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