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Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-dimensions
Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage
Flexible Option Learning
Landscape analysis of an improved power method for tensor decomposition
Explicit loss asymptotics in the gradient descent training of neural networks
COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features
$\alpha$-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression
A Minimalist Approach to Offline Reinforcement Learning
Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods
Conservative Data Sharing for Multi-Task Offline Reinforcement Learning
Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as a Target for NLP
Spherical Motion Dynamics: Learning Dynamics of Normalized Neural Network using SGD and Weight Decay
A Gaussian Process-Bayesian Bernoulli Mixture Model for Multi-Label Active Learning
Exploring Social Posterior Collapse in Variational Autoencoder for Interaction Modeling
Sparse Training via Boosting Pruning Plasticity with Neuroregeneration
Large-Scale Unsupervised Object Discovery
Across-animal odor decoding by probabilistic manifold alignment
Score-based Generative Neural Networks for Large-Scale Optimal Transport
On Plasticity, Invariance, and Mutually Frozen Weights in Sequential Task Learning
Statistical Regeneration Guarantees of the Wasserstein Autoencoder with Latent Space Consistency
EIGNN: Efficient Infinite-Depth Graph Neural Networks
Unsupervised Noise Adaptive Speech Enhancement by Discriminator-Constrained Optimal Transport
Credal Self-Supervised Learning
Distributed Deep Learning In Open Collaborations
Skipping the Frame-Level: Event-Based Piano Transcription With Neural Semi-CRFs
Profiling Pareto Front With Multi-Objective Stein Variational Gradient Descent
Sequential Algorithms for Testing Closeness of Distributions
INDIGO: GNN-Based Inductive Knowledge Graph Completion Using Pair-Wise Encoding
Detecting Moments and Highlights in Videos via Natural Language Queries
Joint Inference for Neural Network Depth and Dropout Regularization
Lifelong Domain Adaptation via Consolidated Internal Distribution
Learning latent causal graphs via mixture oracles
Container: Context Aggregation Networks
Semialgebraic Representation of Monotone Deep Equilibrium Models and Applications to Certification
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective
Temporally Abstract Partial Models
Near-Optimal Offline Reinforcement Learning via Double Variance Reduction
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective
One Explanation is Not Enough: Structured Attention Graphs for Image Classification
Overinterpretation reveals image classification model pathologies
Good Classification Measures and How to Find Them
BNS: Building Network Structures Dynamically for Continual Learning
Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks
TRS: Transferability Reduced Ensemble via Promoting Gradient Diversity and Model Smoothness
Automorphic Equivalence-aware Graph Neural Network
Direct Multi-view Multi-person 3D Pose Estimation
Learnability of Linear Thresholds from Label Proportions
Regret Bounds for Gaussian-Process Optimization in Large Domains
On Episodes, Prototypical Networks, and Few-Shot Learning
Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions
CATs: Cost Aggregation Transformers for Visual Correspondence
Efficient Training of Retrieval Models using Negative Cache
Differentiable Multiple Shooting Layers
Deep Explicit Duration Switching Models for Time Series
Offline RL Without Off-Policy Evaluation
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning
Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach
Validation Free and Replication Robust Volume-based Data Valuation
Graph Neural Networks with Adaptive Residual
Efficient Combination of Rematerialization and Offloading for Training DNNs
Conservative Offline Distributional Reinforcement Learning
On Model Calibration for Long-Tailed Object Detection and Instance Segmentation
Generative Occupancy Fields for 3D Surface-Aware Image Synthesis
TNASP: A Transformer-based NAS Predictor with a Self-evolution Framework
Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Prediction
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)
Influence Patterns for Explaining Information Flow in BERT
Towards mental time travel: a hierarchical memory for reinforcement learning agents
Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks
Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss
Online Knapsack with Frequency Predictions
Neural Regression, Representational Similarity, Model Zoology & Neural Taskonomy at Scale in Rodent Visual Cortex
The future is log-Gaussian: ResNets and their infinite-depth-and-width limit at initialization
A Mathematical Framework for Quantifying Transferability in Multi-source Transfer Learning
Panoptic 3D Scene Reconstruction From a Single RGB Image
PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning
Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning
3DP3: 3D Scene Perception via Probabilistic Programming
Learning with Holographic Reduced Representations
Convex Polytope Trees
You Are the Best Reviewer of Your Own Papers: An Owner-Assisted Scoring Mechanism
Online Control of Unknown Time-Varying Dynamical Systems
Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language
Counterbalancing Learning and Strategic Incentives in Allocation Markets
Low-dimensional Structure in the Space of Language Representations is Reflected in Brain Responses
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations
Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings
Reinforcement Learning with Latent Flow
Behavior From the Void: Unsupervised Active Pre-Training
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting
Probing Inter-modality: Visual Parsing with Self-Attention for Vision-and-Language Pre-training
Towards Gradient-based Bilevel Optimization with Non-convex Followers and Beyond
Information is Power: Intrinsic Control via Information Capture
CHIP: CHannel Independence-based Pruning for Compact Neural Networks
Improving Generalization in Meta-RL with Imaginary Tasks from Latent Dynamics Mixture
Instance-optimal Mean Estimation Under Differential Privacy
Weak-shot Fine-grained Classification via Similarity Transfer
A Continuous Mapping For Augmentation Design
Towards robust vision by multi-task learning on monkey visual cortex
SE(3)-equivariant prediction of molecular wavefunctions and electronic densities
Analysis of one-hidden-layer neural networks via the resolvent method
A Probabilistic State Space Model for Joint Inference from Differential Equations and Data
Differentiable Simulation of Soft Multi-body Systems
Hierarchical Skills for Efficient Exploration
Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games
Improved Transformer for High-Resolution GANs
Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation
Tractable Regularization of Probabilistic Circuits
Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization
Sim and Real: Better Together
Matrix factorisation and the interpretation of geodesic distance
Marginalised Gaussian Processes with Nested Sampling
Grounding Spatio-Temporal Language with Transformers
K-Net: Towards Unified Image Segmentation
Neural Algorithmic Reasoners are Implicit Planners
Active 3D Shape Reconstruction from Vision and Touch
Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning
Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations
A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs
Fast Approximate Dynamic Programming for Infinite-Horizon Markov Decision Processes
Speedy Performance Estimation for Neural Architecture Search
Scalable Thompson Sampling using Sparse Gaussian Process Models
Reliable Decisions with Threshold Calibration
MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images
Learning Large Neighborhood Search Policy for Integer Programming
Corruption Robust Active Learning
A Critical Look at the Consistency of Causal Estimation with Deep Latent Variable Models
Non-local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation
Piper: Multidimensional Planner for DNN Parallelization
Post-Contextual-Bandit Inference
CrypTen: Secure Multi-Party Computation Meets Machine Learning
Continuous Mean-Covariance Bandits
Controlling Neural Networks with Rule Representations
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition
Hierarchical Clustering: $O(1)$-Approximation for Well-Clustered Graphs
Efficient constrained sampling via the mirror-Langevin algorithm
Towards Robust Bisimulation Metric Learning
Amortized Variational Inference for Simple Hierarchical Models
Repulsive Deep Ensembles are Bayesian
Algorithmic stability and generalization of an unsupervised feature selection algorithm
LSH-SMILE: Locality Sensitive Hashing Accelerated Simulation and Learning
Towards Better Understanding of Training Certifiably Robust Models against Adversarial Examples
Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning
RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem
AC-GC: Lossy Activation Compression with Guaranteed Convergence
Near-Optimal No-Regret Learning in General Games
It Has Potential: Gradient-Driven Denoisers for Convergent Solutions to Inverse Problems
Shift Invariance Can Reduce Adversarial Robustness
DNN-based Topology Optimisation: Spatial Invariance and Neural Tangent Kernel
Neural Production Systems
Neural Active Learning with Performance Guarantees
Equivariant Manifold Flows
Reinforcement Learning in Newcomblike Environments
Disrupting Deep Uncertainty Estimation Without Harming Accuracy
Fairness in Ranking under Uncertainty
Identifiable Generative models for Missing Not at Random Data Imputation
Multi-view Contrastive Graph Clustering
Unifying lower bounds on prediction dimension of convex surrogates
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound
Misspecified Gaussian Process Bandit Optimization
Reliable Estimation of KL Divergence using a Discriminator in Reproducing Kernel Hilbert Space
DeepGEM: Generalized Expectation-Maximization for Blind Inversion
Parameter-free HE-friendly Logistic Regression
Imitation with Neural Density Models
SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning
Fair Exploration via Axiomatic Bargaining
No Regrets for Learning the Prior in Bandits
Privately Publishable Per-instance Privacy
Learning the optimal Tikhonov regularizer for inverse problems
Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge Distillation
Fast Algorithms for $L_\infty$-constrained S-rectangular Robust MDPs
A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration
Weighted model estimation for offline model-based reinforcement learning
Bellman-consistent Pessimism for Offline Reinforcement Learning
Settling the Variance of Multi-Agent Policy Gradients
Cortico-cerebellar networks as decoupling neural interfaces
Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias
A flow-based latent state generative model of neural population responses to natural images
Monte Carlo Tree Search With Iteratively Refining State Abstractions
Editing a classifier by rewriting its prediction rules
Training Neural Networks with Fixed Sparse Masks
Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis
ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE
Particle Cloud Generation with Message Passing Generative Adversarial Networks
Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics
PSD Representations for Effective Probability Models
Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs
Memory-efficient Patch-based Inference for Tiny Deep Learning
Boost Neural Networks by Checkpoints
Explainable Semantic Space by Grounding Language to Vision with Cross-Modal Contrastive Learning
SOAT: A Scene- and Object-Aware Transformer for Vision-and-Language Navigation
Learning 3D Dense Correspondence via Canonical Point Autoencoder
On the interplay between data structure and loss function in classification problems
Robust Compressed Sensing MRI with Deep Generative Priors
Scalable Intervention Target Estimation in Linear Models
Hierarchical Reinforcement Learning with Timed Subgoals
Selective Sampling for Online Best-arm Identification
Excess Capacity and Backdoor Poisoning
Multimodal and Multilingual Embeddings for Large-Scale Speech Mining
Learning Frequency Domain Approximation for Binary Neural Networks
Clustering Effect of Adversarial Robust Models
Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation
Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification
Functional Neural Networks for Parametric Image Restoration Problems
Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning
Understanding the Generalization Benefit of Model Invariance from a Data Perspective
Predicting Deep Neural Network Generalization with Perturbation Response Curves
Escape saddle points by a simple gradient-descent based algorithm
Your head is there to move you around: Goal-driven models of the primate dorsal pathway
SUPER-ADAM: Faster and Universal Framework of Adaptive Gradients
SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs
Decentralized Q-learning in Zero-sum Markov Games
Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation
Channel Permutations for N:M Sparsity
Dynamic influence maximization
Closing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation
Human-Adversarial Visual Question Answering
Non-approximate Inference for Collective Graphical Models on Path Graphs via Discrete Difference of Convex Algorithm
What Matters for Adversarial Imitation Learning?
Accumulative Poisoning Attacks on Real-time Data
IQ-Learn: Inverse soft-Q Learning for Imitation
ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions
MarioNette: Self-Supervised Sprite Learning
Regulating algorithmic filtering on social media
Visualizing the Emergence of Intermediate Visual Patterns in DNNs
CBP: backpropagation with constraint on weight precision using a pseudo-Lagrange multiplier method
Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning
On The Structure of Parametric Tournaments with Application to Ranking from Pairwise Comparisons
A Geometric Perspective towards Neural Calibration via Sensitivity Decomposition
Improving Transferability of Representations via Augmentation-Aware Self-Supervision
Policy Learning Using Weak Supervision
Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters
Passive attention in artificial neural networks predicts human visual selectivity
Do Vision Transformers See Like Convolutional Neural Networks?
Information-constrained optimization: can adaptive processing of gradients help?
Fast Tucker Rank Reduction for Non-Negative Tensors Using Mean-Field Approximation
Learning Hard Optimization Problems: A Data Generation Perspective
Similarity and Matching of Neural Network Representations
NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform
Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition
Scaling up Continuous-Time Markov Chains Helps Resolve Underspecification
Noisy Recurrent Neural Networks
Multi-modal Dependency Tree for Video Captioning
Deep Reinforcement Learning at the Edge of the Statistical Precipice
Shaping embodied agent behavior with activity-context priors from egocentric video
Representation Learning Beyond Linear Prediction Functions
How Modular should Neural Module Networks Be for Systematic Generalization?
PolarStream: Streaming Object Detection and Segmentation with Polar Pillars
SSMF: Shifting Seasonal Matrix Factorization
Average-Reward Learning and Planning with Options
Nonsmooth Implicit Differentiation for Machine-Learning and Optimization
Numerical influence of ReLU’(0) on backpropagation
Optimal Gradient-based Algorithms for Non-concave Bandit Optimization
Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors
PettingZoo: Gym for Multi-Agent Reinforcement Learning
HNPE: Leveraging Global Parameters for Neural Posterior Estimation
Partition-Based Formulations for Mixed-Integer Optimization of Trained ReLU Neural Networks
Gradient-based Hyperparameter Optimization Over Long Horizons
On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations
Multimodal Virtual Point 3D Detection
VidLanKD: Improving Language Understanding via Video-Distilled Knowledge Transfer
Convergence and Alignment of Gradient Descent with Random Backpropagation Weights
Implicit Task-Driven Probability Discrepancy Measure for Unsupervised Domain Adaptation
Referring Transformer: A One-step Approach to Multi-task Visual Grounding
Learning Diverse Policies in MOBA Games via Macro-Goals
Deformable Butterfly: A Highly Structured and Sparse Linear Transform
MAP Propagation Algorithm: Faster Learning with a Team of Reinforcement Learning Agents
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias
Understanding the Effect of Stochasticity in Policy Optimization
Deep Learning on a Data Diet: Finding Important Examples Early in Training
Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation
Bayesian Optimization of Function Networks
Prior-independent Dynamic Auctions for a Value-maximizing Buyer
Constrained Robust Submodular Partitioning
Iterative Connecting Probability Estimation for Networks
Forster Decomposition and Learning Halfspaces with Noise
On Joint Learning for Solving Placement and Routing in Chip Design
End-to-End Weak Supervision
A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning
Characterizing the risk of fairwashing
Searching the Search Space of Vision Transformer
On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources
Perceptual Score: What Data Modalities Does Your Model Perceive?
On UMAP's True Loss Function
Stable, Fast and Accurate: Kernelized Attention with Relative Positional Encoding
Adder Attention for Vision Transformer
Provably Efficient Black-Box Action Poisoning Attacks Against Reinforcement Learning
COMBO: Conservative Offline Model-Based Policy Optimization
Learning to Schedule Heuristics in Branch and Bound
TAAC: Temporally Abstract Actor-Critic for Continuous Control
Spectrum-to-Kernel Translation for Accurate Blind Image Super-Resolution
Unintended Selection: Persistent Qualification Rate Disparities and Interventions
Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks
Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation
Open-set Label Noise Can Improve Robustness Against Inherent Label Noise
Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks
Deeply Shared Filter Bases for Parameter-Efficient Convolutional Neural Networks
Doubly Robust Thompson Sampling with Linear Payoffs
Only Train Once: A One-Shot Neural Network Training And Pruning Framework
Fairness via Representation Neutralization
A No-go Theorem for Robust Acceleration in the Hyperbolic Plane
Probabilistic Transformer For Time Series Analysis
Learning Robust Hierarchical Patterns of Human Brain across Many fMRI Studies
Self-Adaptable Point Processes with Nonparametric Time Decays
RED : Looking for Redundancies for Data-FreeStructured Compression of Deep Neural Networks
Adversarially Robust Change Point Detection
The Limits of Optimal Pricing in the Dark
Making the most of your day: online learning for optimal allocation of time
Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases
Invertible DenseNets with Concatenated LipSwish
Pareto-Optimal Learning-Augmented Algorithms for Online Conversion Problems
Non-asymptotic Error Bounds for Bidirectional GANs
Iterative Teacher-Aware Learning
Stochastic $L^\natural$-convex Function Minimization
BatchQuant: Quantized-for-all Architecture Search with Robust Quantizer
Post-Training Sparsity-Aware Quantization
Accurately Solving Rod Dynamics with Graph Learning
Online and Offline Reinforcement Learning by Planning with a Learned Model
Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory
Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting
Greedy and Random Quasi-Newton Methods with Faster Explicit Superlinear Convergence
Heavy Ball Momentum for Conditional Gradient
On the Out-of-distribution Generalization of Probabilistic Image Modelling
Neural Architecture Dilation for Adversarial Robustness
Even your Teacher Needs Guidance: Ground-Truth Targets Dampen Regularization Imposed by Self-Distillation
Revisiting Smoothed Online Learning
Learning interaction rules from multi-animal trajectories via augmented behavioral models
A Constant Approximation Algorithm for Sequential Random-Order No-Substitution k-Median Clustering
Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent
Dynamic Sasvi: Strong Safe Screening for Norm-Regularized Least Squares
Scalable Inference of Sparsely-changing Gaussian Markov Random Fields
Capturing implicit hierarchical structure in 3D biomedical images with self-supervised hyperbolic representations
Adaptive Risk Minimization: Learning to Adapt to Domain Shift
Analyzing the Confidentiality of Undistillable Teachers in Knowledge Distillation
Scalable Rule-Based Representation Learning for Interpretable Classification
Parallel and Efficient Hierarchical k-Median Clustering
Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State
Conic Blackwell Algorithm: Parameter-Free Convex-Concave Saddle-Point Solving
Robust Allocations with Diversity Constraints
Optimality and Stability in Federated Learning: A Game-theoretic Approach
Dynamic Causal Bayesian Optimization
An Efficient Pessimistic-Optimistic Algorithm for Stochastic Linear Bandits with General Constraints
Robustifying Algorithms of Learning Latent Trees with Vector Variables
Generalization Guarantee of SGD for Pairwise Learning
Universal Off-Policy Evaluation
Calibration and Consistency of Adversarial Surrogate Losses
On the Convergence of Step Decay Step-Size for Stochastic Optimization
Unsupervised Speech Recognition
TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification
DOBF: A Deobfuscation Pre-Training Objective for Programming Languages
On the Expected Complexity of Maxout Networks
Tactical Optimism and Pessimism for Deep Reinforcement Learning
An online passive-aggressive algorithm for difference-of-squares classification
Learning State Representations from Random Deep Action-conditional Predictions
Exact Privacy Guarantees for Markov Chain Implementations of the Exponential Mechanism with Artificial Atoms
Relative stability toward diffeomorphisms indicates performance in deep nets
Sparse Uncertainty Representation in Deep Learning with Inducing Weights
Online Active Learning with Surrogate Loss Functions
Reverse engineering learned optimizers reveals known and novel mechanisms
A Near-Optimal Algorithm for Debiasing Trained Machine Learning Models
Efficient and Accurate Gradients for Neural SDEs
Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models
Sparse Spiking Gradient Descent
Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance
Few-Shot Data-Driven Algorithms for Low Rank Approximation
Privately Learning Mixtures of Axis-Aligned Gaussians
Hash Layers For Large Sparse Models
List-Decodable Mean Estimation in Nearly-PCA Time
Automatic Unsupervised Outlier Model Selection
Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Sparse Neural Networks
Ising Model Selection Using $\ell_{1}$-Regularized Linear Regression: A Statistical Mechanics Analysis
Dynamic Bottleneck for Robust Self-Supervised Exploration
USCO-Solver: Solving Undetermined Stochastic Combinatorial Optimization Problems
Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarking
Understanding Interlocking Dynamics of Cooperative Rationalization
Differentiable Equilibrium Computation with Decision Diagrams for Stackelberg Models of Combinatorial Congestion Games
Understanding Adaptive, Multiscale Temporal Integration In Deep Speech Recognition Systems
Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning
Online Market Equilibrium with Application to Fair Division
Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets
Bias and variance of the Bayesian-mean decoder
MLP-Mixer: An all-MLP Architecture for Vision
Learning Knowledge Graph-based World Models of Textual Environments
Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection
Refined Learning Bounds for Kernel and Approximate $k$-Means
Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning
Faster Directional Convergence of Linear Neural Networks under Spherically Symmetric Data
An Exponential Lower Bound for Linearly Realizable MDP with Constant Suboptimality Gap
Coresets for Classification – Simplified and Strengthened
Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)
Submodular + Concave
Understanding Partial Multi-Label Learning via Mutual Information
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style
Improved Coresets and Sublinear Algorithms for Power Means in Euclidean Spaces
Multi-Armed Bandits with Bounded Arm-Memory: Near-Optimal Guarantees for Best-Arm Identification and Regret Minimization
Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations
Vector-valued Distance and Gyrocalculus on the Space of Symmetric Positive Definite Matrices
Self-Instantiated Recurrent Units with Dynamic Soft Recursion
Near-Optimal Lower Bounds For Convex Optimization For All Orders of Smoothness
Improved Learning Rates of a Functional Lasso-type SVM with Sparse Multi-Kernel Representation
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks
A Hierarchical Reinforcement Learning Based Optimization Framework for Large-scale Dynamic Pickup and Delivery Problems
Morié Attack (MA): A New Potential Risk of Screen Photos
Towards Understanding Why Lookahead Generalizes Better Than SGD and Beyond
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification
An Empirical Study of Adder Neural Networks for Object Detection
Medical Dead-ends and Learning to Identify High-Risk States and Treatments
Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems
Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark
Local plasticity rules can learn deep representations using self-supervised contrastive predictions
Approximating the Permanent with Deep Rejection Sampling
Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation
How Data Augmentation affects Optimization for Linear Regression
Spot the Difference: Detection of Topological Changes via Geometric Alignment
Deep Extended Hazard Models for Survival Analysis
Scaling Gaussian Processes with Derivative Information Using Variational Inference
On the Expressivity of Markov Reward
Credit Assignment in Neural Networks through Deep Feedback Control
KS-GNN: Keywords Search over Incomplete Graphs via Graphs Neural Network
A novel notion of barycenter for probability distributions based on optimal weak mass transport
Confident Anchor-Induced Multi-Source Free Domain Adaptation
Learning from Inside: Self-driven Siamese Sampling and Reasoning for Video Question Answering
Iterative Amortized Policy Optimization
The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning
Encoding Robustness to Image Style via Adversarial Feature Perturbations
Structure-Aware Random Fourier Kernel for Graphs
Risk Monotonicity in Statistical Learning
Recognizing Vector Graphics without Rasterization
Large-Scale Learning with Fourier Features and Tensor Decompositions
Implicit Semantic Response Alignment for Partial Domain Adaptation
Exponential Separation between Two Learning Models and Adversarial Robustness
SBO-RNN: Reformulating Recurrent Neural Networks via Stochastic Bilevel Optimization
Variational Continual Bayesian Meta-Learning
Learning One Representation to Optimize All Rewards
Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket Perspective
Linear Convergence of Gradient Methods for Estimating Structured Transition Matrices in High-dimensional Vector Autoregressive Models
Raw Nav-merge Seismic Data to Subsurface Properties with MLP based Multi-Modal Information Unscrambler
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method
Robust Optimization for Multilingual Translation with Imbalanced Data
SPANN: Highly-efficient Billion-scale Approximate Nearest Neighborhood Search
Class-Incremental Learning via Dual Augmentation
Mitigating Forgetting in Online Continual Learning with Neuron Calibration
Robust Auction Design in the Auto-bidding World
CROCS: Clustering and Retrieval of Cardiac Signals Based on Patient Disease Class, Sex, and Age
Multi-Objective SPIBB: Seldonian Offline Policy Improvement with Safety Constraints in Finite MDPs
The Pareto Frontier of model selection for general Contextual Bandits
Asymptotically Exact Error Characterization of Offline Policy Evaluation with Misspecified Linear Models
Causal Bandits with Unknown Graph Structure
Emergent Discrete Communication in Semantic Spaces
On the Stochastic Stability of Deep Markov Models
Statistical Inference with M-Estimators on Adaptively Collected Data
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation
Locality Sensitive Teaching
RL for Latent MDPs: Regret Guarantees and a Lower Bound
Distribution-free inference for regression: discrete, continuous, and in between
Lattice partition recovery with dyadic CART
The Flip Side of the Reweighted Coin: Duality of Adaptive Dropout and Regularization
Beyond the Signs: Nonparametric Tensor Completion via Sign Series
Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature
Tensor decompositions of higher-order correlations by nonlinear Hebbian plasticity
Curriculum Learning for Vision-and-Language Navigation
Information Directed Sampling for Sparse Linear Bandits
A generative nonparametric Bayesian model for whole genomes
Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity
A Unified View of cGANs with and without Classifiers
Sub-Linear Memory: How to Make Performers SLiM
Challenges and Opportunities in High Dimensional Variational Inference
On the Existence of The Adversarial Bayes Classifier
Cross-modal Domain Adaptation for Cost-Efficient Visual Reinforcement Learning
To Beam Or Not To Beam: That is a Question of Cooperation for Language GANs
True Few-Shot Learning with Language Models
Label Disentanglement in Partition-based Extreme Multilabel Classification
Towards understanding retrosynthesis by energy-based models
Rectangular Flows for Manifold Learning
Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution
Stability & Generalisation of Gradient Descent for Shallow Neural Networks without the Neural Tangent Kernel
DiBS: Differentiable Bayesian Structure Learning
BARTScore: Evaluating Generated Text as Text Generation
Fast and accurate randomized algorithms for low-rank tensor decompositions
Nearly Horizon-Free Offline Reinforcement Learning
CogView: Mastering Text-to-Image Generation via Transformers
Private and Non-private Uniformity Testing for Ranking Data
Universal Graph Convolutional Networks
Causal Inference for Event Pairs in Multivariate Point Processes
Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning
On the Power of Differentiable Learning versus PAC and SQ Learning
SOLQ: Segmenting Objects by Learning Queries
Bandits with Knapsacks beyond the Worst Case
Counterfactual Invariance to Spurious Correlations in Text Classification
Sample-Efficient Reinforcement Learning for Linearly-Parameterized MDPs with a Generative Model
Identity testing for Mallows model
Generalized and Discriminative Few-Shot Object Detection via SVD-Dictionary Enhancement
Localization with Sampling-Argmax
Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs
Automated Discovery of Adaptive Attacks on Adversarial Defenses
Learning with Labeling Induced Abstentions
Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem
Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness
Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization
A Regression Approach to Learning-Augmented Online Algorithms
Revenue maximization via machine learning with noisy data
Planning from Pixels in Environments with Combinatorially Hard Search Spaces
Privately Learning Subspaces
Which Mutual-Information Representation Learning Objectives are Sufficient for Control?
Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding
Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures
Spatial Ensemble: a Novel Model Smoothing Mechanism for Student-Teacher Framework
Online Multi-Armed Bandits with Adaptive Inference
Equilibrium and non-Equilibrium regimes in the learning of Restricted Boltzmann Machines
Contextual Recommendations and Low-Regret Cutting-Plane Algorithms
UniDoc: Unified Pretraining Framework for Document Understanding
The effectiveness of feature attribution methods and its correlation with automatic evaluation scores
Subquadratic Overparameterization for Shallow Neural Networks
Learning Semantic Representations to Verify Hardware Designs
Constrained Optimization to Train Neural Networks on Critical and Under-Represented Classes
Surrogate Regret Bounds for Polyhedral Losses
A Variational Perspective on Diffusion-Based Generative Models and Score Matching
Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization
Shapeshifter: a Parameter-efficient Transformer using Factorized Reshaped Matrices
A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics
Cardinality constrained submodular maximization for random streams
On Calibration and Out-of-Domain Generalization
Nonuniform Negative Sampling and Log Odds Correction with Rare Events Data
For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets
Intriguing Properties of Contrastive Losses
Answering Complex Causal Queries With the Maximum Causal Set Effect
Generalizable Multi-linear Attention Network
Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning
Test-time Collective Prediction
Statistical Undecidability in Linear, Non-Gaussian Causal Models in the Presence of Latent Confounders
Are Transformers more robust than CNNs?
Approximate optimization of convex functions with outlier noise
Reliable and Trustworthy Machine Learning for Health Using Dataset Shift Detection
SimiGrad: Fine-Grained Adaptive Batching for Large Scale Training using Gradient Similarity Measurement
Bootstrap Your Object Detector via Mixed Training
Can fMRI reveal the representation of syntactic structure in the brain?
On the Algorithmic Stability of Adversarial Training
Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization
Can multi-label classification networks know what they don’t know?
AFEC: Active Forgetting of Negative Transfer in Continual Learning
Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning
Exploring Forensic Dental Identification with Deep Learning
Dissecting the Diffusion Process in Linear Graph Convolutional Networks
Solving Graph-based Public Goods Games with Tree Search and Imitation Learning
NeurWIN: Neural Whittle Index Network For Restless Bandits Via Deep RL
On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry
Safe Pontryagin Differentiable Programming
Generic Neural Architecture Search via Regression
Graph Differentiable Architecture Search with Structure Learning
Reinforcement Learning in Reward-Mixing MDPs
A Highly-Efficient Group Elastic Net Algorithm with an Application to Function-On-Scalar Regression
Not All Low-Pass Filters are Robust in Graph Convolutional Networks
Implicit Regularization in Matrix Sensing via Mirror Descent
Generalized DataWeighting via Class-Level Gradient Manipulation
Online Robust Reinforcement Learning with Model Uncertainty
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection
Non-Asymptotic Analysis for Two Time-scale TDC with General Smooth Function Approximation
Continuized Accelerations of Deterministic and Stochastic Gradient Descents, and of Gossip Algorithms
Oracle Complexity in Nonsmooth Nonconvex Optimization
Unlabeled Principal Component Analysis
Residual2Vec: Debiasing graph embedding with random graphs
Towards Context-Agnostic Learning Using Synthetic Data
Modality-Agnostic Topology Aware Localization
A Closer Look at the Worst-case Behavior of Multi-armed Bandit Algorithms
Asynchronous Stochastic Optimization Robust to Arbitrary Delays
Graph Neural Networks with Local Graph Parameters
Towards Sharper Generalization Bounds for Structured Prediction
L2ight: Enabling On-Chip Learning for Optical Neural Networks via Efficient in-situ Subspace Optimization
Coresets for Time Series Clustering
MCMC Variational Inference via Uncorrected Hamiltonian Annealing
Adversarial Examples for k-Nearest Neighbor Classifiers Based on Higher-Order Voronoi Diagrams
Implicit Sparse Regularization: The Impact of Depth and Early Stopping
Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems
Risk-Aware Transfer in Reinforcement Learning using Successor Features
A Biased Graph Neural Network Sampler with Near-Optimal Regret
Coresets for Decision Trees of Signals
Quantifying and Improving Transferability in Domain Generalization
Online Selective Classification with Limited Feedback
Concentration inequalities under sub-Gaussian and sub-exponential conditions
Subgaussian and Differentiable Importance Sampling for Off-Policy Evaluation and Learning
Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces
MAU: A Motion-Aware Unit for Video Prediction and Beyond
Width-based Lookaheads with Learnt Base Policies and Heuristics Over the Atari-2600 Benchmark
FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition
STEP: Out-of-Distribution Detection in the Presence of Limited In-Distribution Labeled Data
The Complexity of Bayesian Network Learning: Revisiting the Superstructure
Tighter Expected Generalization Error Bounds via Wasserstein Distance
Differentiable Learning Under Triage
Analytical Study of Momentum-Based Acceleration Methods in Paradigmatic High-Dimensional Non-Convex Problems
GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph
Hyperbolic Busemann Learning with Ideal Prototypes
Meta-Learning for Relative Density-Ratio Estimation
TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learning
ROI Maximization in Stochastic Online Decision-Making
Asymptotics of representation learning in finite Bayesian neural networks
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems
Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning
Revisiting ResNets: Improved Training and Scaling Strategies
Communication-efficient SGD: From Local SGD to One-Shot Averaging
DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning
Well-tuned Simple Nets Excel on Tabular Datasets
A Central Limit Theorem for Differentially Private Query Answering
A Little Robustness Goes a Long Way: Leveraging Robust Features for Targeted Transfer Attacks
Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention
Diffusion Models Beat GANs on Image Synthesis
Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models
A Contrastive Learning Approach for Training Variational Autoencoder Priors
Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training
Coresets for Clustering with Missing Values
Representation Learning on Spatial Networks
Chasing Sparsity in Vision Transformers: An End-to-End Exploration
Cycle Self-Training for Domain Adaptation
Self-Supervised Multi-Object Tracking with Cross-input Consistency
Generalizable Imitation Learning from Observation via Inferring Goal Proximity
Information-theoretic generalization bounds for black-box learning algorithms
Disentangled Contrastive Learning on Graphs
Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression
Adversarial Regression with Doubly Non-negative Weighting Matrices
ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs
Combinatorial Pure Exploration with Bottleneck Reward Function
Few-Shot Object Detection via Association and DIscrimination
Coarse-to-fine Animal Pose and Shape Estimation
Memory-Efficient Approximation Algorithms for Max-k-Cut and Correlation Clustering
An Online Method for A Class of Distributionally Robust Optimization with Non-convex Objectives
Regime Switching Bandits
Conformal Bayesian Computation
Two steps to risk sensitivity
Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training
Causal Influence Detection for Improving Efficiency in Reinforcement Learning
NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem
Learning to Time-Decode in Spiking Neural Networks Through the Information Bottleneck
Uncertainty-Driven Loss for Single Image Super-Resolution
Continual World: A Robotic Benchmark For Continual Reinforcement Learning
Spectral embedding for dynamic networks with stability guarantees
Decentralized Learning in Online Queuing Systems
Framing RNN as a kernel method: A neural ODE approach
Algorithmic Instabilities of Accelerated Gradient Descent
Dual Progressive Prototype Network for Generalized Zero-Shot Learning
Distributed Principal Component Analysis with Limited Communication
Efficient Active Learning for Gaussian Process Classification by Error Reduction
E(n) Equivariant Normalizing Flows
Scalable Bayesian GPFA with automatic relevance determination and discrete noise models
Sharp Impossibility Results for Hyper-graph Testing
Private learning implies quantum stability
IRM—when it works and when it doesn't: A test case of natural language inference
GemNet: Universal Directional Graph Neural Networks for Molecules
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize
Learning to Combine Per-Example Solutions for Neural Program Synthesis
Causal Navigation by Continuous-time Neural Networks
Provable Representation Learning for Imitation with Contrastive Fourier Features
Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensembles
Parametrized Quantum Policies for Reinforcement Learning
Parameter Prediction for Unseen Deep Architectures
A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes
How can classical multidimensional scaling go wrong?
Deep Extrapolation for Attribute-Enhanced Generation
Separation Results between Fixed-Kernel and Feature-Learning Probability Metrics
Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience
Neural optimal feedback control with local learning rules
HyperSPNs: Compact and Expressive Probabilistic Circuits
Robust Generalization despite Distribution Shift via Minimum Discriminating Information
Tracking Without Re-recognition in Humans and Machines
Luna: Linear Unified Nested Attention
Modified Frank Wolfe in Probability Space
EDGE: Explaining Deep Reinforcement Learning Policies
An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers
Differentially Private n-gram Extraction
Heuristic-Guided Reinforcement Learning
A Note on Sparse Generalized Eigenvalue Problem
On Empirical Risk Minimization with Dependent and Heavy-Tailed Data
Celebrating Diversity in Shared Multi-Agent Reinforcement Learning
Mirror Langevin Monte Carlo: the Case Under Isoperimetry
HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning
Exploration-Exploitation in Multi-Agent Competition: Convergence with Bounded Rationality
Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance
Neural Additive Models: Interpretable Machine Learning with Neural Nets
You Never Cluster Alone
Rethinking the Pruning Criteria for Convolutional Neural Network
Bridging Explicit and Implicit Deep Generative Models via Neural Stein Estimators
Distilling Object Detectors with Feature Richness
Support Recovery of Sparse Signals from a Mixture of Linear Measurements
Residual Relaxation for Multi-view Representation Learning
Latent Execution for Neural Program Synthesis Beyond Domain-Specific Languages
Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning
Optimal Rates for Random Order Online Optimization
Autonomous Reinforcement Learning via Subgoal Curricula
Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer
$\texttt{LeadCache}$: Regret-Optimal Caching in Networks
Noise2Score: Tweedie’s Approach to Self-Supervised Image Denoising without Clean Images
Adversarial Robustness with Non-uniform Perturbations
Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning
Optimal Uniform OPE and Model-based Offline Reinforcement Learning in Time-Homogeneous, Reward-Free and Task-Agnostic Settings
Online Matching in Sparse Random Graphs: Non-Asymptotic Performances of Greedy Algorithm
All Tokens Matter: Token Labeling for Training Better Vision Transformers
The decomposition of the higher-order homology embedding constructed from the $k$-Laplacian
Catch-A-Waveform: Learning to Generate Audio from a Single Short Example
Curriculum Disentangled Recommendation with Noisy Multi-feedback
Unsupervised Motion Representation Learning with Capsule Autoencoders
On Margin-Based Cluster Recovery with Oracle Queries
Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models
Mixture weights optimisation for Alpha-Divergence Variational Inference
Fast and Memory Efficient Differentially Private-SGD via JL Projections
Conformal Time-series Forecasting
A Max-Min Entropy Framework for Reinforcement Learning
Instance-Dependent Partial Label Learning
Leveraging Distribution Alignment via Stein Path for Cross-Domain Cold-Start Recommendation
Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones
Adaptive Diffusion in Graph Neural Networks
Explaining Latent Representations with a Corpus of Examples
Sparse Quadratic Optimisation over the Stiefel Manifold with Application to Permutation Synchronisation
Knowledge-inspired 3D Scene Graph Prediction in Point Cloud
Regularization in ResNet with Stochastic Depth
Photonic Differential Privacy with Direct Feedback Alignment
Few-Round Learning for Federated Learning
Multiclass Boosting and the Cost of Weak Learning
On Optimal Robustness to Adversarial Corruption in Online Decision Problems
Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial Networks
Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning
ABC: Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning
Rethinking Calibration of Deep Neural Networks: Do Not Be Afraid of Overconfidence
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning
Learning Interpretable Decision Rule Sets: A Submodular Optimization Approach
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee
Boosted CVaR Classification
MICo: Improved representations via sampling-based state similarity for Markov decision processes
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing
Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints
Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification
BAST: Bayesian Additive Regression Spanning Trees for Complex Constrained Domain
On Memorization in Probabilistic Deep Generative Models
Assessing Fairness in the Presence of Missing Data
Entropy-based adaptive Hamiltonian Monte Carlo
DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning
An Improved Analysis and Rates for Variance Reduction under Without-replacement Sampling Orders
Taxonomizing local versus global structure in neural network loss landscapes
Making a (Counterfactual) Difference One Rationale at a Time
RIM: Reliable Influence-based Active Learning on Graphs
SOFT: Softmax-free Transformer with Linear Complexity
Node Dependent Local Smoothing for Scalable Graph Learning
Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning
A Geometric Analysis of Neural Collapse with Unconstrained Features
Noisy Adaptation Generates Lévy Flights in Attractor Neural Networks
Reverse-Complement Equivariant Networks for DNA Sequences
Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization
Statistical Query Lower Bounds for List-Decodable Linear Regression
The Unbalanced Gromov Wasserstein Distance: Conic Formulation and Relaxation
Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks
Adversarial Teacher-Student Representation Learning for Domain Generalization
Neural Bootstrapper
Learning to Draw: Emergent Communication through Sketching
Counterfactual Maximum Likelihood Estimation for Training Deep Networks
Fitting summary statistics of neural data with a differentiable spiking network simulator
Littlestone Classes are Privately Online Learnable
Can contrastive learning avoid shortcut solutions?
Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP
Contrastive Learning for Neural Topic Model
Scallop: From Probabilistic Deductive Databases to Scalable Differentiable Reasoning
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation
Last iterate convergence of SGD for Least-Squares in the Interpolation regime.
Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning
Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables
Differentiable Unsupervised Feature Selection based on a Gated Laplacian
Uniform Concentration Bounds toward a Unified Framework for Robust Clustering
Risk-Averse Bayes-Adaptive Reinforcement Learning
Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components
Lower and Upper Bounds on the Pseudo-Dimension of Tensor Network Models
Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning
Federated Reconstruction: Partially Local Federated Learning
Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints
K-level Reasoning for Zero-Shot Coordination in Hanabi
A Theory of the Distortion-Perception Tradeoff in Wasserstein Space
Learning a Single Neuron with Bias Using Gradient Descent
Offline Meta Reinforcement Learning -- Identifiability Challenges and Effective Data Collection Strategies
The Many Faces of Adversarial Risk
Re-ranking for image retrieval and transductive few-shot classification
Impression learning: Online representation learning with synaptic plasticity
Adaptive Conformal Inference Under Distribution Shift
Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers
Neural Distance Embeddings for Biological Sequences
REMIPS: Physically Consistent 3D Reconstruction of Multiple Interacting People under Weak Supervision
Adaptive wavelet distillation from neural networks through interpretations
Credit Assignment Through Broadcasting a Global Error Vector
Robust Online Correlation Clustering
DOCTOR: A Simple Method for Detecting Misclassification Errors
Out-of-Distribution Generalization in Kernel Regression
Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery
Efficiently Learning One Hidden Layer ReLU Networks From Queries
Truncated Marginal Neural Ratio Estimation
Learning Barrier Certificates: Towards Safe Reinforcement Learning with Zero Training-time Violations
Hyperparameter Optimization Is Deceiving Us, and How to Stop It
Scalable and Stable Surrogates for Flexible Classifiers with Fairness Constraints
Pointwise Bounds for Distribution Estimation under Communication Constraints
Backward-Compatible Prediction Updates: A Probabilistic Approach
Universal Rate-Distortion-Perception Representations for Lossy Compression
Autobahn: Automorphism-based Graph Neural Nets
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise
Learning to Ground Multi-Agent Communication with Autoencoders
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery
Turing Completeness of Bounded-Precision Recurrent Neural Networks
Interpretable agent communication from scratch (with a generic visual processor emerging on the side)
Mean-based Best Arm Identification in Stochastic Bandits under Reward Contamination
A Provably Efficient Sample Collection Strategy for Reinforcement Learning
Searching for Efficient Transformers for Language Modeling
Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration
On Large-Cohort Training for Federated Learning
A/B Testing for Recommender Systems in a Two-sided Marketplace
Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning
Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression
A Prototype-Oriented Framework for Unsupervised Domain Adaptation
Probabilistic Attention for Interactive Segmentation
Safe Policy Optimization with Local Generalized Linear Function Approximations
Locally Valid and Discriminative Prediction Intervals for Deep Learning Models
Extracting Deformation-Aware Local Features by Learning to Deform
NxMTransformer: Semi-Structured Sparsification for Natural Language Understanding via ADMM
Lip to Speech Synthesis with Visual Context Attentional GAN
Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents
Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators
Fine-Grained Zero-Shot Learning with DNA as Side Information
Debiased Visual Question Answering from Feature and Sample Perspectives
Towards a Theoretical Framework of Out-of-Distribution Generalization
Handling Long-tailed Feature Distribution in AdderNets
Gradient-Free Adversarial Training Against Image Corruption for Learning-based Steering
Increasing Liquid State Machine Performance with Edge-of-Chaos Dynamics Organized by Astrocyte-modulated Plasticity
Capacity and Bias of Learned Geometric Embeddings for Directed Graphs
Word2Fun: Modelling Words as Functions for Diachronic Word Representation
Provably Faster Algorithms for Bilevel Optimization
MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data
Practical Near Neighbor Search via Group Testing
Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization
Fast Abductive Learning by Similarity-based Consistency Optimization
Posterior Collapse and Latent Variable Non-identifiability
See More for Scene: Pairwise Consistency Learning for Scene Classification
Adversarial Attack Generation Empowered by Min-Max Optimization
PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition
Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods
When Is Unsupervised Disentanglement Possible?
Robust Pose Estimation in Crowded Scenes with Direct Pose-Level Inference
Beyond BatchNorm: Towards a Unified Understanding of Normalization in Deep Learning
You are caught stealing my winning lottery ticket! Making a lottery ticket claim its ownership
Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficial
Discerning Decision-Making Process of Deep Neural Networks with Hierarchical Voting Transformation
Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization
Learning to Adapt via Latent Domains for Adaptive Semantic Segmentation
Near Optimal Policy Optimization via REPS
Per-Pixel Classification is Not All You Need for Semantic Segmentation
Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems
Optimal Algorithms for Stochastic Contextual Preference Bandits
Batch Normalization Orthogonalizes Representations in Deep Random Networks
Exploiting Chain Rule and Bayes' Theorem to Compare Probability Distributions
Multi-Objective Meta Learning
Efficiently Identifying Task Groupings for Multi-Task Learning
Continuous Doubly Constrained Batch Reinforcement Learning
ELLA: Exploration through Learned Language Abstraction
PortaSpeech: Portable and High-Quality Generative Text-to-Speech
A mechanistic multi-area recurrent network model of decision-making
Localization, Convexity, and Star Aggregation
Learning to delegate for large-scale vehicle routing
Maximum Likelihood Training of Score-Based Diffusion Models
Graphical Models in Heavy-Tailed Markets
Reliable Post hoc Explanations: Modeling Uncertainty in Explainability
Relaxing Local Robustness
Improving Calibration through the Relationship with Adversarial Robustness
Consistent Non-Parametric Methods for Maximizing Robustness
Representation Learning for Event-based Visuomotor Policies
Towards Understanding Cooperative Multi-Agent Q-Learning with Value Factorization
Off-Policy Risk Assessment in Contextual Bandits
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs
Joint Semantic Mining for Weakly Supervised RGB-D Salient Object Detection
The Inductive Bias of Quantum Kernels
Hindsight Task Relabelling: Experience Replay for Sparse Reward Meta-RL
Coordinated Proximal Policy Optimization
Estimating High Order Gradients of the Data Distribution by Denoising
Stabilizing Dynamical Systems via Policy Gradient Methods
What Makes Multi-Modal Learning Better than Single (Provably)
Cardinality-Regularized Hawkes-Granger Model
Deep Contextual Video Compression
Designing Counterfactual Generators using Deep Model Inversion
Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data
Offline Reinforcement Learning as One Big Sequence Modeling Problem
G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators
Emergent Communication of Generalizations
Glance-and-Gaze Vision Transformer
On the Sample Complexity of Privately Learning Axis-Aligned Rectangles
Teachable Reinforcement Learning via Advice Distillation
Sampling with Trusthworthy Constraints: A Variational Gradient Framework
Anti-Backdoor Learning: Training Clean Models on Poisoned Data
Control Variates for Slate Off-Policy Evaluation
TriBERT: Human-centric Audio-visual Representation Learning
How Powerful are Performance Predictors in Neural Architecture Search?
RoMA: Robust Model Adaptation for Offline Model-based Optimization
Sample Complexity Bounds for Active Ranking from Multi-wise Comparisons
Understanding and Improving Early Stopping for Learning with Noisy Labels
NeRS: Neural Reflectance Surfaces for Sparse-view 3D Reconstruction in the Wild
Sageflow: Robust Federated Learning against Both Stragglers and Adversaries
A Universal Law of Robustness via Isoperimetry
Understanding the Under-Coverage Bias in Uncertainty Estimation
Improving Anytime Prediction with Parallel Cascaded Networks and a Temporal-Difference Loss
Differentially Private Model Personalization
Multi-Agent Reinforcement Learning in Stochastic Networked Systems
BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining
Robust Predictable Control
Revisiting Model Stitching to Compare Neural Representations
Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation
Iterative Causal Discovery in the Possible Presence of Latent Confounders and Selection Bias
Fast Extra Gradient Methods for Smooth Structured Nonconvex-Nonconcave Minimax Problems
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
Detecting Anomalous Event Sequences with Temporal Point Processes
ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias
End-to-end Multi-modal Video Temporal Grounding
Subgroup Generalization and Fairness of Graph Neural Networks
Improving Visual Quality of Image Synthesis by A Token-based Generator with Transformers
Online Convex Optimization with Continuous Switching Constraint
Never Go Full Batch (in Stochastic Convex Optimization)
PCA Initialization for Approximate Message Passing in Rotationally Invariant Models
Evaluating State-of-the-Art Classification Models Against Bayes Optimality
Policy Optimization in Adversarial MDPs: Improved Exploration via Dilated Bonuses
LADA: Look-Ahead Data Acquisition via Augmentation for Deep Active Learning
Searching Parameterized AP Loss for Object Detection
Matrix encoding networks for neural combinatorial optimization
Probabilistic Margins for Instance Reweighting in Adversarial Training
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation
Learning Riemannian metric for disease progression modeling
Random Noise Defense Against Query-Based Black-Box Attacks
Exploiting Domain-Specific Features to Enhance Domain Generalization
Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning
What’s a good imputation to predict with missing values?
Local policy search with Bayesian optimization
Twice regularized MDPs and the equivalence between robustness and regularization
Supervising the Transfer of Reasoning Patterns in VQA
On Robust Optimal Transport: Computational Complexity and Barycenter Computation
Deconvolutional Networks on Graph Data
Set Prediction in the Latent Space
Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence
Ensembling Graph Predictions for AMR Parsing
PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization
Predicting Event Memorability from Contextual Visual Semantics
Bounds all around: training energy-based models with bidirectional bounds
Online Sign Identification: Minimization of the Number of Errors in Thresholding Bandits
Artistic Style Transfer with Internal-external Learning and Contrastive Learning
Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning
Consistency Regularization for Variational Auto-Encoders
The Implicit Bias of Minima Stability: A View from Function Space
What can linearized neural networks actually say about generalization?
Neighborhood Reconstructing Autoencoders
SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression
On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness
Variational Multi-Task Learning with Gumbel-Softmax Priors
Noether’s Learning Dynamics: Role of Symmetry Breaking in Neural Networks
Posterior Meta-Replay for Continual Learning
SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
Variational Diffusion Models
Collaborative Uncertainty in Multi-Agent Trajectory Forecasting
ResT: An Efficient Transformer for Visual Recognition
Unsupervised Object-Level Representation Learning from Scene Images
Locality defeats the curse of dimensionality in convolutional teacher-student scenarios
Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks
Fast rates for prediction with limited expert advice
Unsupervised Representation Transfer for Small Networks: I Believe I Can Distill On-the-Fly
Episodic Multi-agent Reinforcement Learning with Curiosity-driven Exploration
Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network
Rectifying the Shortcut Learning of Background for Few-Shot Learning
To The Point: Correspondence-driven monocular 3D category reconstruction
Robustness via Uncertainty-aware Cycle Consistency
Counterexample Guided RL Policy Refinement Using Bayesian Optimization
Foundations of Symbolic Languages for Model Interpretability
Wisdom of the Crowd Voting: Truthful Aggregation of Voter Information and Preferences
Rate-Optimal Subspace Estimation on Random Graphs
Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System
Convergence of adaptive algorithms for constrained weakly convex optimization
Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning
Model-Based Reinforcement Learning via Imagination with Derived Memory
Pareto Domain Adaptation
Optimal Rates for Nonparametric Density Estimation under Communication Constraints
Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck
Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction
Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis
DRIVE: One-bit Distributed Mean Estimation
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms
Do Transformers Really Perform Badly for Graph Representation?
Diversity Enhanced Active Learning with Strictly Proper Scoring Rules
Fast Federated Learning in the Presence of Arbitrary Device Unavailability
Clockwork Variational Autoencoders
Learning Domain Invariant Representations in Goal-conditioned Block MDPs
Rethinking conditional GAN training: An approach using geometrically structured latent manifolds
Learning Space Partitions for Path Planning
Independent Prototype Propagation for Zero-Shot Compositionality
A Normative and Biologically Plausible Algorithm for Independent Component Analysis
Representing Hyperbolic Space Accurately using Multi-Component Floats
Compacter: Efficient Low-Rank Hypercomplex Adapter Layers
Proportional Participatory Budgeting with Additive Utilities
Landmark-RxR: Solving Vision-and-Language Navigation with Fine-Grained Alignment Supervision
Streaming Linear System Identification with Reverse Experience Replay
Estimating Multi-cause Treatment Effects via Single-cause Perturbation
DualNet: Continual Learning, Fast and Slow
End-to-end reconstruction meets data-driven regularization for inverse problems
Garment4D: Garment Reconstruction from Point Cloud Sequences
Identification of the Generalized Condorcet Winner in Multi-dueling Bandits
Learning Collaborative Policies to Solve NP-hard Routing Problems
Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN
On the Second-order Convergence Properties of Random Search Methods
Combating Noise: Semi-supervised Learning by Region Uncertainty Quantification
Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction
Natural continual learning: success is a journey, not (just) a destination
Parameterized Knowledge Transfer for Personalized Federated Learning
ToAlign: Task-Oriented Alignment for Unsupervised Domain Adaptation
Integrated Latent Heterogeneity and Invariance Learning in Kernel Space
Variational Inference for Continuous-Time Switching Dynamical Systems
Improve Agents without Retraining: Parallel Tree Search with Off-Policy Correction
3D Pose Transfer with Correspondence Learning and Mesh Refinement
Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections
Efficient Learning of Discrete-Continuous Computation Graphs
From global to local MDI variable importances for random forests and when they are Shapley values
Limiting fluctuation and trajectorial stability of multilayer neural networks with mean field training
Nearly-Tight and Oblivious Algorithms for Explainable Clustering
Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions
Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training
Safe Reinforcement Learning by Imagining the Near Future
BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
Structured Denoising Diffusion Models in Discrete State-Spaces
An Information-theoretic Approach to Distribution Shifts
Offline Reinforcement Learning with Reverse Model-based Imagination
On learning sparse vectors from mixture of responses
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
The Role of Global Labels in Few-Shot Classification and How to Infer Them
Predify: Augmenting deep neural networks with brain-inspired predictive coding dynamics
Meta-Learning Sparse Implicit Neural Representations
Pruning Randomly Initialized Neural Networks with Iterative Randomization
Periodic Activation Functions Induce Stationarity
Stateful Strategic Regression
On the Estimation Bias in Double Q-Learning
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA
A Faster Maximum Cardinality Matching Algorithm with Applications in Machine Learning
Few-Shot Segmentation via Cycle-Consistent Transformer
Augmented Shortcuts for Vision Transformers
Adversarial Reweighting for Partial Domain Adaptation
Instance-dependent Label-noise Learning under a Structural Causal Model
Auto-Encoding Knowledge Graph for Unsupervised Medical Report Generation
Optimizing Reusable Knowledge for Continual Learning via Metalearning
Breaking the centralized barrier for cross-device federated learning
Towards Enabling Meta-Learning from Target Models
Universal Semi-Supervised Learning
The Emergence of Objectness: Learning Zero-shot Segmentation from Videos
CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions
Confidence-Aware Imitation Learning from Demonstrations with Varying Optimality
Unsupervised Foreground Extraction via Deep Region Competition
DeepSITH: Efficient Learning via Decomposition of What and When Across Time Scales
Factored Policy Gradients: Leveraging Structure for Efficient Learning in MOMDPs
From Canonical Correlation Analysis to Self-supervised Graph Neural Networks
Powerpropagation: A sparsity inducing weight reparameterisation
Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness
Deep Residual Learning in Spiking Neural Networks
Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning
Learning curves of generic features maps for realistic datasets with a teacher-student model
Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret
SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes
Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation
Recurrent Bayesian Classifier Chains for Exact Multi-Label Classification
Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks
When Are Solutions Connected in Deep Networks?
SWAD: Domain Generalization by Seeking Flat Minima
Efficient Neural Network Training via Forward and Backward Propagation Sparsification
Least Square Calibration for Peer Reviews
Differentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks
Choose a Transformer: Fourier or Galerkin
MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps
Gauge Equivariant Transformer
An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning
Mastering Atari Games with Limited Data
Contextual Similarity Aggregation with Self-attention for Visual Re-ranking
Distributed Saddle-Point Problems Under Data Similarity
Online Variational Filtering and Parameter Learning
Support vector machines and linear regression coincide with very high-dimensional features
Information Directed Reward Learning for Reinforcement Learning
Rank Overspecified Robust Matrix Recovery: Subgradient Method and Exact Recovery
Associating Objects with Transformers for Video Object Segmentation
Learning in Non-Cooperative Configurable Markov Decision Processes
Partial success in closing the gap between human and machine vision
No-regret Online Learning over Riemannian Manifolds
On Effective Scheduling of Model-based Reinforcement Learning
Dual Parameterization of Sparse Variational Gaussian Processes
Online Facility Location with Multiple Advice
Agent Modelling under Partial Observability for Deep Reinforcement Learning
Self-Supervised Learning Disentangled Group Representation as Feature
MOMA: Multi-Object Multi-Actor Activity Parsing
Optimizing Information-theoretical Generalization Bound via Anisotropic Noise of SGLD
Batched Thompson Sampling
Shape your Space: A Gaussian Mixture Regularization Approach to Deterministic Autoencoders
On the Bias-Variance-Cost Tradeoff of Stochastic Optimization
R-Drop: Regularized Dropout for Neural Networks
Hard-Attention for Scalable Image Classification
A Faster Decentralized Algorithm for Nonconvex Minimax Problems
Co-evolution Transformer for Protein Contact Prediction
Dynamic COVID risk assessment accounting for community virus exposure from a spatial-temporal transmission model
The balancing principle for parameter choice in distance-regularized domain adaptation
Large-Scale Wasserstein Gradient Flows
Non-Gaussian Gaussian Processes for Few-Shot Regression
Robustness between the worst and average case
Alignment Attention by Matching Key and Query Distributions
Learning Conjoint Attentions for Graph Neural Nets
FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization
Neural Symplectic Form: Learning Hamiltonian Equations on General Coordinate Systems
Open Rule Induction
Biological learning in key-value memory networks
Mixed Supervised Object Detection by Transferring Mask Prior and Semantic Similarity
An Improved Analysis of Gradient Tracking for Decentralized Machine Learning
Task-Adaptive Neural Network Search with Meta-Contrastive Learning
Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers
On Inductive Biases for Heterogeneous Treatment Effect Estimation
Deconditional Downscaling with Gaussian Processes
Understanding Instance-based Interpretability of Variational Auto-Encoders
Self-Supervised GANs with Label Augmentation
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data
Goal-Aware Cross-Entropy for Multi-Target Reinforcement Learning
Fair Scheduling for Time-dependent Resources
Distilling Image Classifiers in Object Detectors
Discovery of Options via Meta-Learned Subgoals
Balanced Chamfer Distance as a Comprehensive Metric for Point Cloud Completion
CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum
Topographic VAEs learn Equivariant Capsules
Self-Consistent Models and Values
Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions
On Linear Stability of SGD and Input-Smoothness of Neural Networks
Adversarial Training Helps Transfer Learning via Better Representations
Going Beyond Linear Transformers with Recurrent Fast Weight Programmers
Regret Minimization Experience Replay in Off-Policy Reinforcement Learning
PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning
Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction
Learning to Learn Graph Topologies
AutoGEL: An Automated Graph Neural Network with Explicit Link Information
Interpreting Representation Quality of DNNs for 3D Point Cloud Processing
Low-Rank Constraints for Fast Inference in Structured Models
On the Equivalence between Neural Network and Support Vector Machine
Active Assessment of Prediction Services as Accuracy Surface Over Attribute Combinations
Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling
Adversarially robust learning for security-constrained optimal power flow
Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence
MST: Masked Self-Supervised Transformer for Visual Representation
Generalized Shape Metrics on Neural Representations
Faster Neural Network Training with Approximate Tensor Operations
Personalized Federated Learning With Gaussian Processes
ReSSL: Relational Self-Supervised Learning with Weak Augmentation
Aligned Structured Sparsity Learning for Efficient Image Super-Resolution
Differentiable Quality Diversity
Recurrence along Depth: Deep Convolutional Neural Networks with Recurrent Layer Aggregation
Efficient Equivariant Network
The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning
Alias-Free Generative Adversarial Networks
Statistically and Computationally Efficient Linear Meta-representation Learning
Towards Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games
Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning
Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data
Safe Reinforcement Learning with Natural Language Constraints
Improving Self-supervised Learning with Automated Unsupervised Outlier Arbitration
Dynamical Wasserstein Barycenters for Time-series Modeling
Global-aware Beam Search for Neural Abstractive Summarization
Optimal Order Simple Regret for Gaussian Process Bandits
Invariant Causal Imitation Learning for Generalizable Policies
Directed Probabilistic Watershed
Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects
STORM+: Fully Adaptive SGD with Recursive Momentum for Nonconvex Optimization
Counterfactual Explanations in Sequential Decision Making Under Uncertainty
Diversity Matters When Learning From Ensembles
Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples
Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications
Beyond Bandit Feedback in Online Multiclass Classification
Learning Fast-Inference Bayesian Networks
Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling
Directed Graph Contrastive Learning
Neural Auto-Curricula in Two-Player Zero-Sum Games
Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD
Asynchronous Decentralized Online Learning
Diffusion Normalizing Flow
A sampling-based circuit for optimal decision making
Demystifying and Generalizing BinaryConnect
Learning Transferable Features for Point Cloud Detection via 3D Contrastive Co-training
Bayesian Optimization with High-Dimensional Outputs
Using Random Effects to Account for High-Cardinality Categorical Features and Repeated Measures in Deep Neural Networks
HRFormer: High-Resolution Vision Transformer for Dense Predict
Graph Adversarial Self-Supervised Learning
The Image Local Autoregressive Transformer
Fine-grained Generalization Analysis of Inductive Matrix Completion
Canonical Capsules: Self-Supervised Capsules in Canonical Pose
On the Power of Edge Independent Graph Models
On the Theory of Reinforcement Learning with Once-per-Episode Feedback
Conflict-Averse Gradient Descent for Multi-task learning
Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems
Predicting What You Already Know Helps: Provable Self-Supervised Learning
Fair Sortition Made Transparent
Denoising Normalizing Flow
TopicNet: Semantic Graph-Guided Topic Discovery
Effective Meta-Regularization by Kernelized Proximal Regularization
No RL, No Simulation: Learning to Navigate without Navigating
Knowledge-Adaptation Priors
Universal Approximation Using Well-Conditioned Normalizing Flows
Domain Invariant Representation Learning with Domain Density Transformations
OSOA: One-Shot Online Adaptation of Deep Generative Models for Lossless Compression
Is Automated Topic Model Evaluation Broken? The Incoherence of Coherence
VAST: Value Function Factorization with Variable Agent Sub-Teams
Relaxed Marginal Consistency for Differentially Private Query Answering
Neural Flows: Efficient Alternative to Neural ODEs
Square Root Principal Component Pursuit: Tuning-Free Noisy Robust Matrix Recovery
Fast Training Method for Stochastic Compositional Optimization Problems
Fast Projection onto the Capped Simplex with Applications to Sparse Regression in Bioinformatics
MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility Prediction
Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions
Adaptive Sampling for Minimax Fair Classification
Relative Flatness and Generalization
Mini-Batch Consistent Slot Set Encoder for Scalable Set Encoding
Stochastic Solutions for Linear Inverse Problems using the Prior Implicit in a Denoiser
RelaySum for Decentralized Deep Learning on Heterogeneous Data
FMMformer: Efficient and Flexible Transformer via Decomposed Near-field and Far-field Attention
Gaussian Kernel Mixture Network for Single Image Defocus Deblurring
Global Filter Networks for Image Classification
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data
Linear-Time Probabilistic Solution of Boundary Value Problems
Adaptive Online Packing-guided Search for POMDPs
Topological Relational Learning on Graphs
MobILE: Model-Based Imitation Learning From Observation Alone
Multi-Label Learning with Pairwise Relevance Ordering
Volume Rendering of Neural Implicit Surfaces
Loss function based second-order Jensen inequality and its application to particle variational inference
Towards Robust and Reliable Algorithmic Recourse
DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras
Does Preprocessing Help Training Over-parameterized Neural Networks?
Adversarial Robustness with Semi-Infinite Constrained Learning
Don’t Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence
Optimizing Conditional Value-At-Risk of Black-Box Functions
Learning to dehaze with polarization
TestRank: Bringing Order into Unlabeled Test Instances for Deep Learning Tasks
Federated Linear Contextual Bandits
Fast Doubly-Adaptive MCMC to Estimate the Gibbs Partition Function with Weak Mixing Time Bounds
An Efficient Transfer Learning Framework for Multiagent Reinforcement Learning
Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration
Implicit Transformer Network for Screen Content Image Continuous Super-Resolution
Do Input Gradients Highlight Discriminative Features?
Three Operator Splitting with Subgradients, Stochastic Gradients, and Adaptive Learning Rates
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients
3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds
Training for the Future: A Simple Gradient Interpolation Loss to Generalize Along Time
Dense Unsupervised Learning for Video Segmentation
Scalable Quasi-Bayesian Inference for Instrumental Variable Regression
Ultrahyperbolic Neural Networks
Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning
Neural Hybrid Automata: Learning Dynamics With Multiple Modes and Stochastic Transitions
Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embedding
Gradient Inversion with Generative Image Prior
Action-guided 3D Human Motion Prediction
SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness
Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes
Learning to Learn Dense Gaussian Processes for Few-Shot Learning
Achieving Rotational Invariance with Bessel-Convolutional Neural Networks
Online Learning and Control of Complex Dynamical Systems from Sensory Input
Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning
End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering
Reinforcement learning for optimization of variational quantum circuit architectures
Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination
Active clustering for labeling training data
Dynamic Normalization and Relay for Video Action Recognition
Local Differential Privacy for Regret Minimization in Reinforcement Learning
Predicting Molecular Conformation via Dynamic Graph Score Matching
Identification and Estimation of Joint Probabilities of Potential Outcomes in Observational Studies with Covariate Information
Residual Pathway Priors for Soft Equivariance Constraints
Robust Deep Reinforcement Learning through Adversarial Loss
Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives
Interesting Object, Curious Agent: Learning Task-Agnostic Exploration
ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation Learning
Smooth Normalizing Flows
Directional Message Passing on Molecular Graphs via Synthetic Coordinates
Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks
On Contrastive Representations of Stochastic Processes
Joint inference and input optimization in equilibrium networks
Black Box Probabilistic Numerics
STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning
NTopo: Mesh-free Topology Optimization using Implicit Neural Representations
A 3D Generative Model for Structure-Based Drug Design
Circa: Stochastic ReLUs for Private Deep Learning
Explaining Hyperparameter Optimization via Partial Dependence Plots
Learning Causal Semantic Representation for Out-of-Distribution Prediction
Charting and Navigating the Space of Solutions for Recurrent Neural Networks
Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect
Manipulating SGD with Data Ordering Attacks
Practical Large-Scale Linear Programming using Primal-Dual Hybrid Gradient
Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition
Do Different Tracking Tasks Require Different Appearance Models?
Online Learning in Periodic Zero-Sum Games
CentripetalText: An Efficient Text Instance Representation for Scene Text Detection
Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data
Image Generation using Continuous Filter Atoms
Beltrami Flow and Neural Diffusion on Graphs
Multimodal Few-Shot Learning with Frozen Language Models
Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent
Fast Bayesian Inference for Gaussian Cox Processes via Path Integral Formulation
NORESQA: A Framework for Speech Quality Assessment using Non-Matching References
Duplex Sequence-to-Sequence Learning for Reversible Machine Translation
Coupled Gradient Estimators for Discrete Latent Variables
Perturbation-based Regret Analysis of Predictive Control in Linear Time Varying Systems
Metropolis-Hastings Data Augmentation for Graph Neural Networks
Private Non-smooth ERM and SCO in Subquadratic Steps
Automatic Symmetry Discovery with Lie Algebra Convolutional Network
Finding Bipartite Components in Hypergraphs
Gone Fishing: Neural Active Learning with Fisher Embeddings
SketchGen: Generating Constrained CAD Sketches
Dueling Bandits with Team Comparisons
The Effect of the Intrinsic Dimension on the Generalization of Quadratic Classifiers
Exponential Graph is Provably Efficient for Decentralized Deep Training
A Convergence Analysis of Gradient Descent on Graph Neural Networks
Design of Experiments for Stochastic Contextual Linear Bandits
Think Big, Teach Small: Do Language Models Distil Occam’s Razor?
Towards Calibrated Model for Long-Tailed Visual Recognition from Prior Perspective
On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning
Solving Min-Max Optimization with Hidden Structure via Gradient Descent Ascent
Cooperative Stochastic Bandits with Asynchronous Agents and Constrained Feedback
Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons
On Locality of Local Explanation Models
Learning Signal-Agnostic Manifolds of Neural Fields
Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training
Unsupervised Learning of Compositional Energy Concepts
Neural Circuit Synthesis from Specification Patterns
Spatiotemporal Joint Filter Decomposition in 3D Convolutional Neural Networks
Grounding Representation Similarity Through Statistical Testing
Uncertainty Calibration for Ensemble-Based Debiasing Methods
Activation Sharing with Asymmetric Paths Solves Weight Transport Problem without Bidirectional Connection
Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception
Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families
Who Leads and Who Follows in Strategic Classification?
Catalytic Role Of Noise And Necessity Of Inductive Biases In The Emergence Of Compositional Communication
Bandits with many optimal arms
Exploiting a Zoo of Checkpoints for Unseen Tasks
Offline Model-based Adaptable Policy Learning
Formalizing the Generalization-Forgetting Trade-off in Continual Learning
(Almost) Free Incentivized Exploration from Decentralized Learning Agents
Emergent Communication under Varying Sizes and Connectivities
Meta Learning Backpropagation And Improving It
Adaptable Agent Populations via a Generative Model of Policies
Faster Algorithms and Constant Lower Bounds for the Worst-Case Expected Error
Preserved central model for faster bidirectional compression in distributed settings
InfoGCL: Information-Aware Graph Contrastive Learning
Reinforced Few-Shot Acquisition Function Learning for Bayesian Optimization
Boosting with Multiple Sources
Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation
Towards Biologically Plausible Convolutional Networks
Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex Optimization
Actively Identifying Causal Effects with Latent Variables Given Only Response Variable Observable
Gradual Domain Adaptation without Indexed Intermediate Domains
Understanding How Encoder-Decoder Architectures Attend
Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices
PLUGIn: A simple algorithm for inverting generative models with recovery guarantees
Relative Uncertainty Learning for Facial Expression Recognition
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels
Manifold Topology Divergence: a Framework for Comparing Data Manifolds.
Bayesian Bellman Operators
Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees
Compositional Reinforcement Learning from Logical Specifications
One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval
Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations
Dynamic population-based meta-learning for multi-agent communication with natural language
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data
Model, sample, and epoch-wise descents: exact solution of gradient flow in the random feature model
Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning
Instance-Conditional Knowledge Distillation for Object Detection
Entropic Desired Dynamics for Intrinsic Control
A unified framework for bandit multiple testing
Memory Efficient Meta-Learning with Large Images
A single gradient step finds adversarial examples on random two-layers neural networks
The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations
A Unified Approach to Fair Online Learning via Blackwell Approachability
On Component Interactions in Two-Stage Recommender Systems
Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic
CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator
ReAct: Out-of-distribution Detection With Rectified Activations
Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models
Stability and Deviation Optimal Risk Bounds with Convergence Rate $O(1/n)$
Efficient Training of Visual Transformers with Small Datasets
Combiner: Full Attention Transformer with Sparse Computation Cost
On the Frequency Bias of Generative Models
SSAL: Synergizing between Self-Training and Adversarial Learning for Domain Adaptive Object Detection
High-probability Bounds for Non-Convex Stochastic Optimization with Heavy Tails
Discrete-Valued Neural Communication
Robust Contrastive Learning Using Negative Samples with Diminished Semantics
Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote
Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers
XDO: A Double Oracle Algorithm for Extensive-Form Games
From Optimality to Robustness: Adaptive Re-Sampling Strategies in Stochastic Bandits
History Aware Multimodal Transformer for Vision-and-Language Navigation
Reformulating Zero-shot Action Recognition for Multi-label Actions
The Utility of Explainable AI in Ad Hoc Human-Machine Teaming
Understanding Deflation Process in Over-parametrized Tensor Decomposition
Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation
MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge
Generalized Linear Bandits with Local Differential Privacy
Scalable Diverse Model Selection for Accessible Transfer Learning
Unbiased Classification through Bias-Contrastive and Bias-Balanced Learning
Snowflake: Scaling GNNs to high-dimensional continuous control via parameter freezing
Distributional Reinforcement Learning for Multi-Dimensional Reward Functions
Learning Nonparametric Volterra Kernels with Gaussian Processes
Estimating the Unique Information of Continuous Variables
Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation
Rethinking Graph Transformers with Spectral Attention
Continual Learning via Local Module Composition
Local Explanation of Dialogue Response Generation
Robust Visual Reasoning via Language Guided Neural Module Networks
Robust and differentially private mean estimation
Differentially Private Stochastic Optimization: New Results in Convex and Non-Convex Settings
Accurate Point Cloud Registration with Robust Optimal Transport
Efficient and Local Parallel Random Walks
RMM: Reinforced Memory Management for Class-Incremental Learning
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability
Comprehensive Knowledge Distillation with Causal Intervention
How Does it Sound?
Contrastive Laplacian Eigenmaps
Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space
Online Meta-Learning via Learning with Layer-Distributed Memory
Neural Program Generation Modulo Static Analysis
$(\textrm{Implicit})^2$: Implicit Layers for Implicit Representations
Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains
Leveraging the Inductive Bias of Large Language Models for Abstract Textual Reasoning
Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction
Exact marginal prior distributions of finite Bayesian neural networks
Functional Regularization for Reinforcement Learning via Learned Fourier Features
Discovering Dynamic Salient Regions for Spatio-Temporal Graph Neural Networks
Training Over-parameterized Models with Non-decomposable Objectives
Stochastic Multi-Armed Bandits with Control Variates
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods
Reinforcement Learning Enhanced Explainer for Graph Neural Networks
RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning
Directed Spectrum Measures Improve Latent Network Models Of Neural Populations
Antipodes of Label Differential Privacy: PATE and ALIBI
POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples
Proper Value Equivalence
Data driven semi-supervised learning
Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation
CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networks
An Online Riemannian PCA for Stochastic Canonical Correlation Analysis
Deep Learning with Label Differential Privacy
Label Noise SGD Provably Prefers Flat Global Minimizers
Sparse Flows: Pruning Continuous-depth Models
Adversarial Examples in Multi-Layer Random ReLU Networks
Fast Certified Robust Training with Short Warmup
Realistic evaluation of transductive few-shot learning
Active Offline Policy Selection
Finding Discriminative Filters for Specific Degradations in Blind Super-Resolution
Systematic Generalization with Edge Transformers
Contrastive Active Inference
Relational Self-Attention: What's Missing in Attention for Video Understanding
Blending Anti-Aliasing into Vision Transformer
Data-Efficient Instance Generation from Instance Discrimination
Scalable Neural Data Server: A Data Recommender for Transfer Learning
High Probability Complexity Bounds for Line Search Based on Stochastic Oracles
Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs
Non-convex Distributionally Robust Optimization: Non-asymptotic Analysis
Multi-View Representation Learning via Total Correlation Objective
Bandit Phase Retrieval
Object DGCNN: 3D Object Detection using Dynamic Graphs
IA-RED$^2$: Interpretability-Aware Redundancy Reduction for Vision Transformers
XCiT: Cross-Covariance Image Transformers
Optimal Policies Tend To Seek Power
On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay
SalKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning
Multiclass versus Binary Differentially Private PAC Learning
Partition and Code: learning how to compress graphs
An Image is Worth More Than a Thousand Words: Towards Disentanglement in The Wild
Error Compensated Distributed SGD Can Be Accelerated
Revisiting Deep Learning Models for Tabular Data
Conditioning Sparse Variational Gaussian Processes for Online Decision-making
Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis
Implicit SVD for Graph Representation Learning
Explanation-based Data Augmentation for Image Classification
On the Universality of Graph Neural Networks on Large Random Graphs
Navigating to the Best Policy in Markov Decision Processes
Identifying and Benchmarking Natural Out-of-Context Prediction Problems
Asynchronous Decentralized SGD with Quantized and Local Updates
Analogous to Evolutionary Algorithm: Designing a Unified Sequence Model
Grammar-Based Grounded Lexicon Learning
SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition
Retiring Adult: New Datasets for Fair Machine Learning
Space-time Mixing Attention for Video Transformer
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation
Weisfeiler and Lehman Go Cellular: CW Networks
Iterative Teaching by Label Synthesis
Lossy Compression for Lossless Prediction
T-LoHo: A Bayesian Regularization Model for Structured Sparsity and Smoothness on Graphs
Contrastive Reinforcement Learning of Symbolic Reasoning Domains
How Tight Can PAC-Bayes be in the Small Data Regime?
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization
Deep Markov Factor Analysis: Towards Concurrent Temporal and Spatial Analysis of fMRI Data
Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies
AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks
Synthetic Design: An Optimization Approach to Experimental Design with Synthetic Controls
Going Beyond Linear RL: Sample Efficient Neural Function Approximation
One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective
De-randomizing MCMC dynamics with the diffusion Stein operator
On the Provable Generalization of Recurrent Neural Networks
A first-order primal-dual method with adaptivity to local smoothness
Encoding Spatial Distribution of Convolutional Features for Texture Representation
Curriculum Offline Imitating Learning
Sparse is Enough in Scaling Transformers
Federated Graph Classification over Non-IID Graphs
Adaptive Denoising via GainTuning
Rates of Estimation of Optimal Transport Maps using Plug-in Estimators via Barycentric Projections
Attention Approximates Sparse Distributed Memory
ViSER: Video-Specific Surface Embeddings for Articulated 3D Shape Reconstruction
Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training
Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate
NeuroMLR: Robust & Reliable Route Recommendation on Road Networks
ATISS: Autoregressive Transformers for Indoor Scene Synthesis
Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models
Pure Exploration in Kernel and Neural Bandits
On the Cryptographic Hardness of Learning Single Periodic Neurons
Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning
Meta-learning with an Adaptive Task Scheduler
Multi-Facet Clustering Variational Autoencoders
Soft Calibration Objectives for Neural Networks
Complexity Lower Bounds for Nonconvex-Strongly-Concave Min-Max Optimization
Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks
Learning rule influences recurrent network representations but not attractor structure in decision-making tasks
Techniques for Symbol Grounding with SATNet
Improved Guarantees for Offline Stochastic Matching via new Ordered Contention Resolution Schemes
A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance
Continuous Latent Process Flows
How does a Neural Network's Architecture Impact its Robustness to Noisy Labels?
When does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?
On the Importance of Gradients for Detecting Distributional Shifts in the Wild
Learning to Simulate Self-driven Particles System with Coordinated Policy Optimization
Evaluating Efficient Performance Estimators of Neural Architectures
Multiwavelet-based Operator Learning for Differential Equations
Bubblewrap: Online tiling and real-time flow prediction on neural manifolds
Dirichlet Energy Constrained Learning for Deep Graph Neural Networks
S$^3$: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks
The staircase property: How hierarchical structure can guide deep learning
Topological Attention for Time Series Forecasting
Compressive Visual Representations
When False Positive is Intolerant: End-to-End Optimization with Low FPR for Multipartite Ranking
Best-case lower bounds in online learning
Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs
Computer-Aided Design as Language
No-Press Diplomacy from Scratch
Efficient Mirror Descent Ascent Methods for Nonsmooth Minimax Problems
Data Sharing and Compression for Cooperative Networked Control
DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer
Adaptive First-Order Methods Revisited: Convex Minimization without Lipschitz Requirements
Backdoor Attack with Imperceptible Input and Latent Modification
Teaching an Active Learner with Contrastive Examples
On sensitivity of meta-learning to support data
Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing
Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks
Inverse Problems Leveraging Pre-trained Contrastive Representations
H-NeRF: Neural Radiance Fields for Rendering and Temporal Reconstruction of Humans in Motion
Slice Sampling Reparameterization Gradients
Why Do Better Loss Functions Lead to Less Transferable Features?
Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data
Revisit Multimodal Meta-Learning through the Lens of Multi-Task Learning
Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization
Fast Axiomatic Attribution for Neural Networks
Targeted Neural Dynamical Modeling
On the Role of Optimization in Double Descent: A Least Squares Study
Attention Bottlenecks for Multimodal Fusion
Stochastic Bias-Reduced Gradient Methods
Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training data
Interactive Label Cleaning with Example-based Explanations
Parameter Inference with Bifurcation Diagrams
Logarithmic Regret from Sublinear Hints
Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis
Learning to Predict Trustworthiness with Steep Slope Loss
Breaking the Linear Iteration Cost Barrier for Some Well-known Conditional Gradient Methods Using MaxIP Data-structures
Kernel Identification Through Transformers
Convex-Concave Min-Max Stackelberg Games
Three-dimensional spike localization and improved motion correction for Neuropixels recordings
Outcome-Driven Reinforcement Learning via Variational Inference
Transformers Generalize DeepSets and Can be Extended to Graphs & Hypergraphs
Efficient Generalization with Distributionally Robust Learning
How to transfer algorithmic reasoning knowledge to learn new algorithms?
Fast Routing under Uncertainty: Adaptive Learning in Congestion Games via Exponential Weights
Absolute Neighbour Difference based Correlation Test for Detecting Heteroscedastic Relationships
Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels
On Optimal Interpolation in Linear Regression
Towards Sample-efficient Overparameterized Meta-learning
Self-Supervised Learning with Kernel Dependence Maximization
Instance-Conditioned GAN
Optimal prediction of Markov chains with and without spectral gap
Overlapping Spaces for Compact Graph Representations
Long Short-Term Transformer for Online Action Detection
Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer
Neural Pseudo-Label Optimism for the Bank Loan Problem
Differentially Private Learning with Adaptive Clipping
Nested Counterfactual Identification from Arbitrary Surrogate Experiments
On Provable Benefits of Depth in Training Graph Convolutional Networks
Robust Counterfactual Explanations on Graph Neural Networks
Perturb-and-max-product: Sampling and learning in discrete energy-based models
Class-Disentanglement and Applications in Adversarial Detection and Defense
Hypergraph Propagation and Community Selection for Objects Retrieval
Aligning Silhouette Topology for Self-Adaptive 3D Human Pose Recovery
Robust Implicit Networks via Non-Euclidean Contractions
Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement Learning
Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time
Generative vs. Discriminative: Rethinking The Meta-Continual Learning
Controllable and Compositional Generation with Latent-Space Energy-Based Models
CoFiNet: Reliable Coarse-to-fine Correspondences for Robust PointCloud Registration
Automatic and Harmless Regularization with Constrained and Lexicographic Optimization: A Dynamic Barrier Approach
Joint Modeling of Visual Objects and Relations for Scene Graph Generation
Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL
Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning
Visual Adversarial Imitation Learning using Variational Models
Online false discovery rate control for anomaly detection in time series
Double Machine Learning Density Estimation for Local Treatment Effects with Instruments
An analysis of Ermakov-Zolotukhin quadrature using kernels
An Exponential Improvement on the Memorization Capacity of Deep Threshold Networks
NAS-Bench-x11 and the Power of Learning Curves
Reinforcement Learning based Disease Progression Model for Alzheimer’s Disease
Scalable Online Planning via Reinforcement Learning Fine-Tuning
Differentiable Optimization of Generalized Nondecomposable Functions using Linear Programs
Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement
Explicable Reward Design for Reinforcement Learning Agents
Robust and Fully-Dynamic Coreset for Continuous-and-Bounded Learning (With Outliers) Problems
Remember What You Want to Forget: Algorithms for Machine Unlearning
Faster Matchings via Learned Duals
A Separation Result Between Data-oblivious and Data-aware Poisoning Attacks
Learning to Select Exogenous Events for Marked Temporal Point Process
Score-based Generative Modeling in Latent Space
Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks
Center Smoothing: Certified Robustness for Networks with Structured Outputs
Numerical Composition of Differential Privacy
The Semi-Random Satisfaction of Voting Axioms
Better Algorithms for Individually Fair $k$-Clustering
A Near-Optimal Algorithm for Stochastic Bilevel Optimization via Double-Momentum
One More Step Towards Reality: Cooperative Bandits with Imperfect Communication
Discovering and Achieving Goals via World Models
Learning-to-learn non-convex piecewise-Lipschitz functions
Tracking People with 3D Representations
Efficient Truncated Linear Regression with Unknown Noise Variance
Moser Flow: Divergence-based Generative Modeling on Manifolds
Stateful ODE-Nets using Basis Function Expansions
Adversarial Graph Augmentation to Improve Graph Contrastive Learning
Latent Matters: Learning Deep State-Space Models
Permuton-induced Chinese Restaurant Process
Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy to Game
A Gang of Adversarial Bandits
Bayesian Adaptation for Covariate Shift
Differentiable Synthesis of Program Architectures
Fair Classification with Adversarial Perturbations
Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks
Stochastic Online Linear Regression: the Forward Algorithm to Replace Ridge
Strategic Behavior is Bliss: Iterative Voting Improves Social Welfare
Sifting through the noise: Universal first-order methods for stochastic variational inequalities
The Complexity of Sparse Tensor PCA
Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models
CAPE: Encoding Relative Positions with Continuous Augmented Positional Embeddings
Double/Debiased Machine Learning for Dynamic Treatment Effects
Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information
Do Wider Neural Networks Really Help Adversarial Robustness?
Hyperparameter Tuning is All You Need for LISTA
Learning Stable Deep Dynamics Models for Partially Observed or Delayed Dynamical Systems
Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems
FLEX: Unifying Evaluation for Few-Shot NLP
TokenLearner: Adaptive Space-Time Tokenization for Videos
Adjusting for Autocorrelated Errors in Neural Networks for Time Series
The Benefits of Implicit Regularization from SGD in Least Squares Problems
Teaching via Best-Case Counterexamples in the Learning-with-Equivalence-Queries Paradigm
SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios
Shapley Residuals: Quantifying the limits of the Shapley value for explanations
Instance-Dependent Bounds for Zeroth-order Lipschitz Optimization with Error Certificates
Robust Regression Revisited: Acceleration and Improved Estimation Rates
Reinforcement Learning with State Observation Costs in Action-Contingent Noiselessly Observable Markov Decision Processes
On the Sample Complexity of Learning under Geometric Stability
Scaling Ensemble Distribution Distillation to Many Classes with Proxy Targets
Subgoal Search For Complex Reasoning Tasks
Consistent Estimation for PCA and Sparse Regression with Oblivious Outliers
Curriculum Design for Teaching via Demonstrations: Theory and Applications
Uncertain Decisions Facilitate Better Preference Learning
Asymptotically Best Causal Effect Identification with Multi-Armed Bandits
Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial Outcomes
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks
Improving Compositionality of Neural Networks by Decoding Representations to Inputs
Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization
Learning to See by Looking at Noise
Parametric Complexity Bounds for Approximating PDEs with Neural Networks
General Nonlinearities in SO(2)-Equivariant CNNs
Representing Long-Range Context for Graph Neural Networks with Global Attention
Subgame solving without common knowledge
Towards a Unified Information-Theoretic Framework for Generalization
Learning to Synthesize Programs as Interpretable and Generalizable Policies
Distributed Estimation with Multiple Samples per User: Sharp Rates and Phase Transition
Structured Dropout Variational Inference for Bayesian Neural Networks
Local Signal Adaptivity: Provable Feature Learning in Neural Networks Beyond Kernels
Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update
Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning
Scatterbrain: Unifying Sparse and Low-rank Attention
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis
Minimizing Polarization and Disagreement in Social Networks via Link Recommendation
Adversarial Examples Make Strong Poisons
Laplace Redux - Effortless Bayesian Deep Learning
Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization
A Multi-Implicit Neural Representation for Fonts
Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning
Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?
Learning Distilled Collaboration Graph for Multi-Agent Perception
Generalization Bounds for (Wasserstein) Robust Optimization
Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning
Compositional Transformers for Scene Generation
Structural Credit Assignment in Neural Networks using Reinforcement Learning
Reducing the Covariate Shift by Mirror Samples in Cross Domain Alignment
Characterizing possible failure modes in physics-informed neural networks
Fast Training of Neural Lumigraph Representations using Meta Learning
Correlated Stochastic Block Models: Exact Graph Matching with Applications to Recovering Communities
Can Information Flows Suggest Targets for Interventions in Neural Circuits?
Kernel Functional Optimisation
Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation
Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds
ReLU Regression with Massart Noise
Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning
Fair Clustering Under a Bounded Cost
Pragmatic Image Compression for Human-in-the-Loop Decision-Making
Second-Order Neural ODE Optimizer
Early Convolutions Help Transformers See Better
PatchGame: Learning to Signal Mid-level Patches in Referential Games
Structured Reordering for Modeling Latent Alignments in Sequence Transduction
ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees
MERLOT: Multimodal Neural Script Knowledge Models
Novel Upper Bounds for the Constrained Most Probable Explanation Task
Low-Fidelity Video Encoder Optimization for Temporal Action Localization
Replay-Guided Adversarial Environment Design
Voxel-based 3D Detection and Reconstruction of Multiple Objects from a Single Image
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity
Beyond Smoothness: Incorporating Low-Rank Analysis into Nonparametric Density Estimation
A Geometric Structure of Acceleration and Its Role in Making Gradients Small Fast
Differentiable Spline Approximations
Measuring Generalization with Optimal Transport
Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval
Optimal Sketching for Trace Estimation
Robustness of Graph Neural Networks at Scale
Dynamic Inference with Neural Interpreters
Stochastic bandits with groups of similar arms.
Identification of Partially Observed Linear Causal Models: Graphical Conditions for the Non-Gaussian and Heterogeneous Cases
Continual Auxiliary Task Learning
Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic
On the Rate of Convergence of Regularized Learning in Games: From Bandits and Uncertainty to Optimism and Beyond
CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation
Causal Abstractions of Neural Networks
Optimal Underdamped Langevin MCMC Method
Greedy Approximation Algorithms for Active Sequential Hypothesis Testing
Towards Deeper Deep Reinforcement Learning with Spectral Normalization
NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction
Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals
When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting
KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support
Hyperbolic Procrustes Analysis Using Riemannian Geometry
MADE: Exploration via Maximizing Deviation from Explored Regions
Federated Multi-Task Learning under a Mixture of Distributions
Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima
Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyond
Collapsed Variational Bounds for Bayesian Neural Networks
Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning
Improving Deep Learning Interpretability by Saliency Guided Training
Label consistency in overfitted generalized $k$-means
Meta Internal Learning
Analytic Insights into Structure and Rank of Neural Network Hessian Maps
LEADS: Learning Dynamical Systems that Generalize Across Environments
Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks
Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks
Uniform Sampling over Episode Difficulty
GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles
Efficient Algorithms for Learning Depth-2 Neural Networks with General ReLU Activations
A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose
AugMax: Adversarial Composition of Random Augmentations for Robust Training
Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks
Overparameterization Improves Robustness to Covariate Shift in High Dimensions
MagNet: A Neural Network for Directed Graphs
Dr Jekyll & Mr Hyde: the strange case of off-policy policy updates
Stronger NAS with Weaker Predictors
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs
Adaptive Machine Unlearning
Time-independent Generalization Bounds for SGLD in Non-convex Settings
NeRV: Neural Representations for Videos
Causal Effect Inference for Structured Treatments
Learning-Augmented Dynamic Power Management with Multiple States via New Ski Rental Bounds
Implicit Finite-Horizon Approximation and Efficient Optimal Algorithms for Stochastic Shortest Path
Finite Sample Analysis of Average-Reward TD Learning and $Q$-Learning
Stochastic optimization under time drift: iterate averaging, step-decay schedules, and high probability guarantees
Focal Attention for Long-Range Interactions in Vision Transformers
Iteratively Reweighted Least Squares for Basis Pursuit with Global Linear Convergence Rate
COHESIV: Contrastive Object and Hand Embedding Segmentation In Video
Scalars are universal: Equivariant machine learning, structured like classical physics
Rethinking gradient sparsification as total error minimization
Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation
Habitat 2.0: Training Home Assistants to Rearrange their Habitat
Language models enable zero-shot prediction of the effects of mutations on protein function
Label-Imbalanced and Group-Sensitive Classification under Overparameterization
Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time
TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive?
Two-sided fairness in rankings via Lorenz dominance
Decoupling the Depth and Scope of Graph Neural Networks
Learning in two-player zero-sum partially observable Markov games with perfect recall
Mixture Proportion Estimation and PU Learning:A Modern Approach
A Law of Iterated Logarithm for Multi-Agent Reinforcement Learning
Refining Language Models with Compositional Explanations
Noether Networks: meta-learning useful conserved quantities
Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging
Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation
Stochastic Anderson Mixing for Nonconvex Stochastic Optimization
NovelD: A Simple yet Effective Exploration Criterion
Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification
Row-clustering of a Point Process-valued Matrix
Optimal Best-Arm Identification Methods for Tail-Risk Measures
Deep Networks Provably Classify Data on Curves
Global Convergence to Local Minmax Equilibrium in Classes of Nonconvex Zero-Sum Games
A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness
EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback
On the Generative Utility of Cyclic Conditionals
CAFE: Catastrophic Data Leakage in Vertical Federated Learning
Topological Detection of Trojaned Neural Networks
Proxy-Normalizing Activations to Match Batch Normalization while Removing Batch Dependence
Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration
The Causal-Neural Connection: Expressiveness, Learnability, and Inference
SEAL: Self-supervised Embodied Active Learning using Exploration and 3D Consistency
Redesigning the Transformer Architecture with Insights from Multi-particle Dynamical Systems
Interpolation can hurt robust generalization even when there is no noise
OctField: Hierarchical Implicit Functions for 3D Modeling
Test-Time Personalization with a Transformer for Human Pose Estimation
Dense Keypoints via Multiview Supervision
Functional Variational Inference based on Stochastic Process Generators
Overcoming the Convex Barrier for Simplex Inputs
Look at What I’m Doing: Self-Supervised Spatial Grounding of Narrations in Instructional Videos
CLIP-It! Language-Guided Video Summarization
The Lazy Online Subgradient Algorithm is Universal on Strongly Convex Domains
Adversarial Robustness without Adversarial Training: A Teacher-Guided Curriculum Learning Approach
An Exact Characterization of the Generalization Error for the Gibbs Algorithm
Evaluating model performance under worst-case subpopulations
DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples
Risk-averse Heteroscedastic Bayesian Optimization
Mining the Benefits of Two-stage and One-stage HOI Detection
Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Models
Learning Equilibria in Matching Markets from Bandit Feedback
Improving black-box optimization in VAE latent space using decoder uncertainty
On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms
Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory
TöRF: Time-of-Flight Radiance Fields for Dynamic Scene View Synthesis
Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond
Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models
Adapting to function difficulty and growth conditions in private optimization
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers
Leveraging SE(3) Equivariance for Self-supervised Category-Level Object Pose Estimation from Point Clouds
AutoBalance: Optimized Loss Functions for Imbalanced Data
Gradient Descent on Two-layer Nets: Margin Maximization and Simplicity Bias
TransMatcher: Deep Image Matching Through Transformers for Generalizable Person Re-identification
You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection
Towards Efficient and Effective Adversarial Training
Neural Dubber: Dubbing for Videos According to Scripts
Revealing and Protecting Labels in Distributed Training
Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers
Preconditioned Gradient Descent for Over-Parameterized Nonconvex Matrix Factorization
Scaling Up Exact Neural Network Compression by ReLU Stability
Revisiting 3D Object Detection From an Egocentric Perspective
Learning Debiased Representation via Disentangled Feature Augmentation
ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis
SQALER: Scaling Question Answering by Decoupling Multi-Hop and Logical Reasoning
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism
Object-Centric Representation Learning with Generative Spatial-Temporal Factorization
Post-processing for Individual Fairness
Linear and Kernel Classification in the Streaming Model: Improved Bounds for Heavy Hitters
Bridging the Imitation Gap by Adaptive Insubordination
Particle Dual Averaging: Optimization of Mean Field Neural Network with Global Convergence Rate Analysis
Gradient-based Editing of Memory Examples for Online Task-free Continual Learning
Last-iterate Convergence in Extensive-Form Games
GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement
On Blame Attribution for Accountable Multi-Agent Sequential Decision Making
Locally private online change point detection
A Causal Lens for Controllable Text Generation
Unsupervised Part Discovery from Contrastive Reconstruction
PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement Learning
Beyond Tikhonov: faster learning with self-concordant losses, via iterative regularization
Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable Approach
Topology-Imbalance Learning for Semi-Supervised Node Classification
FACMAC: Factored Multi-Agent Centralised Policy Gradients
Exploring Cross-Video and Cross-Modality Signals for Weakly-Supervised Audio-Visual Video Parsing
Benign Overfitting in Multiclass Classification: All Roads Lead to Interpolation
Continuous vs. Discrete Optimization of Deep Neural Networks
Post-Training Quantization for Vision Transformer
Edge Representation Learning with Hypergraphs
SILG: The Multi-domain Symbolic Interactive Language Grounding Benchmark
Conditional Generation Using Polynomial Expansions
Model-Based Episodic Memory Induces Dynamic Hybrid Controls
Property-Aware Relation Networks for Few-Shot Molecular Property Prediction
Deep Learning Through the Lens of Example Difficulty
Understanding Bandits with Graph Feedback
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence
Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning
Online Adaptation to Label Distribution Shift
Sample Selection for Fair and Robust Training
Integrating Tree Path in Transformer for Code Representation
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Neural Network Robustness Verification
VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media
Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation
Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression
A Non-commutative Extension of Lee-Seung's Algorithm for Positive Semidefinite Factorizations
The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle
Differentially Private Federated Bayesian Optimization with Distributed Exploration
SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimization
Deep Conditional Gaussian Mixture Model for Constrained Clustering
EditGAN: High-Precision Semantic Image Editing
Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations
VoiceMixer: Adversarial Voice Style Mixup
BCORLE($\lambda$): An Offline Reinforcement Learning and Evaluation Framework for Coupons Allocation in E-commerce Market
Unsupervised Object-Based Transition Models For 3D Partially Observable Environments
Learning Graph Models for Retrosynthesis Prediction
On Success and Simplicity: A Second Look at Transferable Targeted Attacks
Variational Model Inversion Attacks
A Computationally Efficient Method for Learning Exponential Family Distributions
Streaming Belief Propagation for Community Detection
Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection
Learning Generalized Gumbel-max Causal Mechanisms
A PAC-Bayes Analysis of Adversarial Robustness
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning
Recursive Bayesian Networks: Generalising and Unifying Probabilistic Context-Free Grammars and Dynamic Bayesian Networks
Improved Regularization and Robustness for Fine-tuning in Neural Networks
Pay Better Attention to Attention: Head Selection in Multilingual and Multi-Domain Sequence Modeling
Dataset Distillation with Infinitely Wide Convolutional Networks
Multi-Person 3D Motion Prediction with Multi-Range Transformers
Efficient Bayesian network structure learning via local Markov boundary search
SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL
On the Value of Infinite Gradients in Variational Autoencoder Models
Sequential Causal Imitation Learning with Unobserved Confounders
Faster Non-asymptotic Convergence for Double Q-learning
CorticalFlow: A Diffeomorphic Mesh Transformer Network for Cortical Surface Reconstruction
Provably Strict Generalisation Benefit for Invariance in Kernel Methods
Accelerating Quadratic Optimization with Reinforcement Learning
Multi-armed Bandit Requiring Monotone Arm Sequences
Non-asymptotic convergence bounds for Wasserstein approximation using point clouds
Early-stopped neural networks are consistent
Class-agnostic Reconstruction of Dynamic Objects from Videos
Meta-Adaptive Nonlinear Control: Theory and Algorithms
A nonparametric method for gradual change problems with statistical guarantees
Analyzing the Generalization Capability of SGLD Using Properties of Gaussian Channels
An Uncertainty Principle is a Price of Privacy-Preserving Microdata
Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch
Neural Routing by Memory
Improved Regret Bounds for Tracking Experts with Memory
Adversarial Attacks on Graph Classifiers via Bayesian Optimisation
Learning where to learn: Gradient sparsity in meta and continual learning
Fuzzy Clustering with Similarity Queries
User-Level Differentially Private Learning via Correlated Sampling
Learning to Generate Visual Questions with Noisy Supervision
Scaling Vision with Sparse Mixture of Experts
What training reveals about neural network complexity
Dimensionality Reduction for Wasserstein Barycenter
Gradient Starvation: A Learning Proclivity in Neural Networks
Reusing Combinatorial Structure: Faster Iterative Projections over Submodular Base Polytopes
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection
Dynamic Resolution Network
Probabilistic Forecasting: A Level-Set Approach
Learnable Fourier Features for Multi-dimensional Spatial Positional Encoding
Pipeline Combinators for Gradual AutoML
Play to Grade: Testing Coding Games as Classifying Markov Decision Process
Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces
Collaborating with Humans without Human Data
Constrained Two-step Look-Ahead Bayesian Optimization
A Stochastic Newton Algorithm for Distributed Convex Optimization
Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi
Adversarial Feature Desensitization
Shared Independent Component Analysis for Multi-Subject Neuroimaging
Nested Variational Inference
Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks
Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning
Pooling by Sliced-Wasserstein Embedding
Exploiting Local Convergence of Quasi-Newton Methods Globally: Adaptive Sample Size Approach
Meta-learning to Improve Pre-training
Stylized Dialogue Generation with Multi-Pass Dual Learning
Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble
Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning
Object-aware Contrastive Learning for Debiased Scene Representation
Dynamic Grained Encoder for Vision Transformers
Contrastively Disentangled Sequential Variational Autoencoder
A Surrogate Objective Framework for Prediction+Programming with Soft Constraints
The Adaptive Doubly Robust Estimator and a Paradox Concerning Logging Policy
Robust and Decomposable Average Precision for Image Retrieval
Learning Transferable Adversarial Perturbations
Efficient Online Estimation of Causal Effects by Deciding What to Observe
Pay Attention to MLPs
Robust Learning of Optimal Auctions
Asymptotics of the Bootstrap via Stability with Applications to Inference with Model Selection
Locally differentially private estimation of functionals of discrete distributions
Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose
Breaking the Dilemma of Medical Image-to-image Translation
PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations
Machine Learning for Variance Reduction in Online Experiments
Learning with Noisy Correspondence for Cross-modal Matching
Indexed Minimum Empirical Divergence for Unimodal Bandits
Learning Graph Cellular Automata
The Skellam Mechanism for Differentially Private Federated Learning
Logarithmic Regret in Feature-based Dynamic Pricing
SNIPS: Solving Noisy Inverse Problems Stochastically
A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis
Learning to Compose Visual Relations
Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr\"om Method
Machine versus Human Attention in Deep Reinforcement Learning Tasks
Analytic Study of Families of Spurious Minima in Two-Layer ReLU Neural Networks: A Tale of Symmetry II
Global Convergence of Gradient Descent for Asymmetric Low-Rank Matrix Factorization
Coupled Segmentation and Edge Learning via Dynamic Graph Propagation
Online learning in MDPs with linear function approximation and bandit feedback.
Dual Adaptivity: A Universal Algorithm for Minimizing the Adaptive Regret of Convex Functions
Temporal-attentive Covariance Pooling Networks for Video Recognition
Improving Conditional Coverage via Orthogonal Quantile Regression
Speech-T: Transducer for Text to Speech and Beyond
Machine learning structure preserving brackets for forecasting irreversible processes
TransformerFusion: Monocular RGB Scene Reconstruction using Transformers
Group Equivariant Subsampling
GRIN: Generative Relation and Intention Network for Multi-agent Trajectory Prediction
Tree in Tree: from Decision Trees to Decision Graphs
Generalized Proximal Policy Optimization with Sample Reuse
DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks
Diverse Message Passing for Attribute with Heterophily
Matching a Desired Causal State via Shift Interventions
Learning to Assimilate in Chaotic Dynamical Systems
Independent mechanism analysis, a new concept?
Representation Costs of Linear Neural Networks: Analysis and Design
Active Learning of Convex Halfspaces on Graphs
Environment Generation for Zero-Shot Compositional Reinforcement Learning
ScaleCert: Scalable Certified Defense against Adversarial Patches with Sparse Superficial Layers
Grounding inductive biases in natural images: invariance stems from variations in data
Efficient Statistical Assessment of Neural Network Corruption Robustness
Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning
argmax centroid
Rethinking the Variational Interpretation of Accelerated Optimization Methods
Adaptive Proximal Gradient Methods for Structured Neural Networks
Decision Transformer: Reinforcement Learning via Sequence Modeling
Scaling Neural Tangent Kernels via Sketching and Random Features
Fast Pure Exploration via Frank-Wolfe
Learning with Algorithmic Supervision via Continuous Relaxations
SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks
On the Variance of the Fisher Information for Deep Learning
On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs)
How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?
Twins: Revisiting the Design of Spatial Attention in Vision Transformers
Auditing Black-Box Prediction Models for Data Minimization Compliance
Regularized Softmax Deep Multi-Agent Q-Learning
BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation
Task-Agnostic Undesirable Feature Deactivation Using Out-of-Distribution Data
When Expressivity Meets Trainability: Fewer than $n$ Neurons Can Work
Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chess
Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering
Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels
Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots
Invertible Tabular GANs: Killing Two Birds with One Stone for Tabular Data Synthesis
Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image Recognition
Learning Debiased and Disentangled Representations for Semantic Segmentation
Stability and Generalization of Bilevel Programming in Hyperparameter Optimization
A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference
Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows
Adversarial Robustness of Streaming Algorithms through Importance Sampling
Video Instance Segmentation using Inter-Frame Communication Transformers
Towards Tight Communication Lower Bounds for Distributed Optimisation
Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networks
Uncertainty Quantification and Deep Ensembles
BooVI: Provably Efficient Bootstrapped Value Iteration
A Framework to Learn with Interpretation
Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning
Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
Improving Robustness using Generated Data
Model Selection for Bayesian Autoencoders
Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime
On Path Integration of Grid Cells: Group Representation and Isotropic Scaling
Unfolding Taylor's Approximations for Image Restoration
Towards Lower Bounds on the Depth of ReLU Neural Networks
Deep Self-Dissimilarities as Powerful Visual Fingerprints
UFC-BERT: Unifying Multi-Modal Controls for Conditional Image Synthesis
Conformal Prediction using Conditional Histograms
D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation
Distributed Machine Learning with Sparse Heterogeneous Data
Associative Memories via Predictive Coding
Adaptive Data Augmentation on Temporal Graphs
Novel Visual Category Discovery with Dual Ranking Statistics and Mutual Knowledge Distillation
Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision
Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer
Meta-Learning Reliable Priors in the Function Space
Neural Ensemble Search for Uncertainty Estimation and Dataset Shift
Federated-EM with heterogeneity mitigation and variance reduction
Recovering Latent Causal Factor for Generalization to Distributional Shifts
Dual-stream Network for Visual Recognition
Shape As Points: A Differentiable Poisson Solver
Spatio-Temporal Variational Gaussian Processes
Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization
Long-Short Transformer: Efficient Transformers for Language and Vision
Momentum Centering and Asynchronous Update for Adaptive Gradient Methods
Unadversarial Examples: Designing Objects for Robust Vision
Reward is enough for convex MDPs
Dangers of Bayesian Model Averaging under Covariate Shift
Differentially Private Sampling from Distributions
Provably efficient, succinct, and precise explanations
Storchastic: A Framework for General Stochastic Automatic Differentiation
Differentially Private Multi-Armed Bandits in the Shuffle Model
Program Synthesis Guided Reinforcement Learning for Partially Observed Environments
Offline Constrained Multi-Objective Reinforcement Learning via Pessimistic Dual Value Iteration
Hessian Eigenspectra of More Realistic Nonlinear Models
Motif-based Graph Self-Supervised Learning for Molecular Property Prediction
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data
Subgraph Federated Learning with Missing Neighbor Generation
Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs
Provably Efficient Causal Reinforcement Learning with Confounded Observational Data
Simple steps are all you need: Frank-Wolfe and generalized self-concordant functions
A Kernel-based Test of Independence for Cluster-correlated Data
Unique sparse decomposition of low rank matrices
Data Augmentation Can Improve Robustness
Fair Sequential Selection Using Supervised Learning Models
Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL
Escaping Saddle Points with Compressed SGD
The Difficulty of Passive Learning in Deep Reinforcement Learning
Multilingual Pre-training with Universal Dependency Learning
Self-Supervised Bug Detection and Repair
Neural Trees for Learning on Graphs
When Is Generalizable Reinforcement Learning Tractable?
On the Representation of Solutions to Elliptic PDEs in Barron Spaces
Towards optimally abstaining from prediction with OOD test examples
Precise characterization of the prior predictive distribution of deep ReLU networks
Random Shuffling Beats SGD Only After Many Epochs on Ill-Conditioned Problems
Looking Beyond Single Images for Contrastive Semantic Segmentation Learning
Rethinking Neural Operations for Diverse Tasks
Training Neural Networks is ER-complete
ErrorCompensatedX: error compensation for variance reduced algorithms
Densely connected normalizing flows
Collaborative Causal Discovery with Atomic Interventions
An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning
SOPE: Spectrum of Off-Policy Estimators
Learning with User-Level Privacy
Neural Tangent Kernel Maximum Mean Discrepancy
Estimating the Long-Term Effects of Novel Treatments
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
Bandit Quickest Changepoint Detection
OpenMatch: Open-Set Semi-supervised Learning with Open-set Consistency Regularization
How Well do Feature Visualizations Support Causal Understanding of CNN Activations?
Margin-Independent Online Multiclass Learning via Convex Geometry
Does enforcing fairness mitigate biases caused by subpopulation shift?
Batch Active Learning at Scale
Variational Bayesian Optimistic Sampling
Mind the Gap: Assessing Temporal Generalization in Neural Language Models
Automated Dynamic Mechanism Design
On the Suboptimality of Thompson Sampling in High Dimensions
Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models
Deep Neural Networks as Point Estimates for Deep Gaussian Processes
Learning Treatment Effects in Panels with General Intervention Patterns
PiRank: Scalable Learning To Rank via Differentiable Sorting
Ranking Policy Decisions
Local Disentanglement in Variational Auto-Encoders Using Jacobian $L_1$ Regularization
CoAtNet: Marrying Convolution and Attention for All Data Sizes
Multiple Descent: Design Your Own Generalization Curve
Generating High-Quality Explanations for Navigation in Partially-Revealed Environments
Solving Soft Clustering Ensemble via $k$-Sparse Discrete Wasserstein Barycenter
Learning Models for Actionable Recourse
A variational approximate posterior for the deep Wishart process
Bayesian decision-making under misspecified priors with applications to meta-learning
Infinite Time Horizon Safety of Bayesian Neural Networks
Network-to-Network Regularization: Enforcing Occam's Razor to Improve Generalization
Pretraining Representations for Data-Efficient Reinforcement Learning
Domain Adaptation with Invariant Representation Learning: What Transformations to Learn?
BayesIMP: Uncertainty Quantification for Causal Data Fusion
Self-Interpretable Model with Transformation Equivariant Interpretation
Generalization Bounds for Meta-Learning via PAC-Bayes and Uniform Stability
Roto-translated Local Coordinate Frames For Interacting Dynamical Systems
Distributed Zero-Order Optimization under Adversarial Noise
Scalable Inference in SDEs by Direct Matching of the Fokker–Planck–Kolmogorov Equation
Parallelizing Thompson Sampling
Differential Privacy Over Riemannian Manifolds
GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training
Sliced Mutual Information: A Scalable Measure of Statistical Dependence
Smooth Bilevel Programming for Sparse Regularization
Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling
Variance-Aware Off-Policy Evaluation with Linear Function Approximation
On the Representation Power of Set Pooling Networks
Dimension-free empirical entropy estimation
Geometry Processing with Neural Fields
Provably efficient multi-task reinforcement learning with model transfer
DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networks
Deep Synoptic Monte-Carlo Planning in Reconnaissance Blind Chess
Attention over Learned Object Embeddings Enables Complex Visual Reasoning
Unbalanced Optimal Transport through Non-negative Penalized Linear Regression
Closing the Gap: Tighter Analysis of Alternating Stochastic Gradient Methods for Bilevel Problems
A Topological Perspective on Causal Inference
Shifted Chunk Transformer for Spatio-Temporal Representational Learning
Distilling Meta Knowledge on Heterogeneous Graph for Illicit Drug Trafficker Detection on Social Media
Continuous-time edge modelling using non-parametric point processes
Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions
Intriguing Properties of Vision Transformers
Arbitrary Conditional Distributions with Energy
Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning
UCB-based Algorithms for Multinomial Logistic Regression Bandits
BooVAE: Boosting Approach for Continual Learning of VAE
Conditionally Parameterized, Discretization-Aware Neural Networks for Mesh-Based Modeling of Physical Systems
Why Spectral Normalization Stabilizes GANs: Analysis and Improvements
Rebounding Bandits for Modeling Satiation Effects
Efficient methods for Gaussian Markov random fields under sparse linear constraints
Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling
Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions
Revisiting the Calibration of Modern Neural Networks
Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning
Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning
NN-Baker: A Neural-network Infused Algorithmic Framework for Optimization Problems on Geometric Intersection Graphs
Bootstrapping the Error of Oja's Algorithm
Towards Stable and Robust AdderNets
Probability Paths and the Structure of Predictions over Time
Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning
Global Convergence of Online Optimization for Nonlinear Model Predictive Control
ProTo: Program-Guided Transformer for Program-Guided Tasks
Oracle-Efficient Regret Minimization in Factored MDPs with Unknown Structure
Observation-Free Attacks on Stochastic Bandits
Contrastive Learning of Global and Local Video Representations
A Theoretical Analysis of Fine-tuning with Linear Teachers
On Training Implicit Models
Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity
Reconstruction for Powerful Graph Representations
Deep Molecular Representation Learning via Fusing Physical and Chemical Information
Implicit Generative Copulas
Automatic Data Augmentation for Generalization in Reinforcement Learning
Local Hyper-Flow Diffusion
Analysis of Sensing Spectral for Signal Recovery under a Generalized Linear Model
Differentially Private Empirical Risk Minimization under the Fairness Lens
Adversarial Neuron Pruning Purifies Backdoored Deep Models
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair
Adversarial Intrinsic Motivation for Reinforcement Learning
Embedding Principle of Loss Landscape of Deep Neural Networks
Progressive Feature Interaction Search for Deep Sparse Network
Towards Multi-Grained Explainability for Graph Neural Networks
Multi-task Learning of Order-Consistent Causal Graphs
Sequence-to-Sequence Learning with Latent Neural Grammars
Causal Identification with Matrix Equations
Compressed Video Contrastive Learning
Low-Rank Subspaces in GANs
Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N:M Transposable Masks
Differentiable rendering with perturbed optimizers
iFlow: Numerically Invertible Flows for Efficient Lossless Compression via a Uniform Coder
Controlled Text Generation as Continuous Optimization with Multiple Constraints
Dynamic Analysis of Higher-Order Coordination in Neuronal Assemblies via De-Sparsified Orthogonal Matching Pursuit
Best of Both Worlds: Practical and Theoretically Optimal Submodular Maximization in Parallel
Individual Privacy Accounting via a Rényi Filter
Improving Contrastive Learning on Imbalanced Data via Open-World Sampling
A Comprehensively Tight Analysis of Gradient Descent for PCA
CCVS: Context-aware Controllable Video Synthesis
Adaptive Ensemble Q-learning: Minimizing Estimation Bias via Error Feedback
Multi-Scale Representation Learning on Proteins
Exploring the Limits of Out-of-Distribution Detection
The best of both worlds: stochastic and adversarial episodic MDPs with unknown transition
The Value of Information When Deciding What to Learn
Minimax Regret for Stochastic Shortest Path
Tensor Normal Training for Deep Learning Models
Fair Algorithms for Multi-Agent Multi-Armed Bandits
Nested Graph Neural Networks
General Low-rank Matrix Optimization: Geometric Analysis and Sharper Bounds
Variational Bayesian Reinforcement Learning with Regret Bounds
A Gradient Method for Multilevel Optimization
A universal probabilistic spike count model reveals ongoing modulation of neural variability
Shape Registration in the Time of Transformers
Towards Instance-Optimal Offline Reinforcement Learning with Pessimism
Optimality of variational inference for stochasticblock model with missing links
Dynamic Trace Estimation
Zero Time Waste: Recycling Predictions in Early Exit Neural Networks
Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme
Learning Student-Friendly Teacher Networks for Knowledge Distillation
Towards Best-of-All-Worlds Online Learning with Feedback Graphs
A$^2$-Net: Learning Attribute-Aware Hash Codes for Large-Scale Fine-Grained Image Retrieval
Progressive Coordinate Transforms for Monocular 3D Object Detection
Neural Human Performer: Learning Generalizable Radiance Fields for Human Performance Rendering
Learning and Generalization in RNNs
Counterfactual Explanations Can Be Manipulated
Scheduling jobs with stochastic holding costs
On the Value of Interaction and Function Approximation in Imitation Learning
Nonparametric estimation of continuous DPPs with kernel methods
Learning Disentangled Behavior Embeddings
Topic Modeling Revisited: A Document Graph-based Neural Network Perspective
Dueling Bandits with Adversarial Sleeping
Inverse-Weighted Survival Games
Identifiability in inverse reinforcement learning
Modular Gaussian Processes for Transfer Learning
Faster proximal algorithms for matrix optimization using Jacobi-based eigenvalue methods
Neural Relightable Participating Media Rendering
Time-series Generation by Contrastive Imitation
Exploiting Opponents Under Utility Constraints in Sequential Games
Model-Based Domain Generalization
The Elastic Lottery Ticket Hypothesis
Hybrid Regret Bounds for Combinatorial Semi-Bandits and Adversarial Linear Bandits
Learning Optimal Predictive Checklists
Learning Markov State Abstractions for Deep Reinforcement Learning
Learning to Elect
Projected GANs Converge Faster
Certifying Robustness to Programmable Data Bias in Decision Trees
M-FAC: Efficient Matrix-Free Approximations of Second-Order Information
Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms
Transformer in Transformer
Neural Scene Flow Prior
MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers
Neural Rule-Execution Tracking Machine For Transformer-Based Text Generation
Dynamics-regulated kinematic policy for egocentric pose estimation
TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up
A/B/n Testing with Control in the Presence of Subpopulations
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization
Baby Intuitions Benchmark (BIB): Discerning the goals, preferences, and actions of others
Introspective Distillation for Robust Question Answering
Bandit Learning with Delayed Impact of Actions
DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification
Heavy Ball Neural Ordinary Differential Equations
Recurrent Submodular Welfare and Matroid Blocking Semi-Bandits
Online Learning Of Neural Computations From Sparse Temporal Feedback
PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators
Testing Probabilistic Circuits
Aligning Pretraining for Detection via Object-Level Contrastive Learning
Perturbation Theory for the Information Bottleneck
Equilibrium Refinement for the Age of Machines: The One-Sided Quasi-Perfect Equilibrium
DRONE: Data-aware Low-rank Compression for Large NLP Models
Pseudo-Spherical Contrastive Divergence
How Fine-Tuning Allows for Effective Meta-Learning
Learning in Multi-Stage Decentralized Matching Markets
Structured in Space, Randomized in Time: Leveraging Dropout in RNNs for Efficient Training
Cross-view Geo-localization with Layer-to-Layer Transformer
Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent
Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning
FINE Samples for Learning with Noisy Labels
Distributionally Robust Imitation Learning
Probabilistic Tensor Decomposition of Neural Population Spiking Activity
Change Point Detection via Multivariate Singular Spectrum Analysis
Mixability made efficient: Fast online multiclass logistic regression
Does Knowledge Distillation Really Work?
Risk Bounds and Calibration for a Smart Predict-then-Optimize Method
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