reproducibilityindex.ai

International Conference on Machine Learning (ICML) - 2020

Conference Proceedings:

Key: PC - Pseudocode, OSC - Open Source Code, OSD - Open Datasets, DS - Dataset Splits, HS - Hardware Specification, SD - Software Dependencies, ES - 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