Downloads 2021
Number of events: 1231
- 12-Lead ECG Reconstruction via Koopman Operators
- 1-bit Adam: Communication Efficient Large-Scale Training with Adam's Convergence Speed
- 8th ICML Workshop on Automated Machine Learning (AutoML 2021)
- A Bit More Bayesian: Domain-Invariant Learning with Uncertainty
- A Blessing in Disguise: The Prospects and Perils of Adversarial Machine Learning
- Accelerate CNNs from Three Dimensions: A Comprehensive Pruning Framework
- Accelerated Algorithms for Smooth Convex-Concave Minimax Problems with O(1/k^2) Rate on Squared Gradient Norm
- Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving
- Accelerating Gossip SGD with Periodic Global Averaging
- Accelerating Safe Reinforcement Learning with Constraint-mismatched Baseline Policies
- Acceleration via Fractal Learning Rate Schedules
- Accumulated Decoupled Learning with Gradient Staleness Mitigation for Convolutional Neural Networks
- Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
- Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization
- Accurate Post Training Quantization With Small Calibration Sets
- ACE: Explaining cluster from an adversarial perspective
- Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously
- A Collective Learning Framework to Boost GNN Expressiveness for Node Classification
- Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills
- Active Covering
- Active Deep Probabilistic Subsampling
- Active Feature Acquisition with Generative Surrogate Models
- Active Learning for Distributionally Robust Level-Set Estimation
- Active Learning of Continuous-time Bayesian Networks through Interventions
- Active Slices for Sliced Stein Discrepancy
- Active Testing: Sample-Efficient Model Evaluation
- ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training
- Adapting to Delays and Data in Adversarial Multi-Armed Bandits
- Adapting to misspecification in contextual bandits with offline regression oracles
- Adaptive Newton Sketch: Linear-time Optimization with Quadratic Convergence and Effective Hessian Dimensionality
- Adaptive Sampling for Best Policy Identification in Markov Decision Processes
- AdaXpert: Adapting Neural Architecture for Growing Data
- Additive Error Guarantees for Weighted Low Rank Approximation
- Addressing Catastrophic Forgetting in Few-Shot Problems
- A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation
- A Differentiable Point Process with Its Application to Spiking Neural Networks
- A Discriminative Technique for Multiple-Source Adaptation
- A Distribution-dependent Analysis of Meta Learning
- ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks
- Adversarial Combinatorial Bandits with General Non-linear Reward Functions
- Adversarial Dueling Bandits
- Adversarial Multi Class Learning under Weak Supervision with Performance Guarantees
- Adversarial Option-Aware Hierarchical Imitation Learning
- Adversarial Policy Learning in Two-player Competitive Games
- Adversarial Purification with Score-based Generative Models
- Adversarial Robustness Guarantees for Random Deep Neural Networks
- Affine Invariant Analysis of Frank-Wolfe on Strongly Convex Sets
- A Framework for Private Matrix Analysis in Sliding Window Model
- A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration
- A Functional Perspective on Learning Symmetric Functions with Neural Networks
- A General Framework For Detecting Anomalous Inputs to DNN Classifiers
- AGENT: A Benchmark for Core Psychological Reasoning
- Aggregating From Multiple Target-Shifted Sources
- Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins
- A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization
- A Hybrid Variance-Reduced Method for Decentralized Stochastic Non-Convex Optimization
- A Language for Counterfactual Generative Models
- A large-scale benchmark for few-shot program induction and synthesis
- Align, then memorise: the dynamics of learning with feedback alignment
- Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits
- A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning
- AlphaNet: Improved Training of Supernets with Alpha-Divergence
- Alternative Microfoundations for Strategic Classification
- A Modular Analysis of Provable Acceleration via Polyak's Momentum: Training a Wide ReLU Network and a Deep Linear Network
- Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation
- An Algorithm for Stochastic and Adversarial Bandits with Switching Costs
- Analysis of stochastic Lanczos quadrature for spectrum approximation
- Analyzing the tree-layer structure of Deep Forests
- An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming
- A New Formalism, Method and Open Issues for Zero-Shot Coordination
- A New Representation of Successor Features for Transfer across Dissimilar Environments
- An exact solver for the Weston-Watkins SVM subproblem
- An Identifiable Double VAE For Disentangled Representations
- An Information-Geometric Distance on the Space of Tasks
- An Integer Linear Programming Framework for Mining Constraints from Data
- Annealed Flow Transport Monte Carlo
- A Novel Method to Solve Neural Knapsack Problems
- A Novel Sequential Coreset Method for Gradient Descent Algorithms
- A Nullspace Property for Subspace-Preserving Recovery
- A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning
- Approximate Group Fairness for Clustering
- Approximating a Distribution Using Weight Queries
- Approximation Theory Based Methods for RKHS Bandits
- Approximation Theory of Convolutional Architectures for Time Series Modelling
- A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups
- A Precise Performance Analysis of Support Vector Regression
- A Probabilistic Approach to Neural Network Pruning
- A Proxy Variable View of Shared Confounding
- APS: Active Pretraining with Successor Features
- A Receptor Skeleton for Capsule Neural Networks
- A Regret Minimization Approach to Iterative Learning Control
- A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-Learning
- A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distance
- ARMS: Antithetic-REINFORCE-Multi-Sample Gradient for Binary Variables
- ASAM: Adaptive Sharpness-Aware Minimization for Scale-Invariant Learning of Deep Neural Networks
- A Sampling-Based Method for Tensor Ring Decomposition
- A Scalable Deterministic Global Optimization Algorithm for Clustering Problems
- A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few Samples
- A Second look at Exponential and Cosine Step Sizes: Simplicity, Adaptivity, and Performance
- A Sharp Analysis of Model-based Reinforcement Learning with Self-Play
- A statistical perspective on distillation
- A Structured Observation Distribution for Generative Biological Sequence Prediction and Forecasting
- Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections
- Asymmetric Loss Functions for Learning with Noisy Labels
- Asymptotic Normality and Confidence Intervals for Prediction Risk of the Min-Norm Least Squares Estimator
- Asymptotics of Ridge Regression in Convolutional Models
- Asynchronous Decentralized Optimization With Implicit Stochastic Variance Reduction
- Asynchronous Distributed Learning : Adapting to Gradient Delays without Prior Knowledge
- A Tale of Two Efficient and Informative Negative Sampling Distributions
- A theory of high dimensional regression with arbitrary correlations between input features and target functions: sample complexity, multiple descent curves and a hierarchy of phase transitions
- A Theory of Label Propagation for Subpopulation Shift
- Attention is not all you need: pure attention loses rank doubly exponentially with depth
- Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment
- A Unified Generative Adversarial Network Training via Self-Labeling and Self-Attention
- A Unified Lottery Ticket Hypothesis for Graph Neural Networks
- AutoAttend: Automated Attention Representation Search
- Autoencoder Image Interpolation by Shaping the Latent Space
- Autoencoding Under Normalization Constraints
- Automatic variational inference with cascading flows
- Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators
- Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting
- AutoSampling: Search for Effective Data Sampling Schedules
- A Value-Function-based Interior-point Method for Non-convex Bi-level Optimization
- Average-Reward Off-Policy Policy Evaluation with Function Approximation
- A Wasserstein Minimax Framework for Mixed Linear Regression
- A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimization
- Backdoor Scanning for Deep Neural Networks through K-Arm Optimization
- Backpropagated Neighborhood Aggregation for Accurate Training of Spiking Neural Networks
- BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining
- Barlow Twins: Self-Supervised Learning via Redundancy Reduction
- BASE Layers: Simplifying Training of Large, Sparse Models
- BASGD: Buffered Asynchronous SGD for Byzantine Learning
- BasisDeVAE: Interpretable Simultaneous Dimensionality Reduction and Feature-Level Clustering with Derivative-Based Variational Autoencoders
- Batch Value-function Approximation with Only Realizability
- Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information
- Bayesian Attention Belief Networks
- Bayesian Deep Learning via Subnetwork Inference
- Bayesian Optimistic Optimisation with Exponentially Decaying Regret
- Bayesian Optimization over Hybrid Spaces
- Bayesian Quadrature on Riemannian Data Manifolds
- Bayesian Structural Adaptation for Continual Learning
- Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement
- Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks
- Best Arm Identification in Graphical Bilinear Bandits
- Best Model Identification: A Rested Bandit Formulation
- Better Training using Weight-Constrained Stochastic Dynamics
- Beyond $log^2(T)$ regret for decentralized bandits in matching markets
- Beyond first-order methods in machine learning systems
- Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design
- Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization
- Bias-Free Scalable Gaussian Processes via Randomized Truncations
- Bias-Robust Bayesian Optimization via Dueling Bandits
- Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning
- Bilevel Optimization: Convergence Analysis and Enhanced Design
- Bilinear Classes: A Structural Framework for Provable Generalization in RL
- Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification
- Black-box density function estimation using recursive partitioning
- Blind Pareto Fairness and Subgroup Robustness
- Boosting for Online Convex Optimization
- Boosting the Throughput and Accelerator Utilization of Specialized CNN Inference Beyond Increasing Batch Size
- Bootstrapping Fitted Q-Evaluation for Off-Policy Inference
- BORE: Bayesian Optimization by Density-Ratio Estimation
- Breaking the Deadly Triad with a Target Network
- Breaking the Limits of Message Passing Graph Neural Networks
- Break-It-Fix-It: Unsupervised Learning for Program Repair
- Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation
- Budgeted Heterogeneous Treatment Effect Estimation
- Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data
- Calibrate Before Use: Improving Few-shot Performance of Language Models
- Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization?
- CARTL: Cooperative Adversarially-Robust Transfer Learning
- Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization
- CATE: Computation-aware Neural Architecture Encoding with Transformers
- Catformer: Designing Stable Transformers via Sensitivity Analysis
- Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning
- Causality-aware counterfactual confounding adjustment as an alternative to linear residualization in anticausal prediction tasks based on linear learners
- ChaCha for Online AutoML
- Challenges in Deploying and monitoring Machine Learning Systems
- Characterizing Fairness Over the Set of Good Models Under Selective Labels
- Characterizing Structural Regularities of Labeled Data in Overparameterized Models
- Characterizing the Gap Between Actor-Critic and Policy Gradient
- Chebyshev Polynomial Codes: Task Entanglement-based Coding for Distributed Matrix Multiplication
- CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection
- Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels
- Classification with Rejection Based on Cost-sensitive Classification
- Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed
- CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients
- Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels
- Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning
- Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition
- Coded-InvNet for Resilient Prediction Serving Systems
- Collaborative Bayesian Optimization with Fair Regret
- Combinatorial Blocking Bandits with Stochastic Delays
- Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning
- CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints
- Communication-Efficient Distributed Optimization with Quantized Preconditioners
- Communication-Efficient Distributed SVD via Local Power Iterations
- Commutative Lie Group VAE for Disentanglement Learning
- Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved Generalization
- Composing Normalizing Flows for Inverse Problems
- Compositional Video Synthesis with Action Graphs
- Compressed Maximum Likelihood
- Concentric mixtures of Mallows models for top-$k$ rankings: sampling and identifiability
- Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression
- Conditional Temporal Neural Processes with Covariance Loss
- Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
- Confidence-Budget Matching for Sequential Budgeted Learning
- Confidence Scores Make Instance-dependent Label-noise Learning Possible
- Conformal prediction interval for dynamic time-series
- Conjugate Energy-Based Models
- Connecting Interpretability and Robustness in Decision Trees through Separation
- Connecting Optimal Ex-Ante Collusion in Teams to Extensive-Form Correlation: Faster Algorithms and Positive Complexity Results
- Connecting Sphere Manifolds Hierarchically for Regularization
- Consensus Control for Decentralized Deep Learning
- Conservative Objective Models for Effective Offline Model-Based Optimization
- Consistent Nonparametric Methods for Network Assisted Covariate Estimation
- Consistent regression when oblivious outliers overwhelm
- Context-Aware Online Collective Inference for Templated Graphical Models
- Continual Learning in the Teacher-Student Setup: Impact of Task Similarity
- Continual Learning with Deep Architectures
- Continuous Coordination As a Realistic Scenario for Lifelong Learning
- Continuous-time Model-based Reinforcement Learning
- Contrastive Learning Inverts the Data Generating Process
- Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks
- Convex Regularization in Monte-Carlo Tree Search
- ConvexVST: A Convex Optimization Approach to Variance-stabilizing Transformation
- ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases
- Cooperative Exploration for Multi-Agent Deep Reinforcement Learning
- Correcting Exposure Bias for Link Recommendation
- Correlation Clustering in Constant Many Parallel Rounds
- Counterfactual Credit Assignment in Model-Free Reinforcement Learning
- CountSketches, Feature Hashing and the Median of Three
- CRFL: Certifiably Robust Federated Learning against Backdoor Attacks
- Cross-domain Imitation from Observations
- Cross-Gradient Aggregation for Decentralized Learning from Non-IID Data
- Cross-model Back-translated Distillation for Unsupervised Machine Translation
- Crowdsourcing via Annotator Co-occurrence Imputation and Provable Symmetric Nonnegative Matrix Factorization
- CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee
- Cryospheric Science and Emergence of Machine Learning
- Crystallization Learning with the Delaunay Triangulation
- Cumulants of Hawkes Processes are Robust to Observation Noise
- CURI: A Benchmark for Productive Concept Learning Under Uncertainty
- Cyclically Equivariant Neural Decoders for Cyclic Codes
- DAGs with No Curl: An Efficient DAG Structure Learning Approach
- DANCE: Enhancing saliency maps using decoys
- Dash: Semi-Supervised Learning with Dynamic Thresholding
- Data augmentation for deep learning based accelerated MRI reconstruction with limited data
- Data Augmentation for Meta-Learning
- Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps
- Data-efficient Hindsight Off-policy Option Learning
- Data-Free Knowledge Distillation for Heterogeneous Federated Learning
- Dataset Condensation with Differentiable Siamese Augmentation
- Dataset Dynamics via Gradient Flows in Probability Space
- Debiasing a First-order Heuristic for Approximate Bi-level Optimization
- Debiasing Model Updates for Improving Personalized Federated Training
- Decentralized Riemannian Gradient Descent on the Stiefel Manifold
- Decentralized Single-Timescale Actor-Critic on Zero-Sum Two-Player Stochastic Games
- Deciding What to Learn: A Rate-Distortion Approach
- Decision-Making Under Selective Labels: Optimal Finite-Domain Policies and Beyond
- Decomposable Submodular Function Minimization via Maximum Flow
- Decomposed Mutual Information Estimation for Contrastive Representation Learning
- Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices
- Decoupling Representation Learning from Reinforcement Learning
- Decoupling Value and Policy for Generalization in Reinforcement Learning
- Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design
- Deep Coherent Exploration for Continuous Control
- Deep Continuous Networks
- Deep Generative Learning via Schrödinger Bridge
- Deep kernel processes
- Deep Latent Graph Matching
- Deep Learning for Functional Data Analysis with Adaptive Basis Layers
- Deeply-Debiased Off-Policy Interval Estimation
- DeepReDuce: ReLU Reduction for Fast Private Inference
- Deep Reinforcement Learning amidst Continual Structured Non-Stationarity
- DeepWalking Backwards: From Embeddings Back to Graphs
- Defense against backdoor attacks via robust covariance estimation
- Delving into Deep Imbalanced Regression
- Demonstration-Conditioned Reinforcement Learning for Few-Shot Imitation
- Demystifying Inductive Biases for (Beta-)VAE Based Architectures
- Dense for the Price of Sparse: Improved Performance of Sparsely Initialized Networks via a Subspace Offset
- Density Constrained Reinforcement Learning
- Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers
- Detecting Rewards Deterioration in Episodic Reinforcement Learning
- Detection of Signal in the Spiked Rectangular Models
- DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
- DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs
- Dichotomous Optimistic Search to Quantify Human Perception
- Differentiable Dynamic Quantization with Mixed Precision and Adaptive Resolution
- Differentiable Particle Filtering via Entropy-Regularized Optimal Transport
- Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision
- Differentiable Spatial Planning using Transformers
- Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message
- Differentially Private Bayesian Inference for Generalized Linear Models
- Differentially-Private Clustering of Easy Instances
- Differentially Private Correlation Clustering
- Differentially Private Densest Subgraph Detection
- Differentially Private Quantiles
- Differentially Private Query Release Through Adaptive Projection
- Differentially Private Sliced Wasserstein Distance
- Diffusion Earth Mover's Distance and Distribution Embeddings
- Diffusion Source Identification on Networks with Statistical Confidence
- Dimensionality Reduction for the Sum-of-Distances Metric
- Directed Graph Embeddings in Pseudo-Riemannian Manifolds
- Directional Bias Amplification
- Directional Graph Networks
- Disambiguation of Weak Supervision leading to Exponential Convergence rates
- Discovering symbolic policies with deep reinforcement learning
- Discrete-Valued Latent Preference Matrix Estimation with Graph Side Information
- Discretization Drift in Two-Player Games
- Discriminative Complementary-Label Learning with Weighted Loss
- Disentangling Sampling and Labeling Bias for Learning in Large-output Spaces
- Disentangling syntax and semantics in the brain with deep networks
- Dissecting Supervised Constrastive Learning
- Distributed Nystr\"{o}m Kernel Learning with Communications
- Distributed Second Order Methods with Fast Rates and Compressed Communication
- Distributionally Robust Optimization with Markovian Data
- Distribution-Free Calibration Guarantees for Histogram Binning without Sample Splitting
- Ditto: Fair and Robust Federated Learning Through Personalization
- Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration
- Domain Generalization using Causal Matching
- Don’t Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification
- DORO: Distributional and Outlier Robust Optimization
- Double-Win Quant: Aggressively Winning Robustness of Quantized Deep Neural Networks via Random Precision Training and Inference
- Doubly Robust Off-Policy Actor-Critic: Convergence and Optimality
- DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning
- Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training
- DriftSurf: Stable-State / Reactive-State Learning under Concept Drift
- Dropout: Explicit Forms and Capacity Control
- Dual Principal Component Pursuit for Robust Subspace Learning: Theory and Algorithms for a Holistic Approach
- Dueling Convex Optimization
- Dynamic Balancing for Model Selection in Bandits and RL
- Dynamic Game Theoretic Neural Optimizer
- Dynamic Planning and Learning under Recovering Rewards
- Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games
- Efficient Differentiable Simulation of Articulated Bodies
- Efficient Generative Modelling of Protein Structure Fragments using a Deep Markov Model
- Efficient Iterative Amortized Inference for Learning Symmetric and Disentangled Multi-Object Representations
- Efficient Lottery Ticket Finding: Less Data is More
- Efficient Message Passing for 0–1 ILPs with Binary Decision Diagrams
- EfficientNetV2: Smaller Models and Faster Training
- Efficient Online Learning for Dynamic k-Clustering
- Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations
- Efficient Statistical Tests: A Neural Tangent Kernel Approach
- Efficient Training of Robust Decision Trees Against Adversarial Examples
- EfficientTTS: An Efficient and High-Quality Text-to-Speech Architecture
- Elastic Graph Neural Networks
- EL-Attention: Memory Efficient Lossless Attention for Generation
- Elementary superexpressive activations
- EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL
- Emergent Social Learning via Multi-agent Reinforcement Learning
- Emphatic Algorithms for Deep Reinforcement Learning
- Encoding and Decoding Speech From the Human Brain
- End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series
- E(n) Equivariant Graph Neural Networks
- Enhancing Robustness of Neural Networks through Fourier Stabilization
- Ensemble Bootstrapping for Q-Learning
- Environment Inference for Invariant Learning
- Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes
- Equivariant message passing for the prediction of tensorial properties and molecular spectra
- Equivariant Networks for Pixelized Spheres
- Esther Duflo, Plumbers and Mechanics: How ML can complement RCT in policy experiments
- Estimating $\alpha$-Rank from A Few Entries with Low Rank Matrix Completion
- Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning
- Estimation and Quantization of Expected Persistence Diagrams
- Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?
- Evaluating the Implicit Midpoint Integrator for Riemannian Hamiltonian Monte Carlo
- Event Outlier Detection in Continuous Time
- Evolving Attention with Residual Convolutions
- Exact Gap between Generalization Error and Uniform Convergence in Random Feature Models
- Exact Optimization of Conformal Predictors via Incremental and Decremental Learning
- Examining and Combating Spurious Features under Distribution Shift
- Explainable Automated Graph Representation Learning with Hyperparameter Importance
- Explaining Time Series Predictions with Dynamic Masks
- Explanations for Monotonic Classifiers.
- Exploiting Shared Representations for Personalized Federated Learning
- Exploiting structured data for learning contagious diseases under incomplete testing
- Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning
- Explore Visual Concept Formation for Image Classification
- Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL
- Exponentially Many Local Minima in Quantum Neural Networks
- Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics
- Expressive 1-Lipschitz Neural Networks for Robust Multiple Graph Learning against Adversarial Attacks
- Factor-analytic inverse regression for high-dimension, small-sample dimensionality reduction
- Fair Classification with Noisy Protected Attributes: A Framework with Provable Guarantees
- Fairness and Bias in Online Selection
- Fairness for Image Generation with Uncertain Sensitive Attributes
- Fairness of Exposure in Stochastic Bandits
- Fair Selective Classification Via Sufficiency
- Fast active learning for pure exploration in reinforcement learning
- Fast Algorithms for Stackelberg Prediction Game with Least Squares Loss
- Faster Kernel Matrix Algebra via Density Estimation
- Fast margin maximization via dual acceleration
- Fast Projection Onto Convex Smooth Constraints
- Fast Sketching of Polynomial Kernels of Polynomial Degree
- Fast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction
- f-Domain Adversarial Learning: Theory and Algorithms
- Feature Clustering for Support Identification in Extreme Regions
- Federated Composite Optimization
- Federated Continual Learning with Weighted Inter-client Transfer
- Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity
- Federated Learning of User Verification Models Without Sharing Embeddings
- Federated Learning under Arbitrary Communication Patterns
- Few-Shot Conformal Prediction with Auxiliary Tasks
- Few-shot Language Coordination by Modeling Theory of Mind
- Few-Shot Neural Architecture Search
- FILTRA: Rethinking Steerable CNN by Filter Transform
- Finding k in Latent $k-$ polytope
- Finding Relevant Information via a Discrete Fourier Expansion
- Finding the Stochastic Shortest Path with Low Regret: the Adversarial Cost and Unknown Transition Case
- Finite mixture models do not reliably learn the number of components
- Finite-Sample Analysis of Off-Policy Natural Actor-Critic Algorithm
- First-Order Methods for Wasserstein Distributionally Robust MDP
- Fixed-Parameter and Approximation Algorithms for PCA with Outliers
- FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis
- Flow-based Attribution in Graphical Models: A Recursive Shapley Approach
- Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design
- Follow-the-Regularized-Leader Routes to Chaos in Routing Games
- FOP: Factorizing Optimal Joint Policy of Maximum-Entropy Multi-Agent Reinforcement Learning
- From Local Structures to Size Generalization in Graph Neural Networks
- From Local to Global Norm Emergence: Dissolving Self-reinforcing Substructures with Incremental Social Instruments
- From ML research to ML products: A path towards building models with real-world impact
- From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization
- Functional Space Analysis of Local GAN Convergence
- Function Contrastive Learning of Transferable Meta-Representations
- Fundamental Tradeoffs in Distributionally Adversarial Training
- Fused Acoustic and Text Encoding for Multimodal Bilingual Pretraining and Speech Translation
- GANMEX: One-vs-One Attributions using GAN-based Model Explainability
- Gaussian Process-Based Real-Time Learning for Safety Critical Applications
- GBHT: Gradient Boosting Histogram Transform for Density Estimation
- Generalised Lipschitz Regularisation Equals Distributional Robustness
- Generalizable Episodic Memory for Deep Reinforcement Learning
- Generalization Bounds in the Presence of Outliers: a Median-of-Means Study
- Generalization Error Bound for Hyperbolic Ordinal Embedding
- Generalization Guarantees for Neural Architecture Search with Train-Validation Split
- Generalized Doubly Reparameterized Gradient Estimators
- Generating images with sparse representations
- Generative Adversarial Networks for Markovian Temporal Dynamics: Stochastic Continuous Data Generation
- Generative Adversarial Transformers
- Generative Causal Explanations for Graph Neural Networks
- Generative Particle Variational Inference via Estimation of Functional Gradients
- Generative Video Transformer: Can Objects be the Words?
- GeomCA: Geometric Evaluation of Data Representations
- Geometric convergence of elliptical slice sampling
- Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances
- Global Convergence of Policy Gradient for Linear-Quadratic Mean-Field Control/Game in Continuous Time
- Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes
- Globally-Robust Neural Networks
- Global Optimality Beyond Two Layers: Training Deep ReLU Networks via Convex Programs
- Global Prosody Style Transfer Without Text Transcriptions
- GLSearch: Maximum Common Subgraph Detection via Learning to Search
- GMAC: A Distributional Perspective on Actor-Critic Framework
- GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings
- Goal-Conditioned Reinforcement Learning with Imagined Subgoals
- GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning
- Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix
- GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training
- Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech
- GRAND: Graph Neural Diffusion
- Graph Contrastive Learning Automated
- Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization
- Graph Cuts Always Find a Global Optimum for Potts Models (With a Catch)
- GraphDF: A Discrete Flow Model for Molecular Graph Generation
- Graph Mixture Density Networks
- Graph Neural Networks Inspired by Classical Iterative Algorithms
- GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training
- Grey-box Extraction of Natural Language Models
- Grid-Functioned Neural Networks
- Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning
- Group Fisher Pruning for Practical Network Compression
- Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings
- Guarantees for Tuning the Step Size using a Learning-to-Learn Approach
- Guided Exploration with Proximal Policy Optimization using a Single Demonstration
- HardCoRe-NAS: Hard Constrained diffeRentiable Neural Architecture Search
- HAWQ-V3: Dyadic Neural Network Quantization
- HEMET: A Homomorphic-Encryption-Friendly Privacy-Preserving Mobile Neural Network Architecture
- Heterogeneity for the Win: One-Shot Federated Clustering
- Heterogeneous Risk Minimization
- "Hey, that's not an ODE": Faster ODE Adjoints via Seminorms
- Hierarchical Agglomerative Graph Clustering in Nearly-Linear Time
- Hierarchical Clustering of Data Streams: Scalable Algorithms and Approximation Guarantees
- Hierarchical VAEs Know What They Don’t Know
- High Confidence Generalization for Reinforcement Learning
- High-dimensional Experimental Design and Kernel Bandits
- High-Dimensional Gaussian Process Inference with Derivatives
- High-Performance Large-Scale Image Recognition Without Normalization
- Homomorphic Sensing: Sparsity and Noise
- HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical Projections
- Householder Sketch for Accurate and Accelerated Least-Mean-Squares Solvers
- How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference
- How could Neural Networks understand Programs?
- How Do Adam and Training Strategies Help BNNs Optimization
- How Does Loss Function Affect Generalization Performance of Deep Learning? Application to Human Age Estimation
- How Framelets Enhance Graph Neural Networks
- How Important is the Train-Validation Split in Meta-Learning?
- How rotational invariance of common kernels prevents generalization in high dimensions
- How to Learn when Data Reacts to Your Model: Performative Gradient Descent
- Human-AI Collaboration in Sequential Decision-Making
- HyperHyperNetwork for the Design of Antenna Arrays
- Hyperparameter Selection for Imitation Learning
- I-BERT: Integer-only BERT Quantization
- ICML 2021 Workshop on Computational Biology
- ICML 2021 Workshop on Unsupervised Reinforcement Learning
- ICML Workshop on Algorithmic Recourse
- ICML Workshop on Human in the Loop Learning (HILL)
- ICML Workshop on Representation Learning for Finance and E-Commerce Applications
- ICML Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI
- iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients
- Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection
- Imitation by Predicting Observations
- Implicit Bias of Linear RNNs
- Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold
- Implicit rate-constrained optimization of non-decomposable objectives
- Implicit Regularization in Tensor Factorization
- Improved Algorithms for Agnostic Pool-based Active Classification
- Improved Confidence Bounds for the Linear Logistic Model and Applications to Bandits
- Improved Contrastive Divergence Training of Energy-Based Models
- Improved Corruption Robust Algorithms for Episodic Reinforcement Learning
- Improved Denoising Diffusion Probabilistic Models
- Improved, Deterministic Smoothing for L_1 Certified Robustness
- Improved OOD Generalization via Adversarial Training and Pretraing
- Improved Regret Bound and Experience Replay in Regularized Policy Iteration
- Improved Regret Bounds of Bilinear Bandits using Action Space Analysis
- Improving Breadth-Wise Backpropagation in Graph Neural Networks Helps Learning Long-Range Dependencies.
- Improving Generalization in Meta-learning via Task Augmentation
- Improving Gradient Regularization using Complex-Valued Neural Networks
- Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding
- Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity
- Improving Predictors via Combination Across Diverse Task Categories
- Improving Ultrametrics Embeddings Through Coresets
- Incentivized Bandit Learning with Self-Reinforcing User Preferences
- Incentivizing Compliance with Algorithmic Instruments
- In-Database Regression in Input Sparsity Time
- Inference for Network Regression Models with Community Structure
- Inferring Latent Dynamics Underlying Neural Population Activity via Neural Differential Equations
- Inferring serial correlation with dynamic backgrounds
- Infinite-Dimensional Optimization for Zero-Sum Games via Variational Transport
- Information Obfuscation of Graph Neural Networks
- Information-Theoretic Methods for Rigorous, Responsible, and Reliable Machine Learning (ITR3)
- INNF+: Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models
- Instabilities of Offline RL with Pre-Trained Neural Representation
- Instance-Optimal Compressed Sensing via Posterior Sampling
- Instance Specific Approximations for Submodular Maximization
- Integer Programming for Causal Structure Learning in the Presence of Latent Variables
- Integrated Defense for Resilient Graph Matching
- Interaction-Grounded Learning
- Interactive Learning from Activity Description
- Intermediate Layer Optimization for Inverse Problems using Deep Generative Models
- International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2021 (FL-ICML'21)
- Interpretable Machine Learning in Healthcare
- Interpretable Stability Bounds for Spectral Graph Filters
- Interpretable Stein Goodness-of-fit Tests on Riemannian Manifold
- Interpreting and Disentangling Feature Components of Various Complexity from DNNs
- Inverse Constrained Reinforcement Learning
- Inverse Decision Modeling: Learning Interpretable Representations of Behavior
- Isometric Gaussian Process Latent Variable Model for Dissimilarity Data
- Is Pessimism Provably Efficient for Offline RL?
- Is Space-Time Attention All You Need for Video Understanding?
- Joining datasets via data augmentation in the label space for neural networks
- Joint Online Learning and Decision-making via Dual Mirror Descent
- Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks
- Just Train Twice: Improving Group Robustness without Training Group Information
- KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation
- Kernel-Based Reinforcement Learning: A Finite-Time Analysis
- Kernel Continual Learning
- Kernel Stein Discrepancy Descent
- Keyframe-Focused Visual Imitation Learning
- KNAS: Green Neural Architecture Search
- Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks
- KO codes: inventing nonlinear encoding and decoding for reliable wireless communication via deep-learning
- K-shot NAS: Learnable Weight-Sharing for NAS with K-shot Supernets
- Label Distribution Learning Machine
- Label Inference Attacks from Log-loss Scores
- Label-Only Membership Inference Attacks
- LAMDA: Label Matching Deep Domain Adaptation
- Large-Margin Contrastive Learning with Distance Polarization Regularizer
- Large-Scale Meta-Learning with Continual Trajectory Shifting
- Large-Scale Multi-Agent Deep FBSDEs
- Large Scale Private Learning via Low-rank Reparametrization
- LARNet: Lie Algebra Residual Network for Face Recognition
- Latent Programmer: Discrete Latent Codes for Program Synthesis
- Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classification
- Learn2Hop: Learned Optimization on Rough Landscapes
- Learner-Private Convex Optimization
- Learning and Planning in Average-Reward Markov Decision Processes
- Learning and Planning in Complex Action Spaces
- Learning a Universal Template for Few-shot Dataset Generalization
- Learning Binary Decision Trees by Argmin Differentiation
- Learning Bounds for Open-Set Learning
- Learning by Turning: Neural Architecture Aware Optimisation
- Learning Curves for Analysis of Deep Networks
- Learning Deep Neural Networks under Agnostic Corrupted Supervision
- Learning de-identified representations of prosody from raw audio
- Learning disentangled representations via product manifold projection
- Learning Diverse-Structured Networks for Adversarial Robustness
- Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning
- Learning from Biased Data: A Semi-Parametric Approach
- Learning from History for Byzantine Robust Optimization
- Learning from Nested Data with Ornstein Auto-Encoders
- Learning from Noisy Labels with No Change to the Training Process
- Learning from Similarity-Confidence Data
- Learning Generalized Intersection Over Union for Dense Pixelwise Prediction
- Learning Gradient Fields for Molecular Conformation Generation
- Learning in Nonzero-Sum Stochastic Games with Potentials
- Learning Interaction Kernels for Agent Systems on Riemannian Manifolds
- Learning Intra-Batch Connections for Deep Metric Learning
- Learning Neural Network Subspaces
- Learning Node Representations Using Stationary Flow Prediction on Large Payment and Cash Transaction Networks
- Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization
- Learning Online Algorithms with Distributional Advice
- Learning Optimal Auctions with Correlated Valuations from Samples
- Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis
- Learning Randomly Perturbed Structured Predictors for Direct Loss Minimization
- Learning Representations by Humans, for Humans
- Learning Routines for Effective Off-Policy Reinforcement Learning
- Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation
- Learning Stochastic Behaviour from Aggregate Data
- Learning Task Informed Abstractions
- Learning to Generate Noise for Multi-Attack Robustness
- Learning to Price Against a Moving Target
- Learning to Rehearse in Long Sequence Memorization
- Learning to Weight Imperfect Demonstrations
- Learning Transferable Visual Models From Natural Language Supervision
- Learning While Playing in Mean-Field Games: Convergence and Optimality
- Learn-to-Share: A Hardware-friendly Transfer Learning Framework Exploiting Computation and Parameter Sharing
- LEGO: Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs
- Lenient Regret and Good-Action Identification in Gaussian Process Bandits
- Let's Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework
- Leveraged Weighted Loss for Partial Label Learning
- Leveraging Good Representations in Linear Contextual Bandits
- Leveraging Language to Learn Program Abstractions and Search Heuristics
- Leveraging Non-uniformity in First-order Non-convex Optimization
- Leveraging Public Data for Practical Private Query Release
- Leveraging Sparse Linear Layers for Debuggable Deep Networks
- LieTransformer: Equivariant Self-Attention for Lie Groups
- Light RUMs
- LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning
- Linear Transformers Are Secretly Fast Weight Programmers
- Link Prediction with Persistent Homology: An Interactive View
- Lipschitz normalization for self-attention layers with application to graph neural networks
- Local Algorithms for Finding Densely Connected Clusters
- Local Correlation Clustering with Asymmetric Classification Errors
- Locally Adaptive Label Smoothing Improves Predictive Churn
- Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards
- Locally Private k-Means in One Round
- Logarithmic Regret for Reinforcement Learning with Linear Function Approximation
- LogME: Practical Assessment of Pre-trained Models for Transfer Learning
- Lossless Compression of Efficient Private Local Randomizers
- Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling
- Lottery Ticket Preserves Weight Correlation: Is It Desirable or Not?
- Lower-Bounded Proper Losses for Weakly Supervised Classification
- Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries
- Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision
- Low-Rank Sinkhorn Factorization
- LTL2Action: Generalizing LTL Instructions for Multi-Task RL
- Machine Learning for Data: Automated Creation, Privacy, Bias
- Machine Learning for Molecular Science
- Machine Unlearning for Random Forests
- Making Paper Reviewing Robust to Bid Manipulation Attacks
- Making transport more robust and interpretable by moving data through a small number of anchor points
- Mandoline: Model Evaluation under Distribution Shift
- Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data
- Marginalized Stochastic Natural Gradients for Black-Box Variational Inference
- MARINA: Faster Non-Convex Distributed Learning with Compression
- Markpainting: Adversarial Machine Learning meets Inpainting
- Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling
- Matrix Completion with Model-free Weighting
- Matrix Sketching for Secure Collaborative Machine Learning
- Maximum Mean Discrepancy Test is Aware of Adversarial Attacks
- MC-LSTM: Mass-Conserving LSTM
- Measuring Robustness in Deep Learning Based Compressive Sensing
- Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences
- Megaverse: Simulating Embodied Agents at One Million Experiences per Second
- Memory Efficient Online Meta Learning
- Memory-Efficient Pipeline-Parallel DNN Training
- Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning
- Meta-Cal: Well-controlled Post-hoc Calibration by Ranking
- MetaCURE: Meta Reinforcement Learning with Empowerment-Driven Exploration
- Meta-Learning Bidirectional Update Rules
- Meta Learning for Support Recovery in High-dimensional Precision Matrix Estimation
- Meta-learning Hyperparameter Performance Prediction with Neural Processes
- Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation
- Meta-Thompson Sampling
- Mind the Box: $l_1$-APGD for Sparse Adversarial Attacks on Image Classifiers
- Mixed Cross Entropy Loss for Neural Machine Translation
- Mixed Nash Equilibria in the Adversarial Examples Game
- Model-based Reinforcement Learning for Continuous Control with Posterior Sampling
- Model-Based Reinforcement Learning via Latent-Space Collocation
- Model Distillation for Revenue Optimization: Interpretable Personalized Pricing
- Model-Free and Model-Based Policy Evaluation when Causality is Uncertain
- Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity
- Model Fusion for Personalized Learning
- Modeling Hierarchical Structures with Continuous Recursive Neural Networks
- Modelling Behavioural Diversity for Learning in Open-Ended Games
- Model Performance Scaling with Multiple Data Sources
- Model-Targeted Poisoning Attacks with Provable Convergence
- Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment
- Momentum Residual Neural Networks
- Monotonic Robust Policy Optimization with Model Discrepancy
- Monte Carlo Variational Auto-Encoders
- Moreau-Yosida $f$-divergences
- More Powerful and General Selective Inference for Stepwise Feature Selection using Homotopy Method
- MorphVAE: Generating Neural Morphologies from 3D-Walks using a Variational Autoencoder with Spherical Latent Space
- MOTS: Minimax Optimal Thompson Sampling
- MSA Transformer
- Muesli: Combining Improvements in Policy Optimization
- Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers
- Multi-Dimensional Classification via Sparse Label Encoding
- Multidimensional Scaling: Approximation and Complexity
- Multi-group Agnostic PAC Learnability
- Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning
- Multiplicative Noise and Heavy Tails in Stochastic Optimization
- Multiplying Matrices Without Multiplying
- Multi-Receiver Online Bayesian Persuasion
- Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference
- Multi-Task Reinforcement Learning with Context-based Representations
- MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning
- Narrow Margins: Classification, Margins and Fat Tails
- Natural-XAI: Explainable AI with Natural Language Explanations
- Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation
- Near-Optimal Algorithms for Explainable k-Medians and k-Means
- Near-Optimal Confidence Sequences for Bounded Random Variables
- Near-Optimal Entrywise Anomaly Detection for Low-Rank Matrices with Sub-Exponential Noise
- Near-Optimal Linear Regression under Distribution Shift
- Near-Optimal Model-Free Reinforcement Learning in Non-Stationary Episodic MDPs
- Near-Optimal Representation Learning for Linear Bandits and Linear RL
- Near Optimal Reward-Free Reinforcement Learning
- Necessary and sufficient conditions for causal feature selection in time series with latent common causes
- Neighborhood Contrastive Learning Applied to Online Patient Monitoring
- NeRF-VAE: A Geometry Aware 3D Scene Generative Model
- Network Inference and Influence Maximization from Samples
- Neural Architecture Search without Training
- Neural Feature Matching in Implicit 3D Representations
- Neural Pharmacodynamic State Space Modeling
- Neural-Pull: Learning Signed Distance Function from Point clouds by Learning to Pull Space onto Surface
- Neural Rough Differential Equations for Long Time Series
- Neural SDEs as Infinite-Dimensional GANs
- Neural Symbolic Regression that scales
- Neural Tangent Generalization Attacks
- Neural Transformation Learning for Deep Anomaly Detection Beyond Images
- Neuro-algorithmic Policies Enable Fast Combinatorial Generalization
- Newton Method over Networks is Fast up to the Statistical Precision
- Noise and Fluctuation of Finite Learning Rate Stochastic Gradient Descent
- Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction
- Nondeterminism and Instability in Neural Network Optimization
- Non-Exponentially Weighted Aggregation: Regret Bounds for Unbounded Loss Functions
- Nonmyopic Multifidelity Acitve Search
- Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation
- Nonparametric Decomposition of Sparse Tensors
- Nonparametric Hamiltonian Monte Carlo
- No-regret Algorithms for Capturing Events in Poisson Point Processes
- Not All Memories are Created Equal: Learning to Forget by Expiring
- Objective Bound Conditional Gaussian Process for Bayesian Optimization
- Object Segmentation Without Labels with Large-Scale Generative Models
- Oblivious Sketching-based Central Path Method for Linear Programming
- Oblivious Sketching for Logistic Regression
- Off-Belief Learning
- Offline Contextual Bandits with Overparameterized Models
- Offline Meta-Reinforcement Learning with Advantage Weighting
- Offline Reinforcement Learning with Fisher Divergence Critic Regularization
- Offline Reinforcement Learning with Pseudometric Learning
- Off-Policy Confidence Sequences
- Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap
- OmniNet: Omnidirectional Representations from Transformers
- On a Combination of Alternating Minimization and Nesterov's Momentum
- On Characterizing GAN Convergence Through Proximal Duality Gap
- On Disentangled Representations Learned from Correlated Data
- One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning
- On Energy-Based Models with Overparametrized Shallow Neural Networks
- One Pass Late Fusion Multi-view Clustering
- Oneshot Differentially Private Top-k Selection
- One-sided Frank-Wolfe algorithms for saddle problems
- On Estimation in Latent Variable Models
- On Explainability of Graph Neural Networks via Subgraph Explorations
- On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting
- On Limited-Memory Subsampling Strategies for Bandits
- Online and non-stochastic control
- Online A-Optimal Design and Active Linear Regression
- On Linear Identifiability of Learned Representations
- Online Graph Dictionary Learning
- Online Learning for Load Balancing of Unknown Monotone Resource Allocation Games
- Online Learning in Unknown Markov Games
- Online Learning with Optimism and Delay
- Online Limited Memory Neural-Linear Bandits with Likelihood Matching
- Online Optimization in Games via Control Theory: Connecting Regret, Passivity and Poincaré Recurrence
- Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with √T Regret
- Online Selection Problems against Constrained Adversary
- Online Submodular Resource Allocation with Applications to Rebalancing Shared Mobility Systems
- Online Unrelated Machine Load Balancing with Predictions Revisited
- On Lower Bounds for Standard and Robust Gaussian Process Bandit Optimization
- On Monotonic Linear Interpolation of Neural Network Parameters
- On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification
- On Perceptual Lossy Compression: The Cost of Perceptual Reconstruction and An Optimal Training Framework
- On-Policy Deep Reinforcement Learning for the Average-Reward Criterion
- On Proximal Policy Optimization's Heavy-tailed Gradients
- On Recovering from Modeling Errors Using Testing Bayesian Networks
- On Reinforcement Learning with Adversarial Corruption and Its Application to Block MDP
- On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game
- On Robust Mean Estimation under Coordinate-level Corruption
- On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes
- On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients
- On the difficulty of unbiased alpha divergence minimization
- On the Explicit Role of Initialization on the Convergence and Implicit Bias of Overparametrized Linear Networks
- On-the-fly Rectification for Robust Large-Vocabulary Topic Inference
- On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models
- On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent
- On the Inherent Regularization Effects of Noise Injection During Training
- On the Optimality of Batch Policy Optimization Algorithms
- On the Power of Localized Perceptron for Label-Optimal Learning of Halfspaces with Adversarial Noise
- On the Predictability of Pruning Across Scales
- On the price of explainability for some clustering problems
- On the Problem of Underranking in Group-Fair Ranking
- On the Proof of Global Convergence of Gradient Descent for Deep ReLU Networks with Linear Widths
- On the Random Conjugate Kernel and Neural Tangent Kernel
- On Variational Inference in Biclustering Models
- Oops I Took A Gradient: Scalable Sampling for Discrete Distributions
- Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics
- Operationalizing Complex Causes: A Pragmatic View of Mediation
- OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation
- Optimal Complexity in Decentralized Training
- Optimal Counterfactual Explanations in Tree Ensembles
- Optimal Estimation of High Dimensional Smooth Additive Function Based on Noisy Observations
- Optimal Non-Convex Exact Recovery in Stochastic Block Model via Projected Power Method
- Optimal Off-Policy Evaluation from Multiple Logging Policies
- Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization
- Optimal Streaming Algorithms for Multi-Armed Bandits
- Optimal Thompson Sampling strategies for support-aware CVaR bandits
- Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search
- Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth
- Optimization Planning for 3D ConvNets
- Optimizing Black-box Metrics with Iterative Example Weighting
- Optimizing persistent homology based functions
- Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translation
- Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation
- Outlier-Robust Optimal Transport
- Out-of-Distribution Generalization via Risk Extrapolation (REx)
- Outside the Echo Chamber: Optimizing the Performative Risk
- Overcoming Catastrophic Forgetting by Bayesian Generative Regularization
- Over-parameterization: Pitfalls and Opportunities
- PAC-Learning for Strategic Classification
- PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees
- PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization
- PAPRIKA: Private Online False Discovery Rate Control
- Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics
- Parallel Droplet Control in MEDA Biochips using Multi-Agent Reinforcement Learning
- Parallelizing Legendre Memory Unit Training
- Parallel tempering on optimized paths
- Parameter-free Locally Accelerated Conditional Gradients
- Parameterless Transductive Feature Re-representation for Few-Shot Learning
- Parametric Graph for Unimodal Ranking Bandit
- Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions
- Partially Observed Exchangeable Modeling
- Path Planning using Neural A* Search
- PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration
- PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training
- Perceiver: General Perception with Iterative Attention
- Permutation Weighting
- Personalized Federated Learning using Hypernetworks
- Phase Transitions, Distance Functions, and Implicit Neural Representations
- Phasic Policy Gradient
- PHEW : Constructing Sparse Networks that Learn Fast and Generalize Well without Training Data
- PID Accelerated Value Iteration Algorithm
- PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models
- PixelTransformer: Sample Conditioned Signal Generation
- PODS: Policy Optimization via Differentiable Simulation
- Pointwise Binary Classification with Pairwise Confidence Comparisons
- Poisson-Randomised DirBN: Large Mutation is Needed in Dirichlet Belief Networks
- Policy Analysis using Synthetic Controls in Continuous-Time
- Policy Caches with Successor Features
- Policy Gradient Bayesian Robust Optimization for Imitation Learning
- Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning
- Poolingformer: Long Document Modeling with Pooling Attention
- PopSkipJump: Decision-Based Attack for Probabilistic Classifiers
- Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization
- Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization
- Posterior Value Functions: Hindsight Baselines for Policy Gradient Methods
- Post-selection inference with HSIC-Lasso
- Practical and Private (Deep) Learning Without Sampling or Shuffling
- Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations
- Predict then Interpolate: A Simple Algorithm to Learn Stable Classifiers
- Preferential Temporal Difference Learning
- Principal Bit Analysis: Autoencoding with Schur-Concave Loss
- Principal Component Hierarchy for Sparse Quadratic Programs
- Principled Exploration via Optimistic Bootstrapping and Backward Induction
- Principled Simplicial Neural Networks for Trajectory Prediction
- Prior Image-Constrained Reconstruction using Style-Based Generative Models
- Prioritized Level Replay
- Privacy in learning: Basics and the interplay
- Privacy-Preserving Feature Selection with Secure Multiparty Computation
- Privacy-Preserving Video Classification with Convolutional Neural Networks
- Private Adaptive Gradient Methods for Convex Optimization
- Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates
- Private Stochastic Convex Optimization: Optimal Rates in L1 Geometry
- Probabilistic Generating Circuits
- Probabilistic Programs with Stochastic Conditioning
- Probabilistic Sequential Shrinking: A Best Arm Identification Algorithm for Stochastic Bandits with Corruptions
- Problem Dependent View on Structured Thresholding Bandit Problems
- ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations
- Progressive-Scale Boundary Blackbox Attack via Projective Gradient Estimation
- Projection Robust Wasserstein Barycenters
- Projection techniques to update the truncated SVD of evolving matrices with applications
- Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise
- Provable Lipschitz Certification for Generative Models
- Provable Meta-Learning of Linear Representations
- Provable Robustness of Adversarial Training for Learning Halfspaces with Noise
- Provably Correct Optimization and Exploration with Non-linear Policies
- Provably Efficient Algorithms for Multi-Objective Competitive RL
- Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions
- Provably Efficient Learning of Transferable Rewards
- Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping
- Provably End-to-end Label-noise Learning without Anchor Points
- Provably Strict Generalisation Benefit for Equivariant Models
- Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction
- PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning
- Pure Exploration and Regret Minimization in Matching Bandits
- Putting the ``Learning" into Learning-Augmented Algorithms for Frequency Estimation
- Quantifying and Reducing Bias in Maximum Likelihood Estimation of Structured Anomalies
- Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability
- Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding
- Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels
- Quantile Bandits for Best Arms Identification
- Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding
- Quantization Algorithms for Random Fourier Features
- Quantum algorithms for reinforcement learning with a generative model
- Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data
- Query Complexity of Adversarial Attacks
- Randomized Algorithms for Submodular Function Maximization with a $k$-System Constraint
- Randomized Dimensionality Reduction for Facility Location and Single-Linkage Clustering
- Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning
- Randomized Exploration in Reinforcement Learning with General Value Function Approximation
- Random Matrix Theory and ML (RMT+ML)
- Rate-Distortion Analysis of Minimum Excess Risk in Bayesian Learning
- RATT: Leveraging Unlabeled Data to Guarantee Generalization
- Reasoning Over Virtual Knowledge Bases With Open Predicate Relations
- Recomposing the Reinforcement Learning Building Blocks with Hypernetworks
- Recovering AES Keys with a Deep Cold Boot Attack
- Regret and Cumulative Constraint Violation Analysis for Online Convex Optimization with Long Term Constraints
- Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach
- Regularized Online Allocation Problems: Fairness and Beyond
- Regularized Submodular Maximization at Scale
- Regularizing towards Causal Invariance: Linear Models with Proxies
- Reinforcement Learning for Cost-Aware Markov Decision Processes
- Reinforcement Learning for Real Life
- Reinforcement Learning of Implicit and Explicit Control Flow Instructions
- Reinforcement Learning Under Moral Uncertainty
- Reinforcement Learning with Prototypical Representations
- Relative Deviation Margin Bounds
- Relative Positional Encoding for Transformers with Linear Complexity
- REPAINT: Knowledge Transfer in Deep Reinforcement Learning
- Representational aspects of depth and conditioning in normalizing flows
- Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data
- Representation Matters: Offline Pretraining for Sequential Decision Making
- Representation Subspace Distance for Domain Adaptation Regression
- Reserve Price Optimization for First Price Auctions in Display Advertising
- Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism
- Responsible AI in Industry: Practical Challenges and Lessons Learned
- Rethinking Drug Discovery in the Era of Digital Biology
- Rethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives
- Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss
- Re-understanding Finite-State Representations of Recurrent Policy Networks
- Revealing the Structure of Deep Neural Networks via Convex Duality
- Revenue-Incentive Tradeoffs in Dynamic Reserve Pricing
- Revisiting Peng's Q($\lambda$) for Modern Reinforcement Learning
- Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline
- Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research
- Reward Identification in Inverse Reinforcement Learning
- Riemannian Convex Potential Maps
- Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning
- Risk-Sensitive Reinforcement Learning with Function Approximation: A Debiasing Approach
- Rissanen Data Analysis: Examining Dataset Characteristics via Description Length
- RNNRepair: Automatic RNN Repair via Model-based Analysis
- RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting
- Robust Asymmetric Learning in POMDPs
- Robust Density Estimation from Batches: The Best Things in Life are (Nearly) Free
- Robust Inference for High-Dimensional Linear Models via Residual Randomization
- Robust Learning-Augmented Caching: An Experimental Study
- Robust Learning for Data Poisoning Attacks
- Robust Policy Gradient against Strong Data Corruption
- Robust Pure Exploration in Linear Bandits with Limited Budget
- Robust Reinforcement Learning using Least Squares Policy Iteration with Provable Performance Guarantees
- Robust Representation Learning via Perceptual Similarity Metrics
- Robust Testing and Estimation under Manipulation Attacks
- Robust Unsupervised Learning via L-statistic Minimization
- RRL: Resnet as representation for Reinforcement Learning
- Run-Sort-ReRun: Escaping Batch Size Limitations in Sliced Wasserstein Generative Models
- Safe Reinforcement Learning Using Advantage-Based Intervention
- Safe Reinforcement Learning with Linear Function Approximation
- SagaNet: A Small Sample Gated Network for Pediatric Cancer Diagnosis
- SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning
- Sample Complexity of Robust Linear Classification on Separated Data
- Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity
- Sample-Optimal PAC Learning of Halfspaces with Malicious Noise
- Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network
- Scalable Certified Segmentation via Randomized Smoothing
- Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks
- Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot
- Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
- Scalable Normalizing Flows for Permutation Invariant Densities
- Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More
- Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition
- Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing
- Scaling Properties of Deep Residual Networks
- Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
- SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II
- SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies
- Segmenting Hybrid Trajectories using Latent ODEs
- Selecting Data Augmentation for Simulating Interventions
- Self-Attention for Computer Vision
- Self-Damaging Contrastive Learning
- Self-Improved Retrosynthetic Planning
- Selfish Sparse RNN Training
- Self Normalizing Flows
- Self-Paced Context Evaluation for Contextual Reinforcement Learning
- Self-supervised and Supervised Joint Training for Resource-rich Machine Translation
- Self-supervised Graph-level Representation Learning with Local and Global Structure
- Self-Supervised Learning for Reasoning and Perception
- Self-Tuning for Data-Efficient Deep Learning
- Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
- SGA: A Robust Algorithm for Partial Recovery of Tree-Structured Graphical Models with Noisy Samples
- SGLB: Stochastic Gradient Langevin Boosting
- SG-PALM: a Fast Physically Interpretable Tensor Graphical Model
- Sharf: Shape-conditioned Radiance Fields from a Single View
- Sharing Less is More: Lifelong Learning in Deep Networks with Selective Layer Transfer
- Sharper Generalization Bounds for Clustering
- Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks
- SiameseXML: Siamese Networks meet Extreme Classifiers with 100M Labels
- SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data
- Signatured Deep Fictitious Play for Mean Field Games with Common Noise
- SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks
- Simple and Effective VAE Training with Calibrated Decoders
- Simultaneous Similarity-based Self-Distillation for Deep Metric Learning
- Single Pass Entrywise-Transformed Low Rank Approximation
- SinIR: Efficient General Image Manipulation with Single Image Reconstruction
- Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training
- Size-Invariant Graph Representations for Graph Classification Extrapolations
- SketchEmbedNet: Learning Novel Concepts by Imitating Drawings
- Skew Orthogonal Convolutions
- SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes
- Skill Discovery for Exploration and Planning using Deep Skill Graphs
- Sliced Iterative Normalizing Flows
- Slot Machines: Discovering Winning Combinations of Random Weights in Neural Networks
- SMG: A Shuffling Gradient-Based Method with Momentum
- Smooth $p$-Wasserstein Distance: Structure, Empirical Approximation, and Statistical Applications
- Social Implications of Large Language Models
- Soft then Hard: Rethinking the Quantization in Neural Image Compression
- Solving Challenging Dexterous Manipulation Tasks With Trajectory Optimisation and Reinforcement Learning
- Solving high-dimensional parabolic PDEs using the tensor train format
- Solving Inverse Problems with a Flow-based Noise Model
- SoundDet: Polyphonic Moving Sound Event Detection and Localization from Raw Waveform
- SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation
- Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm
- Sparse Bayesian Learning via Stepwise Regression
- SparseBERT: Rethinking the Importance Analysis in Self-attention
- Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient
- Sparse within Sparse Gaussian Processes using Neighbor Information
- Sparsifying Networks via Subdifferential Inclusion
- Sparsity-Agnostic Lasso Bandit
- Spectral Normalisation for Deep Reinforcement Learning: An Optimisation Perspective
- Spectral Smoothing Unveils Phase Transitions in Hierarchical Variational Autoencoders
- Spectral vertex sparsifiers and pair-wise spanners over distributed graphs
- SpreadsheetCoder: Formula Prediction from Semi-structured Context
- Stability and Convergence of Stochastic Gradient Clipping: Beyond Lipschitz Continuity and Smoothness
- Stability and Generalization of Stochastic Gradient Methods for Minimax Problems
- Stabilizing Equilibrium Models by Jacobian Regularization
- State Entropy Maximization with Random Encoders for Efficient Exploration
- State Relevance for Off-Policy Evaluation
- Statistical Estimation from Dependent Data
- Stochastic Iterative Graph Matching
- Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions
- Stochastic Sign Descent Methods: New Algorithms and Better Theory
- Straight to the Gradient: Learning to Use Novel Tokens for Neural Text Generation
- Strategic Classification in the Dark
- Strategic Classification Made Practical
- Streaming and Distributed Algorithms for Robust Column Subset Selection
- Streaming Bayesian Deep Tensor Factorization
- STRODE: Stochastic Boundary Ordinary Differential Equation
- Structured Convolutional Kernel Networks for Airline Crew Scheduling
- Structured World Belief for Reinforcement Learning in POMDP
- Submodular Maximization subject to a Knapsack Constraint: Combinatorial Algorithms with Near-optimal Adaptive Complexity
- Subset Selection in Machine Learning: From Theory to Applications
- SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning
- Supervised Tree-Wasserstein Distance
- Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approach
- Synthesizer: Rethinking Self-Attention for Transformer Models
- Synthetic Healthcare Data Generation and Assessment: Challenges, Methods, and Impact on Machine Learning
- Systematic Analysis of Cluster Similarity Indices: How to Validate Validation Measures
- Tackling Climate Change with Machine Learning
- Targeted Data Acquisition for Evolving Negotiation Agents
- Task-Optimal Exploration in Linear Dynamical Systems
- Taylor Expansion of Discount Factors
- TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL
- Temporal Difference Learning as Gradient Splitting
- Temporally Correlated Task Scheduling for Sequence Learning
- Temporal Predictive Coding For Model-Based Planning In Latent Space
- TempoRL: Learning When to Act
- Tensor Programs IIb: Architectural Universality Of Neural Tangent Kernel Training Dynamics
- Tensor Programs IV: Feature Learning in Infinite-Width Neural Networks
- TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models
- Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning
- Testing DNN-based Autonomous Driving Systems under Critical Environmental Conditions
- Testing Group Fairness via Optimal Transport Projections
- TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer
- The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation
- The Earth Mover's Pinball Loss: Quantiles for Histogram-Valued Regression
- The Emergence of Individuality
- The Heavy-Tail Phenomenon in SGD
- The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning
- The Impact of Record Linkage on Learning from Feature Partitioned Data
- The Implicit Bias for Adaptive Optimization Algorithms on Homogeneous Neural Networks
- The Limits of Min-Max Optimization Algorithms: Convergence to Spurious Non-Critical Sets
- The Lipschitz Constant of Self-Attention
- The Logical Options Framework
- The Neglected Assumptions In Causal Inference
- Theory and Foundation of Continual Learning
- Theory and Practice of Differential Privacy
- Theory of Spectral Method for Union of Subspaces-Based Random Geometry Graph
- The Power of Adaptivity for Stochastic Submodular Cover
- The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy Optimization
- The Symmetry between Arms and Knapsacks: A Primal-Dual Approach for Bandits with Knapsacks
- Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces
- Thinking Like Transformers
- Three Operator Splitting with a Nonconvex Loss Function
- Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks
- Tightening the Dependence on Horizon in the Sample Complexity of Q-Learning
- Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients
- Tilting the playing field: Dynamical loss functions for machine learning
- Time Series Workshop
- To be Robust or to be Fair: Towards Fairness in Adversarial Training
- Top-k eXtreme Contextual Bandits with Arm Hierarchy
- Toward Better Generalization Bounds with Locally Elastic Stability
- Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawing
- Towards Better Robust Generalization with Shift Consistency Regularization
- Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neurons
- Towards Defending against Adversarial Examples via Attack-Invariant Features
- Towards Distraction-Robust Active Visual Tracking
- Towards Domain-Agnostic Contrastive Learning
- Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning
- Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach
- Towards Practical Mean Bounds for Small Samples
- Towards Rigorous Interpretations: a Formalisation of Feature Attribution
- Towards the Unification and Robustness of Perturbation and Gradient Based Explanations
- Towards Tight Bounds on the Sample Complexity of Average-reward MDPs
- Towards Understanding and Mitigating Social Biases in Language Models
- Towards Understanding Learning in Neural Networks with Linear Teachers
- Toward Understanding the Feature Learning Process of Self-supervised Contrastive Learning
- Tractable structured natural-gradient descent using local parameterizations
- Training Adversarially Robust Sparse Networks via Bayesian Connectivity Sampling
- Training data-efficient image transformers & distillation through attention
- Training Data Subset Selection for Regression with Controlled Generalization Error
- Training Graph Neural Networks with 1000 Layers
- Training Quantized Neural Networks to Global Optimality via Semidefinite Programming
- Training Recurrent Neural Networks via Forward Propagation Through Time
- Train simultaneously, generalize better: Stability of gradient-based minimax learners
- Trajectory Diversity for Zero-Shot Coordination
- Transfer-Based Semantic Anomaly Detection
- Trees with Attention for Set Prediction Tasks
- T-SCI: A Two-Stage Conformal Inference Algorithm with Guaranteed Coverage for Cox-MLP
- Two Heads are Better Than One: Hypergraph-Enhanced Graph Reasoning for Visual Event Ratiocination
- Two-way kernel matrix puncturing: towards resource-efficient PCA and spectral clustering
- UCB Momentum Q-learning: Correcting the bias without forgetting
- Unbalanced minibatch Optimal Transport; applications to Domain Adaptation
- Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies
- Uncertainty and Robustness in Deep Learning
- Uncertainty Principles of Encoding GANs
- Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning
- Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability
- Understanding and Mitigating Accuracy Disparity in Regression
- Understanding Failures in Out-of-Distribution Detection with Deep Generative Models
- Understanding Instance-Level Label Noise: Disparate Impacts and Treatments
- Understanding Invariance via Feedforward Inversion of Discriminatively Trained Classifiers
- Understanding Noise Injection in GANs
- Understanding self-supervised learning dynamics without contrastive pairs
- Understanding the Dynamics of Gradient Flow in Overparameterized Linear models
- UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning
- UnICORNN: A recurrent model for learning very long time dependencies
- Unified Robust Semi-Supervised Variational Autoencoder
- Uniform Convergence, Adversarial Spheres and a Simple Remedy
- Unifying Vision-and-Language Tasks via Text Generation
- UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data
- Unitary Branching Programs: Learnability and Lower Bounds
- Unsupervised Co-part Segmentation through Assembly
- Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot Classification
- Unsupervised Learning for Reinforcement Learning
- Unsupervised Learning of Visual 3D Keypoints for Control
- Unsupervised Part Representation by Flow Capsules
- Unsupervised Representation Learning via Neural Activation Coding
- Unsupervised Skill Discovery with Bottleneck Option Learning
- Valid Causal Inference with (Some) Invalid Instruments
- Value Alignment Verification
- Value-at-Risk Optimization with Gaussian Processes
- Value Iteration in Continuous Actions, States and Time
- Variance Reduced Training with Stratified Sampling for Forecasting Models
- Variance Reduction via Primal-Dual Accelerated Dual Averaging for Nonsmooth Convex Finite-Sums
- Variational Auto-Regressive Gaussian Processes for Continual Learning
- Variational Data Assimilation with a Learned Inverse Observation Operator
- Variational Empowerment as Representation Learning for Goal-Conditioned Reinforcement Learning
- Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models
- Vector Quantized Models for Planning
- Versatile Verification of Tree Ensembles
- ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
- Voice2Series: Reprogramming Acoustic Models for Time Series Classification
- Wasserstein Distributional Normalization For Robust Distributional Certification of Noisy Labeled Data
- Watermarking Deep Neural Networks with Greedy Residuals
- Weight-covariance alignment for adversarially robust neural networks
- Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
- WGAN with an Infinitely Wide Generator Has No Spurious Stationary Points
- What Are Bayesian Neural Network Posteriors Really Like?
- What does LIME really see in images?
- What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?
- What Makes for End-to-End Object Detection?
- What's in the Box? Exploring the Inner Life of Neural Networks with Robust Rules
- When All We Need is a Piece of the Pie: A Generic Framework for Optimizing Two-way Partial AUC
- When Does Data Augmentation Help With Membership Inference Attacks?
- Which transformer architecture fits my data? A vocabulary bottleneck in self-attention
- Whitening and Second Order Optimization Both Make Information in the Dataset Unusable During Training, and Can Reduce or Prevent Generalization
- Whitening for Self-Supervised Representation Learning
- Whittle Networks: A Deep Likelihood Model for Time Series
- WILDS: A Benchmark of in-the-Wild Distribution Shifts
- Winograd Algorithm for AdderNet
- Workshop on Computational Approaches to Mental Health @ ICML 2021
- Workshop on Distribution-Free Uncertainty Quantification
- Workshop on Reinforcement Learning Theory
- Workshop on Socially Responsible Machine Learning
- World Model as a Graph: Learning Latent Landmarks for Planning
- XOR-CD: Linearly Convergent Constrained Structure Generation
- You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling
- Zero-Shot Knowledge Distillation from a Decision-Based Black-Box Model
- Zero-Shot Text-to-Image Generation
- Zeroth-Order Non-Convex Learning via Hierarchical Dual Averaging
- Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting
- Zoo-Tuning: Adaptive Transfer from A Zoo of Models
- Sparsity in Deep Learning: Pruning and growth for efficient inference and training
Successful Page Load