| $p$-Laplacian Based Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| (Non-)Convergence Results for Predictive Coding Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| 3D Infomax improves GNNs for Molecular Property Prediction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| 3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| 3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| A Closer Look at Smoothness in Domain Adversarial Training |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| A Completely Tuning-Free and Robust Approach to Sparse Precision Matrix Estimation |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| A Consistent and Efficient Evaluation Strategy for Attribution Methods |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| A Context-Integrated Transformer-Based Neural Network for Auction Design |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Convergence Theory for SVGD in the Population Limit under Talagrand’s Inequality T1 |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Convergent and Dimension-Independent Min-Max Optimization Algorithm |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A Difference Standardization Method for Mutual Transfer Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A Differential Entropy Estimator for Training Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| A Dynamical System Perspective for Lipschitz Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| A Framework for Learning to Request Rich and Contextually Useful Information from Humans |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Functional Information Perspective on Model Interpretation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| A General Recipe for Likelihood-free Bayesian Optimization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Hierarchical Bayesian Approach to Inverse Reinforcement Learning with Symbolic Reward Machines |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Hierarchical Transitive-Aligned Graph Kernel for Un-attributed Graphs |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| A Joint Exponential Mechanism For Differentially Private Top-$k$ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| A Langevin-like Sampler for Discrete Distributions |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| A Marriage between Adversarial Team Games and 2-player Games: Enabling Abstractions, No-regret Learning, and Subgame Solving |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Model-Agnostic Randomized Learning Framework based on Random Hypothesis Subspace Sampling |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| A Modern Self-Referential Weight Matrix That Learns to Modify Itself |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A Multi-objective / Multi-task Learning Framework Induced by Pareto Stationarity |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| A Natural Actor-Critic Framework for Zero-Sum Markov Games |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Neural Tangent Kernel Perspective of GANs |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| A New Perspective on the Effects of Spectrum in Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Parametric Class of Approximate Gradient Updates for Policy Optimization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Psychological Theory of Explainability |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| A Random Matrix Analysis of Data Stream Clustering: Coping With Limited Memory Resources |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| A Reduction from Linear Contextual Bandits Lower Bounds to Estimations Lower Bounds |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Regret Minimization Approach to Multi-Agent Control |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Resilient Distributed Boosting Algorithm |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Simple Guard for Learned Optimizers |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| A Simple Reward-free Approach to Constrained Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Simple Unified Framework for High Dimensional Bandit Problems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Simple yet Universal Strategy for Online Convex Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Single-Loop Gradient Descent and Perturbed Ascent Algorithm for Nonconvex Functional Constrained Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| A State-Distribution Matching Approach to Non-Episodic Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Statistical Manifold Framework for Point Cloud Data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Stochastic Multi-Rate Control Framework For Modeling Distributed Optimization Algorithms |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| A Study of Face Obfuscation in ImageNet |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A Study on the Ramanujan Graph Property of Winning Lottery Tickets |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Temporal-Difference Approach to Policy Gradient Estimation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Theoretical Comparison of Graph Neural Network Extensions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Tighter Analysis of Spectral Clustering, and Beyond |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| A Unified View on PAC-Bayes Bounds for Meta-Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A data-driven approach for learning to control computers |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A deep convolutional neural network that is invariant to time rescaling |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A new similarity measure for covariate shift with applications to nonparametric regression |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A query-optimal algorithm for finding counterfactuals |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A$^3$T: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and Editing |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| AGNAS: Attention-Guided Micro and Macro-Architecture Search |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| ASAP.SGD: Instance-based Adaptiveness to Staleness in Asynchronous SGD |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Accelerated Federated Learning with Decoupled Adaptive Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Accelerated Gradient Methods for Geodesically Convex Optimization: Tractable Algorithms and Convergence Analysis |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Accelerated, Optimal and Parallel: Some results on model-based stochastic optimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Accelerating Shapley Explanation via Contributive Cooperator Selection |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Accurate Quantization of Measures via Interacting Particle-based Optimization |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Achieving Fairness at No Utility Cost via Data Reweighing with Influence |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Achieving Minimax Rates in Pool-Based Batch Active Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Action-Sufficient State Representation Learning for Control with Structural Constraints |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Active Multi-Task Representation Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Active Nearest Neighbor Regression Through Delaunay Refinement |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Active Sampling for Min-Max Fairness |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Active fairness auditing |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| ActiveHedge: Hedge meets Active Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Actor-Critic based Improper Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| AdaGrad Avoids Saddle Points |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Adapting k-means Algorithms for Outliers |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adapting the Linearised Laplace Model Evidence for Modern Deep Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Adapting to Mixing Time in Stochastic Optimization with Markovian Data |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Adaptive Accelerated (Extra-)Gradient Methods with Variance Reduction |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Adaptive Conformal Predictions for Time Series |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Adaptive Data Analysis with Correlated Observations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Adaptive Gaussian Process Change Point Detection |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Adaptive Model Design for Markov Decision Process |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Adaptive Random Walk Gradient Descent for Decentralized Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adaptive Second Order Coresets for Data-efficient Machine Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Additive Gaussian Processes Revisited |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Adversarial Attack and Defense for Non-Parametric Two-Sample Tests |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Adversarial Attacks on Gaussian Process Bandits |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Adversarial Masking for Self-Supervised Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Adversarial Robustness against Multiple and Single $l_p$-Threat Models via Quick Fine-Tuning of Robust Classifiers |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Adversarial Vulnerability of Randomized Ensembles |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Adversarially Trained Actor Critic for Offline Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Adversarially trained neural representations are already as robust as biological neural representations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Agnostic Learnability of Halfspaces via Logistic Loss |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Algorithms for the Communication of Samples |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| An Analytical Update Rule for General Policy Optimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| An Equivalence Between Data Poisoning and Byzantine Gradient Attacks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| An Exact Symbolic Reduction of Linear Smart Predict+Optimize to Mixed Integer Linear Programming |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| An Initial Alignment between Neural Network and Target is Needed for Gradient Descent to Learn |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| An Intriguing Property of Geophysics Inversion |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Analysis of Stochastic Processes through Replay Buffers |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Analyzing and Mitigating Interference in Neural Architecture Search |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Anarchic Federated Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Antibody-Antigen Docking and Design via Hierarchical Structure Refinement |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Anticorrelated Noise Injection for Improved Generalization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| AnyMorph: Learning Transferable Polices By Inferring Agent Morphology |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Anytime Information Cascade Popularity Prediction via Self-Exciting Processes |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
5 |
| Approximate Bayesian Computation with Domain Expert in the Loop |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Approximate Frank-Wolfe Algorithms over Graph-structured Support Sets |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Approximately Equivariant Networks for Imperfectly Symmetric Dynamics |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Architecture Agnostic Federated Learning for Neural Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Asking for Knowledge (AFK): Training RL Agents to Query External Knowledge Using Language |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Asymptotically-Optimal Gaussian Bandits with Side Observations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Attentional Meta-learners for Few-shot Polythetic Classification |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Augment with Care: Contrastive Learning for Combinatorial Problems |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| AutoIP: A United Framework to Integrate Physics into Gaussian Processes |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| AutoSNN: Towards Energy-Efficient Spiking Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Auxiliary Learning with Joint Task and Data Scheduling |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| BAMDT: Bayesian Additive Semi-Multivariate Decision Trees for Nonparametric Regression |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| BabelTower: Learning to Auto-parallelized Program Translation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Balancing Discriminability and Transferability for Source-Free Domain Adaptation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Balancing Sample Efficiency and Suboptimality in Inverse Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Batch Greenkhorn Algorithm for Entropic-Regularized Multimarginal Optimal Transport: Linear Rate of Convergence and Iteration Complexity |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Batched Dueling Bandits |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Bayesian Continuous-Time Tucker Decomposition |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Bayesian Deep Embedding Topic Meta-Learner |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Bayesian Imitation Learning for End-to-End Mobile Manipulation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bayesian Model Selection, the Marginal Likelihood, and Generalization |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Bayesian Nonparametric Learning for Point Processes with Spatial Homogeneity: A Spatial Analysis of NBA Shot Locations |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Bayesian Nonparametrics for Offline Skill Discovery |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Bayesian Optimization for Distributionally Robust Chance-constrained Problem |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Bayesian Optimization under Stochastic Delayed Feedback |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Be Like Water: Adaptive Floating Point for Machine Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Being Properly Improper |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Benchmarking and Analyzing Point Cloud Classification under Corruptions |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Biased Gradient Estimate with Drastic Variance Reduction for Meta Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Biological Sequence Design with GFlowNets |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Bit Prioritization in Variational Autoencoders via Progressive Coding |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Black-Box Tuning for Language-Model-as-a-Service |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Boosting Graph Structure Learning with Dummy Nodes |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Bounding Training Data Reconstruction in Private (Deep) Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Bounding the Width of Neural Networks via Coupled Initialization A Worst Case Analysis |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Branching Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Breaking the $\sqrtT$ Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Bregman Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Bregman Power k-Means for Clustering Exponential Family Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Bregman Proximal Langevin Monte Carlo via Bregman-Moreau Envelopes |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Building Robust Ensembles via Margin Boosting |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Burst-Dependent Plasticity and Dendritic Amplification Support Target-Based Learning and Hierarchical Imitation Learning |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| ButterflyFlow: Building Invertible Layers with Butterfly Matrices |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| C*-algebra Net: A New Approach Generalizing Neural Network Parameters to C*-algebra |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| C-MinHash: Improving Minwise Hashing with Circulant Permutation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| CITRIS: Causal Identifiability from Temporal Intervened Sequences |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| COAT: Measuring Object Compositionality in Emergent Representations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| COLA: Consistent Learning with Opponent-Learning Awareness |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Calibrated Learning to Defer with One-vs-All Classifiers |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Calibrated and Sharp Uncertainties in Deep Learning via Density Estimation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Cascaded Gaps: Towards Logarithmic Regret for Risk-Sensitive Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Causal Conceptions of Fairness and their Consequences |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Causal Dynamics Learning for Task-Independent State Abstraction |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Causal Imitation Learning under Temporally Correlated Noise |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Causal Inference Through the Structural Causal Marginal Problem |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Causal Transformer for Estimating Counterfactual Outcomes |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Causal structure-based root cause analysis of outliers |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Centroid Approximation for Bootstrap: Improving Particle Quality at Inference |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| CerDEQ: Certifiable Deep Equilibrium Model |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Certified Adversarial Robustness Under the Bounded Support Set |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Certified Neural Network Watermarks with Randomized Smoothing |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Certified Robustness Against Natural Language Attacks by Causal Intervention |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Certifying Out-of-Domain Generalization for Blackbox Functions |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Channel Importance Matters in Few-Shot Image Classification |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Characterizing and Overcoming the Greedy Nature of Learning in Multi-modal Deep Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Choosing Answers in Epsilon-Best-Answer Identification for Linear Bandits |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
4 |
| Class-Imbalanced Semi-Supervised Learning with Adaptive Thresholding |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Closed-Form Diffeomorphic Transformations for Time Series Alignment |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Co-training Improves Prompt-based Learning for Large Language Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Coin Flipping Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Collaboration of Experts: Achieving 80% Top-1 Accuracy on ImageNet with 100M FLOPs |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Combining Diverse Feature Priors |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Communicating via Markov Decision Processes |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Communication-Efficient Adaptive Federated Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Communication-efficient Distributed Learning for Large Batch Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Composing Partial Differential Equations with Physics-Aware Neural Networks |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Conditional GANs with Auxiliary Discriminative Classifier |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Confidence Score for Source-Free Unsupervised Domain Adaptation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Conformal Prediction Sets with Limited False Positives |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Congested Bandits: Optimal Routing via Short-term Resets |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Consensus Multiplicative Weights Update: Learning to Learn using Projector-based Game Signatures |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Consistent Polyhedral Surrogates for Top-k Classification and Variants |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Constants Matter: The Performance Gains of Active Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Constrained Discrete Black-Box Optimization using Mixed-Integer Programming |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Constrained Offline Policy Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Constrained Optimization with Dynamic Bound-scaling for Effective NLP Backdoor Defense |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Constrained Variational Policy Optimization for Safe Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Constraint-based graph network simulator |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Content Addressable Memory Without Catastrophic Forgetting by Heteroassociation with a Fixed Scaffold |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Context-Aware Drift Detection |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Contextual Bandits with Large Action Spaces: Made Practical |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Contextual Information-Directed Sampling |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Continual Learning via Sequential Function-Space Variational Inference |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Continual Learning with Guarantees via Weight Interval Constraints |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Continual Repeated Annealed Flow Transport Monte Carlo |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Continuous Control with Action Quantization from Demonstrations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Continuous-Time Analysis of Accelerated Gradient Methods via Conservation Laws in Dilated Coordinate Systems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Contrastive Learning with Boosted Memorization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Controlling Conditional Language Models without Catastrophic Forgetting |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Convergence Rates of Non-Convex Stochastic Gradient Descent Under a Generic Lojasiewicz Condition and Local Smoothness |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Convergence and Recovery Guarantees of the K-Subspaces Method for Subspace Clustering |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Convergence of Invariant Graph Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Convergence of Policy Gradient for Entropy Regularized MDPs with Neural Network Approximation in the Mean-Field Regime |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Convergence of Uncertainty Sampling for Active Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Convolutional and Residual Networks Provably Contain Lottery Tickets |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Cooperative Online Learning in Stochastic and Adversarial MDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Coordinated Double Machine Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Correct-N-Contrast: a Contrastive Approach for Improving Robustness to Spurious Correlations |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Correlated Quantization for Distributed Mean Estimation and Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Correlation Clustering via Strong Triadic Closure Labeling: Fast Approximation Algorithms and Practical Lower Bounds |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Counterfactual Prediction for Outcome-Oriented Treatments |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Counterfactual Transportability: A Formal Approach |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Cross-Space Active Learning on Graph Convolutional Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Curriculum Reinforcement Learning via Constrained Optimal Transport |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| Cycle Representation Learning for Inductive Relation Prediction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| DAVINZ: Data Valuation using Deep Neural Networks at Initialization |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| DNA: Domain Generalization with Diversified Neural Averaging |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| DNNR: Differential Nearest Neighbors Regression |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| DRAGONN: Distributed Randomized Approximate Gradients of Neural Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| DRIBO: Robust Deep Reinforcement Learning via Multi-View Information Bottleneck |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Data Augmentation as Feature Manipulation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP) |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Data Scaling Laws in NMT: The Effect of Noise and Architecture |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Data-Efficient Double-Win Lottery Tickets from Robust Pre-training |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Data-SUITE: Data-centric identification of in-distribution incongruous examples |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Datamodels: Understanding Predictions with Data and Data with Predictions |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Dataset Condensation via Efficient Synthetic-Data Parameterization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Dataset Condensation with Contrastive Signals |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| De novo mass spectrometry peptide sequencing with a transformer model |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Debiaser Beware: Pitfalls of Centering Regularized Transport Maps |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Decentralized Online Convex Optimization in Networked Systems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Deciphering Lasso-based Classification Through a Large Dimensional Analysis of the Iterative Soft-Thresholding Algorithm |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Decision-Focused Learning: Through the Lens of Learning to Rank |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Decomposing Temporal High-Order Interactions via Latent ODEs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deconfounded Value Decomposition for Multi-Agent Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deduplicating Training Data Mitigates Privacy Risks in Language Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Deep Causal Metric Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Deep Hierarchy in Bandits |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep Network Approximation in Terms of Intrinsic Parameters |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Deep Networks on Toroids: Removing Symmetries Reveals the Structure of Flat Regions in the Landscape Geometry |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Deep Probability Estimation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Deep Reference Priors: What is the best way to pretrain a model? |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep Squared Euclidean Approximation to the Levenshtein Distance for DNA Storage |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Deep Variational Graph Convolutional Recurrent Network for Multivariate Time Series Anomaly Detection |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Deep and Flexible Graph Neural Architecture Search |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Deep equilibrium networks are sensitive to initialization statistics |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Deep symbolic regression for recurrence prediction |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Delay-Adaptive Step-sizes for Asynchronous Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Delayed Reinforcement Learning by Imitation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Deletion Robust Submodular Maximization over Matroids |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Demystifying the Adversarial Robustness of Random Transformation Defenses |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Denoised MDPs: Learning World Models Better Than the World Itself |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Describing Differences between Text Distributions with Natural Language |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Detached Error Feedback for Distributed SGD with Random Sparsification |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Detecting Corrupted Labels Without Training a Model to Predict |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Dialog Inpainting: Turning Documents into Dialogs |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Difference Advantage Estimation for Multi-Agent Policy Gradients |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Differentiable Top-k Classification Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Differentially Private Approximate Quantiles |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Differentially Private Community Detection for Stochastic Block Models |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Differentially Private Coordinate Descent for Composite Empirical Risk Minimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Differentially Private Maximal Information Coefficients |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Diffusion Models for Adversarial Purification |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Diffusion bridges vector quantized variational autoencoders |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Dimension-free Complexity Bounds for High-order Nonconvex Finite-sum Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Direct Behavior Specification via Constrained Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Directed Acyclic Transformer for Non-Autoregressive Machine Translation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Discrete Probabilistic Inverse Optimal Transport |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Discrete Tree Flows via Tree-Structured Permutations |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Disentangling Disease-related Representation from Obscure for Disease Prediction |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Distinguishing rule and exemplar-based generalization in learning systems |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Distribution Regression with Sliced Wasserstein Kernels |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Distributionally Robust $Q$-Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Distributionally-Aware Kernelized Bandit Problems for Risk Aversion |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
5 |
| Divergence-Regularized Multi-Agent Actor-Critic |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Diversified Adversarial Attacks based on Conjugate Gradient Method |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Do Differentiable Simulators Give Better Policy Gradients? |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Do More Negative Samples Necessarily Hurt In Contrastive Learning? |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Does the Data Induce Capacity Control in Deep Learning? |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Domain Adaptation for Time Series Forecasting via Attention Sharing |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Double Sampling Randomized Smoothing |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Dual Decomposition of Convex Optimization Layers for Consistent Attention in Medical Images |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Dual Perspective of Label-Specific Feature Learning for Multi-Label Classification |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| DynaMixer: A Vision MLP Architecture with Dynamic Mixing |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Dynamic Regret of Online Markov Decision Processes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Dynamic Topic Models for Temporal Document Networks |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Easy Variational Inference for Categorical Models via an Independent Binary Approximation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Efficient Approximate Inference for Stationary Kernel on Frequency Domain |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Efficient Computation of Higher-Order Subgraph Attribution via Message Passing |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Efficient Distributionally Robust Bayesian Optimization with Worst-case Sensitivity |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Efficient Learning for AlphaZero via Path Consistency |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Efficient Learning of CNNs using Patch Based Features |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Efficient Low Rank Convex Bounds for Pairwise Discrete Graphical Models |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Efficient Online ML API Selection for Multi-Label Classification Tasks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Efficient PAC Learning from the Crowd with Pairwise Comparisons |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Representation Learning via Adaptive Context Pooling |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Efficient Test-Time Model Adaptation without Forgetting |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Efficient Variance Reduction for Meta-learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficiently Learning the Topology and Behavior of a Networked Dynamical System Via Active Queries |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| End-to-End Balancing for Causal Continuous Treatment-Effect Estimation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Entropic Causal Inference: Graph Identifiability |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Entropic Gromov-Wasserstein between Gaussian Distributions |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| EqR: Equivariant Representations for Data-Efficient Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Equivalence Analysis between Counterfactual Regret Minimization and Online Mirror Descent |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Equivariance versus Augmentation for Spherical Images |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Equivariant Diffusion for Molecule Generation in 3D |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Equivariant Priors for compressed sensing with unknown orientation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Equivariant Quantum Graph Circuits |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Estimating and Penalizing Induced Preference Shifts in Recommender Systems |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
5 |
| Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models |
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5 |
| Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing |
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1 |
| Evaluating the Adversarial Robustness of Adaptive Test-time Defenses |
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4 |
| Evolving Curricula with Regret-Based Environment Design |
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❌ |
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6 |
| Exact Learning of Preference Structure: Single-peaked Preferences and Beyond |
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1 |
| Exact Optimal Accelerated Complexity for Fixed-Point Iterations |
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1 |
| Examining Scaling and Transfer of Language Model Architectures for Machine Translation |
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3 |
| Exploiting Independent Instruments: Identification and Distribution Generalization |
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4 |
| Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups |
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5 |
| Exploring and Exploiting Hubness Priors for High-Quality GAN Latent Sampling |
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5 |
| Exploring the Gap between Collapsed & Whitened Features in Self-Supervised Learning |
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4 |
| Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control |
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4 |
| Extended Unconstrained Features Model for Exploring Deep Neural Collapse |
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2 |
| Extracting Latent State Representations with Linear Dynamics from Rich Observations |
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2 |
| FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting |
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6 |
| FITNESS: (Fine Tune on New and Similar Samples) to detect anomalies in streams with drift and outliers |
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3 |
| FOCUS: Familiar Objects in Common and Uncommon Settings |
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3 |
| Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data |
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2 |
| Fair Generalized Linear Models with a Convex Penalty |
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4 |
| Fair Representation Learning through Implicit Path Alignment |
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4 |
| Fair and Fast k-Center Clustering for Data Summarization |
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5 |
| Fairness Interventions as (Dis)Incentives for Strategic Manipulation |
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❌ |
✅ |
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2 |
| Fairness with Adaptive Weights |
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❌ |
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3 |
| Fast Aquatic Swimmer Optimization with Differentiable Projective Dynamics and Neural Network Hydrodynamic Models |
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1 |
| Fast Composite Optimization and Statistical Recovery in Federated Learning |
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3 |
| Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions |
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7 |
| Fast Finite Width Neural Tangent Kernel |
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5 |
| Fast Lossless Neural Compression with Integer-Only Discrete Flows |
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6 |
| Fast Population-Based Reinforcement Learning on a Single Machine |
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5 |
| Fast Provably Robust Decision Trees and Boosting |
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5 |
| Fast Relative Entropy Coding with A* coding |
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4 |
| Fast and Provable Nonconvex Tensor RPCA |
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5 |
| Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack |
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6 |
| Fast rates for noisy interpolation require rethinking the effect of inductive bias |
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3 |
| Fast-Rate PAC-Bayesian Generalization Bounds for Meta-Learning |
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❌ |
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3 |
| Faster Algorithms for Learning Convex Functions |
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7 |
| Faster Fundamental Graph Algorithms via Learned Predictions |
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3 |
| Faster Privacy Accounting via Evolving Discretization |
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2 |
| Fat–Tailed Variational Inference with Anisotropic Tail Adaptive Flows |
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❌ |
✅ |
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5 |
| Feature Learning and Signal Propagation in Deep Neural Networks |
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4 |
| Feature Space Particle Inference for Neural Network Ensembles |
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5 |
| Feature and Parameter Selection in Stochastic Linear Bandits |
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3 |
| Feature selection using e-values |
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6 |
| FedNL: Making Newton-Type Methods Applicable to Federated Learning |
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3 |
| FedNest: Federated Bilevel, Minimax, and Compositional Optimization |
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5 |
| FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning |
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4 |
| FedScale: Benchmarking Model and System Performance of Federated Learning at Scale |
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7 |
| Federated Learning with Label Distribution Skew via Logits Calibration |
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4 |
| Federated Learning with Partial Model Personalization |
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5 |
| Federated Learning with Positive and Unlabeled Data |
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3 |
| Federated Minimax Optimization: Improved Convergence Analyses and Algorithms |
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5 |
| Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling |
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1 |
| Fenrir: Physics-Enhanced Regression for Initial Value Problems |
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3 |
| Fictitious Play and Best-Response Dynamics in Identical Interest and Zero-Sum Stochastic Games |
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1 |
| Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming |
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3 |
| Finding Global Homophily in Graph Neural Networks When Meeting Heterophily |
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6 |
| Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks |
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7 |
| Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications |
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5 |
| First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach |
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1 |
| Fisher SAM: Information Geometry and Sharpness Aware Minimisation |
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4 |
| Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification |
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6 |
| Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization |
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6 |
| Flashlight: Enabling Innovation in Tools for Machine Learning |
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7 |
| Flow-Guided Sparse Transformer for Video Deblurring |
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4 |
| Flow-based Recurrent Belief State Learning for POMDPs |
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3 |
| Flowformer: Linearizing Transformers with Conservation Flows |
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5 |
| Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension |
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❌ |
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1 |
| For Learning in Symmetric Teams, Local Optima are Global Nash Equilibria |
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4 |
| Forget-free Continual Learning with Winning Subnetworks |
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6 |
| Forward Operator Estimation in Generative Models with Kernel Transfer Operators |
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5 |
| Fourier Learning with Cyclical Data |
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❌ |
❌ |
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4 |
| Framework for Evaluating Faithfulness of Local Explanations |
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4 |
| FriendlyCore: Practical Differentially Private Aggregation |
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4 |
| From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses |
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3 |
| From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model |
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5 |
| From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers |
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6 |
| From data to functa: Your data point is a function and you can treat it like one |
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3 |
| Frustratingly Easy Transferability Estimation |
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5 |
| Fully-Connected Network on Noncompact Symmetric Space and Ridgelet Transform based on Helgason-Fourier Analysis |
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❌ |
❌ |
❌ |
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0 |
| Function-space Inference with Sparse Implicit Processes |
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5 |
| Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions |
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4 |
| Functional Output Regression with Infimal Convolution: Exploring the Huber and $ε$-insensitive Losses |
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4 |
| G$^2$CN: Graph Gaussian Convolution Networks with Concentrated Graph Filters |
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4 |
| G-Mixup: Graph Data Augmentation for Graph Classification |
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5 |
| GACT: Activation Compressed Training for Generic Network Architectures |
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5 |
| GALAXY: Graph-based Active Learning at the Extreme |
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4 |
| GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models |
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5 |
| GLaM: Efficient Scaling of Language Models with Mixture-of-Experts |
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4 |
| GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks |
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❌ |
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6 |
| GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing |
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3 |
| Gating Dropout: Communication-efficient Regularization for Sparsely Activated Transformers |
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5 |
| Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification |
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5 |
| Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications |
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4 |
| GenLabel: Mixup Relabeling using Generative Models |
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6 |
| General-purpose, long-context autoregressive modeling with Perceiver AR |
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5 |
| Generalised Policy Improvement with Geometric Policy Composition |
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❌ |
❌ |
❌ |
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2 |
| Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers |
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4 |
| Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling |
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2 |
| Generalization and Robustness Implications in Object-Centric Learning |
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5 |
| Generalized Beliefs for Cooperative AI |
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4 |
| Generalized Data Distribution Iteration |
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4 |
| Generalized Federated Learning via Sharpness Aware Minimization |
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5 |
| Generalized Leverage Scores: Geometric Interpretation and Applications |
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3 |
| Generalized Results for the Existence and Consistency of the MLE in the Bradley-Terry-Luce Model |
❌ |
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❌ |
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2 |
| Generalized Strategic Classification and the Case of Aligned Incentives |
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✅ |
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❌ |
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4 |
| Generalizing Gaussian Smoothing for Random Search |
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3 |
| Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder |
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4 |
| Generalizing to New Physical Systems via Context-Informed Dynamics Model |
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6 |
| Generating 3D Molecules for Target Protein Binding |
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2 |
| Generating Distributional Adversarial Examples to Evade Statistical Detectors |
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❌ |
✅ |
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❌ |
❌ |
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2 |
| Generative Coarse-Graining of Molecular Conformations |
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4 |
| Generative Cooperative Networks for Natural Language Generation |
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4 |
| Generative Flow Networks for Discrete Probabilistic Modeling |
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5 |
| Generative Modeling for Multi-task Visual Learning |
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5 |
| Generative Trees: Adversarial and Copycat |
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6 |
| Generic Coreset for Scalable Learning of Monotonic Kernels: Logistic Regression, Sigmoid and more |
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❌ |
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4 |
| Geometric Multimodal Contrastive Representation Learning |
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❌ |
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3 |
| Global Optimization Networks |
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❌ |
❌ |
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4 |
| Global Optimization of K-Center Clustering |
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✅ |
❌ |
❌ |
✅ |
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5 |
| Goal Misgeneralization in Deep Reinforcement Learning |
❌ |
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❌ |
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4 |
| Going Deeper into Permutation-Sensitive Graph Neural Networks |
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6 |
| Gradient Based Clustering |
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✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Gradient Descent on Neurons and its Link to Approximate Second-order Optimization |
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✅ |
❌ |
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❌ |
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4 |
| Gradient-Free Method for Heavily Constrained Nonconvex Optimization |
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❌ |
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4 |
| Graph Neural Architecture Search Under Distribution Shifts |
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5 |
| Graph-Coupled Oscillator Networks |
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5 |
| GraphFM: Improving Large-Scale GNN Training via Feature Momentum |
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✅ |
6 |
| Greedy based Value Representation for Optimal Coordination in Multi-agent Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Greedy when Sure and Conservative when Uncertain about the Opponents |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| H-Consistency Bounds for Surrogate Loss Minimizers |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Hardness and Algorithms for Robust and Sparse Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Hermite Polynomial Features for Private Data Generation |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Hessian-Free High-Resolution Nesterov Acceleration For Sampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models. |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| High Probability Guarantees for Nonconvex Stochastic Gradient Descent with Heavy Tails |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Hindering Adversarial Attacks with Implicit Neural Representations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| History Compression via Language Models in Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| HousE: Knowledge Graph Embedding with Householder Parameterization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| How Powerful are Spectral Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| How Tempering Fixes Data Augmentation in Bayesian Neural Networks |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| How to Leverage Unlabeled Data in Offline Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| How to Stay Curious while avoiding Noisy TVs using Aleatoric Uncertainty Estimation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| How to Steer Your Adversary: Targeted and Efficient Model Stealing Defenses with Gradient Redirection |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| How to Train Your Wide Neural Network Without Backprop: An Input-Weight Alignment Perspective |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| HyperImpute: Generalized Iterative Imputation with Automatic Model Selection |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| HyperPrompt: Prompt-based Task-Conditioning of Transformers |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| IDYNO: Learning Nonparametric DAGs from Interventional Dynamic Data |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Identifiability Conditions for Domain Adaptation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Identification of Linear Non-Gaussian Latent Hierarchical Structure |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Identity-Disentangled Adversarial Augmentation for Self-supervised Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Imitation Learning by Estimating Expertise of Demonstrators |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Implicit Bias of Linear Equivariant Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Implicit Bias of the Step Size in Linear Diagonal Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Implicit Regularization with Polynomial Growth in Deep Tensor Factorization |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Importance Weighted Kernel Bayes’ Rule |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Improve Single-Point Zeroth-Order Optimization Using High-Pass and Low-Pass Filters |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved No-Regret Algorithms for Stochastic Shortest Path with Linear MDP |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved Regret for Differentially Private Exploration in Linear MDP |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved StyleGAN-v2 based Inversion for Out-of-Distribution Images |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Improving Adversarial Robustness via Mutual Information Estimation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Improving Ensemble Distillation With Weight Averaging and Diversifying Perturbation |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Improving Language Models by Retrieving from Trillions of Tokens |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Improving Mini-batch Optimal Transport via Partial Transportation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Improving Out-of-Distribution Robustness via Selective Augmentation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Improving Policy Optimization with Generalist-Specialist Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improving Screening Processes via Calibrated Subset Selection |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Improving Task-free Continual Learning by Distributionally Robust Memory Evolution |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Improving Transformers with Probabilistic Attention Keys |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| In defense of dual-encoders for neural ranking |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Individual Preference Stability for Clustering |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Individual Reward Assisted Multi-Agent Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Inducing Causal Structure for Interpretable Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Inductive Biases and Variable Creation in Self-Attention Mechanisms |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
3 |
| Inductive Matrix Completion: No Bad Local Minima and a Fast Algorithm |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
4 |
| Inferring Cause and Effect in the Presence of Heteroscedastic Noise |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Influence-Augmented Local Simulators: a Scalable Solution for Fast Deep RL in Large Networked Systems |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
2 |
| Information Discrepancy in Strategic Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Informed Learning by Wide Neural Networks: Convergence, Generalization and Sampling Complexity |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Injecting Logical Constraints into Neural Networks via Straight-Through Estimators |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Input Dependent Sparse Gaussian Processes |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Instance Dependent Regret Analysis of Kernelized Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Instrumental Variable Regression with Confounder Balancing |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Interactive Correlation Clustering with Existential Cluster Constraints |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Interactive Inverse Reinforcement Learning for Cooperative Games |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| Interactively Learning Preference Constraints in Linear Bandits |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Interpretable Off-Policy Learning via Hyperbox Search |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Interventional Contrastive Learning with Meta Semantic Regularizer |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Intriguing Properties of Input-Dependent Randomized Smoothing |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Invariant Ancestry Search |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Inverse Contextual Bandits: Learning How Behavior Evolves over Time |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Investigating Generalization by Controlling Normalized Margin |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Investigating Why Contrastive Learning Benefits Robustness against Label Noise |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Iterative Double Sketching for Faster Least-Squares Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Iterative Hard Thresholding with Adaptive Regularization: Sparser Solutions Without Sacrificing Runtime |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| It’s Raw! Audio Generation with State-Space Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Kernel Methods for Radial Transformed Compositional Data with Many Zeros |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Kernelized Multiplicative Weights for 0/1-Polyhedral Games: Bridging the Gap Between Learning in Extensive-Form and Normal-Form Games |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Kill a Bird with Two Stones: Closing the Convergence Gaps in Non-Strongly Convex Optimization by Directly Accelerated SVRG with Double Compensation and Snapshots |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Knowledge Base Question Answering by Case-based Reasoning over Subgraphs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| LCANets: Lateral Competition Improves Robustness Against Corruption and Attack |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| LIMO: Latent Inceptionism for Targeted Molecule Generation |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| LSB: Local Self-Balancing MCMC in Discrete Spaces |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Label Ranking through Nonparametric Regression |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Label-Descriptive Patterns and Their Application to Characterizing Classification Errors |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Label-Free Explainability for Unsupervised Models |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Lagrangian Method for Q-Function Learning (with Applications to Machine Translation) |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Langevin Monte Carlo for Contextual Bandits |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Large Batch Experience Replay |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Large-Scale Graph Neural Architecture Search |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Latent Diffusion Energy-Based Model for Interpretable Text Modelling |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Latent Outlier Exposure for Anomaly Detection with Contaminated Data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Lazy Estimation of Variable Importance for Large Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Augmented Binary Search Trees |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Learning Bellman Complete Representations for Offline Policy Evaluation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Domain Adaptive Object Detection with Probabilistic Teacher |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Dynamics and Generalization in Deep Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Efficient and Robust Ordinary Differential Equations via Invertible Neural Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning General Halfspaces with Adversarial Label Noise via Online Gradient Descent |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Infinite-horizon Average-reward Markov Decision Process with Constraints |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Iterative Reasoning through Energy Minimization |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Mixtures of Linear Dynamical Systems |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Multiscale Transformer Models for Sequence Generation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Pseudometric-based Action Representations for Offline Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Stable Classifiers by Transferring Unstable Features |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Stochastic Shortest Path with Linear Function Approximation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Symmetric Embeddings for Equivariant World Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning fair representation with a parametric integral probability metric |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning from Counterfactual Links for Link Prediction |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Learning from Demonstration: Provably Efficient Adversarial Policy Imitation with Linear Function Approximation |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning from a Learning User for Optimal Recommendations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning inverse folding from millions of predicted structures |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning of Cluster-based Feature Importance for Electronic Health Record Time-series |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning to Cut by Looking Ahead: Cutting Plane Selection via Imitation Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Learning to Estimate and Refine Fluid Motion with Physical Dynamics |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning to Hash Robustly, Guaranteed |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning to Infer Structures of Network Games |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning to Separate Voices by Spatial Regions |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning to Solve PDE-constrained Inverse Problems with Graph Networks |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
3 |
| Learning-based Optimisation of Particle Accelerators Under Partial Observability Without Real-World Training |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Least Squares Estimation using Sketched Data with Heteroskedastic Errors |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Let Invariant Rationale Discovery Inspire Graph Contrastive Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Leverage Score Sampling for Tensor Product Matrices in Input Sparsity Time |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Lie Point Symmetry Data Augmentation for Neural PDE Solvers |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
2 |
| Linear Adversarial Concept Erasure |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Linear Bandit Algorithms with Sublinear Time Complexity |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Linear Complexity Randomized Self-attention Mechanism |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Local Augmentation for Graph Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Local Linear Convergence of Douglas-Rachford for Linear Programming: a Probabilistic Analysis |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Locally Sparse Neural Networks for Tabular Biomedical Data |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Loss Function Learning for Domain Generalization by Implicit Gradient |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Low-Complexity Deep Convolutional Neural Networks on Fully Homomorphic Encryption Using Multiplexed Parallel Convolutions |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Low-Precision Stochastic Gradient Langevin Dynamics |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| LyaNet: A Lyapunov Framework for Training Neural ODEs |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| MAML and ANIL Provably Learn Representations |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Making Linear MDPs Practical via Contrastive Representation Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Marginal Distribution Adaptation for Discrete Sets via Module-Oriented Divergence Minimization |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Marginal Tail-Adaptive Normalizing Flows |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Markov Chain Monte Carlo for Continuous-Time Switching Dynamical Systems |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Maslow’s Hammer in Catastrophic Forgetting: Node Re-Use vs. Node Activation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Massively Parallel $k$-Means Clustering for Perturbation Resilient Instances |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Matching Learned Causal Effects of Neural Networks with Domain Priors |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Matching Normalizing Flows and Probability Paths on Manifolds |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Matching Structure for Dual Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Maximum Likelihood Training for Score-based Diffusion ODEs by High Order Denoising Score Matching |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Meaningfully debugging model mistakes using conceptual counterfactual explanations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Measure Estimation in the Barycentric Coding Model |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Measuring Representational Robustness of Neural Networks Through Shared Invariances |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Measuring dissimilarity with diffeomorphism invariance |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| MemSR: Training Memory-efficient Lightweight Model for Image Super-Resolution |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Memory-Based Model Editing at Scale |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| MetAug: Contrastive Learning via Meta Feature Augmentation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Meta-Learning Hypothesis Spaces for Sequential Decision-making |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Metric-Fair Active Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Metric-Fair Classifier Derandomization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Minimax M-estimation under Adversarial Contamination |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Minimizing Control for Credit Assignment with Strong Feedback |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Minimum Cost Intervention Design for Causal Effect Identification |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Mirror Learning: A Unifying Framework of Policy Optimisation |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Mitigating Gender Bias in Face Recognition using the von Mises-Fisher Mixture Model |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Mitigating Neural Network Overconfidence with Logit Normalization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| ModLaNets: Learning Generalisable Dynamics via Modularity and Physical Inductive Bias |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably) |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Model Agnostic Sample Reweighting for Out-of-Distribution Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Model Selection in Batch Policy Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Model-Free Opponent Shaping |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Model-Value Inconsistency as a Signal for Epistemic Uncertainty |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models and Amortized Policy Search |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Modeling Adversarial Noise for Adversarial Training |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Modeling Irregular Time Series with Continuous Recurrent Units |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Modeling Strong and Human-Like Gameplay with KL-Regularized Search |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Modeling Structure with Undirected Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Modular Conformal Calibration |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Monarch: Expressive Structured Matrices for Efficient and Accurate Training |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| More Efficient Sampling for Tensor Decomposition With Worst-Case Guarantees |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| More Than a Toy: Random Matrix Models Predict How Real-World Neural Representations Generalize |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Multi Resolution Analysis (MRA) for Approximate Self-Attention |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multi-Level Branched Regularization for Federated Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Multi-Task Learning as a Bargaining Game |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Multi-scale Feature Learning Dynamics: Insights for Double Descent |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-slots Online Matching with High Entropy |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multiclass learning with margin: exponential rates with no bias-variance trade-off |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Multicoated Supermasks Enhance Hidden Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multirate Training of Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| N-Penetrate: Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| NOMU: Neural Optimization-based Model Uncertainty |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| NP-Match: When Neural Processes meet Semi-Supervised Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Near-Optimal Algorithms for Autonomous Exploration and Multi-Goal Stochastic Shortest Path |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Near-Optimal Learning of Extensive-Form Games with Imperfect Information |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Near-optimal rate of consistency for linear models with missing values |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Nearly Optimal Catoni’s M-estimator for Infinite Variance |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Nearly Optimal Policy Optimization with Stable at Any Time Guarantee |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Nested Bandits |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Nesterov Accelerated Shuffling Gradient Method for Convex Optimization |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Neural Fisher Discriminant Analysis: Optimal Neural Network Embeddings in Polynomial Time |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Implicit Dictionary Learning via Mixture-of-Expert Training |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Inverse Kinematic |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Neural Inverse Transform Sampler |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Neural Language Models are not Born Equal to Fit Brain Data, but Training Helps |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Neural Laplace: Learning diverse classes of differential equations in the Laplace domain |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Network Poisson Models for Behavioural and Neural Spike Train Data |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Network Pruning Denoises the Features and Makes Local Connectivity Emerge in Visual Tasks |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Tangent Kernel Analysis of Deep Narrow Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Tangent Kernel Beyond the Infinite-Width Limit: Effects of Depth and Initialization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Tangent Kernel Empowered Federated Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural-Symbolic Models for Logical Queries on Knowledge Graphs |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| NeuralEF: Deconstructing Kernels by Deep Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Neuro-Symbolic Hierarchical Rule Induction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural Radiance Fields |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neurocoder: General-Purpose Computation Using Stored Neural Programs |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Neuron Dependency Graphs: A Causal Abstraction of Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Neurotoxin: Durable Backdoors in Federated Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| No-Regret Learning in Partially-Informed Auctions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| No-Regret Learning in Time-Varying Zero-Sum Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Non-Vacuous Generalisation Bounds for Shallow Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Nonlinear Feature Diffusion on Hypergraphs |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Nonparametric Embeddings of Sparse High-Order Interaction Events |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Nonparametric Factor Trajectory Learning for Dynamic Tensor Decomposition |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Nonparametric Involutive Markov Chain Monte Carlo |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Nonparametric Sparse Tensor Factorization with Hierarchical Gamma Processes |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Not All Poisons are Created Equal: Robust Training against Data Poisoning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| NysADMM: faster composite convex optimization via low-rank approximation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Nyström Kernel Mean Embeddings |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Object Permanence Emerges in a Random Walk along Memory |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Off-Policy Evaluation for Large Action Spaces via Embeddings |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Off-Policy Reinforcement Learning with Delayed Rewards |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Offline Meta-Reinforcement Learning with Online Self-Supervision |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Offline RL Policies Should Be Trained to be Adaptive |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On Collective Robustness of Bagging Against Data Poisoning |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| On Convergence of Gradient Descent Ascent: A Tight Local Analysis |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Distribution Shift in Learning-based Bug Detectors |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On Finite-Sample Identifiability of Contrastive Learning-Based Nonlinear Independent Component Analysis |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Implicit Bias in Overparameterized Bilevel Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Last-Iterate Convergence Beyond Zero-Sum Games |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Learning Mixture of Linear Regressions in the Non-Realizable Setting |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On Measuring Causal Contributions via do-interventions |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On Non-local Convergence Analysis of Deep Linear Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On Numerical Integration in Neural Ordinary Differential Equations |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Transportation of Mini-batches: A Hierarchical Approach |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| On Well-posedness and Minimax Optimal Rates of Nonparametric Q-function Estimation in Off-policy Evaluation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Adversarial Robustness of Causal Algorithmic Recourse |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| On the Convergence of Inexact Predictor-Corrector Methods for Linear Programming |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Convergence of Local Stochastic Compositional Gradient Descent with Momentum |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On the Convergence of the Shapley Value in Parametric Bayesian Learning Games |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Difficulty of Defending Self-Supervised Learning against Model Extraction |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On the Effects of Artificial Data Modification |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On the Equivalence Between Temporal and Static Equivariant Graph Representations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On the Finite-Time Complexity and Practical Computation of Approximate Stationarity Concepts of Lipschitz Functions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Finite-Time Performance of the Knowledge Gradient Algorithm |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Generalization Analysis of Adversarial Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Impossibility of Learning to Cooperate with Adaptive Partner Strategies in Repeated Games |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Learning of Non-Autoregressive Transformers |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| On the Optimization Landscape of Neural Collapse under MSE Loss: Global Optimality with Unconstrained Features |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On the Practicality of Deterministic Epistemic Uncertainty |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| On the Robustness of CountSketch to Adaptive Inputs |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Role of Discount Factor in Offline Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Sample Complexity of Learning Infinite-horizon Discounted Linear Kernel MDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Statistical Benefits of Curriculum Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Surrogate Gap between Contrastive and Supervised Losses |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| One-Pass Algorithms for MAP Inference of Nonsymmetric Determinantal Point Processes |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| One-Pass Diversified Sampling with Application to Terabyte-Scale Genomic Sequence Streams |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Online Active Regression |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Online Algorithms with Multiple Predictions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Balanced Experimental Design |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Online Continual Learning through Mutual Information Maximization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Online Decision Transformer |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Online Learning and Pricing with Reusable Resources: Linear Bandits with Sub-Exponential Rewards |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Online Learning for Min Sum Set Cover and Pandora’s Box |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Learning with Knapsacks: the Best of Both Worlds |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Online and Consistent Correlation Clustering |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Only tails matter: Average-Case Universality and Robustness in the Convex Regime |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Open-Sampling: Exploring Out-of-Distribution data for Re-balancing Long-tailed datasets |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Optimal Algorithms for Mean Estimation under Local Differential Privacy |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal Algorithms for Stochastic Multi-Level Compositional Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Optimal Clustering with Noisy Queries via Multi-Armed Bandit |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal Estimation of Policy Gradient via Double Fitted Iteration |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimally Controllable Perceptual Lossy Compression |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Optimization-Induced Graph Implicit Nonlinear Diffusion |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Optimizing Sequential Experimental Design with Deep Reinforcement Learning |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Optimizing Tensor Network Contraction Using Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Order Constraints in Optimal Transport |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Out-of-Distribution Detection with Deep Nearest Neighbors |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Overcoming Oscillations in Quantization-Aware Training |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| PAC-Bayesian Bounds on Rate-Efficient Classifiers |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| PAC-Net: A Model Pruning Approach to Inductive Transfer Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| PDE-Based Optimal Strategy for Unconstrained Online Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| PDO-s3DCNNs: Partial Differential Operator Based Steerable 3D CNNs |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| PINs: Progressive Implicit Networks for Multi-Scale Neural Representations |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| POEM: Out-of-Distribution Detection with Posterior Sampling |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| POET: Training Neural Networks on Tiny Devices with Integrated Rematerialization and Paging |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Pairwise Conditional Gradients without Swap Steps and Sparser Kernel Herding |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Parametric Visual Program Induction with Function Modularization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Parsimonious Learning-Augmented Caching |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Partial Counterfactual Identification from Observational and Experimental Data |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Partial Label Learning via Label Influence Function |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Partial disentanglement for domain adaptation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Particle Transformer for Jet Tagging |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Path-Aware and Structure-Preserving Generation of Synthetically Accessible Molecules |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Path-Gradient Estimators for Continuous Normalizing Flows |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Penalizing Gradient Norm for Efficiently Improving Generalization in Deep Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Permutation Search of Tensor Network Structures via Local Sampling |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Personalized Federated Learning through Local Memorization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Personalized Federated Learning via Variational Bayesian Inference |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Phasic Self-Imitative Reduction for Sparse-Reward Goal-Conditioned Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Plan Your Target and Learn Your Skills: Transferable State-Only Imitation Learning via Decoupled Policy Optimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Planning with Diffusion for Flexible Behavior Synthesis |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| PoF: Post-Training of Feature Extractor for Improving Generalization |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Policy Gradient Method For Robust Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Popular decision tree algorithms are provably noise tolerant |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
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1 |
| Position Prediction as an Effective Pretraining Strategy |
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5 |
| Power-Law Escape Rate of SGD |
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| Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering |
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6 |
| Preconditioning for Scalable Gaussian Process Hyperparameter Optimization |
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6 |
| Predicting Out-of-Distribution Error with the Projection Norm |
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5 |
| Principal Component Flows |
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5 |
| Principled Knowledge Extrapolation with GANs |
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4 |
| Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt |
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5 |
| Privacy for Free: How does Dataset Condensation Help Privacy? |
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3 |
| Private Adaptive Optimization with Side information |
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5 |
| Private Streaming SCO in $\ell_p$ geometry with Applications in High Dimensional Online Decision Making |
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3 |
| Private frequency estimation via projective geometry |
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4 |
| Private optimization in the interpolation regime: faster rates and hardness results |
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1 |
| ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning |
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6 |
| Probabilistic Bilevel Coreset Selection |
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4 |
| Probabilistic ODE Solutions in Millions of Dimensions |
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3 |
| Probabilistically Robust Learning: Balancing Average and Worst-case Performance |
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6 |
| ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training |
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4 |
| Prompting Decision Transformer for Few-Shot Policy Generalization |
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3 |
| Prototype Based Classification from Hierarchy to Fairness |
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3 |
| Prototype-Anchored Learning for Learning with Imperfect Annotations |
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4 |
| Provable Acceleration of Heavy Ball beyond Quadratics for a Class of Polyak-Lojasiewicz Functions when the Non-Convexity is Averaged-Out |
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2 |
| Provable Domain Generalization via Invariant-Feature Subspace Recovery |
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5 |
| Provable Reinforcement Learning with a Short-Term Memory |
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1 |
| Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance |
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6 |
| Provably Adversarially Robust Nearest Prototype Classifiers |
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3 |
| Provably Efficient Offline Reinforcement Learning for Partially Observable Markov Decision Processes |
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1 |
| Proving Theorems using Incremental Learning and Hindsight Experience Replay |
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5 |
| ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! |
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5 |
| Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularization |
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4 |
| Proximal Exploration for Model-guided Protein Sequence Design |
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4 |
| Proximal and Federated Random Reshuffling |
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4 |
| Public Data-Assisted Mirror Descent for Private Model Training |
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5 |
| Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images |
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5 |
| QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning |
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5 |
| Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features |
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7 |
| Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding |
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3 |
| Quantifying and Learning Linear Symmetry-Based Disentanglement |
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5 |
| Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra |
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4 |
| Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization |
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| RECAPP: Crafting a More Efficient Catalyst for Convex Optimization |
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4 |
| REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer |
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| ROCK: Causal Inference Principles for Reasoning about Commonsense Causality |
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| RUMs from Head-to-Head Contests |
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5 |
| Random Forest Density Estimation |
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4 |
| Random Gegenbauer Features for Scalable Kernel Methods |
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3 |
| RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression |
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5 |
| Re-evaluating Word Mover’s Distance |
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5 |
| Reachability Constrained Reinforcement Learning |
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3 |
| Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series |
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5 |
| Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs |
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4 |
| Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks |
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| Refined Convergence Rates for Maximum Likelihood Estimation under Finite Mixture Models |
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4 |
| Region-Based Semantic Factorization in GANs |
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4 |
| Regret Bounds for Stochastic Shortest Path Problems with Linear Function Approximation |
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1 |
| Regret Minimization with Performative Feedback |
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1 |
| Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning |
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4 |
| Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency |
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1 |
| Reinforcement Learning with Action-Free Pre-Training from Videos |
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4 |
| Removing Batch Normalization Boosts Adversarial Training |
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5 |
| Representation Topology Divergence: A Method for Comparing Neural Network Representations. |
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4 |
| Residual-Based Sampling for Online Outlier-Robust PCA |
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2 |
| Resilient and Communication Efficient Learning for Heterogeneous Federated Systems |
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3 |
| Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the $O(ε^-7/4)$ Complexity |
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3 |
| Rethinking Attention-Model Explainability through Faithfulness Violation Test |
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2 |
| Rethinking Fano’s Inequality in Ensemble Learning |
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6 |
| Rethinking Graph Neural Networks for Anomaly Detection |
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5 |
| Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems |
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5 |
| Retrieval-Augmented Reinforcement Learning |
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3 |
| RetrievalGuard: Provably Robust 1-Nearest Neighbor Image Retrieval |
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| Retroformer: Pushing the Limits of End-to-end Retrosynthesis Transformer |
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6 |
| Reverse Engineering $\ell_p$ attacks: A block-sparse optimization approach with recovery guarantees |
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3 |
| Reverse Engineering the Neural Tangent Kernel |
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| Revisiting Consistency Regularization for Deep Partial Label Learning |
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4 |
| Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework |
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3 |
| Revisiting End-to-End Speech-to-Text Translation From Scratch |
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| Revisiting Label Smoothing and Knowledge Distillation Compatibility: What was Missing? |
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6 |
| Revisiting Online Submodular Minimization: Gap-Dependent Regret Bounds, Best of Both Worlds and Adversarial Robustness |
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1 |
| Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning |
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| Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization |
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6 |
| Revisiting the Effects of Stochasticity for Hamiltonian Samplers |
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3 |
| Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes |
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1 |
| Rich Feature Construction for the Optimization-Generalization Dilemma |
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5 |
| RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests |
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3 |
| Ripple Attention for Visual Perception with Sub-quadratic Complexity |
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5 |
| Risk-Averse No-Regret Learning in Online Convex Games |
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2 |
| Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data |
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4 |
| Robust Counterfactual Explanations for Tree-Based Ensembles |
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3 |
| Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum |
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5 |
| Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees |
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4 |
| Robust Group Synchronization via Quadratic Programming |
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4 |
| Robust Imitation Learning against Variations in Environment Dynamics |
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5 |
| Robust Kernel Density Estimation with Median-of-Means principle |
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3 |
| Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile |
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| Robust Models Are More Interpretable Because Attributions Look Normal |
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5 |
| Robust Multi-Objective Bayesian Optimization Under Input Noise |
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5 |
| Robust Policy Learning over Multiple Uncertainty Sets |
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4 |
| Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning |
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3 |
| Robust Task Representations for Offline Meta-Reinforcement Learning via Contrastive Learning |
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4 |
| Robust Training of Neural Networks Using Scale Invariant Architectures |
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3 |
| Robust Training under Label Noise by Over-parameterization |
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7 |
| Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation |
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3 |
| Robustness Implies Generalization via Data-Dependent Generalization Bounds |
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| Robustness Verification for Contrastive Learning |
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| Robustness and Accuracy Could Be Reconcilable by (Proper) Definition |
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4 |
| Robustness in Multi-Objective Submodular Optimization: a Quantile Approach |
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2 |
| Role-based Multiplex Network Embedding |
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3 |
| Rotting Infinitely Many-Armed Bandits |
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3 |
| SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation |
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5 |
| SDQ: Stochastic Differentiable Quantization with Mixed Precision |
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| SE(3) Equivariant Graph Neural Networks with Complete Local Frames |
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5 |
| SPDY: Accurate Pruning with Speedup Guarantees |
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7 |
| SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators |
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5 |
| SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization |
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| Safe Exploration for Efficient Policy Evaluation and Comparison |
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3 |
| Safe Learning in Tree-Form Sequential Decision Making: Handling Hard and Soft Constraints |
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3 |
| Sample Efficient Learning of Predictors that Complement Humans |
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5 |
| Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis |
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| Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost |
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1 |
| Sanity Simulations for Saliency Methods |
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5 |
| Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation |
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5 |
| Scalable Computation of Causal Bounds |
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1 |
| Scalable Deep Gaussian Markov Random Fields for General Graphs |
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4 |
| Scalable Deep Reinforcement Learning Algorithms for Mean Field Games |
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3 |
| Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation |
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4 |
| Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Scalable Spike-and-Slab |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scaling Out-of-Distribution Detection for Real-World Settings |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Scaling Structured Inference with Randomization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Scaling-up Diverse Orthogonal Convolutional Networks by a Paraunitary Framework |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Score-Guided Intermediate Level Optimization: Fast Langevin Mixing for Inverse Problems |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Searching for BurgerFormer with Micro-Meso-Macro Space Design |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Secure Distributed Training at Scale |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Secure Quantized Training for Deep Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Selective Network Linearization for Efficient Private Inference |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Selective Regression under Fairness Criteria |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Self-Organized Polynomial-Time Coordination Graphs |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Self-Supervised Models of Audio Effectively Explain Human Cortical Responses to Speech |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Self-Supervised Representation Learning via Latent Graph Prediction |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Self-conditioning Pre-Trained Language Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Self-supervised Models are Good Teaching Assistants for Vision Transformers |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Self-supervised learning with random-projection quantizer for speech recognition |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Selling Data To a Machine Learner: Pricing via Costly Signaling |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Sequential Covariate Shift Detection Using Classifier Two-Sample Tests |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Sequential and Parallel Constrained Max-value Entropy Search via Information Lower Bound |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Set Based Stochastic Subsampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Short-Term Plasticity Neurons Learning to Learn and Forget |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Showing Your Offline Reinforcement Learning Work: Online Evaluation Budget Matters |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Shuffle Private Linear Contextual Bandits |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Simple and near-optimal algorithms for hidden stratification and multi-group learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Simultaneous Graph Signal Clustering and Graph Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sketching Algorithms and Lower Bounds for Ridge Regression |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Smoothed Adaptive Weighting for Imbalanced Semi-Supervised Learning: Improve Reliability Against Unknown Distribution Data |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Smoothed Adversarial Linear Contextual Bandits with Knapsacks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac Signals |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Solving Stackelberg Prediction Game with Least Squares Loss via Spherically Constrained Least Squares Reformulation |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| SpaceMAP: Visualizing High-Dimensional Data by Space Expansion |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Sparse Double Descent: Where Network Pruning Aggravates Overfitting |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sparse Invariant Risk Minimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Sparsity in Partially Controllable Linear Systems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Spatial-Channel Token Distillation for Vision MLPs |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Spectral Representation of Robustness Measures for Optimization Under Input Uncertainty |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| SpeqNets: Sparsity-aware permutation-equivariant graph networks |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Stability Based Generalization Bounds for Exponential Family Langevin Dynamics |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Stabilizing Off-Policy Deep Reinforcement Learning from Pixels |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Stabilizing Q-learning with Linear Architectures for Provable Efficient Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Stable Conformal Prediction Sets |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Staged Training for Transformer Language Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| State Transition of Dendritic Spines Improves Learning of Sparse Spiking Neural Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Statistical inference with implicit SGD: proximal Robbins-Monro vs. Polyak-Ruppert |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Steerable 3D Spherical Neurons |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Stochastic Continuous Submodular Maximization: Boosting via Non-oblivious Function |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Stochastic Reweighted Gradient Descent |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stochastic Rising Bandits |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Strategic Representation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Strategies for Safe Multi-Armed Bandits with Logarithmic Regret and Risk |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Streaming Algorithm for Monotone k-Submodular Maximization with Cardinality Constraints |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Streaming Algorithms for High-Dimensional Robust Statistics |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Streaming Algorithms for Support-Aware Histograms |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Streaming Inference for Infinite Feature Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Structural Entropy Guided Graph Hierarchical Pooling |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Structure Preserving Neural Networks: A Case Study in the Entropy Closure of the Boltzmann Equation |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
6 |
| Structure-Aware Transformer for Graph Representation Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Structure-preserving GANs |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Structured Stochastic Gradient MCMC |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Style Equalization: Unsupervised Learning of Controllable Generative Sequence Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Sublinear-Time Clustering Oracle for Signed Graphs |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Subspace Learning for Effective Meta-Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Supervised Learning with General Risk Functionals |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Supervised Off-Policy Ranking |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Surrogate Likelihoods for Variational Annealed Importance Sampling |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Symmetric Machine Theory of Mind |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Synergy and Symmetry in Deep Learning: Interactions between the Data, Model, and Inference Algorithm |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| TACTiS: Transformer-Attentional Copulas for Time Series |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| TPC: Transformation-Specific Smoothing for Point Cloud Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| TSPipe: Learn from Teacher Faster with Pipelines |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| TURF: Two-Factor, Universal, Robust, Fast Distribution Learning Algorithm |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Tackling Data Heterogeneity: A New Unified Framework for Decentralized SGD with Sample-induced Topology |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Tackling covariate shift with node-based Bayesian neural networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Task-aware Privacy Preservation for Multi-dimensional Data |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Tell me why! Explanations support learning relational and causal structure |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
3 |
| Temporal Difference Learning for Model Predictive Control |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| The Algebraic Path Problem for Graph Metrics |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The CLRS Algorithmic Reasoning Benchmark |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| The Complexity of k-Means Clustering when Little is Known |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| The Geometry of Robust Value Functions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Importance of Non-Markovianity in Maximum State Entropy Exploration |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Infinite Contextual Graph Markov Model |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| The Neural Race Reduction: Dynamics of Abstraction in Gated Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| The Primacy Bias in Deep Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Role of Deconfounding in Meta-learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| The State of Sparse Training in Deep Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Teaching Dimension of Regularized Kernel Learners |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Unsurprising Effectiveness of Pre-Trained Vision Models for Control |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| The dynamics of representation learning in shallow, non-linear autoencoders |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The power of first-order smooth optimization for black-box non-smooth problems |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Thompson Sampling for (Combinatorial) Pure Exploration |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Thompson Sampling for Robust Transfer in Multi-Task Bandits |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Three-stage Evolution and Fast Equilibrium for SGD with Non-degerate Critical Points |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Thresholded Lasso Bandit |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Tight and Robust Private Mean Estimation with Few Users |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Time Is MattEr: Temporal Self-supervision for Video Transformers |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| To Smooth or Not? When Label Smoothing Meets Noisy Labels |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Topology-aware Generalization of Decentralized SGD |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Toward Compositional Generalization in Object-Oriented World Modeling |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Towards Coherent and Consistent Use of Entities in Narrative Generation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Towards Scaling Difference Target Propagation by Learning Backprop Targets |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Towards Understanding Sharpness-Aware Minimization |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Towards Uniformly Superhuman Autonomy via Subdominance Minimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Towards understanding how momentum improves generalization in deep learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Tractable Uncertainty for Structure Learning |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Training Discrete Deep Generative Models via Gapped Straight-Through Estimator |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Training OOD Detectors in their Natural Habitats |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Training Your Sparse Neural Network Better with Any Mask |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Transfer Learning In Differential Privacy’s Hybrid-Model |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Transfer and Marginalize: Explaining Away Label Noise with Privileged Information |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Transformer Quality in Linear Time |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Transformers are Meta-Reinforcement Learners |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Translatotron 2: High-quality direct speech-to-speech translation with voice preservation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| UAST: Uncertainty-Aware Siamese Tracking |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| UNIREX: A Unified Learning Framework for Language Model Rationale Extraction |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Unaligned Supervision for Automatic Music Transcription in The Wild |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Uncertainty Modeling in Generative Compressed Sensing |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| UnderGrad: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Understanding Contrastive Learning Requires Incorporating Inductive Biases |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Understanding Doubly Stochastic Clustering |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Understanding Gradient Descent on the Edge of Stability in Deep Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Understanding Instance-Level Impact of Fairness Constraints |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Understanding Policy Gradient Algorithms: A Sensitivity-Based Approach |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Understanding Robust Generalization in Learning Regular Languages |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Understanding Robust Overfitting of Adversarial Training and Beyond |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Understanding The Robustness in Vision Transformers |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Understanding and Improving Knowledge Graph Embedding for Entity Alignment |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
5 |
| Understanding the unstable convergence of gradient descent |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| UniRank: Unimodal Bandit Algorithms for Online Ranking |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Unified Scaling Laws for Routed Language Models |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Universal Joint Approximation of Manifolds and Densities by Simple Injective Flows |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Universal and data-adaptive algorithms for model selection in linear contextual bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Universality of Winning Tickets: A Renormalization Group Perspective |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Unsupervised Ground Metric Learning Using Wasserstein Singular Vectors |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Unsupervised Image Representation Learning with Deep Latent Particles |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Utility Theory for Sequential Decision Making |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Utilizing Expert Features for Contrastive Learning of Time-Series Representations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| VLUE: A Multi-Task Multi-Dimension Benchmark for Evaluating Vision-Language Pre-training |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Validating Causal Inference Methods |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Value Function based Difference-of-Convex Algorithm for Bilevel Hyperparameter Selection Problems |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| VarScene: A Deep Generative Model for Realistic Scene Graph Synthesis |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| VariGrow: Variational Architecture Growing for Task-Agnostic Continual Learning based on Bayesian Novelty |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Variational Feature Pyramid Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Variational Inference for Infinitely Deep Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Variational Inference with Locally Enhanced Bounds for Hierarchical Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Variational Mixtures of ODEs for Inferring Cellular Gene Expression Dynamics |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Variational On-the-Fly Personalization |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Variational Sparse Coding with Learned Thresholding |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Variational Wasserstein gradient flow |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Variational nearest neighbor Gaussian process |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Versatile Dueling Bandits: Best-of-both World Analyses for Learning from Relative Preferences |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy Matching |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| ViT-NeT: Interpretable Vision Transformers with Neural Tree Decoder |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Visual Attention Emerges from Recurrent Sparse Reconstruction |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Weisfeiler-Lehman Meets Gromov-Wasserstein |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Welfare Maximization in Competitive Equilibrium: Reinforcement Learning for Markov Exchange Economy |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| What Can Linear Interpolation of Neural Network Loss Landscapes Tell Us? |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| What Dense Graph Do You Need for Self-Attention? |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| What Language Model Architecture and Pretraining Objective Works Best for Zero-Shot Generalization? |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| When Are Linear Stochastic Bandits Attackable? |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| When and How Mixup Improves Calibration |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
2 |
| Why the Rich Get Richer? On the Balancedness of Random Partition Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Wide Neural Networks Forget Less Catastrophically |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Winning the Lottery Ahead of Time: Efficient Early Network Pruning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| XAI for Transformers: Better Explanations through Conservative Propagation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| You Only Cut Once: Boosting Data Augmentation with a Single Cut |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for Everyone |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Zero-Shot Reward Specification via Grounded Natural Language |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Zero-shot AutoML with Pretrained Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| pathGCN: Learning General Graph Spatial Operators from Paths |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |