reproducibilityindex.ai

Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].

International Conference on Machine Learning (ICML) - 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
ICML	2020	1084	0.52	3.43	90.68%	44.05%

Search Papers

	Pseudocode	Open Source Code	Open Datasets	Dataset Splits	Hardware Specification	Software Dependencies	Experiment Setup
(Locally) Differentially Private Combinatorial Semi-Bandits	✅	❌	❌	❌	❌	❌	❌	1
A Chance-Constrained Generative Framework for Sequence Optimization	✅	❌	✅	✅	❌	❌	✅	4
A Distributional Framework For Data Valuation	✅	✅	✅	❌	❌	❌	❌	3
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation	✅	❌	❌	❌	❌	❌	❌	1
A Flexible Framework for Nonparametric Graphical Modeling that Accommodates Machine Learning	❌	❌	❌	❌	❌	❌	❌	0
A Flexible Latent Space Model for Multilayer Networks	✅	❌	✅	❌	❌	❌	✅	3
A Free-Energy Principle for Representation Learning	❌	❌	✅	❌	❌	❌	✅	2
A Game Theoretic Framework for Model Based Reinforcement Learning	✅	❌	❌	❌	❌	❌	❌	1
A Generative Model for Molecular Distance Geometry	❌	✅	✅	✅	✅	❌	❌	4
A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton	❌	❌	✅	✅	✅	❌	✅	4
A Geometric Approach to Archetypal Analysis via Sparse Projections	✅	❌	✅	❌	✅	❌	✅	4
A Graph to Graphs Framework for Retrosynthesis Prediction	❌	❌	✅	✅	✅	❌	✅	4
A Markov Decision Process Model for Socio-Economic Systems Impacted by Climate Change	✅	❌	✅	❌	❌	❌	✅	3
A Mean Field Analysis Of Deep ResNet And Beyond: Towards Provably Optimization Via Overparameterization From Depth	✅	❌	✅	❌	❌	❌	✅	3
A Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits	✅	✅	✅	❌	❌	❌	❌	3
A Nearly-Linear Time Algorithm for Exact Community Recovery in Stochastic Block Model	✅	❌	❌	❌	✅	✅	✅	4
A Pairwise Fair and Community-preserving Approach to k-Center Clustering	✅	✅	✅	❌	❌	❌	✅	4
A Quantile-based Approach for Hyperparameter Transfer Learning	✅	❌	✅	✅	✅	❌	✅	5
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning	❌	❌	❌	❌	❌	❌	❌	0
A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition	✅	❌	✅	✅	❌	❌	✅	4
A Simple Framework for Contrastive Learning of Visual Representations	✅	✅	✅	✅	✅	❌	✅	6
A Swiss Army Knife for Minimax Optimal Transport	✅	✅	✅	❌	❌	❌	✅	4
A Tree-Structured Decoder for Image-to-Markup Generation	❌	❌	✅	✅	✅	❌	✅	4
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates	✅	❌	❌	❌	❌	❌	✅	2
A distributional view on multi-objective policy optimization	✅	✅	❌	❌	❌	✅	✅	4
A general recurrent state space framework for modeling neural dynamics during decision-making	❌	✅	✅	✅	❌	❌	✅	4
A new regret analysis for Adam-type algorithms	✅	❌	❌	❌	❌	❌	❌	1
A simpler approach to accelerated optimization: iterative averaging meets optimism	✅	❌	❌	❌	❌	❌	❌	1
ACFlow: Flow Models for Arbitrary Conditional Likelihoods	❌	✅	✅	❌	❌	❌	❌	2
AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation	❌	❌	✅	❌	❌	❌	❌	1
Abstraction Mechanisms Predict Generalization in Deep Neural Networks	❌	✅	✅	❌	❌	❌	❌	2
Accelerated Message Passing for Entropy-Regularized MAP Inference	✅	❌	❌	❌	❌	❌	✅	2
Accelerated Stochastic Gradient-free and Projection-free Methods	✅	✅	✅	❌	✅	❌	✅	5
Accelerating Large-Scale Inference with Anisotropic Vector Quantization	❌	✅	✅	❌	✅	❌	✅	4
Accelerating the diffusion-based ensemble sampling by non-reversible dynamics	❌	❌	✅	❌	❌	❌	✅	2
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization	✅	❌	✅	❌	❌	❌	✅	3
Acceleration through spectral density estimation	✅	❌	✅	❌	❌	❌	❌	2
Accountable Off-Policy Evaluation With Kernel Bellman Statistics	✅	❌	✅	❌	❌	❌	✅	3
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation	❌	❌	✅	✅	❌	❌	✅	3
Active World Model Learning with Progress Curiosity	✅	❌	❌	❌	❌	❌	✅	2
AdaScale SGD: A User-Friendly Algorithm for Distributed Training	✅	❌	✅	✅	❌	❌	✅	4
Adaptive Adversarial Multi-task Representation Learning	✅	✅	✅	✅	❌	❌	✅	5
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE	✅	✅	✅	❌	✅	❌	✅	5
Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning	❌	✅	❌	❌	❌	❌	✅	2
Adaptive Estimator Selection for Off-Policy Evaluation	❌	✅	✅	❌	❌	❌	✅	3
Adaptive Gradient Descent without Descent	✅	✅	✅	❌	❌	❌	✅	4
Adaptive Region-Based Active Learning	✅	❌	✅	✅	❌	❌	✅	4
Adaptive Reward-Poisoning Attacks against Reinforcement Learning	✅	❌	❌	❌	❌	❌	✅	2
Adaptive Sampling for Estimating Probability Distributions	✅	❌	❌	❌	❌	❌	✅	2
Adaptive Sketching for Fast and Convergent Canonical Polyadic Decomposition	✅	✅	✅	❌	❌	❌	✅	4
Adding seemingly uninformative labels helps in low data regimes	❌	✅	✅	✅	❌	❌	✅	4
Adversarial Attacks on Copyright Detection Systems	❌	❌	❌	❌	❌	❌	❌	0
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models	❌	✅	✅	✅	✅	❌	✅	5
Adversarial Filters of Dataset Biases	✅	✅	✅	✅	❌	❌	✅	5
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks	❌	❌	❌	❌	❌	❌	❌	0
Adversarial Mutual Information for Text Generation	❌	✅	✅	✅	❌	❌	✅	4
Adversarial Neural Pruning with Latent Vulnerability Suppression	✅	✅	✅	❌	❌	❌	✅	4
Adversarial Nonnegative Matrix Factorization	✅	❌	✅	❌	❌	❌	✅	3
Adversarial Risk via Optimal Transport and Optimal Couplings	❌	✅	✅	❌	❌	❌	❌	2
Adversarial Robustness Against the Union of Multiple Perturbation Models	✅	✅	✅	❌	✅	❌	✅	5
Adversarial Robustness for Code	✅	✅	✅	❌	✅	❌	✅	5
Adversarial Robustness via Runtime Masking and Cleansing	✅	✅	✅	✅	✅	❌	✅	6
Agent57: Outperforming the Atari Human Benchmark	❌	❌	✅	❌	❌	❌	✅	2
Aggregation of Multiple Knockoffs	✅	❌	✅	❌	❌	❌	✅	3
Aligned Cross Entropy for Non-Autoregressive Machine Translation	✅	❌	✅	✅	✅	❌	✅	5
All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference	❌	✅	✅	❌	❌	❌	✅	3
Alleviating Privacy Attacks via Causal Learning	❌	✅	✅	❌	❌	✅	✅	4
Almost Tune-Free Variance Reduction	✅	❌	✅	❌	❌	❌	✅	3
Amortised Learning by Wake-Sleep	✅	✅	✅	❌	❌	❌	✅	4
Amortized Finite Element Analysis for Fast PDE-Constrained Optimization	❌	✅	❌	❌	❌	❌	✅	2
Amortized Population Gibbs Samplers with Neural Sufficient Statistics	✅	❌	✅	❌	❌	❌	✅	3
An Accelerated DFO Algorithm for Finite-sum Convex Functions	✅	❌	✅	❌	❌	❌	✅	3
An EM Approach to Non-autoregressive Conditional Sequence Generation	✅	❌	✅	✅	✅	❌	✅	5
An Explicitly Relational Neural Network Architecture	❌	✅	❌	❌	❌	❌	❌	1
An Imitation Learning Approach for Cache Replacement	✅	✅	✅	✅	❌	❌	✅	5
An Investigation of Why Overparameterization Exacerbates Spurious Correlations	❌	✅	✅	❌	❌	❌	✅	3
An Optimistic Perspective on Offline Reinforcement Learning	❌	✅	✅	❌	❌	❌	✅	3
An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm	✅	❌	❌	❌	❌	❌	✅	2
An end-to-end approach for the verification problem: learning the right distance	✅	✅	✅	✅	❌	❌	✅	5
Analytic Marching: An Analytic Meshing Solution from Deep Implicit Surface Networks	❌	❌	✅	❌	✅	❌	✅	3
Anderson Acceleration of Proximal Gradient Methods	❌	❌	❌	❌	❌	❌	✅	1
Angular Visual Hardness	❌	❌	✅	✅	❌	❌	✅	3
Approximating Stacked and Bidirectional Recurrent Architectures with the Delayed Recurrent Neural Network	❌	✅	✅	✅	✅	✅	✅	6
Approximation Capabilities of Neural ODEs and Invertible Residual Networks	❌	❌	✅	✅	✅	❌	✅	4
Approximation Guarantees of Local Search Algorithms via Localizability of Set Functions	✅	❌	❌	❌	✅	❌	✅	3
Associative Memory in Iterated Overparameterized Sigmoid Autoencoders	❌	❌	✅	❌	✅	❌	✅	3
Asynchronous Coagent Networks	❌	❌	❌	❌	❌	❌	❌	0
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger	✅	❌	✅	❌	❌	❌	✅	3
Attentive Group Equivariant Convolutional Networks	❌	✅	✅	✅	❌	❌	✅	4
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks	✅	✅	✅	✅	✅	❌	✅	6
AutoML-Zero: Evolving Machine Learning Algorithms From Scratch	✅	✅	✅	✅	❌	❌	✅	5
Automated Synthetic-to-Real Generalization	✅	✅	✅	✅	❌	❌	✅	5
Automatic Reparameterisation of Probabilistic Programs	✅	✅	✅	❌	❌	❌	✅	4
Automatic Shortcut Removal for Self-Supervised Representation Learning	❌	❌	✅	✅	✅	❌	✅	4
BINOCULARS for efficient, nonmyopic sequential experimental design	✅	❌	✅	❌	❌	❌	✅	3
Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning	❌	❌	✅	❌	❌	❌	✅	2
Bandits for BMO Functions	✅	❌	❌	❌	❌	❌	✅	2
Bandits with Adversarial Scaling	✅	❌	❌	❌	❌	❌	❌	1
Batch Reinforcement Learning with Hyperparameter Gradients	❌	❌	✅	✅	❌	❌	✅	3
Batch Stationary Distribution Estimation	✅	❌	❌	❌	❌	❌	✅	2
Bayesian Differential Privacy for Machine Learning	❌	✅	✅	❌	❌	❌	❌	2
Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation	❌	✅	❌	❌	❌	❌	✅	2
Bayesian Graph Neural Networks with Adaptive Connection Sampling	❌	✅	✅	✅	❌	❌	✅	4
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances	✅	✅	✅	✅	✅	✅	✅	7
Bayesian Optimisation over Multiple Continuous and Categorical Inputs	✅	✅	✅	❌	✅	❌	✅	5
Bayesian Sparsification of Deep C-valued Networks	❌	✅	✅	✅	❌	❌	✅	4
Being Bayesian about Categorical Probability	❌	✅	✅	✅	✅	❌	✅	5
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks	❌	✅	✅	✅	❌	❌	✅	4
Best Arm Identification for Cascading Bandits in the Fixed Confidence Setting	✅	❌	❌	❌	❌	❌	✅	2
Better depth-width trade-offs for neural networks through the lens of dynamical systems	❌	❌	❌	❌	❌	❌	✅	1
Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization?	❌	❌	✅	❌	✅	❌	✅	3
Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels	✅	✅	✅	✅	✅	❌	✅	6
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles	✅	❌	❌	❌	❌	❌	❌	1
Bidirectional Model-based Policy Optimization	✅	❌	✅	❌	❌	❌	❌	2
Bio-Inspired Hashing for Unsupervised Similarity Search	❌	❌	✅	✅	❌	❌	✅	3
Bisection-Based Pricing for Repeated Contextual Auctions against Strategic Buyer	✅	❌	❌	❌	❌	❌	❌	1
Black-Box Methods for Restoring Monotonicity	❌	❌	❌	❌	❌	❌	❌	0
Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics	❌	❌	✅	❌	✅	✅	✅	4
Black-box Certification and Learning under Adversarial Perturbations	❌	❌	❌	❌	❌	❌	❌	0
BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates	✅	✅	✅	✅	❌	❌	✅	5
Boosted Histogram Transform for Regression	✅	❌	❌	✅	❌	❌	✅	3
Boosting Deep Neural Network Efficiency with Dual-Module Inference	✅	❌	✅	✅	✅	✅	✅	6
Boosting Frank-Wolfe by Chasing Gradients	✅	✅	✅	❌	✅	❌	✅	5
Boosting for Control of Dynamical Systems	✅	❌	❌	❌	❌	❌	✅	2
Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning	✅	❌	✅	❌	✅	❌	✅	4
Born-Again Tree Ensembles	✅	✅	✅	✅	✅	✅	✅	7
Bounding the fairness and accuracy of classifiers from population statistics	✅	✅	✅	✅	❌	❌	❌	4
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning	✅	❌	❌	❌	❌	❌	❌	1
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search	✅	✅	✅	✅	❌	❌	✅	5
Bridging the Gap Between f-GANs and Wasserstein GANs	✅	✅	✅	✅	❌	✅	✅	6
Budgeted Online Influence Maximization	✅	❌	✅	❌	❌	❌	✅	3
CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods	✅	✅	✅	✅	❌	❌	❌	4
CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information	✅	✅	✅	❌	❌	❌	✅	4
CURL: Contrastive Unsupervised Representations for Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Calibration, Entropy Rates, and Memory in Language Models	✅	❌	✅	❌	❌	❌	❌	2
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?	✅	✅	✅	✅	❌	❌	✅	5
Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?	✅	✅	✅	✅	❌	❌	✅	5
Can Stochastic Zeroth-Order Frank-Wolfe Method Converge Faster for Non-Convex Problems?	✅	❌	✅	❌	❌	❌	✅	3
Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health	❌	✅	✅	❌	❌	❌	✅	3
Causal Effect Identifiability under Partial-Observability	✅	❌	❌	❌	❌	❌	❌	1
Causal Inference using Gaussian Processes with Structured Latent Confounders	✅	❌	✅	❌	❌	❌	✅	3
Causal Modeling for Fairness In Dynamical Systems	❌	✅	❌	❌	❌	❌	❌	1
Causal Strategic Linear Regression	✅	❌	❌	❌	❌	❌	❌	1
Causal Structure Discovery from Distributions Arising from Mixtures of DAGs	✅	❌	✅	❌	❌	❌	✅	3
Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings	✅	❌	✅	❌	❌	❌	✅	3
Certified Data Removal from Machine Learning Models	✅	✅	✅	❌	❌	❌	✅	4
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing	✅	❌	✅	❌	❌	❌	❌	2
Channel Equilibrium Networks for Learning Deep Representation	❌	✅	✅	✅	✅	✅	✅	6
Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs	❌	✅	❌	❌	❌	❌	✅	2
Choice Set Optimization Under Discrete Choice Models of Group Decisions	✅	✅	✅	❌	❌	❌	✅	4
Circuit-Based Intrinsic Methods to Detect Overfitting	❌	❌	✅	❌	✅	✅	✅	4
Class-Weighted Classification: Trade-offs and Robust Approaches	❌	✅	✅	❌	❌	❌	✅	3
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies	✅	✅	✅	✅	❌	❌	✅	5
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning	✅	✅	✅	✅	❌	❌	❌	4
Closing the convergence gap of SGD without replacement	❌	✅	❌	❌	❌	❌	✅	2
CoMic: Complementary Task Learning & Mimicry for Reusable Skills	❌	❌	✅	❌	❌	❌	✅	2
Collaborative Machine Learning with Incentive-Aware Model Rewards	❌	❌	✅	❌	❌	❌	❌	1
Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems	❌	✅	✅	❌	❌	❌	✅	3
Combinatorial Pure Exploration for Dueling Bandit	✅	❌	❌	❌	❌	❌	❌	1
Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction	❌	✅	❌	❌	✅	❌	✅	3
Communication-Efficient Distributed PCA by Riemannian Optimization	✅	❌	✅	❌	❌	❌	✅	3
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks	✅	❌	✅	❌	❌	❌	✅	3
Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions	✅	❌	✅	❌	❌	❌	✅	3
Composable Sketches for Functions of Frequencies: Beyond the Worst Case	❌	❌	✅	❌	❌	❌	✅	2
Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximation	❌	✅	✅	❌	❌	❌	✅	3
Computational and Statistical Tradeoffs in Inferring Combinatorial Structures of Ising Model	❌	❌	❌	❌	❌	❌	❌	0
ConQUR: Mitigating Delusional Bias in Deep Q-Learning	✅	❌	✅	❌	❌	❌	✅	3
Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions	✅	❌	❌	❌	❌	❌	❌	1
Concept Bottleneck Models	❌	✅	✅	✅	❌	❌	✅	4
Concise Explanations of Neural Networks using Adversarial Training	❌	✅	✅	❌	❌	❌	❌	2
Conditional gradient methods for stochastically constrained convex minimization	✅	❌	✅	❌	❌	❌	✅	3
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting	✅	✅	❌	❌	✅	❌	✅	4
Confidence-Aware Learning for Deep Neural Networks	❌	✅	✅	❌	❌	❌	✅	3
Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks	✅	✅	✅	✅	❌	❌	✅	5
Consistent Estimators for Learning to Defer to an Expert	❌	✅	✅	✅	❌	❌	✅	4
Consistent Structured Prediction with Max-Min Margin Markov Networks	✅	✅	✅	✅	❌	❌	✅	5
Constant Curvature Graph Convolutional Networks	❌	❌	✅	✅	❌	❌	✅	3
Constrained Markov Decision Processes via Backward Value Functions	✅	❌	✅	❌	❌	❌	✅	3
Constructive Universal High-Dimensional Distribution Generation through Deep ReLU Networks	❌	❌	❌	❌	❌	❌	❌	0
Context Aware Local Differential Privacy	❌	❌	✅	❌	❌	❌	✅	2
Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning	✅	✅	✅	❌	❌	❌	❌	3
Continuous Graph Neural Networks	❌	❌	✅	✅	❌	❌	❌	2
Continuous Time Bayesian Networks with Clocks	✅	❌	✅	❌	❌	❌	✅	3
Continuous-time Lower Bounds for Gradient-based Algorithms	❌	❌	❌	❌	❌	❌	✅	1
Continuously Indexed Domain Adaptation	❌	❌	✅	✅	❌	❌	✅	3
Contrastive Multi-View Representation Learning on Graphs	✅	❌	✅	✅	❌	❌	✅	4
Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
ControlVAE: Controllable Variational Autoencoder	✅	✅	✅	✅	❌	❌	✅	5
Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics	✅	✅	✅	❌	✅	❌	✅	5
Convergence Rates of Variational Inference in Sparse Deep Learning	❌	❌	❌	❌	❌	❌	❌	0
Convergence of a Stochastic Gradient Method with Momentum for Non-Smooth Non-Convex Optimization	✅	❌	❌	❌	❌	❌	✅	2
Converging to Team-Maxmin Equilibria in Zero-Sum Multiplayer Games	✅	❌	✅	❌	✅	✅	✅	5
Convex Calibrated Surrogates for the Multi-Label F-Measure	✅	❌	✅	✅	❌	❌	✅	4
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space	❌	❌	✅	❌	❌	❌	✅	2
Convolutional Kernel Networks for Graph-Structured Data	✅	✅	✅	✅	❌	❌	✅	5
Convolutional dictionary learning based auto-encoders for natural exponential-family distributions	✅	✅	✅	✅	❌	❌	✅	5
Cooperative Multi-Agent Bandits with Heavy Tails	✅	❌	✅	❌	❌	❌	✅	3
Coresets for Clustering in Graphs of Bounded Treewidth	✅	✅	✅	❌	✅	❌	✅	5
Coresets for Data-efficient Training of Machine Learning Models	✅	❌	✅	❌	❌	❌	✅	3
Correlation Clustering with Asymmetric Classification Errors	✅	❌	❌	❌	❌	❌	❌	1
Cost-Effective Interactive Attention Learning with Neural Attention Processes	✅	✅	✅	✅	❌	❌	❌	4
Cost-effectively Identifying Causal Effects When Only Response Variable is Observable	✅	❌	✅	❌	❌	❌	✅	3
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models	✅	✅	✅	✅	❌	❌	❌	4
Countering Language Drift with Seeded Iterated Learning	✅	✅	✅	❌	❌	❌	✅	4
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness	❌	✅	✅	❌	❌	❌	✅	3
Curvature-corrected learning dynamics in deep neural networks	❌	❌	❌	❌	❌	❌	✅	1
Customizing ML Predictions for Online Algorithms	✅	❌	❌	✅	❌	❌	✅	3
DINO: Distributed Newton-Type Optimization Method	✅	✅	✅	❌	❌	❌	✅	4
DROCC: Deep Robust One-Class Classification	✅	✅	✅	✅	✅	✅	✅	7
DRWR: A Differentiable Renderer without Rendering for Unsupervised 3D Structure Learning from Silhouette Images	❌	❌	✅	❌	❌	❌	✅	2
Data Amplification: Instance-Optimal Property Estimation	❌	❌	❌	❌	❌	❌	✅	1
Data Valuation using Reinforcement Learning	✅	✅	✅	✅	❌	❌	✅	5
Data preprocessing to mitigate bias: A maximum entropy based approach	✅	✅	✅	✅	✅	❌	✅	6
Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models	✅	❌	✅	❌	❌	❌	✅	3
Data-Efficient Image Recognition with Contrastive Predictive Coding	❌	❌	✅	❌	❌	❌	✅	2
DeBayes: a Bayesian Method for Debiasing Network Embeddings	❌	❌	✅	✅	❌	✅	✅	4
Debiased Sinkhorn barycenters	✅	✅	✅	✅	✅	❌	✅	6
Decentralised Learning with Random Features and Distributed Gradient Descent	❌	❌	✅	❌	❌	❌	✅	2
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions	✅	✅	✅	❌	❌	❌	❌	3
Decision Trees for Decision-Making under the Predict-then-Optimize Framework	❌	✅	✅	✅	❌	❌	✅	4
Decoupled Greedy Learning of CNNs	✅	✅	✅	❌	❌	❌	✅	4
Deep Coordination Graphs	✅	✅	❌	❌	❌	❌	✅	3
Deep Divergence Learning	❌	✅	✅	✅	❌	❌	✅	4
Deep Gaussian Markov Random Fields	✅	✅	✅	❌	✅	❌	❌	4
Deep Graph Random Process for Relational-Thinking-Based Speech Recognition	❌	❌	✅	✅	✅	❌	✅	4
Deep Isometric Learning for Visual Recognition	❌	✅	✅	✅	❌	❌	✅	4
Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks	✅	❌	✅	✅	❌	❌	✅	4
Deep PQR: Solving Inverse Reinforcement Learning using Anchor Actions	✅	❌	✅	❌	❌	❌	✅	3
Deep Reasoning Networks for Unsupervised Pattern De-mixing with Constraint Reasoning	✅	❌	✅	✅	✅	❌	✅	5
Deep Reinforcement Learning with Robust and Smooth Policy	✅	❌	✅	❌	❌	❌	✅	3
Deep Streaming Label Learning	❌	❌	✅	✅	✅	❌	❌	3
Deep k-NN for Noisy Labels	✅	❌	✅	✅	✅	✅	✅	6
DeepCoDA: personalized interpretability for compositional health data	❌	✅	✅	✅	❌	❌	✅	4
DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training	✅	❌	✅	❌	❌	❌	✅	3
Defense Through Diverse Directions	❌	❌	✅	❌	❌	❌	✅	2
DeltaGrad: Rapid retraining of machine learning models	✅	✅	✅	❌	✅	✅	✅	6
Description Based Text Classification with Reinforcement Learning	❌	❌	✅	❌	❌	❌	❌	1
Designing Optimal Dynamic Treatment Regimes: A Causal Reinforcement Learning Approach	✅	❌	✅	❌	❌	❌	❌	2
DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths	✅	✅	✅	❌	❌	❌	✅	4
Detecting Out-of-Distribution Examples with Gram Matrices	✅	✅	✅	✅	❌	❌	❌	4
Differentiable Likelihoods for Fast Inversion of ’Likelihood-Free’ Dynamical Systems	✅	❌	❌	❌	❌	❌	✅	2
Differentiable Product Quantization for End-to-End Embedding Compression	✅	✅	✅	❌	✅	❌	✅	5
Differentially Private Set Union	✅	✅	❌	❌	❌	❌	✅	3
Differentiating through the Fréchet Mean	✅	✅	✅	✅	✅	❌	✅	6
Discount Factor as a Regularizer in Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Discriminative Adversarial Search for Abstractive Summarization	✅	❌	✅	✅	✅	❌	✅	5
Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions	✅	❌	✅	❌	❌	❌	✅	3
Disentangling Trainability and Generalization in Deep Neural Networks	❌	✅	✅	❌	❌	❌	✅	3
Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation	❌	✅	✅	✅	❌	❌	✅	4
Dissecting Non-Vacuous Generalization Bounds based on the Mean-Field Approximation	❌	❌	✅	❌	❌	❌	✅	2
Distance Metric Learning with Joint Representation Diversification	❌	✅	✅	✅	✅	❌	✅	5
Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery	❌	✅	✅	❌	❌	✅	✅	4
Distributed Online Optimization over a Heterogeneous Network with Any-Batch Mirror Descent	❌	❌	✅	❌	❌	❌	❌	1
Distribution Augmentation for Generative Modeling	❌	✅	✅	✅	❌	❌	✅	4
Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits	✅	❌	❌	✅	❌	❌	✅	3
Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks	✅	❌	✅	❌	❌	❌	❌	2
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support	✅	❌	❌	❌	❌	❌	❌	1
Do GANs always have Nash equilibria?	✅	❌	✅	❌	❌	❌	✅	3
Do RNN and LSTM have Long Memory?	❌	✅	✅	✅	❌	❌	✅	4
Do We Need Zero Training Loss After Achieving Zero Training Error?	✅	✅	✅	✅	✅	❌	✅	6
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation	✅	✅	✅	✅	❌	❌	✅	5
Does label smoothing mitigate label noise?	❌	❌	✅	❌	❌	❌	✅	2
Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making	✅	✅	✅	✅	✅	❌	✅	6
Domain Adaptive Imitation Learning	✅	❌	✅	❌	❌	❌	❌	2
Domain Aggregation Networks for Multi-Source Domain Adaptation	✅	✅	✅	✅	❌	❌	❌	4
Don’t Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript	✅	❌	✅	❌	❌	❌	✅	3
Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation	❌	❌	✅	❌	❌	❌	✅	2
Double Trouble in Double Descent: Bias and Variance(s) in the Lazy Regime	❌	✅	✅	❌	❌	❌	✅	3
Double-Loop Unadjusted Langevin Algorithm	✅	❌	❌	❌	❌	❌	❌	1
Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables	✅	❌	✅	❌	✅	❌	✅	4
Doubly robust off-policy evaluation with shrinkage	❌	❌	✅	✅	❌	❌	✅	3
DropNet: Reducing Neural Network Complexity via Iterative Pruning	✅	✅	✅	✅	❌	❌	✅	5
Dual Mirror Descent for Online Allocation Problems	✅	✅	✅	❌	❌	❌	✅	4
Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks	✅	❌	✅	✅	❌	❌	✅	4
Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses	✅	❌	✅	✅	❌	❌	❌	3
Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising	✅	❌	❌	❌	❌	❌	❌	1
Dynamics of Deep Neural Networks and Neural Tangent Hierarchy	❌	❌	❌	❌	❌	❌	❌	0
ECLIPSE: An Extreme-Scale Linear Program Solver for Web-Applications	✅	❌	❌	❌	❌	✅	❌	2
Educating Text Autoencoders: Latent Representation Guidance via Denoising	❌	✅	✅	✅	❌	❌	✅	4
Efficient Continuous Pareto Exploration in Multi-Task Learning	✅	✅	✅	❌	❌	❌	✅	4
Efficient Domain Generalization via Common-Specific Low-Rank Decomposition	✅	✅	✅	✅	❌	✅	✅	6
Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets	✅	❌	❌	❌	❌	❌	❌	1
Efficient Intervention Design for Causal Discovery with Latents	✅	❌	❌	❌	❌	❌	✅	2
Efficient Non-conjugate Gaussian Process Factor Models for Spike Count Data using Polynomial Approximations	❌	✅	✅	❌	❌	❌	✅	3
Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation	✅	❌	❌	❌	❌	❌	❌	1
Efficient Policy Learning from Surrogate-Loss Classification Reductions	❌	❌	✅	✅	❌	❌	✅	3
Efficient Proximal Mapping of the 1-path-norm of Shallow Networks	✅	❌	✅	❌	❌	❌	✅	3
Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More	✅	✅	✅	✅	❌	❌	✅	5
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors	❌	✅	✅	❌	❌	❌	✅	3
Efficient nonparametric statistical inference on population feature importance using Shapley values	✅	✅	✅	✅	✅	❌	✅	6
Efficiently Learning Adversarially Robust Halfspaces with Noise	❌	❌	❌	❌	❌	❌	❌	0
Efficiently Solving MDPs with Stochastic Mirror Descent	✅	❌	❌	❌	❌	❌	❌	1
Efficiently sampling functions from Gaussian process posteriors	❌	✅	❌	❌	❌	❌	✅	2
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits	✅	✅	✅	✅	✅	❌	✅	6
Eliminating the Invariance on the Loss Landscape of Linear Autoencoders	❌	❌	✅	❌	❌	❌	✅	2
Emergence of Separable Manifolds in Deep Language Representations	❌	✅	✅	✅	❌	❌	❌	3
Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models	❌	❌	✅	✅	❌	❌	✅	3
Encoding Musical Style with Transformer Autoencoders	❌	✅	✅	✅	❌	❌	✅	4
Energy-Based Processes for Exchangeable Data	✅	✅	✅	❌	❌	❌	❌	3
Enhanced POET: Open-ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions	✅	✅	✅	❌	❌	❌	✅	4
Enhancing Simple Models by Exploiting What They Already Know	✅	❌	✅	✅	❌	✅	✅	5
Entropy Minimization In Emergent Languages	❌	✅	✅	✅	❌	❌	✅	4
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities	❌	❌	❌	❌	❌	❌	✅	1
Equivariant Neural Rendering	❌	✅	✅	✅	✅	❌	✅	5
Error Estimation for Sketched SVD via the Bootstrap	✅	❌	✅	❌	❌	❌	✅	3
Error-Bounded Correction of Noisy Labels	✅	✅	✅	✅	❌	❌	✅	5
Estimating Generalization under Distribution Shifts via Domain-Invariant Representations	✅	✅	✅	❌	❌	❌	❌	3
Estimating Model Uncertainty of Neural Networks in Sparse Information Form	✅	✅	✅	✅	✅	❌	✅	6
Estimating Q(s,s’) with Deep Deterministic Dynamics Gradients	✅	✅	✅	❌	❌	❌	❌	3
Estimating the Error of Randomized Newton Methods: A Bootstrap Approach	✅	❌	✅	❌	❌	❌	✅	3
Estimating the Number and Effect Sizes of Non-null Hypotheses	❌	✅	✅	❌	❌	❌	✅	3
Estimation of Bounds on Potential Outcomes For Decision Making	✅	✅	✅	✅	❌	❌	✅	5
Evaluating Lossy Compression Rates of Deep Generative Models	❌	✅	✅	❌	❌	❌	✅	3
Evaluating Machine Accuracy on ImageNet	❌	❌	✅	✅	❌	❌	✅	3
Evaluating the Performance of Reinforcement Learning Algorithms	✅	✅	✅	❌	❌	❌	✅	4
Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination	✅	✅	✅	❌	❌	❌	✅	4
Evolutionary Topology Search for Tensor Network Decomposition	✅	✅	✅	❌	✅	❌	✅	5
Expert Learning through Generalized Inverse Multiobjective Optimization: Models, Insights, and Algorithms	✅	❌	❌	❌	❌	❌	❌	1
Explainable and Discourse Topic-aware Neural Language Understanding	✅	✅	✅	❌	❌	❌	✅	4
Explainable k-Means and k-Medians Clustering	✅	❌	❌	❌	❌	❌	❌	1
Explaining Groups of Points in Low-Dimensional Representations	✅	✅	✅	❌	❌	❌	✅	4
Explicit Gradient Learning for Black-Box Optimization	✅	✅	✅	❌	❌	❌	✅	4
Exploration Through Reward Biasing: Reward-Biased Maximum Likelihood Estimation for Stochastic Multi-Armed Bandits	✅	❌	❌	❌	❌	❌	✅	2
Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills	❌	✅	❌	❌	❌	❌	❌	1
Extra-gradient with player sampling for faster convergence in n-player games	✅	✅	✅	❌	❌	❌	✅	4
Extrapolation for Large-batch Training in Deep Learning	✅	❌	✅	✅	❌	❌	✅	4
Extreme Multi-label Classification from Aggregated Labels	✅	❌	✅	✅	❌	❌	❌	3
FACT: A Diagnostic for Group Fairness Trade-offs	❌	✅	✅	❌	❌	❌	❌	2
FR-Train: A Mutual Information-Based Approach to Fair and Robust Training	❌	✅	✅	✅	✅	❌	✅	5
Fair Generative Modeling via Weak Supervision	✅	✅	✅	✅	❌	✅	✅	6
Fair Learning with Private Demographic Data	✅	✅	✅	✅	❌	❌	✅	5
Fair k-Centers via Maximum Matching	✅	❌	✅	❌	✅	❌	✅	4
Fairwashing explanations with off-manifold detergent	❌	✅	✅	✅	✅	❌	✅	5
Familywise Error Rate Control by Interactive Unmasking	✅	✅	✅	❌	❌	✅	✅	5
Fast Adaptation to New Environments via Policy-Dynamics Value Functions	❌	✅	✅	✅	❌	❌	✅	4
Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance	✅	❌	✅	❌	✅	❌	✅	4
Fast Differentiable Sorting and Ranking	❌	✅	✅	✅	✅	❌	✅	5
Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case	✅	❌	❌	❌	❌	❌	✅	2
Fast OSCAR and OWL Regression via Safe Screening Rules	✅	❌	✅	❌	✅	❌	✅	4
Fast and Consistent Learning of Hidden Markov Models by Incorporating Non-Consecutive Correlations	❌	❌	❌	❌	❌	✅	❌	1
Fast and Private Submodular and $k$-Submodular Functions Maximization with Matroid Constraints	✅	❌	❌	❌	❌	❌	❌	1
Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods	✅	✅	✅	✅	❌	❌	❌	4
Fast computation of Nash Equilibria in Imperfect Information Games	❌	❌	❌	❌	❌	❌	✅	1
Faster Graph Embeddings via Coarsening	✅	❌	✅	✅	✅	❌	✅	5
Feature Noise Induces Loss Discrepancy Across Groups	❌	✅	✅	❌	❌	❌	✅	3
Feature Quantization Improves GAN Training	✅	✅	✅	❌	✅	❌	✅	5
Feature Selection using Stochastic Gates	✅	✅	✅	✅	❌	❌	✅	5
Feature-map-level Online Adversarial Knowledge Distillation	❌	❌	✅	❌	❌	❌	✅	2
FedBoost: A Communication-Efficient Algorithm for Federated Learning	✅	❌	✅	❌	❌	❌	✅	3
Federated Learning with Only Positive Labels	✅	❌	✅	❌	❌	❌	✅	3
FetchSGD: Communication-Efficient Federated Learning with Sketching	✅	✅	✅	✅	❌	❌	✅	5
Few-shot Domain Adaptation by Causal Mechanism Transfer	✅	❌	✅	✅	✅	❌	✅	5
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs	✅	❌	✅	✅	❌	❌	✅	4
Fiduciary Bandits	✅	❌	❌	❌	❌	❌	❌	1
Fiedler Regularization: Learning Neural Networks with Graph Sparsity	✅	✅	✅	❌	✅	✅	✅	6
Finding trainable sparse networks through Neural Tangent Transfer	❌	❌	✅	❌	❌	❌	✅	2
Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent	❌	❌	❌	❌	❌	❌	❌	0
Finite-Time Convergence in Continuous-Time Optimization	❌	❌	❌	❌	❌	❌	✅	1
Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games	✅	❌	❌	❌	❌	❌	❌	1
Flexible and Efficient Long-Range Planning Through Curious Exploration	✅	❌	❌	❌	❌	❌	❌	1
Forecasting Sequential Data Using Consistent Koopman Autoencoders	❌	✅	✅	❌	❌	❌	✅	3
FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis	✅	✅	❌	❌	❌	❌	✅	3
Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos	❌	❌	❌	❌	❌	❌	❌	0
Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise	❌	✅	✅	❌	❌	❌	✅	3
Frequency Bias in Neural Networks for Input of Non-Uniform Density	❌	❌	❌	❌	❌	❌	❌	0
Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions	✅	❌	✅	❌	❌	❌	✅	3
From Chaos to Order: Symmetry and Conservation Laws in Game Dynamics	❌	❌	❌	❌	❌	❌	❌	0
From ImageNet to Image Classification: Contextualizing Progress on Benchmarks	❌	❌	✅	✅	❌	❌	❌	2
From Importance Sampling to Doubly Robust Policy Gradient	❌	❌	✅	❌	❌	❌	✅	2
From Local SGD to Local Fixed-Point Methods for Federated Learning	✅	❌	✅	❌	✅	❌	✅	4
From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model	✅	❌	❌	❌	❌	❌	✅	2
From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models	✅	❌	✅	❌	❌	❌	✅	3
Frustratingly Simple Few-Shot Object Detection	❌	✅	✅	❌	❌	❌	✅	3
Full Law Identification in Graphical Models of Missing Data: Completeness Results	❌	❌	❌	❌	❌	❌	❌	0
Fully Parallel Hyperparameter Search: Reshaped Space-Filling	❌	✅	✅	✅	❌	❌	✅	4
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations	✅	✅	✅	❌	❌	❌	❌	3
GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation	❌	✅	✅	✅	✅	❌	✅	5
Gamification of Pure Exploration for Linear Bandits	✅	❌	❌	❌	❌	❌	✅	2
Generalisation error in learning with random features and the hidden manifold model	❌	❌	❌	❌	❌	❌	✅	1
Generalization Error of Generalized Linear Models in High Dimensions	✅	✅	❌	❌	❌	❌	✅	3
Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features	✅	❌	✅	✅	✅	❌	❌	4
Generalization and Representational Limits of Graph Neural Networks	❌	❌	❌	❌	❌	❌	❌	0
Generalization to New Actions in Reinforcement Learning	✅	✅	✅	✅	❌	❌	✅	5
Generalized and Scalable Optimal Sparse Decision Trees	✅	✅	✅	❌	❌	❌	✅	4
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data	✅	✅	✅	✅	❌	❌	✅	5
Generating Programmatic Referring Expressions via Program Synthesis	✅	✅	✅	❌	❌	❌	✅	4
Generative Adversarial Imitation Learning with Neural Network Parameterization: Global Optimality and Convergence Rate	✅	❌	❌	❌	❌	❌	❌	1
Generative Flows with Matrix Exponential	✅	✅	✅	❌	✅	❌	✅	5
Generative Pretraining From Pixels	❌	❌	✅	✅	✅	❌	✅	4
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data	❌	✅	✅	✅	✅	❌	✅	5
Global Concavity and Optimization in a Class of Dynamic Discrete Choice Models	✅	❌	❌	❌	❌	❌	✅	2
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks	✅	❌	✅	✅	❌	❌	✅	4
Goal-Aware Prediction: Learning to Model What Matters	✅	✅	✅	❌	❌	❌	✅	4
Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection	✅	✅	✅	✅	❌	❌	✅	5
Goodness-of-Fit Tests for Inhomogeneous Random Graphs	✅	❌	❌	❌	❌	❌	✅	2
Gradient Temporal-Difference Learning with Regularized Corrections	❌	✅	✅	❌	❌	❌	✅	3
Gradient-free Online Learning in Continuous Games with Delayed Rewards	✅	❌	❌	❌	❌	❌	❌	1
GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values	✅	✅	✅	❌	❌	❌	✅	4
Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters	❌	✅	✅	✅	❌	❌	✅	4
Graph Filtration Learning	❌	✅	✅	✅	❌	❌	✅	4
Graph Homomorphism Convolution	✅	✅	✅	✅	❌	❌	✅	5
Graph Optimal Transport for Cross-Domain Alignment	✅	✅	✅	✅	❌	❌	✅	5
Graph Random Neural Features for Distance-Preserving Graph Representations	❌	❌	✅	✅	❌	❌	✅	3
Graph Structure of Neural Networks	❌	❌	✅	✅	✅	❌	✅	4
Graph-based Nearest Neighbor Search: From Practice to Theory	❌	✅	✅	❌	✅	❌	❌	3
Graph-based, Self-Supervised Program Repair from Diagnostic Feedback	❌	✅	✅	✅	✅	❌	✅	5
GraphOpt: Learning Optimization Models of Graph Formation	✅	❌	✅	❌	❌	❌	✅	3
Graphical Models Meet Bandits: A Variational Thompson Sampling Approach	✅	❌	❌	❌	✅	❌	✅	3
Growing Action Spaces	❌	✅	❌	❌	❌	❌	✅	2
Growing Adaptive Multi-hyperplane Machines	✅	✅	✅	❌	✅	❌	✅	5
Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization	✅	❌	✅	✅	❌	❌	✅	4
Haar Graph Pooling	✅	❌	✅	✅	✅	❌	✅	5
Hallucinative Topological Memory for Zero-Shot Visual Planning	❌	✅	❌	❌	❌	❌	✅	2
Handling the Positive-Definite Constraint in the Bayesian Learning Rule	✅	✅	✅	✅	❌	❌	✅	5
Harmonic Decompositions of Convolutional Networks	❌	❌	❌	❌	❌	❌	❌	0
Healing Products of Gaussian Process Experts	❌	✅	✅	❌	❌	❌	✅	3
Hierarchical Generation of Molecular Graphs using Structural Motifs	❌	✅	✅	✅	❌	❌	❌	3
Hierarchical Verification for Adversarial Robustness	✅	❌	✅	❌	✅	✅	✅	5
Hierarchically Decoupled Imitation For Morphological Transfer	✅	✅	✅	❌	❌	❌	❌	3
High-dimensional Robust Mean Estimation via Gradient Descent	✅	❌	❌	❌	❌	❌	❌	1
History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms	✅	❌	✅	❌	❌	❌	✅	3
How Good is the Bayes Posterior in Deep Neural Networks Really?	✅	✅	✅	✅	❌	❌	✅	5
How recurrent networks implement contextual processing in sentiment analysis	❌	❌	✅	✅	❌	❌	✅	3
How to Solve Fair k-Center in Massive Data Models	✅	✅	✅	❌	✅	❌	✅	5
How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization	✅	❌	✅	❌	✅	❌	✅	4
Hybrid Stochastic-Deterministic Minibatch Proximal Gradient: Less-Than-Single-Pass Optimization with Nearly Optimal Generalization	✅	❌	✅	❌	❌	❌	✅	3
Hypernetwork approach to generating point clouds	❌	✅	✅	❌	❌	❌	❌	2
IPBoost – Non-Convex Boosting via Integer Programming	✅	✅	✅	❌	✅	✅	✅	6
Identifying Statistical Bias in Dataset Replication	✅	✅	✅	❌	❌	❌	✅	4
Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation	✅	✅	✅	❌	✅	❌	✅	5
Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability	✅	❌	✅	❌	❌	❌	✅	3
Implicit Generative Modeling for Efficient Exploration	✅	❌	✅	❌	❌	❌	❌	2
Implicit Geometric Regularization for Learning Shapes	❌	✅	✅	❌	❌	❌	✅	3
Implicit Learning Dynamics in Stackelberg Games: Equilibria Characterization, Convergence Analysis, and Empirical Study	✅	❌	✅	❌	❌	❌	✅	3
Implicit Regularization of Random Feature Models	❌	❌	✅	❌	❌	❌	❌	1
Implicit competitive regularization in GANs	❌	❌	✅	❌	❌	❌	✅	2
Implicit differentiation of Lasso-type models for hyperparameter optimization	✅	✅	✅	✅	❌	❌	✅	5
Improved Communication Cost in Distributed PageRank Computation – A Theoretical Study	✅	❌	❌	❌	❌	❌	❌	1
Improved Optimistic Algorithms for Logistic Bandits	✅	❌	❌	❌	❌	❌	❌	1
Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards	✅	❌	❌	❌	❌	❌	✅	2
Improving Generative Imagination in Object-Centric World Models	❌	❌	✅	✅	❌	❌	✅	3
Improving Molecular Design by Stochastic Iterative Target Augmentation	✅	❌	✅	❌	❌	❌	✅	3
Improving Robustness of Deep-Learning-Based Image Reconstruction	✅	❌	✅	✅	❌	❌	✅	4
Improving Transformer Optimization Through Better Initialization	❌	✅	✅	❌	✅	❌	✅	4
Improving generalization by controlling label-noise information in neural network weights	✅	✅	✅	✅	❌	❌	✅	5
Improving the Gating Mechanism of Recurrent Neural Networks	❌	❌	✅	✅	❌	❌	✅	3
Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: Joint Gradient Estimation and Tracking	✅	❌	✅	❌	❌	❌	❌	2
Imputer: Sequence Modelling via Imputation and Dynamic Programming	❌	❌	✅	✅	❌	❌	✅	3
In Defense of Uniform Convergence: Generalization via Derandomization with an Application to Interpolating Predictors	❌	❌	❌	❌	❌	❌	❌	0
Incremental Sampling Without Replacement for Sequence Models	✅	✅	✅	❌	❌	❌	✅	4
Individual Calibration with Randomized Forecasting	❌	✅	✅	❌	❌	❌	✅	3
Individual Fairness for k-Clustering	✅	❌	✅	❌	❌	❌	✅	3
Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks	✅	❌	✅	✅	✅	✅	✅	6
Inductive Relation Prediction by Subgraph Reasoning	❌	✅	✅	✅	❌	❌	✅	4
Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters	❌	❌	✅	❌	✅	❌	❌	2
Inertial Block Proximal Methods for Non-Convex Non-Smooth Optimization	✅	✅	✅	❌	✅	✅	✅	6
Inexact Tensor Methods with Dynamic Accuracies	✅	✅	✅	❌	✅	❌	✅	5
Inferring DQN structure for high-dimensional continuous control	✅	✅	✅	❌	❌	❌	✅	4
Infinite attention: NNGP and NTK for deep attention networks	❌	✅	✅	✅	❌	❌	❌	3
Influenza Forecasting Framework based on Gaussian Processes	✅	❌	✅	✅	❌	✅	✅	5
InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs	✅	✅	✅	❌	❌	❌	✅	4
Information Particle Filter Tree: An Online Algorithm for POMDPs with Belief-Based Rewards on Continuous Domains	✅	✅	✅	❌	✅	❌	✅	5
Information-Theoretic Local Minima Characterization and Regularization	✅	✅	✅	✅	✅	❌	✅	6
Informative Dropout for Robust Representation Learning: A Shape-bias Perspective	✅	✅	✅	✅	❌	❌	❌	4
Input-Sparsity Low Rank Approximation in Schatten Norm	✅	❌	✅	❌	✅	✅	✅	5
InstaHide: Instance-hiding Schemes for Private Distributed Learning	✅	❌	✅	❌	❌	❌	✅	3
Inter-domain Deep Gaussian Processes	❌	✅	✅	❌	❌	❌	✅	3
Interference and Generalization in Temporal Difference Learning	❌	✅	✅	❌	❌	❌	✅	3
Interferometric Graph Transform: a Deep Unsupervised Graph Representation	❌	✅	✅	✅	❌	❌	✅	4
Interpolation between Residual and Non-Residual Networks	❌	❌	✅	❌	❌	❌	✅	2
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions	❌	✅	✅	❌	❌	❌	✅	3
Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure	❌	✅	✅	✅	❌	❌	✅	4
Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge	❌	✅	✅	✅	❌	❌	✅	4
Interpreting Robust Optimization via Adversarial Influence Functions	❌	❌	✅	❌	❌	❌	✅	2
Intrinsic Reward Driven Imitation Learning via Generative Model	✅	✅	✅	❌	❌	❌	✅	4
Invariant Causal Prediction for Block MDPs	✅	✅	✅	❌	❌	❌	✅	4
Invariant Rationalization	❌	✅	✅	✅	❌	❌	✅	4
Invariant Risk Minimization Games	✅	✅	✅	❌	❌	❌	❌	3
Inverse Active Sensing: Modeling and Understanding Timely Decision-Making	✅	❌	❌	❌	❌	❌	✅	2
Invertible generative models for inverse problems: mitigating representation error and dataset bias	❌	❌	✅	❌	✅	❌	❌	2
Involutive MCMC: a Unifying Framework	✅	✅	✅	❌	❌	❌	✅	4
Is Local SGD Better than Minibatch SGD?	❌	❌	❌	❌	❌	❌	✅	1
Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing	❌	❌	❌	❌	❌	❌	❌	0
It’s Not What Machines Can Learn, It’s What We Cannot Teach	❌	❌	❌	❌	✅	❌	✅	2
Kernel Methods for Cooperative Multi-Agent Contextual Bandits	✅	❌	✅	❌	❌	❌	✅	3
Kernel interpolation with continuous volume sampling	❌	❌	❌	❌	❌	❌	✅	1
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data	❌	❌	✅	❌	❌	❌	✅	2
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning	✅	✅	❌	❌	❌	❌	❌	2
Knowing The What But Not The Where in Bayesian Optimization	✅	✅	✅	❌	❌	❌	✅	4
LEEP: A New Measure to Evaluate Transferability of Learned Representations	❌	❌	✅	❌	❌	❌	✅	2
LP-SparseMAP: Differentiable Relaxed Optimization for Sparse Structured Prediction	✅	✅	✅	❌	❌	❌	❌	3
LTF: A Label Transformation Framework for Correcting Label Shift	❌	❌	✅	❌	❌	❌	✅	2
Label-Noise Robust Domain Adaptation	❌	❌	✅	✅	✅	❌	✅	4
Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks	❌	❌	✅	❌	❌	❌	✅	2
Laplacian Regularized Few-Shot Learning	✅	✅	✅	✅	✅	❌	✅	6
Latent Bernoulli Autoencoder	✅	✅	✅	❌	❌	❌	✅	4
Latent Space Factorisation and Manipulation via Matrix Subspace Projection	❌	✅	✅	❌	✅	❌	✅	4
Latent Variable Modelling with Hyperbolic Normalizing Flows	❌	✅	✅	❌	❌	❌	✅	3
Layered Sampling for Robust Optimization Problems	✅	❌	❌	❌	❌	❌	❌	1
LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments	✅	✅	❌	❌	❌	❌	❌	2
Learnable Group Transform For Time-Series	❌	✅	✅	❌	❌	❌	✅	3
Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition	✅	❌	❌	❌	❌	❌	❌	1
Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization	✅	✅	✅	❌	❌	❌	✅	4
Learning Algebraic Multigrid Using Graph Neural Networks	✅	✅	❌	❌	✅	❌	✅	4
Learning Autoencoders with Relational Regularization	✅	✅	✅	✅	✅	❌	✅	6
Learning Calibratable Policies using Programmatic Style-Consistency	✅	✅	✅	✅	✅	✅	✅	7
Learning Compound Tasks without Task-specific Knowledge via Imitation and Self-supervised Learning	❌	❌	✅	❌	✅	❌	❌	2
Learning De-biased Representations with Biased Representations	❌	✅	✅	✅	❌	❌	✅	4
Learning Deep Kernels for Non-Parametric Two-Sample Tests	✅	✅	✅	❌	✅	❌	✅	5
Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information	✅	✅	✅	✅	✅	❌	✅	6
Learning Efficient Multi-agent Communication: An Information Bottleneck Approach	✅	✅	✅	❌	❌	❌	✅	4
Learning Factorized Weight Matrix for Joint Filtering	❌	❌	✅	❌	❌	❌	✅	2
Learning Fair Policies in Multi-Objective (Deep) Reinforcement Learning with Average and Discounted Rewards	❌	❌	❌	❌	✅	❌	❌	1
Learning Flat Latent Manifolds with VAEs	✅	❌	✅	❌	❌	❌	✅	3
Learning Human Objectives by Evaluating Hypothetical Behavior	✅	❌	✅	❌	❌	❌	❌	2
Learning Mixtures of Graphs from Epidemic Cascades	✅	❌	❌	❌	❌	❌	✅	2
Learning Near Optimal Policies with Low Inherent Bellman Error	✅	❌	❌	❌	❌	❌	❌	1
Learning Opinions in Social Networks	✅	❌	❌	❌	❌	❌	❌	1
Learning Optimal Tree Models under Beam Search	✅	❌	✅	✅	✅	❌	✅	5
Learning Portable Representations for High-Level Planning	❌	❌	❌	❌	❌	❌	❌	0
Learning Quadratic Games on Networks	✅	❌	✅	❌	❌	✅	✅	4
Learning Reasoning Strategies in End-to-End Differentiable Proving	✅	✅	✅	✅	❌	❌	✅	5
Learning Representations that Support Extrapolation	❌	❌	✅	❌	❌	❌	✅	2
Learning Robot Skills with Temporal Variational Inference	✅	✅	✅	❌	❌	❌	✅	4
Learning Selection Strategies in Buchberger’s Algorithm	✅	❌	❌	❌	✅	❌	✅	3
Learning Similarity Metrics for Numerical Simulations	❌	✅	✅	❌	❌	❌	✅	3
Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective	✅	❌	✅	❌	❌	❌	✅	3
Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion	✅	❌	✅	❌	❌	❌	❌	2
Learning To Stop While Learning To Predict	✅	✅	✅	✅	❌	❌	✅	5
Learning What to Defer for Maximum Independent Sets	❌	❌	✅	❌	✅	❌	❌	2
Learning and Evaluating Contextual Embedding of Source Code	❌	✅	✅	✅	✅	❌	✅	5
Learning and Sampling of Atomic Interventions from Observations	✅	❌	❌	❌	❌	❌	❌	1
Learning disconnected manifolds: a no GAN’s land	❌	❌	✅	❌	❌	❌	✅	2
Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints	✅	❌	✅	❌	❌	❌	✅	3
Learning from Irregularly-Sampled Time Series: A Missing Data Perspective	❌	✅	✅	✅	❌	❌	✅	4
Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling	✅	❌	✅	✅	❌	❌	✅	4
Learning the Valuations of a $k$-demand Agent	✅	❌	❌	❌	❌	❌	✅	2
Learning the piece-wise constant graph structure of a varying Ising model	❌	✅	✅	✅	❌	❌	✅	4
Learning to Branch for Multi-Task Learning	❌	❌	✅	✅	✅	❌	✅	4
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules	✅	❌	✅	✅	❌	❌	✅	4
Learning to Encode Position for Transformer with Continuous Dynamical Model	❌	❌	✅	✅	❌	❌	✅	3
Learning to Learn Kernels with Variational Random Features	❌	❌	✅	❌	❌	❌	✅	2
Learning to Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning	✅	✅	✅	✅	❌	✅	✅	6
Learning to Rank Learning Curves	✅	❌	✅	✅	❌	❌	✅	4
Learning to Score Behaviors for Guided Policy Optimization	✅	✅	✅	❌	❌	❌	✅	4
Learning to Simulate Complex Physics with Graph Networks	❌	✅	✅	✅	❌	✅	✅	5
Learning to Simulate and Design for Structural Engineering	❌	❌	❌	✅	✅	❌	✅	3
Learning with Bounded Instance and Label-dependent Label Noise	✅	❌	✅	❌	❌	❌	✅	3
Learning with Feature and Distribution Evolvable Streams	❌	❌	✅	❌	❌	❌	✅	2
Learning with Good Feature Representations in Bandits and in RL with a Generative Model	✅	❌	❌	❌	❌	❌	❌	1
Learning with Multiple Complementary Labels	❌	❌	✅	✅	✅	❌	✅	4
Let’s Agree to Agree: Neural Networks Share Classification Order on Real Datasets	❌	❌	✅	✅	❌	❌	❌	2
Leveraging Frequency Analysis for Deep Fake Image Recognition	❌	✅	✅	✅	✅	❌	✅	5
Leveraging Procedural Generation to Benchmark Reinforcement Learning	❌	✅	✅	❌	❌	❌	✅	3
Lifted Disjoint Paths with Application in Multiple Object Tracking	✅	✅	✅	✅	❌	✅	✅	6
Likelihood-free MCMC with Amortized Approximate Ratio Estimators	✅	✅	❌	❌	❌	❌	✅	3
Linear Convergence of Randomized Primal-Dual Coordinate Method for Large-scale Linear Constrained Convex Programming	✅	❌	✅	❌	✅	✅	✅	5
Linear Lower Bounds and Conditioning of Differentiable Games	❌	❌	❌	❌	❌	❌	❌	0
Linear Mode Connectivity and the Lottery Ticket Hypothesis	✅	❌	✅	❌	❌	❌	✅	3
Linear bandits with Stochastic Delayed Feedback	✅	✅	✅	❌	❌	❌	✅	4
Logarithmic Regret for Adversarial Online Control	✅	❌	❌	❌	❌	❌	❌	1
Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently	✅	❌	❌	❌	❌	❌	❌	1
Logistic Regression for Massive Data with Rare Events	❌	❌	❌	❌	❌	❌	❌	0
Lookahead-Bounded Q-learning	✅	✅	❌	❌	❌	❌	✅	3
Lorentz Group Equivariant Neural Network for Particle Physics	❌	❌	✅	✅	✅	❌	✅	4
Loss Function Search for Face Recognition	✅	✅	✅	✅	✅	❌	✅	6
Low Bias Low Variance Gradient Estimates for Boolean Stochastic Networks	✅	❌	✅	❌	❌	❌	❌	2
Low-Rank Bottleneck in Multi-head Attention Models	❌	❌	✅	✅	❌	❌	✅	3
Low-Variance and Zero-Variance Baselines for Extensive-Form Games	✅	✅	✅	❌	❌	❌	✅	4
Low-loss connection of weight vectors: distribution-based approaches	❌	✅	✅	❌	❌	❌	✅	3
LowFER: Low-rank Bilinear Pooling for Link Prediction	❌	❌	✅	❌	❌	❌	✅	2
Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems	❌	❌	❌	❌	❌	❌	❌	0
Manifold Identification for Ultimately Communication-Efficient Distributed Optimization	✅	✅	✅	❌	✅	❌	✅	5
Mapping natural-language problems to formal-language solutions using structured neural representations	❌	❌	✅	❌	❌	❌	❌	1
Margin-aware Adversarial Domain Adaptation with Optimal Transport	❌	✅	✅	✅	❌	❌	✅	4
Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation	❌	✅	✅	✅	❌	❌	✅	4
Maximum-and-Concatenation Networks	❌	❌	✅	❌	❌	❌	❌	1
Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics	❌	✅	❌	❌	❌	✅	❌	2
Median Matrix Completion: from Embarrassment to Optimality	✅	❌	✅	✅	❌	❌	✅	4
Message Passing Least Squares Framework and its Application to Rotation Synchronization	✅	❌	✅	❌	✅	❌	✅	4
Meta Variance Transfer: Learning to Augment from the Others	✅	❌	✅	✅	✅	❌	✅	5
Meta-Learning with Shared Amortized Variational Inference	❌	✅	✅	✅	❌	❌	✅	4
Meta-learning for Mixed Linear Regression	✅	❌	❌	❌	❌	❌	✅	2
Meta-learning with Stochastic Linear Bandits	✅	❌	✅	❌	❌	❌	✅	3
MetaFun: Meta-Learning with Iterative Functional Updates	❌	✅	✅	✅	❌	❌	✅	4
Min-Max Optimization without Gradients: Convergence and Applications to Black-Box Evasion and Poisoning Attacks	✅	✅	✅	❌	❌	❌	✅	4
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack	✅	✅	✅	✅	❌	❌	✅	5
Minimax Pareto Fairness: A Multi Objective Perspective	✅	✅	✅	❌	❌	❌	✅	4
Minimax Rate for Learning From Pairwise Comparisons in the BTL Model	✅	❌	❌	❌	❌	❌	✅	2
Minimax Weight and Q-Function Learning for Off-Policy Evaluation	❌	❌	✅	❌	❌	❌	❌	1
Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation	✅	❌	❌	❌	❌	❌	❌	1
Missing Data Imputation using Optimal Transport	✅	✅	✅	❌	❌	❌	✅	4
Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning	❌	✅	✅	✅	❌	❌	❌	3
MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time	❌	✅	✅	❌	✅	❌	✅	4
Model Fusion with Kullback-Leibler Divergence	❌	✅	✅	❌	❌	❌	✅	3
Model-Based Reinforcement Learning with Value-Targeted Regression	✅	❌	❌	❌	❌	❌	✅	2
Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes	✅	❌	❌	❌	❌	❌	❌	1
Modulating Surrogates for Bayesian Optimization	❌	❌	✅	❌	❌	❌	✅	2
Momentum Improves Normalized SGD	✅	❌	✅	❌	✅	❌	✅	4
Momentum-Based Policy Gradient Methods	✅	✅	✅	❌	❌	❌	✅	4
Moniqua: Modulo Quantized Communication in Decentralized SGD	✅	❌	✅	❌	✅	❌	✅	4
Monte-Carlo Tree Search as Regularized Policy Optimization	❌	❌	✅	❌	✅	❌	✅	3
More Data Can Expand The Generalization Gap Between Adversarially Robust and Standard Models	❌	❌	❌	❌	❌	❌	❌	0
More Information Supervised Probabilistic Deep Face Embedding Learning	✅	❌	✅	❌	✅	❌	✅	4
Multi-Agent Determinantal Q-Learning	✅	✅	✅	❌	❌	❌	✅	4
Multi-Agent Routing Value Iteration Network	❌	✅	✅	✅	❌	❌	✅	4
Multi-Objective Molecule Generation using Interpretable Substructures	✅	✅	✅	✅	❌	❌	✅	5
Multi-Precision Policy Enforced Training (MuPPET) : A Precision-Switching Strategy for Quantised Fixed-Point Training of CNNs	❌	✅	✅	✅	✅	❌	✅	5
Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization	✅	✅	✅	✅	❌	❌	✅	5
Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization	✅	❌	✅	❌	❌	❌	✅	3
Multi-objective Bayesian Optimization using Pareto-frontier Entropy	❌	❌	❌	❌	❌	❌	✅	1
Multi-step Greedy Reinforcement Learning Algorithms	✅	❌	✅	❌	❌	❌	✅	3
Multiclass Neural Network Minimization via Tropical Newton Polytope Approximation	✅	✅	✅	❌	✅	❌	✅	5
Multidimensional Shape Constraints	❌	✅	✅	✅	❌	✅	✅	5
Multigrid Neural Memory	❌	❌	✅	❌	❌	❌	✅	2
Multilinear Latent Conditioning for Generating Unseen Attribute Combinations	❌	❌	✅	✅	❌	✅	✅	4
Multinomial Logit Bandit with Low Switching Cost	✅	❌	❌	❌	❌	❌	❌	1
Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis	✅	✅	✅	✅	❌	❌	✅	5
Mutual Transfer Learning for Massive Data	❌	❌	✅	✅	❌	❌	✅	3
My Fair Bandit: Distributed Learning of Max-Min Fairness with Multi-player Bandits	✅	❌	❌	❌	❌	❌	✅	2
NADS: Neural Architecture Distribution Search for Uncertainty Awareness	❌	❌	✅	❌	✅	❌	✅	3
NGBoost: Natural Gradient Boosting for Probabilistic Prediction	✅	✅	✅	✅	❌	❌	✅	5
Naive Exploration is Optimal for Online LQR	✅	❌	❌	❌	❌	❌	❌	1
Near Input Sparsity Time Kernel Embeddings via Adaptive Sampling	✅	❌	✅	✅	❌	❌	✅	4
Near-Tight Margin-Based Generalization Bounds for Support Vector Machines	❌	❌	❌	❌	❌	❌	❌	0
Near-linear time Gaussian process optimization with adaptive batching and resparsification	✅	✅	✅	❌	❌	❌	✅	4
Near-optimal Regret Bounds for Stochastic Shortest Path	✅	❌	❌	❌	❌	❌	❌	1
Near-optimal sample complexity bounds for learning Latent $k-$polytopes and applications to Ad-Mixtures	❌	❌	❌	❌	❌	❌	❌	0
Nearly Linear Row Sampling Algorithm for Quantile Regression	✅	❌	✅	❌	✅	✅	✅	5
Negative Sampling in Semi-Supervised learning	✅	❌	✅	✅	❌	❌	✅	4
Nested Subspace Arrangement for Representation of Relational Data	✅	✅	✅	✅	❌	✅	✅	6
NetGAN without GAN: From Random Walks to Low-Rank Approximations	✅	✅	✅	✅	❌	❌	✅	5
Neural Architecture Search in A Proxy Validation Loss Landscape	✅	❌	✅	✅	❌	❌	✅	4
Neural Clustering Processes	✅	✅	✅	❌	❌	❌	❌	3
Neural Contextual Bandits with UCB-based Exploration	✅	❌	✅	❌	❌	❌	✅	3
Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification	❌	✅	✅	❌	✅	❌	✅	4
Neural Kernels Without Tangents	✅	❌	✅	✅	✅	❌	✅	5
Neural Network Control Policy Verification With Persistent Adversarial Perturbation	✅	❌	✅	❌	❌	❌	✅	3
Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-layer Networks	❌	❌	✅	❌	❌	❌	✅	2
Neural Topic Modeling with Continual Lifelong Learning	✅	✅	✅	❌	✅	❌	✅	5
Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning"	✅	✅	✅	✅	❌	❌	✅	5
New Oracle-Efficient Algorithms for Private Synthetic Data Release	✅	✅	✅	❌	✅	❌	✅	5
No-Regret Exploration in Goal-Oriented Reinforcement Learning	✅	❌	❌	❌	❌	❌	✅	2
No-Regret and Incentive-Compatible Online Learning	✅	✅	✅	❌	❌	❌	✅	4
Non-Autoregressive Neural Text-to-Speech	❌	❌	❌	❌	✅	❌	✅	2
Non-Stationary Delayed Bandits with Intermediate Observations	✅	❌	❌	❌	❌	❌	✅	2
Non-autoregressive Machine Translation with Disentangled Context Transformer	✅	✅	✅	✅	✅	✅	✅	7
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC	✅	❌	✅	✅	❌	❌	✅	4
Non-separable Non-stationary random fields	❌	✅	✅	✅	❌	❌	✅	4
Nonparametric Score Estimators	✅	✅	✅	❌	✅	❌	✅	5
Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis	❌	❌	✅	❌	❌	❌	✅	2
Normalized Loss Functions for Deep Learning with Noisy Labels	❌	❌	✅	✅	❌	❌	✅	3
Normalizing Flows on Tori and Spheres	❌	❌	❌	❌	❌	❌	✅	1
OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning	✅	❌	❌	❌	❌	❌	✅	2
Obtaining Adjustable Regularization for Free via Iterate Averaging	❌	✅	✅	❌	✅	❌	✅	4
Off-Policy Actor-Critic with Shared Experience Replay	❌	❌	✅	❌	❌	❌	✅	2
On Approximate Thompson Sampling with Langevin Algorithms	✅	❌	❌	❌	❌	❌	✅	2
On Breaking Deep Generative Model-based Defenses and Beyond	✅	✅	✅	❌	❌	❌	✅	4
On Conditional Versus Marginal Bias in Multi-Armed Bandits	❌	❌	❌	❌	❌	❌	✅	1
On Contrastive Learning for Likelihood-free Inference	✅	✅	❌	✅	❌	❌	✅	4
On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent	✅	❌	✅	❌	❌	❌	✅	3
On Coresets for Regularized Regression	❌	✅	✅	❌	✅	✅	✅	5
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data	✅	✅	✅	❌	❌	❌	✅	4
On Efficient Constructions of Checkpoints	✅	❌	✅	❌	✅	❌	✅	4
On Efficient Low Distortion Ultrametric Embedding	❌	❌	✅	❌	❌	❌	✅	2
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems	✅	❌	✅	❌	❌	❌	✅	3
On Implicit Regularization in $β$-VAEs	❌	❌	✅	✅	❌	❌	✅	3
On Layer Normalization in the Transformer Architecture	❌	❌	✅	✅	❌	❌	✅	3
On Learning Language-Invariant Representations for Universal Machine Translation	❌	❌	❌	❌	❌	❌	❌	0
On Learning Sets of Symmetric Elements	❌	❌	✅	❌	✅	❌	❌	2
On Leveraging Pretrained GANs for Generation with Limited Data	❌	✅	✅	❌	✅	❌	✅	4
On Lp-norm Robustness of Ensemble Decision Stumps and Trees	❌	✅	✅	❌	✅	❌	✅	4
On Relativistic f-Divergences	❌	✅	✅	❌	❌	❌	✅	3
On Second-Order Group Influence Functions for Black-Box Predictions	❌	❌	✅	❌	❌	❌	✅	2
On Semi-parametric Inference for BART	❌	❌	❌	❌	❌	❌	❌	0
On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm	✅	❌	✅	❌	❌	❌	✅	3
On Validation and Planning of An Optimal Decision Rule with Application in Healthcare Studies	❌	✅	✅	✅	❌	❌	✅	4
On Variational Learning of Controllable Representations for Text without Supervision	❌	✅	✅	✅	❌	❌	✅	4
On a projective ensemble approach to two sample test for equality of distributions	❌	❌	✅	❌	✅	✅	✅	4
On hyperparameter tuning in general clustering problemsm	✅	❌	✅	✅	✅	❌	✅	5
On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes	❌	❌	❌	❌	❌	❌	❌	0
On the Convergence of Nesterov’s Accelerated Gradient Method in Stochastic Settings	❌	❌	❌	❌	❌	❌	✅	1
On the Expressivity of Neural Networks for Deep Reinforcement Learning	✅	❌	✅	❌	❌	❌	❌	2
On the Generalization Benefit of Noise in Stochastic Gradient Descent	❌	❌	✅	❌	❌	❌	✅	2
On the Generalization Effects of Linear Transformations in Data Augmentation	✅	❌	✅	✅	❌	❌	✅	4
On the Global Convergence Rates of Softmax Policy Gradient Methods	✅	❌	❌	❌	❌	❌	✅	2
On the Global Optimality of Model-Agnostic Meta-Learning	✅	❌	❌	❌	❌	❌	❌	1
On the Iteration Complexity of Hypergradient Computation	✅	✅	✅	✅	❌	❌	✅	5
On the Noisy Gradient Descent that Generalizes as SGD	✅	✅	✅	❌	❌	❌	❌	3
On the Number of Linear Regions of Convolutional Neural Networks	❌	✅	❌	❌	❌	❌	✅	2
On the Power of Compressed Sensing with Generative Models	❌	❌	❌	❌	❌	❌	❌	0
On the Relation between Quality-Diversity Evaluation and Distribution-Fitting Goal in Text Generation	❌	❌	✅	❌	❌	❌	✅	2
On the Sample Complexity of Adversarial Multi-Source PAC Learning	✅	❌	❌	❌	❌	❌	❌	1
On the Theoretical Properties of the Network Jackknife	❌	❌	✅	❌	❌	❌	✅	2
On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness	✅	❌	✅	❌	❌	❌	✅	3
On the consistency of top-k surrogate losses	❌	❌	❌	❌	✅	❌	✅	2
One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control	✅	❌	✅	✅	❌	❌	✅	4
One Size Fits All: Can We Train One Denoiser for All Noise Levels?	❌	❌	✅	✅	❌	❌	✅	3
One-shot Distributed Ridge Regression in High Dimensions	✅	❌	✅	✅	❌	❌	✅	4
Online Bayesian Moment Matching based SAT Solver Heuristics	❌	✅	✅	❌	✅	✅	✅	5
Online Continual Learning from Imbalanced Data	✅	❌	✅	❌	✅	❌	✅	4
Online Control of the False Coverage Rate and False Sign Rate	✅	❌	❌	❌	❌	❌	✅	2
Online Convex Optimization in the Random Order Model	✅	❌	✅	❌	❌	❌	✅	3
Online Dense Subgraph Discovery via Blurred-Graph Feedback	✅	❌	✅	❌	✅	✅	✅	5
Online Learned Continual Compression with Adaptive Quantization Modules	✅	❌	✅	✅	❌	❌	❌	3
Online Learning for Active Cache Synchronization	✅	✅	❌	❌	✅	❌	✅	4
Online Learning with Dependent Stochastic Feedback Graphs	✅	❌	✅	❌	❌	❌	✅	3
Online Learning with Imperfect Hints	✅	❌	❌	❌	❌	❌	❌	1
Online Multi-Kernel Learning with Graph-Structured Feedback	✅	❌	✅	❌	❌	❌	✅	3
Online Pricing with Offline Data: Phase Transition and Inverse Square Law	✅	❌	❌	❌	❌	❌	❌	1
Online metric algorithms with untrusted predictions	✅	✅	✅	❌	❌	❌	✅	4
Online mirror descent and dual averaging: keeping pace in the dynamic case	✅	❌	❌	❌	❌	❌	❌	1
Operation-Aware Soft Channel Pruning using Differentiable Masks	❌	❌	✅	✅	✅	❌	✅	4
Optimal Bounds between f-Divergences and Integral Probability Metrics	❌	❌	❌	❌	❌	❌	❌	0
Optimal Continual Learning has Perfect Memory and is NP-hard	❌	❌	❌	❌	❌	❌	❌	0
Optimal Differential Privacy Composition for Exponential Mechanisms	❌	❌	❌	❌	❌	❌	❌	0
Optimal Estimator for Unlabeled Linear Regression	✅	❌	❌	❌	❌	❌	✅	2
Optimal Non-parametric Learning in Repeated Contextual Auctions with Strategic Buyer	✅	❌	❌	❌	❌	❌	❌	1
Optimal Randomized First-Order Methods for Least-Squares Problems	✅	❌	✅	❌	❌	❌	✅	3
Optimal Robust Learning of Discrete Distributions from Batches	✅	✅	❌	❌	✅	❌	✅	4
Optimal Sequential Maximization: One Interview is Enough!	✅	❌	❌	❌	❌	❌	✅	2
Optimal approximation for unconstrained non-submodular minimization	❌	✅	✅	❌	✅	❌	✅	4
Optimal transport mapping via input convex neural networks	✅	✅	✅	❌	❌	❌	✅	4
Optimally Solving Two-Agent Decentralized POMDPs Under One-Sided Information Sharing	✅	✅	❌	❌	✅	❌	✅	4
Optimistic Bounds for Multi-output Learning	❌	❌	❌	❌	❌	❌	❌	0
Optimistic Policy Optimization with Bandit Feedback	✅	❌	❌	❌	❌	❌	❌	1
Optimization Theory for ReLU Neural Networks Trained with Normalization Layers	❌	❌	❌	❌	❌	❌	✅	1
Optimization and Analysis of the pAp@k Metric for Recommender Systems	✅	✅	✅	❌	❌	❌	✅	4
Optimization from Structured Samples for Coverage Functions	✅	❌	❌	❌	❌	❌	❌	1
Optimizer Benchmarking Needs to Account for Hyperparameter Tuning	✅	❌	✅	✅	❌	❌	✅	4
Optimizing Black-box Metrics with Adaptive Surrogates	✅	✅	✅	✅	❌	❌	✅	5
Optimizing Data Usage via Differentiable Rewards	✅	❌	✅	✅	❌	❌	✅	4
Optimizing Dynamic Structures with Bayesian Generative Search	✅	❌	✅	✅	❌	❌	✅	4
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach	❌	✅	✅	❌	❌	❌	✅	3
Optimizing for the Future in Non-Stationary MDPs	✅	✅	✅	❌	❌	❌	✅	4
Option Discovery in the Absence of Rewards with Manifold Analysis	✅	❌	✅	❌	❌	❌	✅	3
Oracle Efficient Private Non-Convex Optimization	✅	❌	✅	❌	❌	❌	✅	3
Ordinal Non-negative Matrix Factorization for Recommendation	✅	✅	✅	❌	✅	❌	✅	5
Orthogonalized SGD and Nested Architectures for Anytime Neural Networks	✅	❌	✅	❌	❌	❌	✅	3
Overfitting in adversarially robust deep learning	❌	✅	✅	✅	✅	❌	✅	5
PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions	❌	❌	✅	✅	❌	❌	✅	3
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization	✅	✅	✅	✅	❌	❌	✅	5
PENNI: Pruned Kernel Sharing for Efficient CNN Inference	❌	✅	✅	✅	✅	✅	✅	6
PackIt: A Virtual Environment for Geometric Planning	✅	✅	✅	✅	✅	❌	✅	6
Parallel Algorithm for Non-Monotone DR-Submodular Maximization	✅	✅	❌	❌	✅	❌	✅	4
Parameter-free, Dynamic, and Strongly-Adaptive Online Learning	✅	❌	❌	❌	❌	❌	❌	1
Parameterized Rate-Distortion Stochastic Encoder	✅	✅	✅	✅	❌	❌	✅	5
Parametric Gaussian Process Regressors	✅	✅	✅	✅	❌	❌	✅	5
Partial Trace Regression and Low-Rank Kraus Decomposition	❌	✅	✅	❌	❌	❌	✅	3
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates	❌	✅	✅	❌	❌	❌	❌	2
Perceptual Generative Autoencoders	❌	✅	✅	❌	❌	❌	✅	3
Performative Prediction	✅	❌	✅	❌	❌	❌	✅	3
Piecewise Linear Regression via a Difference of Convex Functions	✅	✅	✅	✅	❌	❌	✅	5
Planning to Explore via Self-Supervised World Models	✅	✅	✅	❌	❌	❌	✅	4
PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination	❌	✅	✅	✅	✅	❌	✅	5
Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates	✅	✅	✅	❌	✅	❌	✅	5
Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning	❌	✅	❌	❌	❌	❌	✅	2
PolyGen: An Autoregressive Generative Model of 3D Meshes	❌	❌	✅	✅	✅	❌	✅	4
Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix	✅	✅	✅	✅	❌	❌	✅	5
Population-Based Black-Box Optimization for Biological Sequence Design	✅	❌	✅	❌	❌	❌	✅	3
PowerNorm: Rethinking Batch Normalization in Transformers	✅	✅	✅	✅	❌	❌	✅	5
Predicting Choice with Set-Dependent Aggregation	❌	❌	✅	✅	❌	❌	✅	3
Predicting deliberative outcomes	❌	❌	✅	❌	❌	❌	✅	2
Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control	✅	✅	❌	❌	❌	❌	✅	3
Predictive Coding for Locally-Linear Control	❌	✅	❌	❌	❌	❌	✅	2
Predictive Multiplicity in Classification	✅	✅	✅	❌	✅	❌	✅	5
Predictive Sampling with Forecasting Autoregressive Models	✅	❌	✅	✅	✅	✅	✅	6
Preference Modeling with Context-Dependent Salient Features	❌	❌	✅	✅	❌	❌	✅	3
Preselection Bandits	✅	❌	❌	❌	❌	❌	✅	2
Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters for Extreme Multi-label Text Classification	❌	✅	✅	✅	❌	❌	✅	4
Principled learning method for Wasserstein distributionally robust optimization with local perturbations	❌	✅	✅	❌	❌	❌	❌	2
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead	✅	❌	✅	❌	❌	❌	❌	2
Private Outsourced Bayesian Optimization	✅	❌	✅	❌	❌	❌	✅	3
Private Query Release Assisted by Public Data	✅	❌	❌	❌	❌	❌	❌	1
Private Reinforcement Learning with PAC and Regret Guarantees	✅	❌	❌	❌	❌	❌	❌	1
Privately Learning Markov Random Fields	✅	❌	❌	❌	❌	❌	❌	1
Privately detecting changes in unknown distributions	✅	❌	✅	❌	❌	❌	✅	3
Probing Emergent Semantics in Predictive Agents via Question Answering	❌	❌	❌	❌	❌	❌	✅	1
Problems with Shapley-value-based explanations as feature importance measures	❌	❌	❌	❌	❌	❌	❌	0
Progressive Graph Learning for Open-Set Domain Adaptation	❌	❌	✅	✅	❌	❌	✅	3
Progressive Identification of True Labels for Partial-Label Learning	✅	✅	✅	❌	✅	❌	✅	5
Projection-free Distributed Online Convex Optimization with $O(\sqrtT)$ Communication Complexity	✅	❌	✅	❌	❌	❌	✅	3
Projective Preferential Bayesian Optimization	✅	✅	✅	❌	✅	❌	✅	5
Proper Network Interpretability Helps Adversarial Robustness in Classification	❌	✅	✅	❌	✅	❌	✅	4
Provable Representation Learning for Imitation Learning via Bi-level Optimization	❌	❌	❌	❌	✅	❌	✅	2
Provable Self-Play Algorithms for Competitive Reinforcement Learning	✅	❌	❌	❌	❌	❌	❌	1
Provable Smoothness Guarantees for Black-Box Variational Inference	❌	❌	❌	❌	❌	❌	✅	1
Provable guarantees for decision tree induction: the agnostic setting	✅	❌	❌	❌	❌	❌	❌	1
Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation	✅	✅	✅	❌	❌	❌	✅	4
Provably Efficient Exploration in Policy Optimization	✅	❌	❌	❌	❌	❌	❌	1
Provably Efficient Model-based Policy Adaptation	✅	✅	✅	❌	❌	❌	✅	4
Proving the Lottery Ticket Hypothesis: Pruning is All You Need	❌	❌	❌	❌	❌	❌	❌	0
Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup	✅	✅	✅	❌	❌	❌	✅	4
Q-value Path Decomposition for Deep Multiagent Reinforcement Learning	✅	❌	✅	❌	❌	❌	✅	3
Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints	✅	❌	✅	❌	✅	❌	✅	4
Quantized Decentralized Stochastic Learning over Directed Graphs	✅	❌	✅	❌	❌	❌	✅	3
Quantum Boosting	✅	❌	❌	❌	❌	❌	❌	1
Quantum Expectation-Maximization for Gaussian mixture models	✅	❌	✅	❌	❌	❌	✅	3
R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games	✅	❌	✅	❌	❌	❌	✅	3
RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr	✅	❌	✅	✅	❌	❌	✅	4
ROMA: Multi-Agent Reinforcement Learning with Emergent Roles	❌	✅	✅	❌	❌	❌	✅	3
Radioactive data: tracing through training	❌	❌	✅	❌	❌	❌	✅	2
Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization	✅	❌	✅	❌	❌	❌	✅	3
Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures	❌	❌	✅	✅	❌	❌	✅	3
Random extrapolation for primal-dual coordinate descent	✅	❌	✅	❌	❌	❌	✅	3
Randomization matters How to defend against strong adversarial attacks	✅	❌	✅	❌	❌	❌	✅	3
Randomized Block-Diagonal Preconditioning for Parallel Learning	✅	❌	✅	❌	❌	❌	✅	3
Randomized Smoothing of All Shapes and Sizes	✅	✅	✅	❌	❌	❌	✅	4
Randomly Projected Additive Gaussian Processes for Regression	❌	✅	✅	❌	✅	❌	✅	4
Rank Aggregation from Pairwise Comparisons in the Presence of Adversarial Corruptions	✅	❌	❌	❌	❌	❌	✅	2
Rate-distortion optimization guided autoencoder for isometric embedding in Euclidean latent space	❌	❌	✅	❌	❌	❌	✅	2
Ready Policy One: World Building Through Active Learning	✅	✅	✅	❌	❌	❌	❌	3
Real-Time Optimisation for Online Learning in Auctions	✅	❌	❌	❌	❌	❌	✅	2
Recht-Re Noncommutative Arithmetic-Geometric Mean Conjecture is False	❌	✅	❌	❌	✅	✅	✅	4
Recovery of Sparse Signals from a Mixture of Linear Samples	✅	❌	❌	❌	❌	❌	❌	1
Recurrent Hierarchical Topic-Guided RNN for Language Generation	✅	✅	✅	✅	✅	❌	✅	6
Reducing Sampling Error in Batch Temporal Difference Learning	✅	❌	❌	✅	❌	❌	✅	3
Refined bounds for algorithm configuration: The knife-edge of dual class approximability	✅	❌	✅	✅	✅	✅	✅	6
Regularized Optimal Transport is Ground Cost Adversarial	✅	❌	❌	❌	❌	❌	❌	1
Reinforcement Learning for Integer Programming: Learning to Cut	✅	❌	❌	❌	❌	✅	✅	3
Reinforcement Learning for Molecular Design Guided by Quantum Mechanics	❌	✅	✅	❌	✅	✅	❌	4
Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism	✅	❌	❌	❌	❌	❌	❌	1
Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound	✅	❌	❌	❌	❌	❌	❌	1
Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows	✅	✅	✅	❌	❌	❌	❌	3
Reliable Fidelity and Diversity Metrics for Generative Models	❌	✅	✅	❌	❌	❌	✅	3
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks	✅	✅	✅	✅	❌	❌	✅	5
Representation Learning via Adversarially-Contrastive Optimal Transport	❌	❌	✅	✅	❌	❌	✅	3
Representations for Stable Off-Policy Reinforcement Learning	❌	❌	✅	✅	❌	❌	✅	3
Representing Unordered Data Using Complex-Weighted Multiset Automata	❌	✅	❌	✅	❌	❌	✅	3
Reserve Pricing in Repeated Second-Price Auctions with Strategic Bidders	✅	❌	❌	❌	❌	❌	❌	1
Responsive Safety in Reinforcement Learning by PID Lagrangian Methods	✅	✅	✅	❌	❌	❌	✅	4
Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay	✅	✅	✅	❌	❌	❌	✅	4
Rethinking Bias-Variance Trade-off for Generalization of Neural Networks	✅	✅	✅	❌	❌	❌	✅	4
Retrieval Augmented Language Model Pre-Training	❌	❌	✅	✅	✅	❌	✅	4
Retro: Learning Retrosynthetic Planning with Neural Guided A Search	✅	✅	✅	✅	❌	❌	✅	5
Reverse-engineering deep ReLU networks	✅	❌	✅	❌	❌	❌	✅	3
Revisiting Fundamentals of Experience Replay	❌	❌	✅	❌	❌	❌	✅	2
Revisiting Spatial Invariance with Low-Rank Local Connectivity	✅	✅	✅	✅	✅	❌	✅	6
Revisiting Training Strategies and Generalization Performance in Deep Metric Learning	❌	✅	✅	❌	✅	❌	✅	4
Reward-Free Exploration for Reinforcement Learning	✅	❌	❌	❌	❌	❌	❌	1
Rigging the Lottery: Making All Tickets Winners	✅	✅	✅	❌	❌	❌	✅	4
Robust Bayesian Classification Using An Optimistic Score Ratio	✅	✅	✅	✅	✅	❌	✅	6
Robust Graph Representation Learning via Neural Sparsification	✅	❌	✅	✅	❌	❌	✅	4
Robust Learning with the Hilbert-Schmidt Independence Criterion	✅	✅	✅	✅	❌	❌	✅	5
Robust One-Bit Recovery via ReLU Generative Networks: Near-Optimal Statistical Rate and Global Landscape Analysis	❌	❌	❌	❌	❌	❌	❌	0
Robust Outlier Arm Identification	✅	✅	✅	❌	❌	❌	✅	4
Robust Pricing in Dynamic Mechanism Design	❌	❌	❌	❌	❌	❌	❌	0
Robust and Stable Black Box Explanations	❌	❌	✅	❌	❌	❌	✅	2
Robustifying Sequential Neural Processes	✅	❌	✅	❌	❌	❌	✅	3
Robustness to Programmable String Transformations via Augmented Abstract Training	✅	✅	✅	✅	❌	❌	✅	5
Robustness to Spurious Correlations via Human Annotations	❌	✅	✅	✅	❌	❌	❌	3
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning	✅	❌	✅	❌	❌	❌	✅	3
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates	✅	❌	✅	❌	❌	❌	❌	2
SGD Learns One-Layer Networks in WGANs	✅	❌	❌	❌	❌	❌	✅	2
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust	✅	❌	✅	❌	❌	❌	✅	3
Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data	✅	✅	✅	❌	❌	❌	✅	4
Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences	✅	✅	✅	❌	✅	❌	✅	5
Safe Reinforcement Learning in Constrained Markov Decision Processes	✅	✅	❌	❌	❌	❌	✅	3
Safe screening rules for L0-regression from Perspective Relaxations	❌	❌	✅	❌	✅	✅	✅	4
Sample Amplification: Increasing Dataset Size even when Learning is Impossible	✅	❌	❌	❌	❌	❌	✅	2
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors	❌	❌	✅	❌	❌	❌	✅	2
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning	❌	✅	✅	❌	✅	❌	✅	4
Scalable Deep Generative Modeling for Sparse Graphs	✅	✅	✅	❌	❌	❌	✅	4
Scalable Differentiable Physics for Learning and Control	❌	✅	❌	❌	✅	✅	✅	4
Scalable Differential Privacy with Certified Robustness in Adversarial Learning	✅	✅	✅	❌	❌	❌	✅	4
Scalable Exact Inference in Multi-Output Gaussian Processes	✅	✅	✅	❌	✅	❌	✅	5
Scalable Gaussian Process Separation for Kernels with a Non-Stationary Phase	❌	✅	✅	❌	✅	✅	✅	5
Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM	✅	✅	✅	✅	✅	❌	✅	6
Scalable Nearest Neighbor Search for Optimal Transport	❌	✅	✅	❌	✅	❌	❌	3
Scalable and Efficient Comparison-based Search without Features	✅	❌	✅	❌	❌	❌	✅	3
Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing	✅	✅	❌	❌	❌	❌	✅	3
Schatten Norms in Matrix Streams: Hello Sparsity, Goodbye Dimension	❌	❌	✅	❌	❌	❌	❌	1
Searching to Exploit Memorization Effect in Learning with Noisy Labels	✅	❌	✅	✅	✅	✅	✅	6
Second-Order Provable Defenses against Adversarial Attacks	❌	❌	✅	❌	✅	❌	✅	3
Selective Dyna-Style Planning Under Limited Model Capacity	❌	❌	❌	❌	❌	❌	❌	0
Self-Attentive Associative Memory	❌	✅	✅	❌	❌	❌	✅	3
Self-Attentive Hawkes Process	❌	✅	✅	✅	✅	❌	✅	5
Self-Concordant Analysis of Frank-Wolfe Algorithms	✅	✅	✅	❌	✅	✅	❌	5
Self-Modulating Nonparametric Event-Tensor Factorization	❌	❌	✅	❌	❌	❌	✅	2
Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training	❌	✅	✅	✅	❌	❌	✅	4
Self-supervised Label Augmentation via Input Transformations	✅	✅	✅	❌	❌	❌	✅	4
Semi-Supervised Learning with Normalizing Flows	❌	✅	✅	✅	❌	❌	✅	4
Semi-Supervised StyleGAN for Disentanglement Learning	❌	❌	✅	❌	❌	❌	✅	2
Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees	❌	❌	✅	❌	❌	❌	✅	2
Semismooth Newton Algorithm for Efficient Projections onto $\ell_1, ∞$-norm Ball	✅	✅	✅	❌	✅	❌	✅	5
Sequence Generation with Mixed Representations	❌	✅	✅	✅	❌	❌	✅	4
Sequential Cooperative Bayesian Inference	✅	❌	❌	❌	❌	❌	✅	2
Sequential Transfer in Reinforcement Learning with a Generative Model	✅	❌	✅	❌	❌	❌	✅	3
Set Functions for Time Series	❌	✅	✅	✅	❌	❌	✅	4
Sets Clustering	✅	✅	✅	❌	✅	✅	❌	5
Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion	❌	✅	❌	❌	✅	❌	✅	3
Sharp Statistical Guaratees for Adversarially Robust Gaussian Classification	✅	❌	❌	❌	❌	❌	✅	2
SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification	❌	✅	✅	❌	❌	❌	✅	3
Simple and Deep Graph Convolutional Networks	❌	❌	✅	✅	❌	❌	✅	3
Simple and sharp analysis of k-means\|\|	✅	❌	❌	❌	❌	❌	❌	1
Simultaneous Inference for Massive Data: Distributed Bootstrap	✅	❌	❌	❌	❌	❌	✅	2
Single Point Transductive Prediction	❌	✅	✅	✅	❌	❌	✅	4
Skew-Fit: State-Covering Self-Supervised Reinforcement Learning	✅	❌	✅	❌	❌	❌	✅	3
Small Data, Big Decisions: Model Selection in the Small-Data Regime	❌	❌	✅	✅	❌	❌	✅	3
Small-GAN: Speeding up GAN Training using Core-Sets	✅	❌	✅	❌	✅	❌	✅	4
Smaller, more accurate regression forests using tree alternating optimization	✅	❌	✅	❌	❌	❌	✅	3
Soft Threshold Weight Reparameterization for Learnable Sparsity	✅	✅	✅	✅	✅	❌	✅	6
SoftSort: A Continuous Relaxation for the argsort Operator	✅	✅	✅	✅	❌	❌	✅	5
Source Separation with Deep Generative Priors	✅	✅	✅	❌	❌	❌	✅	4
Sparse Convex Optimization via Adaptively Regularized Hard Thresholding	✅	❌	✅	❌	❌	❌	❌	2
Sparse Gaussian Processes with Spherical Harmonic Features	❌	❌	✅	✅	✅	❌	✅	4
Sparse Shrunk Additive Models	❌	❌	✅	✅	❌	❌	✅	3
Sparse Sinkhorn Attention	❌	❌	✅	❌	✅	❌	✅	3
Sparse Subspace Clustering with Entropy-Norm	❌	❌	✅	❌	✅	❌	✅	3
Sparsified Linear Programming for Zero-Sum Equilibrium Finding	✅	❌	✅	❌	❌	✅	✅	4
Spectral Clustering with Graph Neural Networks for Graph Pooling	❌	✅	✅	✅	❌	❌	❌	3
Spectral Frank-Wolfe Algorithm: Strict Complementarity and Linear Convergence	✅	❌	❌	❌	❌	❌	✅	2
Spectral Graph Matching and Regularized Quadratic Relaxations: Algorithm and Theory	✅	❌	✅	❌	❌	❌	✅	3
Spectral Subsampling MCMC for Stationary Time Series	❌	❌	✅	❌	❌	❌	✅	2
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks	✅	✅	✅	❌	❌	❌	✅	4
Spread Divergence	❌	❌	✅	❌	❌	❌	✅	2
Stabilizing Differentiable Architecture Search via Perturbation-based Regularization	✅	✅	✅	✅	✅	❌	✅	6
Stabilizing Transformers for Reinforcement Learning	❌	❌	✅	❌	❌	❌	✅	2
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes	✅	✅	✅	✅	✅	❌	✅	6
Statistically Efficient Off-Policy Policy Gradients	✅	❌	❌	❌	❌	❌	✅	2
Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization	✅	✅	✅	❌	❌	❌	✅	4
Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization	✅	❌	✅	❌	❌	❌	✅	3
Stochastic Differential Equations with Variational Wishart Diffusions	❌	✅	✅	✅	❌	❌	✅	4
Stochastic Flows and Geometric Optimization on the Orthogonal Group	✅	❌	✅	❌	❌	❌	✅	3
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization	✅	✅	✅	❌	❌	❌	✅	4
Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization	✅	❌	✅	❌	❌	❌	✅	3
Stochastic Gradient and Langevin Processes	❌	❌	✅	✅	❌	❌	✅	3
Stochastic Hamiltonian Gradient Methods for Smooth Games	✅	❌	✅	❌	❌	❌	✅	3
Stochastic Latent Residual Video Prediction	❌	✅	✅	❌	❌	❌	❌	2
Stochastic Optimization for Non-convex Inf-Projection Problems	✅	❌	✅	❌	❌	❌	✅	3
Stochastic Optimization for Regularized Wasserstein Estimators	✅	❌	❌	❌	❌	❌	✅	2
Stochastic Regret Minimization in Extensive-Form Games	✅	❌	❌	❌	❌	❌	✅	2
Stochastic Subspace Cubic Newton Method	✅	❌	✅	❌	❌	❌	❌	2
Stochastic bandits with arm-dependent delays	✅	❌	❌	❌	❌	❌	✅	2
StochasticRank: Global Optimization of Scale-Free Discrete Functions	❌	✅	✅	✅	❌	❌	✅	4
Stochastically Dominant Distributional Reinforcement Learning	✅	❌	✅	❌	❌	❌	❌	2
Strategic Classification is Causal Modeling in Disguise	❌	❌	❌	❌	❌	❌	❌	0
Strategyproof Mean Estimation from Multiple-Choice Questions	❌	❌	❌	❌	❌	❌	❌	0
Streaming Coresets for Symmetric Tensor Factorization	✅	❌	❌	❌	❌	❌	✅	2
Streaming Submodular Maximization under a k-Set System Constraint	✅	❌	✅	❌	❌	❌	✅	3
Streaming k-Submodular Maximization under Noise subject to Size Constraint	✅	✅	✅	❌	❌	❌	✅	4
Strength from Weakness: Fast Learning Using Weak Supervision	✅	❌	✅	❌	❌	❌	❌	2
Striving for Simplicity and Performance in Off-Policy DRL: Output Normalization and Non-Uniform Sampling	✅	✅	✅	❌	❌	❌	✅	4
Stronger and Faster Wasserstein Adversarial Attacks	✅	✅	✅	✅	✅	❌	✅	6
Structural Language Models of Code	❌	✅	✅	✅	✅	❌	✅	5
Structure Adaptive Algorithms for Stochastic Bandits	✅	❌	❌	❌	❌	❌	❌	1
Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis	✅	❌	❌	❌	❌	❌	❌	1
Structured Policy Iteration for Linear Quadratic Regulator	✅	❌	✅	❌	✅	❌	✅	4
Structured Prediction with Partial Labelling through the Infimum Loss	❌	✅	✅	❌	❌	❌	❌	2
Student Specialization in Deep Rectified Networks With Finite Width and Input Dimension	❌	✅	✅	❌	❌	❌	✅	3
Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location	✅	❌	✅	✅	❌	❌	✅	4
Sub-Goal Trees a Framework for Goal-Based Reinforcement Learning	✅	✅	❌	❌	❌	❌	✅	3
Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data	✅	❌	✅	❌	❌	❌	✅	3
Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors	✅	❌	❌	❌	❌	❌	❌	1
Super-efficiency of automatic differentiation for functions defined as a minimum	❌	✅	❌	❌	❌	❌	❌	1
Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent	❌	❌	✅	❌	❌	❌	✅	2
Supervised Quantile Normalization for Low Rank Matrix Factorization	✅	❌	✅	❌	❌	❌	✅	3
Supervised learning: no loss no cry	✅	❌	✅	❌	❌	❌	✅	3
Symbolic Network: Generalized Neural Policies for Relational MDPs	❌	✅	✅	❌	✅	❌	✅	4
T-Basis: a Compact Representation for Neural Networks	❌	✅	✅	✅	❌	❌	✅	4
T-GD: Transferable GAN-generated Images Detection Framework	✅	✅	✅	✅	❌	❌	✅	5
Tails of Lipschitz Triangular Flows	❌	❌	✅	✅	✅	❌	✅	4
Task Understanding from Confusing Multi-task Data	❌	❌	✅	❌	❌	❌	✅	2
Task-Oriented Active Perception and Planning in Environments with Partially Known Semantics	✅	✅	❌	❌	❌	❌	✅	3
TaskNorm: Rethinking Batch Normalization for Meta-Learning	❌	✅	✅	❌	❌	❌	❌	2
Taylor Expansion Policy Optimization	✅	❌	✅	❌	❌	❌	✅	3
Teaching with Limited Information on the Learner’s Behaviour	✅	❌	✅	❌	❌	❌	❌	2
Temporal Logic Point Processes	❌	❌	✅	❌	❌	❌	✅	2
Temporal Phenotyping using Deep Predictive Clustering of Disease Progression	✅	✅	✅	✅	❌	❌	✅	5
Tensor denoising and completion based on ordinal observations	✅	✅	✅	✅	❌	❌	✅	5
Test-Time Training with Self-Supervision for Generalization under Distribution Shifts	❌	✅	✅	❌	❌	❌	✅	3
The Boomerang Sampler	❌	✅	❌	❌	❌	❌	✅	2
The Buckley-Osthus model and the block preferential attachment model: statistical analysis and application	❌	❌	✅	❌	❌	❌	✅	2
The Complexity of Finding Stationary Points with Stochastic Gradient Descent	❌	❌	❌	❌	❌	❌	❌	0
The Cost-free Nature of Optimally Tuning Tikhonov Regularizers and Other Ordered Smoothers	❌	✅	❌	❌	❌	❌	✅	2
The Differentiable Cross-Entropy Method	✅	✅	✅	❌	❌	❌	✅	4
The Effect of Natural Distribution Shift on Question Answering Models	❌	❌	✅	✅	❌	❌	❌	2
The FAST Algorithm for Submodular Maximization	✅	✅	✅	❌	✅	❌	✅	5
The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent	❌	❌	✅	✅	❌	❌	✅	3
The Implicit Regularization of Stochastic Gradient Flow for Least Squares	❌	❌	❌	❌	❌	❌	✅	1
The Implicit and Explicit Regularization Effects of Dropout	✅	✅	✅	✅	❌	❌	✅	5
The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation	❌	❌	❌	❌	❌	❌	✅	1
The Many Shapley Values for Model Explanation	✅	❌	✅	❌	❌	❌	❌	2
The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization	❌	❌	❌	❌	❌	❌	✅	1
The Non-IID Data Quagmire of Decentralized Machine Learning	✅	✅	✅	✅	❌	❌	✅	5
The Performance Analysis of Generalized Margin Maximizers on Separable Data	❌	❌	❌	❌	❌	❌	✅	1
The Role of Regularization in Classification of High-dimensional Noisy Gaussian Mixture	❌	❌	❌	❌	❌	❌	✅	1
The Sample Complexity of Best-$k$ Items Selection from Pairwise Comparisons	✅	✅	✅	❌	❌	❌	✅	4
The Shapley Taylor Interaction Index	❌	❌	✅	❌	❌	❌	✅	2
The Tree Ensemble Layer: Differentiability meets Conditional Computation	✅	✅	✅	✅	❌	✅	✅	6
The Usual Suspects? Reassessing Blame for VAE Posterior Collapse	❌	❌	✅	❌	❌	❌	✅	2
The continuous categorical: a novel simplex-valued exponential family	✅	✅	✅	❌	❌	❌	✅	4
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks	❌	✅	✅	✅	❌	❌	❌	3
Thompson Sampling Algorithms for Mean-Variance Bandits	✅	✅	✅	❌	❌	❌	✅	4
Thompson Sampling via Local Uncertainty	✅	✅	✅	❌	✅	✅	✅	6
Tight Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance	❌	❌	❌	❌	❌	❌	❌	0
Tightening Exploration in Upper Confidence Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders	❌	✅	✅	✅	❌	❌	❌	3
Time-Consistent Self-Supervision for Semi-Supervised Learning	✅	❌	✅	✅	❌	❌	✅	4
Time-aware Large Kernel Convolutions	❌	✅	✅	✅	✅	❌	✅	5
Too Relaxed to Be Fair	✅	✅	✅	✅	❌	❌	✅	5
Topic Modeling via Full Dependence Mixtures	✅	✅	✅	❌	❌	❌	✅	4
Topological Autoencoders	❌	✅	✅	✅	❌	❌	✅	4
Topologically Densified Distributions	❌	✅	✅	✅	❌	❌	✅	4
Towards Accurate Post-training Network Quantization via Bit-Split and Stitching	✅	✅	✅	✅	❌	❌	✅	5
Towards Adaptive Residual Network Training: A Neural-ODE Perspective	✅	✅	✅	✅	✅	❌	✅	6
Towards Non-Parametric Drift Detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD)	✅	✅	✅	❌	❌	❌	❌	3
Towards Understanding the Dynamics of the First-Order Adversaries	❌	❌	✅	❌	❌	❌	❌	1
Towards Understanding the Regularization of Adversarial Robustness on Neural Networks	❌	❌	✅	❌	❌	❌	❌	1
Towards a General Theory of Infinite-Width Limits of Neural Classifiers	❌	❌	✅	❌	❌	❌	✅	2
Train Big, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers	❌	❌	✅	✅	✅	❌	✅	4
Training Binary Neural Networks through Learning with Noisy Supervision	✅	❌	✅	✅	✅	❌	✅	5
Training Binary Neural Networks using the Bayesian Learning Rule	✅	✅	✅	✅	❌	❌	✅	5
Training Deep Energy-Based Models with f-Divergence Minimization	✅	✅	✅	✅	❌	❌	✅	5
Training Linear Neural Networks: Non-Local Convergence and Complexity Results	❌	❌	❌	❌	❌	❌	✅	1
Training Neural Networks for and by Interpolation	✅	✅	✅	✅	❌	❌	✅	5
TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics	❌	✅	✅	✅	❌	❌	✅	4
Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources	✅	✅	✅	✅	❌	❌	✅	5
Transformation of ReLU-based recurrent neural networks from discrete-time to continuous-time	❌	✅	✅	❌	❌	❌	✅	3
Transformer Hawkes Process	❌	✅	✅	❌	❌	❌	❌	2
Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention	✅	✅	✅	❌	✅	❌	✅	5
Transparency Promotion with Model-Agnostic Linear Competitors	✅	❌	✅	✅	❌	❌	✅	4
Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems	❌	❌	✅	❌	❌	❌	✅	2
Two Routes to Scalable Credit Assignment without Weight Symmetry	❌	✅	✅	✅	✅	❌	✅	5
Two Simple Ways to Learn Individual Fairness Metrics from Data	✅	✅	✅	❌	❌	❌	❌	3
Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels	❌	❌	✅	❌	❌	❌	✅	2
Uncertainty Estimation Using a Single Deep Deterministic Neural Network	❌	✅	✅	✅	✅	❌	✅	5
Uncertainty quantification for nonconvex tensor completion: Confidence intervals, heteroscedasticity and optimality	✅	❌	❌	❌	❌	❌	✅	2
Uncertainty-Aware Lookahead Factor Models for Quantitative Investing	❌	❌	❌	✅	✅	❌	✅	3
Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere	✅	✅	✅	✅	❌	❌	✅	5
Understanding Self-Training for Gradual Domain Adaptation	❌	✅	✅	✅	❌	❌	✅	4
Understanding and Mitigating the Tradeoff between Robustness and Accuracy	✅	✅	✅	❌	❌	❌	✅	4
Understanding and Stabilizing GANs’ Training Dynamics Using Control Theory	✅	❌	✅	❌	✅	❌	✅	4
Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling	❌	❌	❌	❌	❌	❌	❌	0
Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle	❌	❌	✅	❌	❌	❌	✅	2
Undirected Graphical Models as Approximate Posteriors	✅	✅	✅	❌	❌	❌	✅	4
UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training	✅	✅	✅	✅	✅	❌	✅	6
Uniform Convergence of Rank-weighted Learning	❌	✅	❌	❌	❌	❌	✅	2
Unique Properties of Flat Minima in Deep Networks	❌	❌	✅	❌	❌	❌	✅	2
Universal Average-Case Optimality of Polyak Momentum	❌	❌	❌	❌	❌	❌	❌	0
Universal Equivariant Multilayer Perceptrons	❌	❌	❌	❌	❌	❌	❌	0
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift	❌	❌	✅	✅	❌	❌	✅	3
Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks	✅	✅	✅	✅	❌	❌	✅	5
Unsupervised Discovery of Interpretable Directions in the GAN Latent Space	❌	✅	✅	❌	✅	❌	✅	4
Unsupervised Speech Decomposition via Triple Information Bottleneck	❌	✅	✅	❌	❌	❌	✅	3
Unsupervised Transfer Learning for Spatiotemporal Predictive Networks	❌	✅	✅	✅	✅	✅	✅	6
Up or Down? Adaptive Rounding for Post-Training Quantization	❌	❌	✅	✅	✅	❌	✅	4
Upper bounds for Model-Free Row-Sparse Principal Component Analysis	✅	❌	❌	❌	✅	✅	✅	4
VFlow: More Expressive Generative Flows with Variational Data Augmentation	❌	✅	✅	✅	✅	❌	✅	5
Variable Skipping for Autoregressive Range Density Estimation	❌	✅	✅	❌	❌	❌	✅	3
Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems	✅	❌	❌	❌	❌	❌	✅	2
Variance Reduction and Quasi-Newton for Particle-Based Variational Inference	✅	❌	✅	❌	✅	❌	✅	4
Variance Reduction in Stochastic Particle-Optimization Sampling	✅	❌	✅	❌	❌	❌	✅	3
Variational Autoencoders with Riemannian Brownian Motion Priors	❌	❌	✅	❌	❌	❌	✅	2
Variational Bayesian Quantization	✅	✅	✅	❌	❌	❌	✅	4
Variational Imitation Learning with Diverse-quality Demonstrations	✅	✅	✅	❌	❌	❌	✅	4
Variational Inference for Sequential Data with Future Likelihood Estimates	❌	❌	✅	❌	❌	❌	✅	2
Variational Label Enhancement	❌	❌	✅	✅	❌	❌	✅	3
Video Prediction via Example Guidance	✅	✅	✅	❌	❌	❌	✅	4
VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video Processing	✅	✅	✅	✅	✅	✅	✅	7
Visual Grounding of Learned Physical Models	❌	❌	❌	❌	❌	❌	✅	1
Voice Separation with an Unknown Number of Multiple Speakers	❌	✅	✅	✅	❌	❌	✅	4
WaveFlow: A Compact Flow-based Model for Raw Audio	❌	✅	✅	❌	✅	❌	✅	4
Weakly-Supervised Disentanglement Without Compromises	❌	❌	✅	❌	✅	❌	✅	3
What Can Learned Intrinsic Rewards Capture?	✅	❌	❌	❌	❌	❌	❌	1
What can I do here? A Theory of Affordances in Reinforcement Learning	✅	✅	❌	❌	❌	❌	✅	3
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?	✅	❌	❌	❌	❌	❌	❌	1
When Demands Evolve Larger and Noisier: Learning and Earning in a Growing Environment	✅	❌	❌	❌	❌	❌	❌	1
When Does Self-Supervision Help Graph Convolutional Networks?	❌	✅	✅	✅	❌	❌	✅	4
When Explanations Lie: Why Many Modified BP Attributions Fail	❌	✅	✅	❌	✅	❌	❌	3
When are Non-Parametric Methods Robust?	✅	❌	❌	❌	❌	❌	✅	2
When deep denoising meets iterative phase retrieval	✅	❌	❌	❌	❌	❌	✅	2
Which Tasks Should Be Learned Together in Multi-task Learning?	❌	✅	✅	✅	❌	❌	✅	4
Why Are Learned Indexes So Effective?	❌	✅	✅	❌	✅	❌	❌	3
Why bigger is not always better: on finite and infinite neural networks	❌	❌	✅	❌	❌	❌	✅	2
Word-Level Speech Recognition With a Letter to Word Encoder	❌	❌	✅	✅	❌	❌	✅	3
Working Memory Graphs	❌	✅	✅	❌	❌	❌	✅	3
XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalisation	❌	✅	✅	✅	❌	❌	✅	4
XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning	✅	✅	✅	✅	❌	❌	✅	5
Zeno++: Robust Fully Asynchronous SGD	✅	❌	✅	✅	❌	❌	✅	4
k-means++: few more steps yield constant approximation	✅	❌	❌	❌	❌	❌	❌	1
p-Norm Flow Diffusion for Local Graph Clustering	✅	✅	✅	❌	❌	❌	✅	4
“Other-Play” for Zero-Shot Coordination	❌	✅	✅	❌	❌	❌	✅	3