reproducibilityindex.ai

International Conference on Machine Learning (ICML) - 2021

Conference Proceedings:

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

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