❌	❌	❌	❌	❌	1
Regularized linear autoencoders recover the principal components, eventually	✅	✅	✅	❌	❌	❌	❌	3
Regularizing Black-box Models for Improved Interpretability	✅	✅	✅	❌	✅	❌	✅	5
Regularizing Towards Permutation Invariance In Recurrent Models	❌	❌	✅	❌	❌	❌	❌	1
Reinforced Molecular Optimization with Neighborhood-Controlled Grammars	❌	✅	✅	❌	❌	❌	✅	3
Reinforcement Learning for Control with Multiple Frequencies	✅	❌	✅	❌	❌	❌	✅	3
Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting	✅	❌	✅	❌	❌	❌	✅	3
Reinforcement Learning with Augmented Data	❌	✅	✅	❌	❌	❌	✅	3
Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing	❌	❌	✅	❌	❌	✅	✅	3
Reinforcement Learning with Feedback Graphs	✅	❌	❌	❌	❌	❌	❌	1
Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension	✅	❌	❌	❌	❌	❌	❌	1
Rel3D: A Minimally Contrastive Benchmark for Grounding Spatial Relations in 3D	❌	✅	✅	✅	❌	❌	✅	4
RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder	❌	✅	✅	✅	✅	✅	✅	6
Relative gradient optimization of the Jacobian term in unsupervised deep learning	❌	✅	✅	✅	✅	❌	✅	5
Reliable Graph Neural Networks via Robust Aggregation	❌	✅	✅	✅	✅	❌	✅	5
Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies	❌	❌	✅	✅	❌	❌	✅	3
RepPoints v2: Verification Meets Regression for Object Detection	❌	✅	✅	✅	✅	❌	✅	5
Reparameterizing Mirror Descent as Gradient Descent	❌	❌	❌	❌	❌	❌	❌	0
Replica-Exchange Nos\'e-Hoover Dynamics for Bayesian Learning on Large Datasets	✅	❌	✅	❌	✅	❌	✅	4
Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatment	❌	❌	✅	✅	✅	❌	✅	4
Rescuing neural spike train models from bad MLE	❌	✅	✅	✅	❌	❌	✅	4
Reservoir Computing meets Recurrent Kernels and Structured Transforms	✅	✅	✅	❌	✅	❌	❌	4
Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts	✅	✅	✅	✅	❌	❌	✅	5
Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis	✅	✅	✅	✅	✅	❌	✅	6
Restless-UCB, an Efficient and Low-complexity Algorithm for Online Restless Bandits	✅	❌	✅	❌	✅	❌	✅	4
Restoring Negative Information in Few-Shot Object Detection	❌	✅	✅	✅	❌	❌	✅	4
Rethinking Importance Weighting for Deep Learning under Distribution Shift	✅	✅	✅	✅	❌	❌	✅	5
Rethinking Learnable Tree Filter for Generic Feature Transform	✅	✅	✅	✅	❌	❌	✅	5
Rethinking Pre-training and Self-training	❌	✅	✅	✅	❌	❌	✅	4
Rethinking pooling in graph neural networks	❌	✅	✅	✅	❌	❌	✅	4
Rethinking the Value of Labels for Improving Class-Imbalanced Learning	❌	✅	✅	❌	❌	❌	✅	3
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks	❌	✅	✅	✅	❌	❌	✅	4
RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist	❌	✅	✅	✅	✅	❌	✅	5
Reverse-engineering recurrent neural network solutions to a hierarchical inference task for mice	❌	✅	✅	✅	❌	✅	✅	5
Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity	✅	❌	❌	❌	❌	❌	✅	2
Revisiting Parameter Sharing for Automatic Neural Channel Number Search	✅	✅	✅	❌	✅	❌	✅	5
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes	❌	✅	✅	❌	✅	❌	❌	3
Reward Propagation Using Graph Convolutional Networks	✅	❌	✅	❌	✅	❌	✅	4
Reward-rational (implicit) choice: A unifying formalism for reward learning	❌	❌	❌	❌	❌	❌	❌	0
Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement	✅	✅	✅	❌	❌	❌	❌	3
Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian	✅	✅	✅	❌	❌	❌	✅	4
Riemannian Continuous Normalizing Flows	❌	❌	✅	❌	❌	❌	✅	2
Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret	✅	❌	❌	❌	❌	❌	❌	1
Robust Correction of Sampling Bias using Cumulative Distribution Functions	✅	❌	✅	✅	❌	❌	✅	4
Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations	✅	✅	✅	❌	❌	❌	✅	4
Robust Density Estimation under Besov IPM Losses	❌	❌	❌	❌	❌	❌	❌	0
Robust Disentanglement of a Few Factors at a Time using rPU-VAE	✅	✅	✅	✅	❌	❌	❌	4
Robust Federated Learning: The Case of Affine Distribution Shifts	✅	❌	✅	❌	❌	❌	✅	3
Robust Gaussian Covariance Estimation in Nearly-Matrix Multiplication Time	✅	❌	❌	❌	❌	❌	❌	1
Robust Meta-learning for Mixed Linear Regression with Small Batches	✅	❌	❌	❌	❌	❌	❌	1
Robust Multi-Agent Reinforcement Learning with Model Uncertainty	✅	❌	✅	❌	❌	❌	✅	3
Robust Multi-Object Matching via Iterative Reweighting of the Graph Connection Laplacian	✅	❌	✅	❌	❌	❌	✅	3
Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation	✅	✅	✅	❌	❌	❌	✅	4
Robust Optimization for Fairness with Noisy Protected Groups	✅	✅	✅	✅	❌	❌	✅	5
Robust Persistence Diagrams using Reproducing Kernels	❌	✅	✅	❌	❌	❌	✅	3
Robust Pre-Training by Adversarial Contrastive Learning	✅	✅	✅	✅	❌	❌	✅	5
Robust Quantization: One Model to Rule Them All	❌	✅	✅	❌	❌	❌	❌	2
Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization	❌	✅	✅	❌	❌	❌	✅	3
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification	✅	✅	✅	❌	❌	❌	✅	4
Robust Reinforcement Learning via Adversarial training with Langevin Dynamics	✅	❌	✅	❌	❌	❌	✅	3
Robust Sequence Submodular Maximization	✅	❌	❌	❌	❌	❌	❌	1
Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing	✅	❌	❌	❌	❌	❌	❌	1
Robust and Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization	✅	❌	❌	❌	❌	❌	❌	1
Robust compressed sensing using generative models	✅	✅	✅	❌	❌	❌	✅	4
Robust large-margin learning in hyperbolic space	✅	❌	✅	❌	❌	❌	✅	3
Robust, Accurate Stochastic Optimization for Variational Inference	✅	❌	✅	❌	❌	❌	✅	3
Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs	✅	✅	✅	❌	❌	❌	❌	3
Robustness Analysis of Non-Convex Stochastic Gradient Descent using Biased Expectations	✅	❌	✅	❌	❌	❌	✅	3
Robustness of Bayesian Neural Networks to Gradient-Based Attacks	❌	✅	✅	❌	❌	❌	✅	3
Robustness of Community Detection to Random Geometric Perturbations	❌	❌	❌	❌	❌	❌	✅	1
Rotated Binary Neural Network	❌	✅	✅	❌	❌	❌	✅	3
Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud	❌	✅	✅	✅	❌	❌	❌	3
SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection	❌	❌	✅	✅	✅	❌	✅	4
SCOP: Scientific Control for Reliable Neural Network Pruning	❌	✅	✅	✅	✅	❌	✅	5
SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images	❌	❌	✅	✅	❌	❌	✅	3
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks	❌	✅	✅	❌	❌	❌	❌	2
SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology	❌	✅	✅	✅	✅	❌	✅	5
SGD with shuffling: optimal rates without component convexity and large epoch requirements	❌	❌	❌	❌	❌	❌	❌	0
SIRI: Spatial Relation Induced Network For Spatial Description Resolution	❌	✅	✅	✅	✅	❌	✅	5
SLIP: Learning to predict in unknown dynamical systems with long-term memory	✅	❌	❌	❌	❌	❌	✅	2
SMYRF - Efficient Attention using Asymmetric Clustering	❌	✅	✅	❌	✅	❌	✅	4
SOLOv2: Dynamic and Fast Instance Segmentation	✅	✅	✅	✅	✅	❌	✅	6
STEER : Simple Temporal Regularization For Neural ODE	❌	❌	✅	✅	❌	❌	✅	3
STLnet: Signal Temporal Logic Enforced Multivariate Recurrent Neural Networks	✅	❌	✅	❌	✅	❌	❌	3
SURF: A Simple, Universal, Robust, Fast Distribution Learning Algorithm	❌	❌	✅	❌	❌	❌	✅	2
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence	✅	❌	❌	❌	❌	❌	✅	2
Safe Reinforcement Learning via Curriculum Induction	✅	✅	✅	❌	❌	❌	✅	4
Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction	✅	❌	❌	❌	❌	❌	❌	1
Sample Complexity of Uniform Convergence for Multicalibration	❌	❌	❌	❌	❌	❌	❌	0
Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation	✅	❌	❌	❌	❌	❌	✅	2
Sample complexity and effective dimension for regression on manifolds	❌	❌	❌	❌	❌	❌	❌	0
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining	✅	✅	✅	✅	❌	❌	✅	5
Sample-Efficient Reinforcement Learning of Undercomplete POMDPs	✅	❌	❌	❌	❌	❌	❌	1
Sampling from a k-DPP without looking at all items	✅	✅	✅	❌	✅	❌	✅	5
Sampling-Decomposable Generative Adversarial Recommender	✅	❌	✅	✅	✅	❌	✅	5
Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot	❌	✅	✅	❌	❌	❌	✅	3
Scalable Belief Propagation via Relaxed Scheduling	❌	✅	✅	❌	✅	✅	✅	5
Scalable Graph Neural Networks via Bidirectional Propagation	✅	❌	✅	✅	✅	❌	✅	5
Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward	✅	❌	❌	❌	❌	❌	✅	2
ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training	✅	❌	✅	❌	✅	❌	✅	4
Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks	❌	❌	✅	✅	❌	❌	❌	2
Searching for Low-Bit Weights in Quantized Neural Networks	✅	❌	✅	❌	❌	❌	✅	3
Second Order Optimality in Decentralized Non-Convex Optimization via Perturbed Gradient Tracking	✅	❌	✅	❌	❌	❌	✅	3
Second Order PAC-Bayesian Bounds for the Weighted Majority Vote	❌	✅	✅	✅	❌	❌	✅	4
Secretary and Online Matching Problems with Machine Learned Advice	✅	❌	❌	❌	❌	❌	❌	1
Security Analysis of Safe and Seldonian Reinforcement Learning Algorithms	✅	❌	✅	❌	❌	✅	✅	4
See, Hear, Explore: Curiosity via Audio-Visual Association	❌	✅	❌	❌	❌	❌	✅	2
Self-Adaptive Training: beyond Empirical Risk Minimization	✅	✅	✅	✅	❌	❌	✅	5
Self-Adaptively Learning to Demoiré from Focused and Defocused Image Pairs	❌	❌	✅	❌	✅	✅	✅	4
Self-Distillation Amplifies Regularization in Hilbert Space	❌	✅	✅	❌	❌	❌	❌	2
Self-Distillation as Instance-Specific Label Smoothing	❌	❌	✅	✅	❌	❌	✅	3
Self-Imitation Learning via Generalized Lower Bound Q-learning	❌	✅	✅	❌	❌	❌	✅	3
Self-Learning Transformations for Improving Gaze and Head Redirection	❌	✅	✅	❌	❌	❌	✅	3
Self-Paced Deep Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Self-Supervised Few-Shot Learning on Point Clouds	❌	❌	✅	❌	❌	❌	❌	1
Self-Supervised Generative Adversarial Compression	❌	❌	✅	❌	✅	❌	✅	3
Self-Supervised Graph Transformer on Large-Scale Molecular Data	❌	❌	✅	✅	✅	❌	✅	4
Self-Supervised Learning by Cross-Modal Audio-Video Clustering	❌	✅	✅	✅	❌	❌	✅	4
Self-Supervised MultiModal Versatile Networks	❌	✅	✅	✅	✅	❌	✅	5
Self-Supervised Relational Reasoning for Representation Learning	✅	✅	✅	✅	❌	❌	✅	5
Self-Supervised Relationship Probing	❌	❌	✅	✅	✅	❌	✅	4
Self-Supervised Visual Representation Learning from Hierarchical Grouping	❌	❌	✅	✅	❌	❌	✅	3
Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID	✅	✅	✅	❌	❌	❌	✅	4
Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs	✅	✅	✅	✅	❌	❌	❌	4
Self-supervised Co-Training for Video Representation Learning	❌	✅	✅	✅	❌	❌	✅	4
Self-supervised learning through the eyes of a child	❌	✅	✅	❌	❌	❌	✅	3
Self-training Avoids Using Spurious Features Under Domain Shift	❌	❌	✅	❌	❌	❌	❌	1
Semantic Visual Navigation by Watching YouTube Videos	❌	✅	✅	✅	❌	❌	✅	4
Semi-Supervised Neural Architecture Search	✅	❌	✅	✅	✅	❌	✅	5
Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization	✅	❌	✅	✅	❌	❌	✅	4
Semialgebraic Optimization for Lipschitz Constants of ReLU Networks	❌	❌	✅	❌	✅	❌	✅	3
Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding	❌	✅	✅	✅	❌	❌	❌	3
Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals	❌	✅	✅	✅	❌	❌	❌	3
Sequential Bayesian Experimental Design with Variable Cost Structure	✅	❌	❌	❌	❌	❌	✅	2
Set2Graph: Learning Graphs From Sets	❌	❌	❌	✅	❌	❌	✅	2
ShapeFlow: Learnable Deformation Flows Among 3D Shapes	❌	❌	✅	✅	❌	❌	❌	2
Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Shared Space Transfer Learning for analyzing multi-site fMRI data	❌	✅	✅	✅	✅	✅	✅	6
Sharp Representation Theorems for ReLU Networks with Precise Dependence on Depth	❌	❌	❌	❌	❌	❌	❌	0
Sharp uniform convergence bounds through empirical centralization	❌	✅	❌	❌	❌	❌	✅	2
Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms	❌	❌	✅	❌	❌	❌	❌	1
Sharper Generalization Bounds for Pairwise Learning	❌	❌	❌	❌	❌	❌	❌	0
ShiftAddNet: A Hardware-Inspired Deep Network	❌	✅	✅	❌	✅	❌	✅	4
Simple and Fast Algorithm for Binary Integer and Online Linear Programming	✅	❌	❌	❌	❌	❌	❌	1
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness	✅	✅	✅	❌	❌	❌	✅	4
Simple and Scalable Sparse k-means Clustering via Feature Ranking	✅	❌	✅	❌	❌	❌	✅	3
Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering	✅	✅	✅	✅	❌	❌	✅	5
Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints	❌	✅	❌	❌	❌	❌	✅	2
Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations	❌	✅	✅	✅	❌	❌	✅	4
Simultaneous Preference and Metric Learning from Paired Comparisons	❌	✅	❌	❌	❌	✅	✅	3
Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition	✅	❌	❌	❌	❌	❌	❌	1
Sinkhorn Barycenter via Functional Gradient Descent	✅	❌	✅	❌	❌	❌	✅	3
Sinkhorn Natural Gradient for Generative Models	❌	❌	✅	❌	❌	❌	✅	2
Skeleton-bridged Point Completion: From Global Inference to Local Adjustment	❌	❌	✅	✅	❌	❌	✅	3
Sliding Window Algorithms for k-Clustering Problems	✅	✅	✅	❌	❌	❌	✅	4
Small Nash Equilibrium Certificates in Very Large Games	✅	❌	✅	❌	❌	✅	❌	3
Smooth And Consistent Probabilistic Regression Trees	❌	✅	✅	✅	✅	❌	✅	5
Smoothed Analysis of Online and Differentially Private Learning	❌	❌	❌	❌	❌	❌	❌	0
Smoothed Geometry for Robust Attribution	❌	✅	✅	❌	✅	❌	✅	4
Smoothly Bounding User Contributions in Differential Privacy	✅	❌	✅	❌	❌	❌	✅	3
SnapBoost: A Heterogeneous Boosting Machine	✅	❌	✅	✅	✅	✅	✅	6
Soft Contrastive Learning for Visual Localization	❌	✅	✅	❌	❌	❌	✅	3
SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds	❌	✅	✅	✅	✅	❌	✅	5
Softmax Deep Double Deterministic Policy Gradients	✅	✅	✅	❌	❌	❌	✅	4
Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers	❌	✅	❌	❌	❌	❌	✅	2
Space-Time Correspondence as a Contrastive Random Walk	✅	❌	✅	❌	❌	❌	✅	3
Sparse Graphical Memory for Robust Planning	✅	✅	✅	❌	❌	❌	✅	4
Sparse Learning with CART	❌	❌	✅	❌	❌	❌	❌	1
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning	✅	✅	✅	❌	✅	❌	✅	5
Sparse Symplectically Integrated Neural Networks	❌	✅	❌	✅	✅	❌	✅	4
Sparse Weight Activation Training	✅	✅	✅	✅	✅	✅	✅	7
Sparse and Continuous Attention Mechanisms	✅	✅	✅	❌	❌	❌	✅	4
Spectra of the Conjugate Kernel and Neural Tangent Kernel for linear-width neural networks	❌	❌	✅	❌	❌	❌	✅	2
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting	❌	❌	✅	✅	❌	❌	✅	3
Spike and slab variational Bayes for high dimensional logistic regression	✅	✅	❌	❌	❌	❌	✅	3
Spin-Weighted Spherical CNNs	❌	✅	✅	✅	❌	❌	✅	4
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses	✅	❌	❌	❌	❌	❌	❌	1
Stable and expressive recurrent vision models	❌	✅	✅	✅	✅	❌	✅	5
Stage-wise Conservative Linear Bandits	✅	❌	❌	❌	❌	❌	✅	2
Stateful Posted Pricing with Vanishing Regret via Dynamic Deterministic Markov Decision Processes	✅	❌	❌	❌	❌	❌	❌	1
Stationary Activations for Uncertainty Calibration in Deep Learning	❌	✅	✅	✅	❌	❌	❌	3
Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits	✅	❌	✅	❌	❌	❌	✅	3
Statistical Guarantees of Distributed Nearest Neighbor Classification	✅	❌	✅	❌	✅	❌	❌	3
Statistical Optimal Transport posed as Learning Kernel Embedding	❌	✅	✅	❌	❌	❌	✅	3
Statistical and Topological Properties of Sliced Probability Divergences	❌	✅	✅	❌	❌	❌	✅	3
Statistical control for spatio-temporal MEG/EEG source imaging with desparsified mutli-task Lasso	✅	✅	✅	❌	❌	❌	✅	4
Statistical-Query Lower Bounds via Functional Gradients	✅	❌	❌	❌	❌	❌	❌	1
Steady State Analysis of Episodic Reinforcement Learning	❌	❌	✅	❌	❌	❌	✅	2
Steering Distortions to Preserve Classes and Neighbors in Supervised Dimensionality Reduction	❌	✅	✅	❌	❌	✅	✅	4
Stein Self-Repulsive Dynamics: Benefits From Past Samples	❌	✅	✅	❌	❌	❌	✅	3
Stochastic Deep Gaussian Processes over Graphs	❌	✅	✅	✅	✅	❌	✅	5
Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes	✅	✅	✅	✅	✅	❌	✅	6
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model	✅	✅	✅	❌	❌	❌	✅	4
Stochastic Normalization	✅	✅	✅	✅	❌	❌	✅	5
Stochastic Normalizing Flows	❌	✅	✅	❌	❌	❌	❌	2
Stochastic Optimization for Performative Prediction	✅	❌	✅	❌	❌	❌	✅	3
Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping	✅	✅	✅	❌	❌	❌	✅	4
Stochastic Optimization with Laggard Data Pipelines	✅	❌	✅	❌	❌	❌	✅	3
Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems	✅	❌	✅	❌	✅	✅	✅	5
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty	❌	✅	✅	✅	❌	❌	✅	4
Stochastic Stein Discrepancies	✅	✅	✅	❌	❌	❌	✅	4
Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function	❌	❌	✅	❌	❌	❌	✅	2
Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning	✅	✅	✅	❌	✅	❌	✅	5
StratLearner: Learning a Strategy for Misinformation Prevention in Social Networks	✅	✅	❌	❌	❌	❌	✅	3
Strictly Batch Imitation Learning by Energy-based Distribution Matching	✅	✅	✅	✅	❌	❌	✅	5
Strongly Incremental Constituency Parsing with Graph Neural Networks	❌	✅	✅	✅	✅	❌	✅	5
Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering	✅	✅	✅	❌	✅	❌	✅	5
Structured Convolutions for Efficient Neural Network Design	❌	❌	✅	❌	✅	❌	✅	3
Structured Prediction for Conditional Meta-Learning	✅	✅	✅	✅	✅	❌	✅	6
Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces	✅	✅	✅	❌	❌	❌	✅	4
Sub-sampling for Efficient Non-Parametric Bandit Exploration	✅	✅	❌	❌	❌	❌	✅	3
Subgraph Neural Networks	✅	✅	✅	❌	✅	❌	✅	5
Subgroup-based Rank-1 Lattice Quasi-Monte Carlo	❌	❌	✅	❌	❌	❌	✅	2
Submodular Maximization Through Barrier Functions	✅	❌	✅	❌	❌	❌	✅	3
Submodular Meta-Learning	✅	❌	✅	❌	❌	❌	❌	2
Succinct and Robust Multi-Agent Communication With Temporal Message Control	✅	✅	✅	❌	❌	❌	✅	4
Sufficient dimension reduction for classification using principal optimal transport direction	✅	❌	✅	✅	❌	❌	✅	4
SuperLoss: A Generic Loss for Robust Curriculum Learning	❌	❌	✅	✅	❌	❌	✅	3
Supermasks in Superposition	✅	✅	✅	✅	✅	❌	✅	6
Supervised Contrastive Learning	❌	✅	✅	✅	✅	❌	✅	5
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows	✅	✅	✅	❌	✅	❌	❌	4
Swapping Autoencoder for Deep Image Manipulation	❌	❌	✅	❌	❌	❌	✅	2
Synbols: Probing Learning Algorithms with Synthetic Datasets	❌	✅	✅	✅	✅	❌	✅	5
Synthesize, Execute and Debug: Learning to Repair for Neural Program Synthesis	✅	❌	✅	✅	❌	❌	✅	4
Synthesizing Tasks for Block-based Programming	❌	✅	✅	❌	❌	❌	✅	3
Synthetic Data Generators -- Sequential and Private	❌	❌	❌	❌	❌	❌	❌	0
System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina	❌	✅	❌	❌	❌	❌	✅	2
TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation	❌	✅	✅	✅	✅	❌	✅	5
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization	✅	✅	✅	❌	❌	❌	✅	4
Taming Discrete Integration via the Boon of Dimensionality	✅	✅	✅	❌	✅	❌	✅	5
Targeted Adversarial Perturbations for Monocular Depth Prediction	✅	❌	✅	✅	✅	❌	✅	5
Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters	❌	✅	✅	✅	❌	❌	✅	4
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes	✅	✅	❌	❌	❌	❌	❌	2
Task-Oriented Feature Distillation	❌	✅	✅	❌	❌	❌	✅	3
Task-Robust Model-Agnostic Meta-Learning	✅	✅	✅	✅	❌	❌	✅	5
Task-agnostic Exploration in Reinforcement Learning	✅	❌	❌	❌	❌	❌	❌	1
TaylorGAN: Neighbor-Augmented Policy Update Towards Sample-Efficient Natural Language Generation	❌	✅	✅	✅	✅	❌	✅	5
Teaching a GAN What Not to Learn	❌	❌	✅	❌	❌	✅	✅	3
Telescoping Density-Ratio Estimation	❌	❌	✅	❌	❌	❌	✅	2
Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation	✅	❌	❌	❌	✅	❌	✅	3
Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks	❌	✅	✅	❌	❌	❌	✅	3
Temporal Variability in Implicit Online Learning	✅	❌	✅	❌	❌	❌	✅	3
Tensor Completion Made Practical	✅	✅	❌	❌	❌	❌	✅	3
Testing Determinantal Point Processes	✅	❌	❌	❌	❌	❌	❌	1
Texture Interpolation for Probing Visual Perception	❌	✅	✅	❌	❌	❌	✅	3
The Adaptive Complexity of Maximizing a Gross Substitutes Valuation	✅	❌	❌	❌	❌	❌	✅	2
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning	✅	✅	✅	❌	❌	❌	✅	4
The All-or-Nothing Phenomenon in Sparse Tensor PCA	❌	❌	❌	❌	❌	❌	❌	0
The Autoencoding Variational Autoencoder	✅	❌	✅	❌	❌	❌	✅	3
The Complete Lasso Tradeoff Diagram	❌	✅	❌	❌	❌	❌	✅	2
The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise	✅	❌	❌	❌	❌	❌	❌	1
The Cone of Silence: Speech Separation by Localization	✅	❌	✅	❌	❌	❌	✅	3
The Convex Relaxation Barrier, Revisited: Tightened Single-Neuron Relaxations for Neural Network Verification	✅	✅	✅	❌	✅	✅	✅	6
The Convolution Exponential and Generalized Sylvester Flows	✅	✅	✅	❌	✅	❌	✅	5
The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models	✅	❌	✅	❌	❌	❌	✅	3
The Dilemma of TriHard Loss and an Element-Weighted TriHard Loss for Person Re-Identification	❌	✅	✅	❌	❌	❌	✅	3
The Discrete Gaussian for Differential Privacy	✅	✅	❌	❌	❌	❌	❌	2
The Diversified Ensemble Neural Network	✅	❌	✅	✅	✅	❌	✅	5
The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space	✅	❌	❌	❌	❌	❌	✅	2
The Generalization-Stability Tradeoff In Neural Network Pruning	❌	✅	✅	❌	❌	❌	✅	3
The Generalized Lasso with Nonlinear Observations and Generative Priors	❌	❌	❌	❌	❌	❌	❌	0
The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes	❌	✅	✅	✅	❌	❌	✅	4
The Implications of Local Correlation on Learning Some Deep Functions	✅	❌	✅	❌	❌	❌	✅	3
The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning	❌	✅	❌	❌	❌	❌	✅	2
The Lottery Ticket Hypothesis for Pre-trained BERT Networks	✅	✅	✅	✅	❌	❌	✅	5
The MAGICAL Benchmark for Robust Imitation	❌	✅	✅	❌	❌	❌	✅	3
The Mean-Squared Error of Double Q-Learning	❌	✅	✅	❌	❌	❌	✅	3
The NetHack Learning Environment	❌	✅	✅	✅	❌	✅	✅	5
The Origins and Prevalence of Texture Bias in Convolutional Neural Networks	❌	❌	✅	✅	❌	❌	✅	3
The Pitfalls of Simplicity Bias in Neural Networks	❌	✅	✅	✅	❌	❌	❌	3
The Potts-Ising model for discrete multivariate data	❌	✅	✅	✅	❌	✅	✅	5
The Power of Comparisons for Actively Learning Linear Classifiers	✅	❌	❌	❌	❌	❌	✅	2
The Power of Predictions in Online Control	✅	❌	❌	❌	❌	❌	✅	2
The Primal-Dual method for Learning Augmented Algorithms	✅	✅	❌	❌	❌	❌	✅	3
The Smoothed Possibility of Social Choice	❌	❌	❌	❌	❌	❌	❌	0
The Statistical Complexity of Early-Stopped Mirror Descent	❌	❌	❌	❌	❌	❌	❌	0
The Statistical Cost of Robust Kernel Hyperparameter Turning	✅	❌	❌	❌	❌	❌	❌	1
The Strong Screening Rule for SLOPE	✅	✅	✅	❌	❌	✅	✅	5
The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks	❌	❌	✅	❌	❌	❌	❌	1
The Value Equivalence Principle for Model-Based Reinforcement Learning	❌	❌	❌	❌	❌	❌	✅	1
The Wasserstein Proximal Gradient Algorithm	❌	❌	❌	❌	❌	❌	✅	1
The interplay between randomness and structure during learning in RNNs	❌	❌	✅	❌	❌	❌	✅	2
The phase diagram of approximation rates for deep neural networks	❌	❌	❌	❌	❌	❌	❌	0
The route to chaos in routing games: When is price of anarchy too optimistic?	❌	❌	❌	❌	❌	❌	❌	0
Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View	❌	❌	❌	❌	❌	❌	✅	1
Theory-Inspired Path-Regularized Differential Network Architecture Search	✅	✅	✅	✅	✅	❌	✅	6
Throughput-Optimal Topology Design for Cross-Silo Federated Learning	✅	✅	✅	❌	✅	❌	✅	5
Thunder: a Fast Coordinate Selection Solver for Sparse Learning	✅	✅	✅	❌	❌	❌	✅	4
Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits	✅	❌	❌	❌	❌	❌	❌	1
Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model	❌	❌	❌	❌	❌	❌	❌	0
Tight last-iterate convergence rates for no-regret learning in multi-player games	❌	❌	❌	❌	❌	❌	❌	0
Time-Reversal Symmetric ODE Network	❌	✅	✅	❌	❌	❌	✅	3
Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network	✅	❌	✅	✅	❌	❌	❌	3
TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning	❌	❌	✅	❌	❌	❌	✅	2
Top-KAST: Top-K Always Sparse Training	❌	❌	✅	❌	❌	❌	❌	1
Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples	✅	❌	✅	❌	✅	❌	✅	4
TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search	✅	✅	❌	❌	❌	❌	✅	3
Toward the Fundamental Limits of Imitation Learning	✅	❌	❌	❌	❌	❌	❌	1
Towards Better Generalization of Adaptive Gradient Methods	✅	❌	✅	✅	❌	❌	✅	4
Towards Convergence Rate Analysis of Random Forests for Classification	✅	❌	❌	❌	❌	❌	❌	1
Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts	❌	✅	✅	❌	✅	❌	✅	4
Towards Deeper Graph Neural Networks with Differentiable Group Normalization	❌	✅	✅	❌	❌	❌	✅	3
Towards Interpretable Natural Language Understanding with Explanations as Latent Variables	✅	✅	✅	✅	❌	❌	✅	5
Towards Learning Convolutions from Scratch	✅	❌	✅	❌	❌	❌	✅	3
Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples	❌	✅	✅	❌	❌	❌	❌	2
Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes	✅	❌	❌	❌	❌	❌	❌	1
Towards More Practical Adversarial Attacks on Graph Neural Networks	✅	❌	✅	✅	❌	❌	✅	4
Towards Neural Programming Interfaces	✅	✅	✅	❌	❌	❌	✅	4
Towards Playing Full MOBA Games with Deep Reinforcement Learning	❌	❌	❌	❌	❌	❌	✅	1
Towards Problem-dependent Optimal Learning Rates	❌	❌	❌	❌	❌	❌	❌	0
Towards Safe Policy Improvement for Non-Stationary MDPs	✅	❌	✅	✅	❌	❌	✅	4
Towards Scalable Bayesian Learning of Causal DAGs	✅	✅	✅	❌	❌	❌	✅	4
Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous GNNs	✅	❌	❌	✅	❌	❌	✅	3
Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam in Deep Learning	❌	✅	✅	❌	❌	❌	❌	2
Towards Understanding Hierarchical Learning: Benefits of Neural Representations	✅	❌	❌	❌	❌	❌	❌	1
Towards a Better Global Loss Landscape of GANs	❌	✅	✅	❌	❌	❌	✅	3
Towards a Combinatorial Characterization of Bounded-Memory Learning	❌	❌	❌	❌	❌	❌	❌	0
Towards practical differentially private causal graph discovery	✅	✅	✅	❌	✅	❌	✅	5
Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation	❌	❌	✅	❌	❌	❌	❌	1
Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering	✅	✅	✅	✅	❌	❌	✅	5
Train-by-Reconnect: Decoupling Locations of Weights from Their Values	✅	❌	✅	❌	❌	❌	✅	3
Training Generative Adversarial Networks by Solving Ordinary Differential Equations	✅	✅	✅	❌	❌	❌	✅	4
Training Generative Adversarial Networks with Limited Data	❌	✅	✅	❌	✅	❌	✅	4
Training Linear Finite-State Machines	✅	❌	✅	❌	✅	❌	✅	4
Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification	❌	✅	✅	❌	❌	❌	✅	3
Training Stronger Baselines for Learning to Optimize	✅	✅	✅	✅	❌	❌	✅	5
Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning	✅	✅	✅	❌	❌	❌	❌	3
Transfer Learning via $\ell_1$ Regularization	✅	❌	✅	✅	❌	❌	✅	4
Transferable Calibration with Lower Bias and Variance in Domain Adaptation	✅	❌	✅	❌	❌	❌	❌	2
Transferable Graph Optimizers for ML Compilers	❌	❌	✅	✅	✅	❌	❌	3
Tree! I am no Tree! I am a low dimensional Hyperbolic Embedding	✅	✅	✅	❌	❌	❌	❌	3
Triple descent and the two kinds of overfitting: where & why do they appear?	❌	✅	❌	❌	❌	❌	✅	2
Truncated Linear Regression in High Dimensions	✅	❌	❌	❌	❌	❌	❌	1
Trust the Model When It Is Confident: Masked Model-based Actor-Critic	✅	❌	✅	❌	❌	❌	✅	3
Truthful Data Acquisition via Peer Prediction	❌	❌	❌	❌	❌	❌	❌	0
UCLID-Net: Single View Reconstruction in Object Space	❌	✅	✅	✅	❌	❌	✅	4
UCSG-NET- Unsupervised Discovering of Constructive Solid Geometry Tree	❌	✅	✅	✅	✅	❌	✅	5
UDH: Universal Deep Hiding for Steganography, Watermarking, and Light Field Messaging	❌	✅	✅	✅	❌	❌	✅	4
UWSOD: Toward Fully-Supervised-Level Capacity Weakly Supervised Object Detection	❌	✅	✅	✅	❌	❌	✅	4
Ultra-Low Precision 4-bit Training of Deep Neural Networks	✅	❌	✅	❌	❌	❌	❌	2
Ultrahyperbolic Representation Learning	✅	❌	✅	❌	❌	❌	✅	3
UnModNet: Learning to Unwrap a Modulo Image for High Dynamic Range Imaging	❌	❌	❌	❌	✅	❌	✅	2
Unbalanced Sobolev Descent	✅	✅	✅	❌	❌	❌	✅	4
Uncertainty Aware Semi-Supervised Learning on Graph Data	❌	✅	✅	❌	❌	❌	❌	2
Uncertainty Quantification for Inferring Hawkes Networks	✅	❌	❌	❌	❌	❌	✅	2
Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation	❌	❌	✅	✅	✅	❌	✅	4
Uncertainty-aware Self-training for Few-shot Text Classification	✅	✅	✅	✅	✅	❌	✅	6
Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence	❌	✅	✅	✅	❌	❌	✅	4
Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features	❌	✅	✅	❌	❌	❌	❌	2
Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks	❌	✅	✅	❌	❌	❌	✅	3
Understanding Deep Architecture with Reasoning Layer	❌	✅	✅	❌	❌	❌	✅	3
Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition	❌	❌	❌	❌	❌	❌	✅	1
Understanding Global Feature Contributions With Additive Importance Measures	✅	✅	✅	✅	❌	❌	❌	4
Understanding Gradient Clipping in Private SGD: A Geometric Perspective	❌	❌	✅	❌	❌	❌	✅	2
Understanding and Exploring the Network with Stochastic Architectures	❌	❌	✅	❌	✅	❌	✅	3
Understanding and Improving Fast Adversarial Training	❌	✅	✅	❌	✅	❌	✅	4
Understanding spiking networks through convex optimization	❌	✅	❌	❌	❌	❌	✅	2
Understanding the Role of Training Regimes in Continual Learning	❌	✅	✅	✅	❌	❌	✅	4
Unfolding recurrence by Green’s functions for optimized reservoir computing	❌	❌	✅	❌	❌	❌	✅	2
Unfolding the Alternating Optimization for Blind Super Resolution	❌	✅	✅	❌	✅	❌	✅	4
Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks	❌	✅	✅	✅	❌	❌	✅	4
Universal Domain Adaptation through Self Supervision	❌	✅	✅	✅	❌	❌	✅	4
Universal Function Approximation on Graphs	✅	✅	✅	✅	❌	❌	✅	5
Universal guarantees for decision tree induction via a higher-order splitting criterion	✅	❌	❌	❌	❌	❌	❌	1
Universally Quantized Neural Compression	❌	❌	✅	❌	✅	❌	✅	3
Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms	✅	✅	✅	❌	❌	❌	✅	4
Unsupervised Data Augmentation for Consistency Training	❌	✅	✅	❌	❌	❌	✅	3
Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models	❌	✅	✅	❌	❌	❌	✅	3
Unsupervised Learning of Dense Visual Representations	❌	❌	✅	✅	❌	❌	✅	3
Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control	❌	✅	✅	❌	❌	❌	✅	3
Unsupervised Learning of Object Landmarks via Self-Training Correspondence	❌	✅	✅	✅	❌	❌	✅	4
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments	❌	✅	✅	✅	✅	❌	✅	5
Unsupervised Representation Learning by Invariance Propagation	❌	❌	✅	✅	❌	❌	✅	3
Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning	❌	✅	✅	✅	✅	❌	✅	5
Unsupervised Sound Separation Using Mixture Invariant Training	❌	✅	✅	❌	✅	❌	✅	4
Unsupervised Text Generation by Learning from Search	✅	✅	✅	✅	✅	❌	✅	6
Unsupervised Translation of Programming Languages	❌	✅	✅	✅	✅	❌	✅	5
Unsupervised object-centric video generation and decomposition in 3D	❌	✅	❌	✅	❌	❌	✅	3
Untangling tradeoffs between recurrence and self-attention in artificial neural networks	✅	❌	✅	❌	✅	❌	✅	4
Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss	✅	❌	❌	❌	❌	❌	❌	1
User-Dependent Neural Sequence Models for Continuous-Time Event Data	❌	✅	✅	✅	❌	❌	✅	4
Using noise to probe recurrent neural network structure and prune synapses	❌	❌	❌	❌	❌	❌	✅	1
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data	❌	✅	✅	❌	❌	❌	❌	2
VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain	❌	✅	✅	❌	❌	❌	✅	3
Value-driven Hindsight Modelling	❌	❌	✅	❌	❌	❌	✅	2
VarGrad: A Low-Variance Gradient Estimator for Variational Inference	✅	✅	✅	✅	❌	❌	✅	5
Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization	✅	❌	✅	❌	❌	❌	✅	3
Variance reduction for Random Coordinate Descent-Langevin Monte Carlo	✅	❌	❌	❌	❌	❌	✅	2
Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis	✅	❌	✅	❌	❌	❌	✅	3
Variational Amodal Object Completion	❌	❌	✅	✅	❌	❌	❌	2
Variational Bayesian Monte Carlo with Noisy Likelihoods	❌	✅	✅	❌	❌	❌	✅	3
Variational Bayesian Unlearning	❌	❌	✅	✅	❌	❌	✅	3
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings	❌	✅	✅	✅	❌	❌	✅	4
Variational Interaction Information Maximization for Cross-domain Disentanglement	❌	❌	✅	✅	❌	❌	❌	2
Variational Policy Gradient Method for Reinforcement Learning with General Utilities	✅	❌	✅	❌	❌	❌	✅	3
Video Frame Interpolation without Temporal Priors	❌	✅	✅	❌	✅	❌	✅	4
Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement	❌	✅	✅	✅	✅	❌	✅	5
WOR and $p$'s: Sketches for $\ell_p$-Sampling Without Replacement	✅	✅	❌	❌	❌	❌	✅	3
Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization	✅	❌	✅	❌	❌	✅	❌	3
Walsh-Hadamard Variational Inference for Bayesian Deep Learning	✅	❌	✅	✅	✅	❌	✅	5
Wasserstein Distances for Stereo Disparity Estimation	❌	✅	✅	✅	❌	❌	✅	4
Watch out! Motion is Blurring the Vision of Your Deep Neural Networks	✅	✅	✅	❌	❌	❌	✅	4
Wavelet Flow: Fast Training of High Resolution Normalizing Flows	❌	✅	✅	✅	✅	❌	✅	5
Weak Form Generalized Hamiltonian Learning	❌	✅	❌	❌	✅	❌	✅	3
Weakly Supervised Deep Functional Maps for Shape Matching	❌	✅	✅	✅	❌	❌	✅	4
Weakly-Supervised Reinforcement Learning for Controllable Behavior	✅	❌	❌	❌	❌	❌	✅	2
Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning	❌	✅	✅	❌	❌	✅	✅	4
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings	❌	✅	✅	❌	❌	❌	❌	2
Weston-Watkins Hinge Loss and Ordered Partitions	❌	❌	❌	❌	❌	❌	❌	0
What Did You Think Would Happen? Explaining Agent Behaviour through Intended Outcomes	❌	✅	✅	❌	❌	❌	✅	3
What Do Neural Networks Learn When Trained With Random Labels?	❌	❌	✅	❌	❌	❌	✅	2
What Makes for Good Views for Contrastive Learning?	❌	❌	✅	❌	❌	❌	✅	2
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation	✅	✅	✅	❌	❌	❌	✅	4
What if Neural Networks had SVDs?	✅	✅	❌	❌	✅	❌	✅	4
What is being transferred in transfer learning?	❌	❌	✅	❌	❌	❌	✅	2
What shapes feature representations? Exploring datasets, architectures, and training	❌	❌	✅	✅	❌	❌	✅	3
What went wrong and when? Instance-wise feature importance for time-series black-box models	✅	✅	✅	❌	❌	❌	❌	3
When Counterpoint Meets Chinese Folk Melodies	❌	❌	✅	✅	❌	❌	❌	2
When Do Neural Networks Outperform Kernel Methods?	❌	✅	✅	❌	❌	❌	✅	3
When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes	✅	✅	✅	❌	❌	❌	✅	4
Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? --- A Neural Tangent Kernel Perspective	❌	❌	✅	❌	❌	❌	✅	2
Why Normalizing Flows Fail to Detect Out-of-Distribution Data	❌	✅	✅	✅	❌	❌	✅	4
Why are Adaptive Methods Good for Attention Models?	✅	❌	✅	✅	❌	❌	✅	4
Winning the Lottery with Continuous Sparsification	✅	✅	✅	❌	❌	❌	✅	4
Wisdom of the Ensemble: Improving Consistency of Deep Learning Models	✅	✅	✅	✅	❌	❌	✅	5
WoodFisher: Efficient Second-Order Approximation for Neural Network Compression	❌	✅	✅	❌	✅	❌	✅	4
Woodbury Transformations for Deep Generative Flows	❌	❌	✅	❌	✅	❌	✅	3
Worst-Case Analysis for Randomly Collected Data	✅	✅	❌	❌	❌	✅	✅	4
X-CAL: Explicit Calibration for Survival Analysis	❌	✅	✅	✅	❌	❌	❌	3
Your Classifier can Secretly Suffice Multi-Source Domain Adaptation	✅	✅	✅	✅	❌	❌	✅	5
Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling	❌	❌	✅	❌	❌	❌	❌	1
Zap Q-Learning With Nonlinear Function Approximation	❌	❌	✅	❌	❌	❌	✅	2
Zero-Resource Knowledge-Grounded Dialogue Generation	✅	✅	✅	✅	❌	❌	✅	5
f-Divergence Variational Inference	❌	❌	✅	✅	❌	❌	✅	3
f-GAIL: Learning f-Divergence for Generative Adversarial Imitation Learning	✅	✅	✅	✅	❌	❌	✅	5
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations	❌	✅	✅	✅	✅	❌	✅	5