# Downloads

Number of events: 1279

- $p$-Laplacian Based Graph Neural Networks
- 1st ICML 2022 Workshop on Safe Learning for Autonomous Driving (SL4AD)
- 2nd Workshop on Interpretable Machine Learning in Healthcare (IMLH)
- 3D Infomax improves GNNs for Molecular Property Prediction
- 3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design
- 3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation
- A$^3$T: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and Editing
- A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks
- Accelerated Federated Learning with Decoupled Adaptive Optimization
- Accelerated Gradient Methods for Geodesically Convex Optimization: Tractable Algorithms and Convergence Analysis
- Accelerated, Optimal and Parallel: Some results on model-based stochastic optimization
- Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders
- Accelerating Shapley Explanation via Contributive Cooperator Selection
- Accurate Quantization of Measures via Interacting Particle-based Optimization
- Achieving Fairness at No Utility Cost via Data Reweighing with Influence
- Achieving Minimax Rates in Pool-Based Batch Active Learning
- A Closer Look at Smoothness in Domain Adversarial Training
- A Completely Tuning-Free and Robust Approach to Sparse Precision Matrix Estimation
- A Consistent and Efficient Evaluation Strategy for Attribution Methods
- A Context-Integrated Transformer-Based Neural Network for Auction Design
- A Convergence Theory for SVGD in the Population Limit under Talagrand's Inequality T1
- A Convergent and Dimension-Independent Min-Max Optimization Algorithm
- Action-Sufficient State Representation Learning for Control with Structural Constraints
- Active fairness auditing
- ActiveHedge: Hedge meets Active Learning
- Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets
- Active Multi-Task Representation Learning
- Active Nearest Neighbor Regression Through Delaunay Refinement
- Active Sampling for Min-Max Fairness
- Actor-Critic based Improper Reinforcement Learning
- AdaGrad Avoids Saddle Points
- Adapting k-means Algorithms for Outliers
- Adapting the Linearised Laplace Model Evidence for Modern Deep Learning
- Adapting to Mixing Time in Stochastic Optimization with Markovian Data
- Adaptive Accelerated (Extra-)Gradient Methods with Variance Reduction
- Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits
- Adaptive Conformal Predictions for Time Series
- Adaptive Data Analysis with Correlated Observations
- Adaptive Experimental Design and Active Learning in the Real World
- Adaptive Gaussian Process Change Point Detection
- Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum
- Adaptive Model Design for Markov Decision Process
- Adaptive Random Walk Gradient Descent for Decentralized Optimization
- Adaptive Second Order Coresets for Data-efficient Machine Learning
- A data-driven approach for learning to control computers
- AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems
- Additive Gaussian Processes Revisited
- Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning
- A deep convolutional neural network that is invariant to time rescaling
- A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications
- A Difference Standardization Method for Mutual Transfer Learning
- A Differential Entropy Estimator for Training Neural Networks
- Adversarial Attack and Defense for Non-Parametric Two-Sample Tests
- Adversarial Attacks on Gaussian Process Bandits
- Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization
- Adversarially Trained Actor Critic for Offline Reinforcement Learning
- Adversarially trained neural representations are already as robust as biological neural representations
- Adversarial Masking for Self-Supervised Learning
- Adversarial Robustness against Multiple and Single $l_p$-Threat Models via Quick Fine-Tuning of Robust Classifiers
- Adversarial Vulnerability of Randomized Ensembles
- A Dynamical System Perspective for Lipschitz Neural Networks
- A Framework for Learning to Request Rich and Contextually Useful Information from Humans
- A Functional Information Perspective on Model Interpretation
- A General Recipe for Likelihood-free Bayesian Optimization
- AGNAS: Attention-Guided Micro- and Macro-Architecture Search
- Agnostic Learnability of Halfspaces via Logistic Loss
- A Hierarchical Bayesian Approach to Inverse Reinforcement Learning with Symbolic Reward Machines
- A Hierarchical Transitive-Aligned Graph Kernel for Un-attributed Graphs
- AI for Agent-Based Modelling (AI4ABM)
- AI for Science
- A Joint Exponential Mechanism For Differentially Private Top-$k$
- A Langevin-like Sampler for Discrete Distributions
- Algorithms for the Communication of Samples
- Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution
- A Marriage between Adversarial Team Games and 2-player Games: Enabling Abstractions, No-regret Learning, and Subgame Solving
- A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes
- A Model-Agnostic Randomized Learning Framework based on Random Hypothesis Subspace Sampling
- A Modern Self-Referential Weight Matrix That Learns to Modify Itself
- A Multi-objective / Multi-task Learning Framework Induced by Pareto Stationarity
- Analysis of Stochastic Processes through Replay Buffers
- Analyzing and Mitigating Interference in Neural Architecture Search
- An Analytical Update Rule for General Policy Optimization
- Anarchic Federated Learning
- An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings
- A Natural Actor-Critic Framework for Zero-Sum Markov Games
- An Equivalence Between Data Poisoning and Byzantine Gradient Attacks
- A Neural Tangent Kernel Perspective of GANs
- A New Perspective on the Effects of Spectrum in Graph Neural Networks
- A new similarity measure for covariate shift with applications to nonparametric regression
- An Exact Symbolic Reduction of Linear Smart Predict+Optimize to Mixed Integer Linear Programming
- An Initial Alignment between Neural Network and Target is Needed for Gradient Descent to Learn
- An Intriguing Property of Geophysics Inversion
- An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees
- Antibody-Antigen Docking and Design via Hierarchical Structure Refinement
- Anticorrelated Noise Injection for Improved Generalization
- AnyMorph: Learning Transferable Polices By Inferring Agent Morphology
- Anytime Information Cascade Popularity Prediction via Self-Exciting Processes
- A Parametric Class of Approximate Gradient Updates for Policy Optimization
- Approximate Bayesian Computation with Domain Expert in the Loop
- Approximate Frank-Wolfe Algorithms over Graph-structured Support Sets
- Approximately Equivariant Networks for Imperfectly Symmetric Dynamics
- A Psychological Theory of Explainability
- A query-optimal algorithm for finding counterfactuals
- A Random Matrix Analysis of Data Stream Clustering: Coping With Limited Memory Resources
- Architecture Agnostic Federated Learning for Neural Networks
- A Reduction from Linear Contextual Bandits Lower Bounds to Estimations Lower Bounds
- A Regret Minimization Approach to Multi-Agent Control
- A Resilient Distributed Boosting Algorithm
- A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions
- ASAP.SGD: Instance-based Adaptiveness to Staleness in Asynchronous SGD
- A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games
- A Simple Guard for Learned Optimizers
- A Simple Reward-free Approach to Constrained Reinforcement Learning
- A Simple Unified Framework for High Dimensional Bandit Problems
- A Simple yet Universal Strategy for Online Convex Optimization
- A Single-Loop Gradient Descent and Perturbed Ascent Algorithm for Nonconvex Functional Constrained Optimization
- Asking for Knowledge (AFK): Training RL Agents to Query External Knowledge Using Language
- A State-Distribution Matching Approach to Non-Episodic Reinforcement Learning
- A Statistical Manifold Framework for Point Cloud Data
- A Stochastic Multi-Rate Control Framework For Modeling Distributed Optimization Algorithms
- A Study of Face Obfuscation in ImageNet
- A Study on the Ramanujan Graph Property of Winning Lottery Tickets
- Asymptotically-Optimal Gaussian Bandits with Side Observations
- A Temporal-Difference Approach to Policy Gradient Estimation
- A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization
- A Theoretical Comparison of Graph Neural Network Extensions
- A Tighter Analysis of Spectral Clustering, and Beyond
- A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources
- Attentional Meta-learners for Few-shot Polythetic Classification
- Augment with Care: Contrastive Learning for Combinatorial Problems
- A Unified View on PAC-Bayes Bounds for Meta-Learning
- A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks
- AutoIP: A United Framework to Integrate Physics into Gaussian Processes
- AutoSNN: Towards Energy-Efficient Spiking Neural Networks
- Auxiliary Learning with Joint Task and Data Scheduling
- BabelTower: Learning to Auto-parallelized Program Translation
- Balancing Discriminability and Transferability for Source-Free Domain Adaptation
- Balancing Sample Efficiency and Suboptimality in Inverse Reinforcement Learning
- BAMDT: Bayesian Additive Semi-Multivariate Decision Trees for Nonparametric Regression
- Batched Dueling Bandits
- Batch Greenkhorn Algorithm for Entropic-Regularized Multimarginal Optimal Transport: Linear Rate of Convergence and Iteration Complexity
- Bayesian Continuous-Time Tucker Decomposition
- Bayesian Deep Embedding Topic Meta-Learner
- Bayesian Imitation Learning for End-to-End Mobile Manipulation
- Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense
- Bayesian Model Selection, the Marginal Likelihood, and Generalization
- Bayesian Nonparametric Learning for Point Processes with Spatial Homogeneity: A Spatial Analysis of NBA Shot Locations
- Bayesian Nonparametrics for Offline Skill Discovery
- Bayesian Optimization for Distributionally Robust Chance-constrained Problem
- Bayesian Optimization under Stochastic Delayed Feedback
- Being Properly Improper
- Be Like Water: Adaptive Floating Point for Machine Learning
- Benchmarking and Analyzing Point Cloud Classification under Corruptions
- Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint
- Beyond Bayes: Paths Towards Universal Reasoning Systems
- Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features
- Beyond Worst-Case Analysis in Stochastic Approximation: Moment Estimation Improves Instance Complexity
- Biased Gradient Estimate with Drastic Variance Reduction for Meta Reinforcement Learning
- Biological Sequence Design with GFlowNets
- Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning
- Bit Prioritization in Variational Autoencoders via Progressive Coding
- Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization
- Black-Box Tuning for Language-Model-as-a-Service
- BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
- Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning
- Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness
- Boosting Graph Structure Learning with Dummy Nodes
- Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization
- Bounding the Width of Neural Networks via Coupled Initialization - A Worst Case Analysis
- Bounding Training Data Reconstruction in Private (Deep) Learning
- Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding
- Branching Reinforcement Learning
- Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities
- Breaking the $\sqrt{T}$ Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits
- Bregman Neural Networks
- Bregman Power k-Means for Clustering Exponential Family Data
- Bregman Proximal Langevin Monte Carlo via Bregman--Moreau Envelopes
- Bridging Learning and Decision Making
- Building Robust Ensembles via Margin Boosting
- Burst-Dependent Plasticity and Dendritic Amplification Support Target-Based Learning and Hierarchical Imitation Learning
- ButterflyFlow: Building Invertible Layers with Butterfly Matrices
- Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums
- C*-algebra Net: A New Approach Generalizing Neural Network Parameters to C*-algebra
- Calibrated and Sharp Uncertainties in Deep Learning via Density Estimation
- Calibrated Learning to Defer with One-vs-All Classifiers
- Cascaded Gaps: Towards Logarithmic Regret for Risk-Sensitive Reinforcement Learning
- Causal Conceptions of Fairness and their Consequences
- Causal Dynamics Learning for Task-Independent State Abstraction
- Causal Fairness Analysis
- Causal Imitation Learning under Temporally Correlated Noise
- Causal Inference Through the Structural Causal Marginal Problem
- Causality and Deep Learning: Synergies, Challenges and the Future
- Causal structure-based root cause analysis of outliers
- Causal Transformer for Estimating Counterfactual Outcomes
- Centroid Approximation for Bootstrap: Improving Particle Quality at Inference
- CerDEQ: Certifiable Deep Equilibrium Model
- Certified Adversarial Robustness Under the Bounded Support Set
- Certified Neural Network Watermarks with Randomized Smoothing
- Certified Robustness Against Natural Language Attacks by Causal Intervention
- Certifying Out-of-Domain Generalization for Blackbox Functions
- Channel Importance Matters in Few-Shot Image Classification
- Characterizing and Overcoming the Greedy Nature of Learning in Multi-modal Deep Neural Networks
- Choosing Answers in Epsilon-Best-Answer Identification for Linear Bandits
- CITRIS: Causal Identifiability from Temporal Intervened Sequences
- Class-Imbalanced Semi-Supervised Learning with Adaptive Thresholding
- Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments
- Climate Change and Machine Learning: Opportunities, Challenges, and Considerations
- Closed-Form Diffeomorphic Transformations for Time Series Alignment
- C-MinHash: Improving Minwise Hashing with Circulant Permutation
- Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets
- COAT: Measuring Object Compositionality in Emergent Representations
- Coin Flipping Neural Networks
- COLA: Consistent Learning with Opponent-Learning Awareness
- Collaboration of Experts: Achieving 80% Top-1 Accuracy on ImageNet with 100M FLOPs
- Combining Diverse Feature Priors
- Communicating via Markov Decision Processes
- Communication-Efficient Adaptive Federated Learning
- Communication-efficient Distributed Learning for Large Batch Optimization
- Complex feedback in online learning
- Composing Partial Differential Equations with Physics-Aware Neural Networks
- Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning
- Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data
- Conditional GANs with Auxiliary Discriminative Classifier
- Confidence Score for Source-Free Unsupervised Domain Adaptation
- Conformal Prediction Sets with Limited False Positives
- Congested Bandits: Optimal Routing via Short-term Resets
- Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation
- Consensus Multiplicative Weights Update: Learning to Learn using Projector-based Game Signatures
- Consistent Polyhedral Surrogates for Top-k Classification and Variants
- Constants Matter: The Performance Gains of Active Learning
- Constrained Discrete Black-Box Optimization using Mixed-Integer Programming
- Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks
- Constrained Offline Policy Optimization
- Constrained Optimization with Dynamic Bound-scaling for Effective NLP Backdoor Defense
- Constrained Variational Policy Optimization for Safe Reinforcement Learning
- Constraint-based graph network simulator
- Content Addressable Memory Without Catastrophic Forgetting by Heteroassociation with a Fixed Scaffold
- ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers
- Context-Aware Drift Detection
- Contextual Bandits with Large Action Spaces: Made Practical
- Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces
- Contextual Information-Directed Sampling
- Continual Learning via Sequential Function-Space Variational Inference
- Continual Learning with Guarantees via Weight Interval Constraints
- Continual Repeated Annealed Flow Transport Monte Carlo
- Continuous Control with Action Quantization from Demonstrations
- Continuous-Time Analysis of Accelerated Gradient Methods via Conservation Laws in Dilated Coordinate Systems
- Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations
- Continuous Time Perspectives in Machine Learning
- Contrastive Learning with Boosted Memorization
- Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness
- Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning
- Controlling Conditional Language Models without Catastrophic Forgetting
- Convergence and Recovery Guarantees of the K-Subspaces Method for Subspace Clustering
- Convergence of Invariant Graph Networks
- Convergence of Policy Gradient for Entropy Regularized MDPs with Neural Network Approximation in the Mean-Field Regime
- Convergence of Uncertainty Sampling for Active Learning
- Convergence Rates of Non-Convex Stochastic Gradient Descent Under a Generic Lojasiewicz Condition and Local Smoothness
- Convolutional and Residual Networks Provably Contain Lottery Tickets
- Cooperative Online Learning in Stochastic and Adversarial MDPs
- Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms
- Coordinated Double Machine Learning
- Correct-N-Contrast: a Contrastive Approach for Improving Robustness to Spurious Correlations
- Correlated Quantization for Distributed Mean Estimation and Optimization
- Correlation Clustering via Strong Triadic Closure Labeling: Fast Approximation Algorithms and Practical Lower Bounds
- Co-training Improves Prompt-based Learning for Large Language Models
- Counterfactual Prediction for Outcome-Oriented Treatments
- Counterfactual Transportability: A Formal Approach
- Cross-Space Active Learning on Graph Convolutional Networks
- CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer
- Curriculum Reinforcement Learning via Constrained Optimal Transport
- Cycle Representation Learning for Inductive Relation Prediction
- DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning
- data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
- Data Augmentation as Feature Manipulation
- Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)
- Data-Efficient Double-Win Lottery Tickets from Robust Pre-training
- Datamodels: Understanding Predictions with Data and Data with Predictions
- DataPerf: Benchmarking Data for Data-Centric AI
- Data Scaling Laws in NMT: The Effect of Noise and Architecture
- Dataset Condensation via Efficient Synthetic-Data Parameterization
- Dataset Condensation with Contrastive Signals
- Data-SUITE: Data-centric identification of in-distribution incongruous examples
- DAVINZ: Data Valuation using Deep Neural Networks at Initialization
- Debiaser Beware: Pitfalls of Centering Regularized Transport Maps
- Decentralized Online Convex Optimization in Networked Systems
- Deciphering Lasso-based Classification Through a Large Dimensional Analysis of the Iterative Soft-Thresholding Algorithm
- Decision Awareness in Reinforcement Learning
- Decision-Focused Learning: Through the Lens of Learning to Rank
- Decomposing Temporal High-Order Interactions via Latent ODEs
- Deconfounded Value Decomposition for Multi-Agent Reinforcement Learning
- Deduplicating Training Data Mitigates Privacy Risks in Language Models
- Deep and Flexible Graph Neural Architecture Search
- Deep Causal Metric Learning
- Deep equilibrium networks are sensitive to initialization statistics
- Deep Hierarchy in Bandits
- Deep Network Approximation in Terms of Intrinsic Parameters
- Deep Networks on Toroids: Removing Symmetries Reveals the Structure of Flat Regions in the Landscape Geometry
- Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning
- Deep Probability Estimation
- Deep Reference Priors: What is the best way to pretrain a model?
- Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm
- DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale
- Deep Squared Euclidean Approximation to the Levenshtein Distance for DNA Storage
- Deep symbolic regression for recurrence prediction
- Deep Variational Graph Convolutional Recurrent Network for Multivariate Time Series Anomaly Detection
- Delay-Adaptive Step-sizes for Asynchronous Learning
- Delayed Reinforcement Learning by Imitation
- Deletion Robust Submodular Maximization over Matroids
- Demystifying the Adversarial Robustness of Random Transformation Defenses
- Denoised MDPs: Learning World Models Better Than the World Itself
- De novo mass spectrometry peptide sequencing with a transformer model
- Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations
- DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks
- Describing Differences between Text Distributions with Natural Language
- Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization
- Design for Inference in Drug Discovery and Development
- Detached Error Feedback for Distributed SGD with Random Sparsification
- Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them
- Detecting Corrupted Labels Without Training a Model to Predict
- Dialog Inpainting: Turning Documents into Dialogs
- Difference Advantage Estimation for Multi-Agent Policy Gradients
- Differentiable Top-k Classification Learning
- Differentially Private Approximate Quantiles
- Differentially Private Community Detection for Stochastic Block Models
- Differentially Private Coordinate Descent for Composite Empirical Risk Minimization
- Differentially Private Maximal Information Coefficients
- Diffusion bridges vector quantized variational autoencoders
- Diffusion Models for Adversarial Purification
- Dimension-free Complexity Bounds for High-order Nonconvex Finite-sum Optimization
- Direct Behavior Specification via Constrained Reinforcement Learning
- Directed Acyclic Transformer for Non-Autoregressive Machine Translation
- Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning
- Discrete Probabilistic Inverse Optimal Transport
- Discrete Tree Flows via Tree-Structured Permutations
- Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations
- Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring
- Disentangling Disease-related Representation from Obscure for Disease Prediction
- Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning
- Disinformation Countermeasures and Machine Learning (DisCoML)
- DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training
- Distinguishing rule- and exemplar-based generalization in learning systems
- Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning
- Distributionally-Aware Kernelized Bandit Problems for Risk Aversion
- Distributionally Robust $Q$-Learning
- Distribution Regression with Sliced Wasserstein Kernels
- Divergence-Regularized Multi-Agent Actor-Critic
- Diversified Adversarial Attacks based on Conjugate Gradient Method
- DNA: Domain Generalization with Diversified Neural Averaging
- DNNR: Differential Nearest Neighbors Regression
- DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning
- Do Differentiable Simulators Give Better Policy Gradients?
- Does the Data Induce Capacity Control in Deep Learning?
- Domain Adaptation for Time Series Forecasting via Attention Sharing
- Do More Negative Samples Necessarily Hurt In Contrastive Learning?
- Double Sampling Randomized Smoothing
- Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning
- DRAGONN: Distributed Randomized Approximate Gradients of Neural Networks
- DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations
- DRIBO: Robust Deep Reinforcement Learning via Multi-View Information Bottleneck
- DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting
- Dual Decomposition of Convex Optimization Layers for Consistent Attention in Medical Images
- Dual Perspective of Label-Specific Feature Learning for Multi-Label Classification
- Dynamic Neural Networks
- Dynamic Regret of Online Markov Decision Processes
- Dynamic Topic Models for Temporal Document Networks
- DynaMixer: A Vision MLP Architecture with Dynamic Mixing
- Easy Variational Inference for Categorical Models via an Independent Binary Approximation
- EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning
- EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning
- Efficient Approximate Inference for Stationary Kernel on Frequency Domain
- Efficient Computation of Higher-Order Subgraph Attribution via Message Passing
- Efficient Distributionally Robust Bayesian Optimization with Worst-case Sensitivity
- Efficient Learning for AlphaZero via Path Consistency
- Efficient Learning of CNNs using Patch Based Features
- Efficient Low Rank Convex Bounds for Pairwise Discrete Graphical Models
- Efficiently Learning the Topology and Behavior of a Networked Dynamical System Via Active Queries
- Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation
- Efficient Online ML API Selection for Multi-Label Classification Tasks
- Efficient PAC Learning from the Crowd with Pairwise Comparisons
- Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach
- Efficient Representation Learning via Adaptive Context Pooling
- Efficient Test-Time Model Adaptation without Forgetting
- Efficient Variance Reduction for Meta-learning
- End-to-End Balancing for Causal Continuous Treatment-Effect Estimation
- Entropic Causal Inference: Graph Identifiability
- Entropic Gromov-Wasserstein between Gaussian Distributions
- EqR: Equivariant Representations for Data-Efficient Reinforcement Learning
- EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction
- Equivalence Analysis between Counterfactual Regret Minimization and Online Mirror Descent
- Equivariance versus Augmentation for Spherical Images
- Equivariant Diffusion for Molecule Generation in 3D
- Equivariant Priors for compressed sensing with unknown orientation
- Equivariant Quantum Graph Circuits
- Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass
- Estimating and Penalizing Induced Preference Shifts in Recommender Systems
- Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network
- Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models
- Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing
- Evaluating the Adversarial Robustness of Adaptive Test-time Defenses
- Evolving Curricula with Regret-Based Environment Design
- Exact Learning of Preference Structure: Single-peaked Preferences and Beyond
- Exact Optimal Accelerated Complexity for Fixed-Point Iterations
- Examining Scaling and Transfer of Language Model Architectures for Machine Translation
- Exploiting Independent Instruments: Identification and Distribution Generalization
- Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups
- Exploring and Exploiting Hubness Priors for High-Quality GAN Latent Sampling
- Exploring the Gap between Collapsed & Whitened Features in Self-Supervised Learning
- Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control
- Extended Unconstrained Features Model for Exploring Deep Neural Collapse
- Extracting Latent State Representations with Linear Dynamics from Rich Observations
- Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data
- Fair and Fast k-Center Clustering for Data Summarization
- Fair Generalized Linear Models with a Convex Penalty
- Fairness Interventions as (Dis)Incentives for Strategic Manipulation
- Fairness with Adaptive Weights
- Fair Representation Learning through Implicit Path Alignment
- Fast and Provable Nonconvex Tensor RPCA
- Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack
- Fast Aquatic Swimmer Optimization with Differentiable Projective Dynamics and Neural Network Hydrodynamic Models
- Fast Composite Optimization and Statistical Recovery in Federated Learning
- Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions
- Faster Algorithms for Learning Convex Functions
- Faster Fundamental Graph Algorithms via Learned Predictions
- Faster Privacy Accounting via Evolving Discretization
- Fast Finite Width Neural Tangent Kernel
- Fast Lossless Neural Compression with Integer-Only Discrete Flows
- Fast Population-Based Reinforcement Learning on a Single Machine
- Fast Provably Robust Decision Trees and Boosting
- Fast-Rate PAC-Bayesian Generalization Bounds for Meta-Learning
- Fast rates for noisy interpolation require rethinking the effect of inductive bias
- Fast Relative Entropy Coding with A* coding
- Fat–Tailed Variational Inference with Anisotropic Tail Adaptive Flows
- Feature and Parameter Selection in Stochastic Linear Bandits
- Feature Learning and Signal Propagation in Deep Neural Networks
- Feature selection using e-values
- Feature Space Particle Inference for Neural Network Ensembles
- Federated Learning with Label Distribution Skew via Logits Calibration
- Federated Learning with Partial Model Personalization
- Federated Learning with Positive and Unlabeled Data
- Federated Minimax Optimization: Improved Convergence Analyses and Algorithms
- Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling
- FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting
- FedNest: Federated Bilevel, Minimax, and Compositional Optimization
- FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning
- FedNL: Making Newton-Type Methods Applicable to Federated Learning
- FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
- Fenrir: Physics-Enhanced Regression for Initial Value Problems
- Fictitious Play and Best-Response Dynamics in Identical Interest and Zero-Sum Stochastic Games
- Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming
- Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
- Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks
- Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications
- First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach
- Fisher SAM: Information Geometry and Sharpness Aware Minimisation
- Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification
- Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization
- FITNESS: (Fine Tune on New and Similar Samples) to detect anomalies in streams with drift and outliers
- Flashlight: Enabling Innovation in Tools for Machine Learning
- Flow-based Recurrent Belief State Learning for POMDPs
- Flowformer: Linearizing Transformers with Conservation Flows
- Flow-Guided Sparse Transformer for Video Deblurring
- Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension
- FOCUS: Familiar Objects in Common and Uncommon Settings
- Forget-free Continual Learning with Winning Subnetworks
- For Learning in Symmetric Teams, Local Optima are Global Nash Equilibria
- Forward Operator Estimation in Generative Models with Kernel Transfer Operators
- Fourier Learning with Cyclical Data
- Framework for Evaluating Faithfulness of Local Explanations
- FriendlyCore: Practical Differentially Private Aggregation
- From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers
- From data to functa: Your data point is a function and you can treat it like one
- From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses
- From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model
- Frustratingly Easy Transferability Estimation
- Fully-Connected Network on Noncompact Symmetric Space and Ridgelet Transform based on Helgason-Fourier Analysis
- Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions
- Functional Output Regression with Infimal Convolution: Exploring the Huber and $\epsilon$-insensitive Losses
- Function-space Inference with Sparse Implicit Processes
- G$^2$CN: Graph Gaussian Convolution Networks with Concentrated Graph Filters
- GACT: Activation Compressed Training for Generic Network Architectures
- GALAXY: Graph-based Active Learning at the Extreme
- Gating Dropout: Communication-efficient Regularization for Sparsely Activated Transformers
- Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification
- Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications
- Generalised Policy Improvement with Geometric Policy Composition
- Generalization and Robustness Implications in Object-Centric Learning
- Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers
- Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling
- Generalized Beliefs for Cooperative AI
- Generalized Data Distribution Iteration
- Generalized Federated Learning via Sharpness Aware Minimization
- Generalized Leverage Scores: Geometric Interpretation and Applications
- Generalized Results for the Existence and Consistency of the MLE in the Bradley-Terry-Luce Model
- Generalized Strategic Classification and the Case of Aligned Incentives
- Generalizing Gaussian Smoothing for Random Search
- Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder
- Generalizing to New Physical Systems via Context-Informed Dynamics Model
- General-purpose, long-context autoregressive modeling with Perceiver AR
- Generating 3D Molecules for Target Protein Binding
- Generating Distributional Adversarial Examples to Evade Statistical Detectors
- Generative Coarse-Graining of Molecular Conformations
- Generative Cooperative Networks for Natural Language Generation
- Generative Flow Networks for Discrete Probabilistic Modeling
- Generative Modeling for Multi-task Visual Learning
- Generative Trees: Adversarial and Copycat
- Generic Coreset for Scalable Learning of Monotonic Kernels: Logistic Regression, Sigmoid and more
- GenLabel: Mixup Relabeling using Generative Models
- Geometric Multimodal Contrastive Representation Learning
- GLaM: Efficient Scaling of Language Models with Mixture-of-Experts
- GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
- Global Optimization Networks
- Global Optimization of K-Center Clustering
- G-Mixup: Graph Data Augmentation for Graph Classification
- GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks
- Goal Misgeneralization in Deep Reinforcement Learning
- Going Deeper into Permutation-Sensitive Graph Neural Networks
- Gradient Based Clustering
- Gradient Descent on Neurons and its Link to Approximate Second-order Optimization
- Gradient-Free Method for Heavily Constrained Nonconvex Optimization
- Graph-Coupled Oscillator Networks
- GraphFM: Improving Large-Scale GNN Training via Feature Momentum
- Graph Neural Architecture Search Under Distribution Shifts
- Greedy based Value Representation for Optimal Coordination in Multi-agent Reinforcement Learning
- Greedy when Sure and Conservative when Uncertain about the Opponents
- GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing
- Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation
- Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance
- Hardness and Algorithms for Robust and Sparse Optimization
- Hardware-aware efficient training (HAET)
- H-Consistency Bounds for Surrogate Loss Minimizers
- Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning
- Hermite Polynomial Features for Private Data Generation
- Hessian-Free High-Resolution Nesterov Acceleration For Sampling
- Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models.
- High Probability Guarantees for Nonconvex Stochastic Gradient Descent with Heavy Tails
- Hindering Adversarial Attacks with Implicit Neural Representations
- History Compression via Language Models in Reinforcement Learning
- HousE: Knowledge Graph Embedding with Householder Parameterization
- How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models
- How Powerful are Spectral Graph Neural Networks
- How Tempering Fixes Data Augmentation in Bayesian Neural Networks
- How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity
- How to Leverage Unlabeled Data in Offline Reinforcement Learning
- How to Stay Curious while avoiding Noisy TVs using Aleatoric Uncertainty Estimation
- How to Steer Your Adversary: Targeted and Efficient Model Stealing Defenses with Gradient Redirection
- How to Train Your Wide Neural Network Without Backprop: An Input-Weight Alignment Perspective
- Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation
- HyperImpute: Generalized Iterative Imputation with Automatic Model Selection
- HyperPrompt: Prompt-based Task-Conditioning of Transformers
- HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning
- ICML 2022 Workshop on Computational Biology
- ICML workshop on Machine Learning for Cybersecurity (ICML-ML4Cyber)
- Identifiability Conditions for Domain Adaptation
- Identification of Linear Non-Gaussian Latent Hierarchical Structure
- Identity-Disentangled Adversarial Augmentation for Self-supervised Learning
- IDYNO: Learning Nonparametric DAGs from Interventional Dynamic Data
- IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages
- Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging
- Imitation Learning by Estimating Expertise of Demonstrators
- Implicit Bias of Linear Equivariant Networks
- Implicit Bias of the Step Size in Linear Diagonal Neural Networks
- Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks
- Implicit Regularization with Polynomial Growth in Deep Tensor Factorization
- Importance Weighted Kernel Bayes' Rule
- Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation
- Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning
- Improved No-Regret Algorithms for Stochastic Shortest Path with Linear MDP
- Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data
- Improved Regret for Differentially Private Exploration in Linear MDP
- Improved StyleGAN-v2 based Inversion for Out-of-Distribution Images
- Improve Single-Point Zeroth-Order Optimization Using High-Pass and Low-Pass Filters
- Improving Adversarial Robustness via Mutual Information Estimation
- Improving Ensemble Distillation With Weight Averaging and Diversifying Perturbation
- Improving Language Models by Retrieving from Trillions of Tokens
- Improving Mini-batch Optimal Transport via Partial Transportation
- Improving Out-of-Distribution Robustness via Selective Augmentation
- Improving Policy Optimization with Generalist-Specialist Learning
- Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification
- Improving Screening Processes via Calibrated Subset Selection
- Improving Task-free Continual Learning by Distributionally Robust Memory Evolution
- Improving Transformers with Probabilistic Attention Keys
- In defense of dual-encoders for neural ranking
- Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence
- Individual Preference Stability for Clustering
- Individual Reward Assisted Multi-Agent Reinforcement Learning
- Inducing Causal Structure for Interpretable Neural Networks
- Inductive Biases and Variable Creation in Self-Attention Mechanisms
- Inductive Matrix Completion: No Bad Local Minima and a Fast Algorithm
- Inferring Cause and Effect in the Presence of Heteroscedastic Noise
- Influence-Augmented Local Simulators: a Scalable Solution for Fast Deep RL in Large Networked Systems
- Information Discrepancy in Strategic Learning
- Informed Learning by Wide Neural Networks: Convergence, Generalization and Sampling Complexity
- Injecting Logical Constraints into Neural Networks via Straight-Through Estimators
- Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing
- Input Dependent Sparse Gaussian Processes
- Instance Dependent Regret Analysis of Kernelized Bandits
- Instrumental Variable Regression with Confounder Balancing
- Interactive Correlation Clustering with Existential Cluster Constraints
- Interactive Inverse Reinforcement Learning for Cooperative Games
- Interactively Learning Preference Constraints in Linear Bandits
- Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism
- Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings
- Interpretable Off-Policy Learning via Hyperbox Search
- Interventional Contrastive Learning with Meta Semantic Regularizer
- Intriguing Properties of Input-Dependent Randomized Smoothing
- Invariant Ancestry Search
- Inverse Contextual Bandits: Learning How Behavior Evolves over Time
- Investigating Generalization by Controlling Normalized Margin
- Investigating Why Contrastive Learning Benefits Robustness against Label Noise
- Iterative Double Sketching for Faster Least-Squares Optimization
- Iterative Hard Thresholding with Adaptive Regularization: Sparser Solutions Without Sacrificing Runtime
- It’s Raw! Audio Generation with State-Space Models
- Kernelized Multiplicative Weights for 0/1-Polyhedral Games: Bridging the Gap Between Learning in Extensive-Form and Normal-Form Games
- Kernel Methods for Radial Transformed Compositional Data with Many Zeros
- Kill a Bird with Two Stones: Closing the Convergence Gaps in Non-Strongly Convex Optimization by Directly Accelerated SVRG with Double Compensation and Snapshots
- Knowledge Base Question Answering by Case-based Reasoning over Subgraphs
- Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations
- Knowledge Retrieval and Language Models
- Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics
- Label-Descriptive Patterns and Their Application to Characterizing Classification Errors
- Label-Free Explainability for Unsupervised Models
- Label Ranking through Nonparametric Regression
- Lagrangian Method for Q-Function Learning (with Applications to Machine Translation)
- Langevin Monte Carlo for Contextual Bandits
- Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents
- Large Batch Experience Replay
- Large-Scale Graph Neural Architecture Search
- Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence
- Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression
- Latent Diffusion Energy-Based Model for Interpretable Text Modelling
- Latent Outlier Exposure for Anomaly Detection with Contaminated Data
- Lazy Estimation of Variable Importance for Large Neural Networks
- LCANets: Lateral Competition Improves Robustness Against Corruption and Attack
- Learning Augmented Binary Search Trees
- Learning-based Optimisation of Particle Accelerators Under Partial Observability Without Real-World Training
- Learning Bellman Complete Representations for Offline Policy Evaluation
- Learning Domain Adaptive Object Detection with Probabilistic Teacher
- Learning Dynamics and Generalization in Deep Reinforcement Learning
- Learning Efficient and Robust Ordinary Differential Equations via Invertible Neural Networks
- Learning fair representation with a parametric integral probability metric
- Learning for Interactive Agents
- Learning from a Learning User for Optimal Recommendations
- Learning from Counterfactual Links for Link Prediction
- Learning from Demonstration: Provably Efficient Adversarial Policy Imitation with Linear Function Approximation
- Learning General Halfspaces with Adversarial Label Noise via Online Gradient Descent
- Learning Infinite-horizon Average-reward Markov Decision Process with Constraints
- Learning inverse folding from millions of predicted structures
- Learning Iterative Reasoning through Energy Minimization
- Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits
- Learning Mixtures of Linear Dynamical Systems
- Learning Multiscale Transformer Models for Sequence Generation
- Learning of Cluster-based Feature Importance for Electronic Health Record Time-series
- Learning Pseudometric-based Action Representations for Offline Reinforcement Learning
- Learning Stable Classifiers by Transferring Unstable Features
- Learning Stochastic Shortest Path with Linear Function Approximation
- Learning Symmetric Embeddings for Equivariant World Models
- Learning to Cut by Looking Ahead: Cutting Plane Selection via Imitation Learning
- Learning to Estimate and Refine Fluid Motion with Physical Dynamics
- Learning to Hash Robustly, Guaranteed
- Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization
- Learning to Infer Structures of Network Games
- Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters
- Learning to Separate Voices by Spatial Regions
- Learning to Solve PDE-constrained Inverse Problems with Graph Networks
- Least Squares Estimation using Sketched Data with Heteroskedastic Errors
- LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation
- Let Invariant Rationale Discovery Inspire Graph Contrastive Learning
- Leverage Score Sampling for Tensor Product Matrices in Input Sparsity Time
- Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity
- LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood
- Lie Point Symmetry Data Augmentation for Neural PDE Solvers
- Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent
- LIMO: Latent Inceptionism for Targeted Molecule Generation
- Linear Adversarial Concept Erasure
- Linear Bandit Algorithms with Sublinear Time Complexity
- Linear Complexity Randomized Self-attention Mechanism
- Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness
- Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs
- Local Augmentation for Graph Neural Networks
- Local Linear Convergence of Douglas-Rachford for Linear Programming: a Probabilistic Analysis
- Locally Sparse Neural Networks for Tabular Biomedical Data
- Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets
- Loss Function Learning for Domain Generalization by Implicit Gradient
- Low-Complexity Deep Convolutional Neural Networks on Fully Homomorphic Encryption Using Multiplexed Parallel Convolutions
- Low-Precision Stochastic Gradient Langevin Dynamics
- LSB: Local Self-Balancing MCMC in Discrete Spaces
- LyaNet: A Lyapunov Framework for Training Neural ODEs
- Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control
- Machine Learning for Astrophysics
- Machine Learning for Audio Synthesis
- MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection
- Making Linear MDPs Practical via Contrastive Representation Learning
- MAML and ANIL Provably Learn Representations
- Marginal Distribution Adaptation for Discrete Sets via Module-Oriented Divergence Minimization
- Marginal Tail-Adaptive Normalizing Flows
- Markov Chain Monte Carlo for Continuous-Time Switching Dynamical Systems
- MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer
- Maslow's Hammer in Catastrophic Forgetting: Node Re-Use vs. Node Activation
- Massively Parallel $k$-Means Clustering for Perturbation Resilient Instances
- Matching Learned Causal Effects of Neural Networks with Domain Priors
- Matching Normalizing Flows and Probability Paths on Manifolds
- Matching Structure for Dual Learning
- Maximum Likelihood Training for Score-based Diffusion ODEs by High Order Denoising Score Matching
- Meaningfully debugging model mistakes using conceptual counterfactual explanations
- Measure Estimation in the Barycentric Coding Model
- Measuring dissimilarity with diffeomorphism invariance
- Measuring Representational Robustness of Neural Networks Through Shared Invariances
- Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments
- ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases
- Memory-Based Model Editing at Scale
- MemSR: Training Memory-efficient Lightweight Model for Image Super-Resolution
- Meta-Learning Hypothesis Spaces for Sequential Decision-making
- MetAug: Contrastive Learning via Meta Feature Augmentation
- Metric-Fair Active Learning
- Metric-Fair Classifier Derandomization
- Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees
- Minimax M-estimation under Adversarial Contamination
- Minimizing Control for Credit Assignment with Strong Feedback
- Minimum Cost Intervention Design for Causal Effect Identification
- Mirror Learning: A Unifying Framework of Policy Optimisation
- Mitigating Gender Bias in Face Recognition using the von Mises-Fisher Mixture Model
- Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization
- Mitigating Neural Network Overconfidence with Logit Normalization
- Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably)
- Model Agnostic Sample Reweighting for Out-of-Distribution Learning
- Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models and Amortized Policy Search
- Model-Free Opponent Shaping
- Modeling Adversarial Noise for Adversarial Training
- Modeling Irregular Time Series with Continuous Recurrent Units
- Modeling Strong and Human-Like Gameplay with KL-Regularized Search
- Modeling Structure with Undirected Neural Networks
- Model Selection in Batch Policy Optimization
- Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
- Model-Value Inconsistency as a Signal for Epistemic Uncertainty
- ModLaNets: Learning Generalisable Dynamics via Modularity and Physical Inductive Bias
- Modular Conformal Calibration
- Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks
- Monarch: Expressive Structured Matrices for Efficient and Accurate Training
- More Efficient Sampling for Tensor Decomposition With Worst-Case Guarantees
- More Than a Toy: Random Matrix Models Predict How Real-World Neural Representations Generalize
- Multiclass learning with margin: exponential rates with no bias-variance trade-off
- Multicoated Supermasks Enhance Hidden Networks
- Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts
- Multi-Level Branched Regularization for Federated Learning
- Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms
- Multirate Training of Neural Networks
- Multi Resolution Analysis (MRA) for Approximate Self-Attention
- Multi-scale Feature Learning Dynamics: Insights for Double Descent
- Multi-slots Online Matching with High Entropy
- Multi-Task Learning as a Bargaining Game
- NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning
- Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning
- Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation
- Nearly Optimal Catoni’s M-estimator for Infinite Variance
- Nearly Optimal Policy Optimization with Stable at Any Time Guarantee
- Near-Optimal Algorithms for Autonomous Exploration and Multi-Goal Stochastic Shortest Path
- Near-Optimal Learning of Extensive-Form Games with Imperfect Information
- Near-optimal rate of consistency for linear models with missing values
- Nested Bandits
- Nesterov Accelerated Shuffling Gradient Method for Convex Optimization
- NeuralEF: Deconstructing Kernels by Deep Neural Networks
- Neural Fisher Discriminant Analysis: Optimal Neural Network Embeddings in Polynomial Time
- Neural Implicit Dictionary Learning via Mixture-of-Expert Training
- Neural Inverse Kinematic
- Neural Inverse Transform Sampler
- Neural Language Models are not Born Equal to Fit Brain Data, but Training Helps
- Neural Laplace: Learning diverse classes of differential equations in the Laplace domain
- Neural Network Poisson Models for Behavioural and Neural Spike Train Data
- Neural Network Pruning Denoises the Features and Makes Local Connectivity Emerge in Visual Tasks
- Neural Network Weights Do Not Converge to Stationary Points: An Invariant Measure Perspective
- Neural-Symbolic Models for Logical Queries on Knowledge Graphs
- Neural Tangent Kernel Analysis of Deep Narrow Neural Networks
- Neural Tangent Kernel Beyond the Infinite-Width Limit: Effects of Depth and Initialization
- Neural Tangent Kernel Empowered Federated Learning
- Neurocoder: General-Purpose Computation Using Stored Neural Programs
- NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural Radiance Fields
- Neuron Dependency Graphs: A Causal Abstraction of Neural Networks
- Neuro-Symbolic Hierarchical Rule Induction
- Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval
- Neurotoxin: Durable Backdoors in Federated Learning
- New Frontiers in Adversarial Machine Learning
- NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks
- NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework
- NOMU: Neural Optimization-based Model Uncertainty
- (Non-)Convergence Results for Predictive Coding Networks
- Nonlinear Feature Diffusion on Hypergraphs
- Nonparametric Embeddings of Sparse High-Order Interaction Events
- Nonparametric Factor Trajectory Learning for Dynamic Tensor Decomposition
- Nonparametric Involutive Markov Chain Monte Carlo
- Nonparametric Sparse Tensor Factorization with Hierarchical Gamma Processes
- Non-Vacuous Generalisation Bounds for Shallow Neural Networks
- No-Regret Learning in Partially-Informed Auctions
- No-Regret Learning in Time-Varying Zero-Sum Games
- Not All Poisons are Created Equal: Robust Training against Data Poisoning
- N-Penetrate: Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations
- NP-Match: When Neural Processes meet Semi-Supervised Learning
- NysADMM: faster composite convex optimization via low-rank approximation
- Nyström Kernel Mean Embeddings
- Object Permanence Emerges in a Random Walk along Memory
- OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework
- Offline Meta-Reinforcement Learning with Online Self-Supervision
- Offline RL Policies Should Be Trained to be Adaptive
- Off-Policy Evaluation for Large Action Spaces via Embeddings
- Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory
- Off-Policy Reinforcement Learning with Delayed Rewards
- Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning
- On Collective Robustness of Bagging Against Data Poisoning
- On Convergence of Gradient Descent Ascent: A Tight Local Analysis
- On Distribution Shift in Learning-based Bug Detectors
- One-Pass Algorithms for MAP Inference of Nonsymmetric Determinantal Point Processes
- One-Pass Diversified Sampling with Application to Terabyte-Scale Genomic Sequence Streams
- On Finite-Sample Identifiability of Contrastive Learning-Based Nonlinear Independent Component Analysis
- On Implicit Bias in Overparameterized Bilevel Optimization
- On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning
- On Last-Iterate Convergence Beyond Zero-Sum Games
- On Learning Mixture of Linear Regressions in the Non-Realizable Setting
- Online Active Regression
- Online Algorithms with Multiple Predictions
- Online and Consistent Correlation Clustering
- Online Balanced Experimental Design
- Online Continual Learning through Mutual Information Maximization
- Online Decision Transformer
- Online Learning and Pricing with Reusable Resources: Linear Bandits with Sub-Exponential Rewards
- Online Learning for Min Sum Set Cover and Pandora’s Box
- Online Learning with Knapsacks: the Best of Both Worlds
- Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback
- Only tails matter: Average-Case Universality and Robustness in the Convex Regime
- On Measuring Causal Contributions via do-interventions
- On Non-local Convergence Analysis of Deep Linear Networks
- On Numerical Integration in Neural Ordinary Differential Equations
- On the Adversarial Robustness of Causal Algorithmic Recourse
- On the Convergence of Inexact Predictor-Corrector Methods for Linear Programming
- On the Convergence of Local Stochastic Compositional Gradient Descent with Momentum
- On the Convergence of the Shapley Value in Parametric Bayesian Learning Games
- On the Difficulty of Defending Self-Supervised Learning against Model Extraction
- On the Effects of Artificial Data Modification
- On the Equivalence Between Temporal and Static Equivariant Graph Representations
- On the Finite-Time Complexity and Practical Computation of Approximate Stationarity Concepts of Lipschitz Functions
- On the Finite-Time Performance of the Knowledge Gradient Algorithm
- On the Generalization Analysis of Adversarial Learning
- On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces
- On the Impossibility of Learning to Cooperate with Adaptive Partner Strategies in Repeated Games
- On the Learning of Non-Autoregressive Transformers
- On the Optimization Landscape of Neural Collapse under MSE Loss: Global Optimality with Unconstrained Features
- On the Practicality of Deterministic Epistemic Uncertainty
- On the Robustness of CountSketch to Adaptive Inputs
- On the Role of Discount Factor in Offline Reinforcement Learning
- On the Sample Complexity of Learning Infinite-horizon Discounted Linear Kernel MDPs
- On the Statistical Benefits of Curriculum Learning
- On the Surrogate Gap between Contrastive and Supervised Losses
- On Transportation of Mini-batches: A Hierarchical Approach
- On Well-posedness and Minimax Optimal Rates of Nonparametric Q-function Estimation in Off-policy Evaluation
- Open-Sampling: Exploring Out-of-Distribution data for Re-balancing Long-tailed datasets
- Optimal Algorithms for Mean Estimation under Local Differential Privacy
- Optimal Algorithms for Stochastic Multi-Level Compositional Optimization
- Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits
- Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training
- Optimal Clustering with Noisy Queries via Multi-Armed Bandit
- Optimal Estimation of Policy Gradient via Double Fitted Iteration
- Optimally Controllable Perceptual Lossy Compression
- Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer
- Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training
- Optimization-Induced Graph Implicit Nonlinear Diffusion
- Optimizing Sequential Experimental Design with Deep Reinforcement Learning
- Optimizing Tensor Network Contraction Using Reinforcement Learning
- Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering
- Order Constraints in Optimal Transport
- Out-of-Distribution Detection with Deep Nearest Neighbors
- Overcoming Oscillations in Quantization-Aware Training
- PAC-Bayesian Bounds on Rate-Efficient Classifiers
- PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs
- PAC-Net: A Model Pruning Approach to Inductive Transfer Learning
- PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation
- Pairwise Conditional Gradients without Swap Steps and Sparser Kernel Herding
- Parametric Visual Program Induction with Function Modularization
- Parsimonious Learning-Augmented Caching
- Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition
- Partial Counterfactual Identification from Observational and Experimental Data
- Partial disentanglement for domain adaptation
- Partial Label Learning via Label Influence Function
- Particle Transformer for Jet Tagging
- Path-Aware and Structure-Preserving Generation of Synthetically Accessible Molecules
- pathGCN: Learning General Graph Spatial Operators from Paths
- Path-Gradient Estimators for Continuous Normalizing Flows
- PDE-Based Optimal Strategy for Unconstrained Online Learning
- PDO-s3DCNNs: Partial Differential Operator Based Steerable 3D CNNs
- Penalizing Gradient Norm for Efficiently Improving Generalization in Deep Learning
- Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning
- Permutation Search of Tensor Network Structures via Local Sampling
- Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning
- Personalized Federated Learning through Local Memorization
- Personalized Federated Learning via Variational Bayesian Inference
- Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning
- Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets
- Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity
- Phasic Self-Imitative Reduction for Sparse-Reward Goal-Conditioned Reinforcement Learning
- PINs: Progressive Implicit Networks for Multi-Scale Neural Representations
- Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification
- Planning with Diffusion for Flexible Behavior Synthesis
- Plan Your Target and Learn Your Skills: Transferable State-Only Imitation Learning via Decoupled Policy Optimization
- PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information
- PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance
- Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations
- Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks
- PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration
- Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
- POEM: Out-of-Distribution Detection with Posterior Sampling
- POET: Training Neural Networks on Tiny Devices with Integrated Rematerialization and Paging
- PoF: Post-Training of Feature Extractor for Improving Generalization
- Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL
- Policy Gradient Method For Robust Reinforcement Learning
- Popular decision tree algorithms are provably noise tolerant
- Position Prediction as an Effective Pretraining Strategy
- Power-Law Escape Rate of SGD
- Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering
- Preconditioning for Scalable Gaussian Process Hyperparameter Optimization
- Predicting Out-of-Distribution Error with the Projection Norm
- Principal Component Flows
- Principled Knowledge Extrapolation with GANs
- Principles of Distribution Shift (PODS)
- Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt
- Privacy for Free: How does Dataset Condensation Help Privacy?
- Private Adaptive Optimization with Side information
- Private frequency estimation via projective geometry
- Private optimization in the interpolation regime: faster rates and hardness results
- Private Streaming SCO in $\ell_p$ geometry with Applications in High Dimensional Online Decision Making
- Probabilistically Robust Learning: Balancing Average- and Worst-case Performance
- Probabilistic Bilevel Coreset Selection
- Probabilistic ODE Solutions in Millions of Dimensions
- ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning
- ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training
- Prompting Decision Transformer for Few-Shot Policy Generalization
- Prototype-Anchored Learning for Learning with Imperfect Annotations
- Prototype Based Classification from Hierarchy to Fairness
- Provable Acceleration of Heavy Ball beyond Quadratics for a Class of Polyak-Lojasiewicz Functions when the Non-Convexity is Averaged-Out
- Provable Domain Generalization via Invariant-Feature Subspace Recovery
- Provable Reinforcement Learning with a Short-Term Memory
- Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance
- Provably Adversarially Robust Nearest Prototype Classifiers
- Provably Efficient Offline Reinforcement Learning for Partially Observable Markov Decision Processes
- Proving Theorems using Incremental Learning and Hindsight Experience Replay
- Proximal and Federated Random Reshuffling
- Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularization
- Proximal Exploration for Model-guided Protein Sequence Design
- ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!
- Public Data-Assisted Mirror Descent for Private Model Training
- Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images
- QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning
- Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features
- Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding
- Quantifying and Learning Linear Symmetry-Based Disentanglement
- Quantitative Reasoning About Data Privacy in Machine Learning
- Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra
- Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization
- Random Forest Density Estimation
- Random Gegenbauer Features for Scalable Kernel Methods
- RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression
- Reachability Constrained Reinforcement Learning
- RECAPP: Crafting a More Efficient Catalyst for Convex Optimization
- Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series
- Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs
- Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks
- Re-evaluating Word Mover's Distance
- Refined Convergence Rates for Maximum Likelihood Estimation under Finite Mixture Models
- Region-Based Semantic Factorization in GANs
- Regret Bounds for Stochastic Shortest Path Problems with Linear Function Approximation
- Regret Minimization with Performative Feedback
- Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning
- Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency
- Reinforcement Learning with Action-Free Pre-Training from Videos
- Removing Batch Normalization Boosts Adversarial Training
- Representation Topology Divergence: A Method for Comparing Neural Network Representations.
- Residual-Based Sampling for Online Outlier-Robust PCA
- Resilient and Communication Efficient Learning for Heterogeneous Federated Systems
- Responsible Decision Making in Dynamic Environments
- Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the $O(\epsilon^{-7/4})$ Complexity
- Rethinking Attention-Model Explainability through Faithfulness Violation Test
- Rethinking Fano’s Inequality in Ensemble Learning
- Rethinking Graph Neural Networks for Anomaly Detection
- Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems
- Retrieval-Augmented Reinforcement Learning
- RetrievalGuard: Provably Robust 1-Nearest Neighbor Image Retrieval
- Retroformer: Pushing the Limits of End-to-end Retrosynthesis Transformer
- Reverse Engineering $\ell_p$ attacks: A block-sparse optimization approach with recovery guarantees
- Reverse Engineering the Neural Tangent Kernel
- Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization
- Revisiting Consistency Regularization for Deep Partial Label Learning
- Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework
- Revisiting End-to-End Speech-to-Text Translation From Scratch
- Revisiting Label Smoothing and Knowledge Distillation Compatibility: What was Missing?
- Revisiting Online Submodular Minimization: Gap-Dependent Regret Bounds, Best of Both Worlds and Adversarial Robustness
- Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning
- Revisiting the Effects of Stochasticity for Hamiltonian Samplers
- REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer
- Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes
- Rich Feature Construction for the Optimization-Generalization Dilemma
- RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests
- Ripple Attention for Visual Perception with Sub-quadratic Complexity
- Risk-Averse No-Regret Learning in Online Convex Games
- Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data
- Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation
- Robust Counterfactual Explanations for Tree-Based Ensembles
- Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum
- Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees
- Robust Group Synchronization via Quadratic Programming
- Robust Imitation Learning against Variations in Environment Dynamics
- Robust Kernel Density Estimation with Median-of-Means principle
- Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile
- Robust Models Are More Interpretable Because Attributions Look Normal
- Robust Multi-Objective Bayesian Optimization Under Input Noise
- Robustness and Accuracy Could Be Reconcilable by (Proper) Definition
- Robustness Implies Generalization via Data-Dependent Generalization Bounds
- Robustness in Multi-Objective Submodular Optimization: a Quantile Approach
- Robustness Verification for Contrastive Learning
- Robust Policy Learning over Multiple Uncertainty Sets
- Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning
- Robust Task Representations for Offline Meta-Reinforcement Learning via Contrastive Learning
- Robust Training of Neural Networks Using Scale Invariant Architectures
- Robust Training under Label Noise by Over-parameterization
- ROCK: Causal Inference Principles for Reasoning about Commonsense Causality
- Role-based Multiplex Network Embedding
- Rotting Infinitely Many-Armed Bandits
- RUMs from Head-to-Head Contests
- Safe Exploration for Efficient Policy Evaluation and Comparison
- Safe Learning in Tree-Form Sequential Decision Making: Handling Hard and Soft Constraints
- Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis
- Sample Efficient Learning of Predictors that Complement Humans
- Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost
- Sampling as First-Order Optimization over a space of probability measures
- Sanity Simulations for Saliency Methods
- Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation
- Scalable Computation of Causal Bounds
- Scalable Deep Gaussian Markov Random Fields for General Graphs
- Scalable Deep Reinforcement Learning Algorithms for Mean Field Games
- Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation
- Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes
- Scalable Spike-and-Slab
- Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times
- Scaling Out-of-Distribution Detection for Real-World Settings
- Scaling Structured Inference with Randomization
- Scaling-up Diverse Orthogonal Convolutional Networks by a Paraunitary Framework
- SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation
- Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations
- Score-Guided Intermediate Level Optimization: Fast Langevin Mixing for Inverse Problems
- Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models
- SDQ: Stochastic Differentiable Quantization with Mixed Precision
- SE(3) Equivariant Graph Neural Networks with Complete Local Frames
- Searching for BurgerFormer with Micro-Meso-Macro Space Design
- Secure Distributed Training at Scale
- Secure Quantized Training for Deep Learning
- Selective Network Linearization for Efficient Private Inference
- Selective Regression under Fairness Criteria
- Self-conditioning Pre-Trained Language Models
- Self-Organized Polynomial-Time Coordination Graphs
- Self-supervised learning with random-projection quantizer for speech recognition
- Self-supervised Models are Good Teaching Assistants for Vision Transformers
- Self-Supervised Models of Audio Effectively Explain Human Cortical Responses to Speech
- Self-Supervised Representation Learning via Latent Graph Prediction
- Selling Data To a Machine Learner: Pricing via Costly Signaling
- Sequential- and Parallel- Constrained Max-value Entropy Search via Information Lower Bound
- Sequential Covariate Shift Detection Using Classifier Two-Sample Tests
- Set Based Stochastic Subsampling
- Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets
- Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood
- Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning
- ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks
- Shift happens: Crowdsourcing metrics and test datasets beyond ImageNet
- Short-Term Plasticity Neurons Learning to Learn and Forget
- Showing Your Offline Reinforcement Learning Work: Online Evaluation Budget Matters
- Shuffle Private Linear Contextual Bandits
- Simple and near-optimal algorithms for hidden stratification and multi-group learning
- Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games
- Simultaneous Graph Signal Clustering and Graph Learning
- Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback
- Sketching Algorithms and Lower Bounds for Ridge Regression
- SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks
- Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification
- Smoothed Adaptive Weighting for Imbalanced Semi-Supervised Learning: Improve Reliability Against Unknown Distribution Data
- Smoothed Adversarial Linear Contextual Bandits with Knapsacks
- Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation
- Solving Stackelberg Prediction Game with Least Squares Loss via Spherically Constrained Least Squares Reformulation
- Solving the Right Problems: Making ML Models Relevant to Healthcare and the Life Sciences
- SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac Signals
- SpaceMAP: Visualizing High-Dimensional Data by Space Expansion
- Sparse Double Descent: Where Network Pruning Aggravates Overfitting
- Sparse Invariant Risk Minimization
- Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxation
- Sparsity in Partially Controllable Linear Systems
- Spatial-Channel Token Distillation for Vision MLPs
- SPDY: Accurate Pruning with Speedup Guarantees
- Spectral Representation of Robustness Measures for Optimization Under Input Uncertainty
- SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators
- SpeqNets: Sparsity-aware permutation-equivariant graph networks
- Spurious correlations, Invariance, and Stability (SCIS)
- SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization
- Stability Based Generalization Bounds for Exponential Family Langevin Dynamics
- Stabilizing Off-Policy Deep Reinforcement Learning from Pixels
- Stabilizing Q-learning with Linear Architectures for Provable Efficient Learning
- Stable Conformal Prediction Sets
- Staged Training for Transformer Language Models
- State Transition of Dendritic Spines Improves Learning of Sparse Spiking Neural Networks
- Statistical inference with implicit SGD: proximal Robbins-Monro vs. Polyak-Ruppert
- Steerable 3D Spherical Neurons
- Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models
- Stochastic Continuous Submodular Maximization: Boosting via Non-oblivious Function
- Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning
- Stochastic Reweighted Gradient Descent
- Stochastic Rising Bandits
- Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification
- Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses
- Strategic Representation
- Strategies for Safe Multi-Armed Bandits with Logarithmic Regret and Risk
- Streaming Algorithm for Monotone k-Submodular Maximization with Cardinality Constraints
- Streaming Algorithms for High-Dimensional Robust Statistics
- Streaming Algorithms for Support-Aware Histograms
- Streaming Inference for Infinite Feature Models
- StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models
- Structural Entropy Guided Graph Hierarchical Pooling
- Structure-Aware Transformer for Graph Representation Learning
- Structured Stochastic Gradient MCMC
- Structure-preserving GANs
- Structure Preserving Neural Networks: A Case Study in the Entropy Closure of the Boltzmann Equation
- Style Equalization: Unsupervised Learning of Controllable Generative Sequence Models
- Sublinear-Time Clustering Oracle for Signed Graphs
- Subspace Learning for Effective Meta-Learning
- Supervised Learning with General Risk Functionals
- Supervised Off-Policy Ranking
- Surrogate Likelihoods for Variational Annealed Importance Sampling
- Symmetric Machine Theory of Mind
- Synergy and Symmetry in Deep Learning: Interactions between the Data, Model, and Inference Algorithm
- Synthetic Control Methods and Difference-In-Differences
- Tackling covariate shift with node-based Bayesian neural networks
- Tackling Data Heterogeneity: A New Unified Framework for Decentralized SGD with Sample-induced Topology
- TACTiS: Transformer-Attentional Copulas for Time Series
- TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification
- Task-aware Privacy Preservation for Multi-dimensional Data
- Tell me why! Explanations support learning relational and causal structure
- Temporal Difference Learning for Model Predictive Control
- Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing
- The 1st Workshop on Healthcare AI and COVID-19
- The Algebraic Path Problem for Graph Metrics
- The CLRS Algorithmic Reasoning Benchmark
- The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks
- The Complexity of k-Means Clustering when Little is Known
- The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention
- The dynamics of representation learning in shallow, non-linear autoencoders
- The First Workshop on Pre-training: Perspectives, Pitfalls, and Paths Forward
- The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning
- The Geometry of Robust Value Functions
- The ICML Expressive Vocalizations (ExVo) Workshop and Competition 2022
- The Importance of Non-Markovianity in Maximum State Entropy Exploration
- The Infinite Contextual Graph Markov Model
- The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks
- The Neural Race Reduction: Dynamics of Abstraction in Gated Networks
- Theory and Practice of Differential Privacy
- The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation
- The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces
- The power of first-order smooth optimization for black-box non-smooth problems
- The Primacy Bias in Deep Reinforcement Learning
- The Role of Deconfounding in Meta-learning
- The State of Sparse Training in Deep Reinforcement Learning
- The Teaching Dimension of Regularized Kernel Learners
- The Unsurprising Effectiveness of Pre-Trained Vision Models for Control
- Thompson Sampling for (Combinatorial) Pure Exploration
- Thompson Sampling for Robust Transfer in Multi-Task Bandits
- Three-stage Evolution and Fast Equilibrium for SGD with Non-degerate Critical Points
- Thresholded Lasso Bandit
- Tight and Robust Private Mean Estimation with Few Users
- Time Is MattEr: Temporal Self-supervision for Video Transformers
- Topology, Algebra, and Geometry in Machine Learning (TAG-ML)
- Topology-aware Generalization of Decentralized SGD
- Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning
- To Smooth or Not? When Label Smoothing Meets Noisy Labels
- Toward Compositional Generalization in Object-Oriented World Modeling
- Towards a Mathematical Theory of Machine Learning
- Towards Coherent and Consistent Use of Entities in Narrative Generation
- Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods
- Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent
- Towards Scaling Difference Target Propagation by Learning Backprop Targets
- Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs
- Towards understanding how momentum improves generalization in deep learning
- Towards Understanding Sharpness-Aware Minimization
- Towards Uniformly Superhuman Autonomy via Subdominance Minimization
- TPC: Transformation-Specific Smoothing for Point Cloud Models
- Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems
- Tractable Uncertainty for Structure Learning
- Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four
- Training Discrete Deep Generative Models via Gapped Straight-Through Estimator
- Training OOD Detectors in their Natural Habitats
- Training Your Sparse Neural Network Better with Any Mask
- Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval
- Transfer and Marginalize: Explaining Away Label Noise with Privileged Information
- Transfer Learning In Differential Privacy's Hybrid-Model
- Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling
- Transformer Quality in Linear Time
- Transformers are Meta-Reinforcement Learners
- Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots
- Translatotron 2: High-quality direct speech-to-speech translation with voice preservation
- TSPipe: Learn from Teacher Faster with Pipelines
- TURF: Two-Factor, Universal, Robust, Fast Distribution Learning Algorithm
- UAST: Uncertainty-Aware Siamese Tracking
- Unaligned Supervision for Automatic Music Transcription in The Wild
- Uncertainty Modeling in Generative Compressed Sensing
- UnderGrad: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees
- Understanding and Improving Knowledge Graph Embedding for Entity Alignment
- Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy
- Understanding Contrastive Learning Requires Incorporating Inductive Biases
- Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information
- Understanding Doubly Stochastic Clustering
- Understanding Gradient Descent on the Edge of Stability in Deep Learning
- Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond
- Understanding Instance-Level Impact of Fairness Constraints
- Understanding Policy Gradient Algorithms: A Sensitivity-Based Approach
- Understanding Robust Generalization in Learning Regular Languages
- Understanding Robust Overfitting of Adversarial Training and Beyond
- Understanding The Robustness in Vision Transformers
- Understanding the unstable convergence of gradient descent
- Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces
- Unified Scaling Laws for Routed Language Models
- UniRank: Unimodal Bandit Algorithms for Online Ranking
- UNIREX: A Unified Learning Framework for Language Model Rationale Extraction
- Universal and data-adaptive algorithms for model selection in linear contextual bandits
- Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models
- Universality of Winning Tickets: A Renormalization Group Perspective
- Universal Joint Approximation of Manifolds and Densities by Simple Injective Flows
- Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers
- Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology
- Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration
- Unsupervised Ground Metric Learning Using Wasserstein Singular Vectors
- Unsupervised Image Representation Learning with Deep Latent Particles
- Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion
- Updatable Machine Learning
- Utility Theory for Sequential Decision Making
- Utilizing Expert Features for Contrastive Learning of Time-Series Representations
- Validating Causal Inference Methods
- Validity, Reliability, and Significance: A Tutorial on Statistical Methods for Reproducible Machine Learning
- Value Function based Difference-of-Convex Algorithm for Bilevel Hyperparameter Selection Problems
- Variational Feature Pyramid Networks
- Variational Inference for Infinitely Deep Neural Networks
- Variational Inference with Locally Enhanced Bounds for Hierarchical Models
- Variational Mixtures of ODEs for Inferring Cellular Gene Expression Dynamics
- Variational nearest neighbor Gaussian process
- Variational On-the-Fly Personalization
- Variational Sparse Coding with Learned Thresholding
- Variational Wasserstein gradient flow
- VariGrow: Variational Architecture Growing for Task-Agnostic Continual Learning based on Bayesian Novelty
- VarScene: A Deep Generative Model for Realistic Scene Graph Synthesis
- Versatile Dueling Bandits: Best-of-both World Analyses for Learning from Relative Preferences
- Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy Matching
- Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning
- Visual Attention Emerges from Recurrent Sparse Reconstruction
- ViT-NeT: Interpretable Vision Transformers with Neural Tree Decoder
- VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix
- VLUE: A Multi-Task Multi-Dimension Benchmark for Evaluating Vision-Language Pre-training
- Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes
- Weisfeiler-Lehman Meets Gromov-Wasserstein
- Welcome to the "Big Model" Era: Techniques and Systems to Train and Serve Bigger Models
- Welfare Maximization in Competitive Equilibrium: Reinforcement Learning for Markov Exchange Economy
- What Can Linear Interpolation of Neural Network Loss Landscapes Tell Us?
- What Dense Graph Do You Need for Self-Attention?
- What Language Model Architecture and Pretraining Objective Works Best for Zero-Shot Generalization?
- When and How Mixup Improves Calibration
- When Are Linear Stochastic Bandits Attackable?
- When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee
- Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error
- Why the Rich Get Richer? On the Balancedness of Random Partition Models
- Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling
- Wide Neural Networks Forget Less Catastrophically
- Winning the Lottery Ahead of Time: Efficient Early Network Pruning
- Workshop on Distribution-Free Uncertainty Quantification
- Workshop on Formal Verification of Machine Learning
- Workshop on Human-Machine Collaboration and Teaming
- Workshop on Machine Learning in Computational Design
- XAI for Transformers: Better Explanations through Conservative Propagation
- You Only Cut Once: Boosting Data Augmentation with a Single Cut
- YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for Everyone
- Zero-shot AutoML with Pretrained Models
- Zero-Shot Reward Specification via Grounded Natural Language