NeurIPS 2022 Papers

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SurDis: A Surface Discontinuity Dataset for Wearable Technology to Assist Blind Navigation in Urban Environments

MOMA-LRG: Language-Refined Graphs for Multi-Object Multi-Actor Activity Parsing

VLMbench: A Compositional Benchmark for Vision-and-Language Manipulation

AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies

Pythae: Unifying Generative Autoencoders in Python - A Benchmarking Use Case

Ambiguous Images With Human Judgments for Robust Visual Event Classification

Towards Better Evaluation for Dynamic Link Prediction

pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models

EgoTaskQA: Understanding Human Tasks in Egocentric Videos

Finding Naturally Occurring Physical Backdoors in Image Datasets

Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Models

GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization

K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions

PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation

Touch and Go: Learning from Human-Collected Vision and Touch

How Transferable are Video Representations Based on Synthetic Data?

Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world

How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios

The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset

A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking

SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments

Hard ImageNet: Segmentations for Objects with Strong Spurious Cues

SCAMPS: Synthetics for Camera Measurement of Physiological Signals

OpenOOD: Benchmarking Generalized Out-of-Distribution Detection

A Large Scale Search Dataset for Unbiased Learning to Rank

FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning

Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems

MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge

Robustness Analysis of Video-Language Models Against Visual and Language Perturbations

ComMU: Dataset for Combinatorial Music Generation

BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs

TAP-Vid: A Benchmark for Tracking Any Point in a Video

DABS 2.0: Improved Datasets and Algorithms for Universal Self-Supervision

Communicating Natural Programs to Humans and Machines

Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability

SkinCon: A skin disease dataset densely annotated by domain experts for fine-grained debugging and analysis

PROSPECT: Labeled Tandem Mass Spectrometry Dataset for Machine Learning in Proteomics

FACT: Learning Governing Abstractions Behind Integer Sequences

Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation

PeRFception: Perception using Radiance Fields

A Greek Parliament Proceedings Dataset for Computational Linguistics and Political Analysis

Addressing Resource Scarcity across Sign Languages with Multilingual Pretraining and Unified-Vocabulary Datasets

TGEA 2.0: A Large-Scale Diagnostically Annotated Dataset with Benchmark Tasks for Text Generation of Pretrained Language Models

BackdoorBench: A Comprehensive Benchmark of Backdoor Learning

3DOS: Towards 3D Open Set Learning - Benchmarking and Understanding Semantic Novelty Detection on Point Clouds

Flare7K: A Phenomenological Nighttime Flare Removal Dataset

DC-BENCH: Dataset Condensation Benchmark

Change Event Dataset for Discovery from Spatio-temporal Remote Sensing Imagery

CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks

Understanding Aesthetics with Language: A Photo Critique Dataset for Aesthetic Assessment

IKEA-Manual: Seeing Shape Assembly Step by Step

FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings

Ontologue: Declarative Benchmark Construction for Ontological Multi-Label Classification

MBW: Multi-view Bootstrapping in the Wild

Enabling Detailed Action Recognition Evaluation Through Video Dataset Augmentation

Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization

NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies

PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding

FlyView: a bio-informed optical flow truth dataset for visual navigation using panoramic stereo vision

Chartalist: Labeled Graph Datasets for UTXO and Account-based Blockchains

A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement Prediction

APT-36K: A Large-scale Benchmark for Animal Pose Estimation and Tracking

OccGen: Selection of Real-world Multilingual Parallel Data Balanced in Gender within Occupations

GriddlyJS: A Web IDE for Reinforcement Learning

Model Zoos: A Dataset of Diverse Populations of Neural Network Models

Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification

Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs

The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games

Breaking Bad: A Dataset for Geometric Fracture and Reassembly

USB: A Unified Semi-supervised Learning Benchmark for Classification

NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search

WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models

BigBio: A Framework for Data-Centric Biomedical Natural Language Processing

MATE: Benchmarking Multi-Agent Reinforcement Learning in Distributed Target Coverage Control

CLEVRER-Humans: Describing Physical and Causal Events the Human Way

HandMeThat: Human-Robot Communication in Physical and Social Environments

OpenSRH: optimizing brain tumor surgery using intraoperative stimulated Raman histology

Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset

BLOX: Macro Neural Architecture Search Benchmark and Algorithms

CEDe: A collection of expert-curated datasets with atom-level entity annotations for Optical Chemical Structure Recognition

M4Singer: A Multi-Style, Multi-Singer and Musical Score Provided Mandarin Singing Corpus

LAION-5B: An open large-scale dataset for training next generation image-text models

MSDS: A Large-Scale Chinese Signature and Token Digit String Dataset for Handwriting Verification

Benchmarking and Analyzing 3D Human Pose and Shape Estimation Beyond Algorithms

ViSioNS: Visual Search in Natural Scenes Benchmark

mRI: Multi-modal 3D Human Pose Estimation Dataset using mmWave, RGB-D, and Inertial Sensors

ActionSense: A Multimodal Dataset and Recording Framework for Human Activities Using Wearable Sensors in a Kitchen Environment

The Dollar Street Dataset: Images Representing the Geographic and Socioeconomic Diversity of the World

FETA: Towards Specializing Foundational Models for Expert Task Applications

AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection

Evaluating Out-of-Distribution Performance on Document Image Classifiers

Open High-Resolution Satellite Imagery: The WorldStrat Dataset – With Application to Super-Resolution

OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics

A Benchmark for Compositional Visual Reasoning

xView3-SAR: Detecting Dark Fishing Activity Using Synthetic Aperture Radar Imagery

Learning Long-Term Crop Management Strategies with CyclesGym

ETAB: A Benchmark Suite for Visual Representation Learning in Echocardiography

EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records

GOOD: A Graph Out-of-Distribution Benchmark

Is one annotation enough? - A data-centric image classification benchmark for noisy and ambiguous label estimation

MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction

CAESAR: An Embodied Simulator for Generating Multimodal Referring Expression Datasets

JAHS-Bench-201: A Foundation For Research On Joint Architecture And Hyperparameter Search

A Dataset for Efforts Towards Achieving the Sustainable Development Goal of Safe Working Environments

Forecasting Future World Events With Neural Networks

TwiBot-22: Towards Graph-Based Twitter Bot Detection

Avalon: A Benchmark for RL Generalization Using Procedurally Generated Worlds

Long Range Graph Benchmark

Geoclidean: Few-Shot Generalization in Euclidean Geometry

CARLANE: A Lane Detection Benchmark for Unsupervised Domain Adaptation from Simulation to multiple Real-World Domains

EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine

How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning?

OpenFilter: A Framework to Democratize Research Access to Social Media AR Filters

Why do tree-based models still outperform deep learning on typical tabular data?

Multi-LexSum: Real-world Summaries of Civil Rights Lawsuits at Multiple Granularities

Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark

Robustness Disparities in Face Detection

AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation

TaiSu: A 166M Large-scale High-Quality Dataset for Chinese Vision-Language Pre-training

Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time

PDEBench: An Extensive Benchmark for Scientific Machine Learning

LIPS - Learning Industrial Physical Simulation benchmark suite

Towards Video Text Visual Question Answering: Benchmark and Baseline

SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learning

NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning

NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks

OpenFWI: Large-scale Multi-structural Benchmark Datasets for Full Waveform Inversion

METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets

DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection

ConfLab: A Data Collection Concept, Dataset, and Benchmark for Machine Analysis of Free-Standing Social Interactions in the Wild

HAPI: A Large-scale Longitudinal Dataset of Commercial ML API Predictions

TempEL: Linking Dynamically Evolving and Newly Emerging Entities

ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models

A Survey and Datasheet Repository of Publicly Available US Criminal Justice Datasets

Myriad: a real-world testbed to bridge trajectory optimization and deep learning

TweetNERD - End to End Entity Linking Benchmark for Tweets

AutoWS-Bench-101: Benchmarking Automated Weak Supervision with 100 Labels

SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles

This is the way: designing and compiling LEPISZCZE, a comprehensive NLP benchmark for Polish

A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks

Kantorovich Strikes Back! Wasserstein GANs are not Optimal Transport?

DART: Articulated Hand Model with Diverse Accessories and Rich Textures

Active-Passive SimStereo - Benchmarking the Cross-Generalization Capabilities of Deep Learning-based Stereo Methods

CGLB: Benchmark Tasks for Continual Graph Learning

ADBench: Anomaly Detection Benchmark

A new dataset for multilingual keyphrase generation

Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems

DDXPlus: A New Dataset For Automatic Medical Diagnosis

Video compression dataset and benchmark of learning-based video-quality metrics

Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning

MVP-N: A Dataset and Benchmark for Real-World Multi-View Object Classification

pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning

Dungeons and Data: A Large-Scale NetHack Dataset

OpenXAI: Towards a Transparent Evaluation of Model Explanations

Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning

ENS-10: A Dataset For Post-Processing Ensemble Weather Forecasts

AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier–Stokes Solutions

EPIC-KITCHENS VISOR Benchmark: VIdeo Segmentations and Object Relations

Multilingual Abusive Comment Detection at Scale for Indic Languages

MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control

FLAIR: Federated Learning Annotated Image Repository

StrokeRehab: A Benchmark Dataset for Sub-second Action Identification

Training Uncertainty-Aware Classifiers with Conformalized Deep Learning

Optimizing Relevance Maps of Vision Transformers Improves Robustness

Quantum Speedups of Optimizing Approximately Convex Functions with Applications to Logarithmic Regret Stochastic Convex Bandits

Low-rank lottery tickets: finding efficient low-rank neural networks via matrix differential equations

Towards Improving Faithfulness in Abstractive Summarization

SIREN: Shaping Representations for Detecting Out-of-Distribution Objects

Implicit Neural Representations with Levels-of-Experts

Uplifting Bandits

Infinite-Fidelity Coregionalization for Physical Simulation

RSA: Reducing Semantic Shift from Aggressive Augmentations for Self-supervised Learning

On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias

On Infinite Separations Between Simple and Optimal Mechanisms

TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction

Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation

Automatic differentiation of nonsmooth iterative algorithms

Efficient coding, channel capacity, and the emergence of retinal mosaics

Decentralized Local Stochastic Extra-Gradient for Variational Inequalities

Toward Equation of Motion for Deep Neural Networks: Continuous-time Gradient Descent and Discretization Error Analysis

Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning

Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering

Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret

Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Media

Globally Gated Deep Linear Networks

Graph Scattering beyond Wavelet Shackles

Aligning individual brains with fused unbalanced Gromov Wasserstein

SoftPatch: Unsupervised Anomaly Detection with Noisy Data

Kernel Interpolation with Sparse Grids

Ask4Help: Learning to Leverage an Expert for Embodied Tasks

TUSK: Task-Agnostic Unsupervised Keypoints

Concept Activation Regions: A Generalized Framework For Concept-Based Explanations

Matrix Multiplicative Weights Updates in Quantum Zero-Sum Games: Conservation Laws & Recurrence

Posted Pricing and Dynamic Prior-independent Mechanisms with Value Maximizers

Training stochastic stabilized supralinear networks by dynamics-neutral growth

Chefs' Random Tables: Non-Trigonometric Random Features

NeuForm: Adaptive Overfitting for Neural Shape Editing

STaR: Bootstrapping Reasoning With Reasoning

A Causal Analysis of Harm

Network change point localisation under local differential privacy

DISCO: Adversarial Defense with Local Implicit Functions

Does GNN Pretraining Help Molecular Representation?

FedAvg with Fine Tuning: Local Updates Lead to Representation Learning

GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images

Re-Analyze Gauss: Bounds for Private Matrix Approximation via Dyson Brownian Motion

Locating and Editing Factual Associations in GPT

Faster Linear Algebra for Distance Matrices

Causal Inference with Non-IID Data using Linear Graphical Models

Extra-Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods

ALMA: Hierarchical Learning for Composite Multi-Agent Tasks

Diversified Recommendations for Agents with Adaptive Preferences

Optimizing Data Collection for Machine Learning

VeriDark: A Large-Scale Benchmark for Authorship Verification on the Dark Web

CoNT: Contrastive Neural Text Generation

Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline

Zeroth-Order Negative Curvature Finding: Escaping Saddle Points without Gradients

Towards Practical Control of Singular Values of Convolutional Layers

Riemannian Neural SDE: Learning Stochastic Representations on Manifolds

Pragmatically Learning from Pedagogical Demonstrations in Multi-Goal Environments

A Contrastive Framework for Neural Text Generation

AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos

Two-Stream Network for Sign Language Recognition and Translation

Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks

Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment

Roadblocks for Temporarily Disabling Shortcuts and Learning New Knowledge

Learning Bipartite Graphs: Heavy Tails and Multiple Components

Vision Transformers provably learn spatial structure

Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoising

Teach Less, Learn More: On the Undistillable Classes in Knowledge Distillation

Hand-Object Interaction Image Generation

Feature Learning in $L_2$-regularized DNNs: Attraction/Repulsion and Sparsity

Bridging the Gap: Unifying the Training and Evaluation of Neural Network Binary Classifiers

On the Discrimination Risk of Mean Aggregation Feature Imputation in Graphs

Efficient and Modular Implicit Differentiation

NeuroSchedule: A Novel Effective GNN-based Scheduling Method for High-level Synthesis

Recursive Reinforcement Learning

Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure

Distribution-Informed Neural Networks for Domain Adaptation Regression

On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity

Exploiting Semantic Relations for Glass Surface Detection

Doubly-Asynchronous Value Iteration: Making Value Iteration Asynchronous in Actions

Function Classes for Identifiable Nonlinear Independent Component Analysis

GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models

Recovering Private Text in Federated Learning of Language Models

Contrastive Language-Image Pre-Training with Knowledge Graphs

Fast Mixing of Stochastic Gradient Descent with Normalization and Weight Decay

Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel

Quantile Constrained Reinforcement Learning: A Reinforcement Learning Framework Constraining Outage Probability

Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members

Diversity vs. Recognizability: Human-like generalization in one-shot generative models

SketchBoost: Fast Gradient Boosted Decision Tree for Multioutput Problems

DeVRF: Fast Deformable Voxel Radiance Fields for Dynamic Scenes

Emergence of Hierarchical Layers in a Single Sheet of Self-Organizing Spiking Neurons

Adapting to Online Label Shift with Provable Guarantees

Offline Multi-Agent Reinforcement Learning with Knowledge Distillation

Visual correspondence-based explanations improve AI robustness and human-AI team accuracy

Large-Scale Differentiable Causal Discovery of Factor Graphs

Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs

A Conditional Randomization Test for Sparse Logistic Regression in High-Dimension

Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Game

On the SDEs and Scaling Rules for Adaptive Gradient Algorithms

Data Augmentation MCMC for Bayesian Inference from Privatized Data

Dynamic Tensor Product Regression

Introspective Learning : A Two-Stage approach for Inference in Neural Networks

Score-Based Diffusion meets Annealed Importance Sampling

Local Identifiability of Deep ReLU Neural Networks: the Theory

Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning

A Continuous Time Framework for Discrete Denoising Models

Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks

Infinite Recommendation Networks: A Data-Centric Approach

Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs

Learning Distributions Generated by Single-Layer ReLU Networks in the Presence of Arbitrary Outliers

RainNet: A Large-Scale Imagery Dataset and Benchmark for Spatial Precipitation Downscaling

VICRegL: Self-Supervised Learning of Local Visual Features

Learning to Find Proofs and Theorems by Learning to Refine Search Strategies: The Case of Loop Invariant Synthesis

Generalization for multiclass classification with overparameterized linear models

Okapi: Generalising Better by Making Statistical Matches Match

Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference

Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design

Adversarial Reprogramming Revisited

A Near-Optimal Best-of-Both-Worlds Algorithm for Online Learning with Feedback Graphs

Uncertainty Estimation Using Riemannian Model Dynamics for Offline Reinforcement Learning

Left Heavy Tails and the Effectiveness of the Policy and Value Networks in DNN-based best-first search for Sokoban Planning

The Pitfalls of Regularization in Off-Policy TD Learning

OmniVL: One Foundation Model for Image-Language and Video-Language Tasks

CCCP is Frank-Wolfe in disguise

Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding

Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning

Adam Can Converge Without Any Modification On Update Rules

A Consistent and Differentiable Lp Canonical Calibration Error Estimator

Exploration-Guided Reward Shaping for Reinforcement Learning under Sparse Rewards

Detection and Localization of Changes in Conditional Distributions

TransTab: Learning Transferable Tabular Transformers Across Tables

Spatial Mixture-of-Experts

TransBoost: Improving the Best ImageNet Performance using Deep Transduction

A Multilabel Classification Framework for Approximate Nearest Neighbor Search

On Efficient Online Imitation Learning via Classification

Inherently Explainable Reinforcement Learning in Natural Language

Inverse Game Theory for Stackelberg Games: the Blessing of Bounded Rationality

Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards

$k$-Sliced Mutual Information: A Quantitative Study of Scalability with Dimension

A Direct Approximation of AIXI Using Logical State Abstractions

Transition to Linearity of General Neural Networks with Directed Acyclic Graph Architecture

Towards Efficient Post-training Quantization of Pre-trained Language Models

A Unified Analysis of Federated Learning with Arbitrary Client Participation

Self-supervised surround-view depth estimation with volumetric feature fusion

Robust Bayesian Regression via Hard Thresholding

On the Efficient Implementation of High Accuracy Optimality of Profile Maximum Likelihood

The price of unfairness in linear bandits with biased feedback

Cooperative Distribution Alignment via JSD Upper Bound

Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis

Dataset Inference for Self-Supervised Models

Active Learning Through a Covering Lens

Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization

Wavelet Score-Based Generative Modeling

Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent

The Curse of Unrolling: Rate of Differentiating Through Optimization

ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Dual-discriminative Graph Neural Network for Imbalanced Graph-level Anomaly Detection

Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and its Application to Optimization

What You See is What You Classify: Black Box Attributions

A Closer Look at Prototype Classifier for Few-shot Image Classification

Graph Reordering for Cache-Efficient Near Neighbor Search

Trade-off between Payoff and Model Rewards in Shapley-Fair Collaborative Machine Learning

Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging

Adaptively Exploiting d-Separators with Causal Bandits

Generative Neural Articulated Radiance Fields

Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos

Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy

BagFlip: A Certified Defense Against Data Poisoning

On the Convergence Theory for Hessian-Free Bilevel Algorithms

On the Sample Complexity of Stabilizing LTI Systems on a Single Trajectory

Gradient Descent Is Optimal Under Lower Restricted Secant Inequality And Upper Error Bound

Maximum Common Subgraph Guided Graph Retrieval: Late and Early Interaction Networks

Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning

Refining Low-Resource Unsupervised Translation by Language Disentanglement of Multilingual Translation Model

Analyzing Data-Centric Properties for Graph Contrastive Learning

RényiCL: Contrastive Representation Learning with Skew Rényi Divergence

Scalable Neural Video Representations with Learnable Positional Features

Towards Improving Calibration in Object Detection Under Domain Shift

GenSDF: Two-Stage Learning of Generalizable Signed Distance Functions

Approaching Quartic Convergence Rates for Quasi-Stochastic Approximation with Application to Gradient-Free Optimization

Neural Circuit Architectural Priors for Embodied Control

Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP

Understanding Deep Neural Function Approximation in Reinforcement Learning via $\epsilon$-Greedy Exploration

LIFT: Language-Interfaced Fine-Tuning for Non-language Machine Learning Tasks

Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?

Stochastic Multiple Target Sampling Gradient Descent

If Influence Functions are the Answer, Then What is the Question?

[Re] Replication Study of DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks

A Projection-free Algorithm for Constrained Stochastic Multi-level Composition Optimization

A composable machine-learning approach for steady-state simulations on high-resolution grids

Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging

[Re] Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation

Amortized Projection Optimization for Sliced Wasserstein Generative Models

Trading Off Resource Budgets For Improved Regret Bounds

Robust On-Policy Sampling for Data-Efficient Policy Evaluation in Reinforcement Learning

Matching in Multi-arm Bandit with Collision

Combinatorial Bandits with Linear Constraints: Beyond Knapsacks and Fairness

Evaluating Graph Generative Models with Contrastively Learned Features

Single-pass Streaming Lower Bounds for Multi-armed Bandits Exploration with Instance-sensitive Sample Complexity

The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm

A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks

Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning

One-shot Neural Backdoor Erasing via Adversarial Weight Masking

Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation

Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts

Movement Penalized Bayesian Optimization with Application to Wind Energy Systems

Two-layer neural network on infinite dimensional data: global optimization guarantee in the mean-field regime

Efficient Aggregated Kernel Tests using Incomplete $U$-statistics

Recurrent Memory Transformer

Unsupervised Learning From Incomplete Measurements for Inverse Problems

An empirical analysis of compute-optimal large language model training

DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems

SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning

House of Cans: Covert Transmission of Internal Datasets via Capacity-Aware Neuron Steganography

A Unifying Framework for Online Optimization with Long-Term Constraints

Better Best of Both Worlds Bounds for Bandits with Switching Costs

Renyi Differential Privacy of Propose-Test-Release and Applications to Private and Robust Machine Learning

Earthformer: Exploring Space-Time Transformers for Earth System Forecasting

Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression

Look Around and Refer: 2D Synthetic Semantics Knowledge Distillation for 3D Visual Grounding

Variational inference via Wasserstein gradient flows

Efficient Risk-Averse Reinforcement Learning

Operator Splitting Value Iteration

Composite Feature Selection Using Deep Ensembles

From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent

Contrastive Adapters for Foundation Model Group Robustness

Domain Generalization by Learning and Removing Domain-specific Features

On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification

Physics-Embedded Neural Networks: Graph Neural PDE Solvers with Mixed Boundary Conditions

SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos

Debiased Self-Training for Semi-Supervised Learning

Learning Recourse on Instance Environment to Enhance Prediction Accuracy

Differentially Private Learning with Margin Guarantees

Provable General Function Class Representation Learning in Multitask Bandits and MDP

Characterization of Excess Risk for Locally Strongly Convex Population Risk

Extrapolative Continuous-time Bayesian Neural Network for Fast Training-free Test-time Adaptation

SNAKE: Shape-aware Neural 3D Keypoint Field

SIXO: Smoothing Inference with Twisted Objectives

Learning Articulated Rigid Body Dynamics with Lagrangian Graph Neural Network

Gradient Descent: The Ultimate Optimizer

Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks

Batch size-invariance for policy optimization

Distributionally robust weighted k-nearest neighbors

On the Importance of Gradient Norm in PAC-Bayesian Bounds

Fair Bayes-Optimal Classifiers Under Predictive Parity

Analyzing Lottery Ticket Hypothesis from PAC-Bayesian Theory Perspective

Counterfactual Fairness with Partially Known Causal Graph

When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Bandits

Efficient identification of informative features in simulation-based inference

Transform Once: Efficient Operator Learning in Frequency Domain

Expansion and Shrinkage of Localization for Weakly-Supervised Semantic Segmentation

Deep Active Learning by Leveraging Training Dynamics

Rate-Optimal Online Convex Optimization in Adaptive Linear Control

SAPipe: Staleness-Aware Pipeline for Data Parallel DNN Training

Understanding Programmatic Weak Supervision via Source-aware Influence Function

Mind Reader: Reconstructing complex images from brain activities

A Neural Corpus Indexer for Document Retrieval

CUP: Critic-Guided Policy Reuse

Low-Rank Modular Reinforcement Learning via Muscle Synergy

RORL: Robust Offline Reinforcement Learning via Conservative Smoothing

Safe Opponent-Exploitation Subgame Refinement

LAPO: Latent-Variable Advantage-Weighted Policy Optimization for Offline Reinforcement Learning

A Primer for Neural Arithmetic Logic Modules

Improving Task-Specific Generalization in Few-Shot Learning via Adaptive Vicinal Risk Minimization

Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers

Look More but Care Less in Video Recognition

Adversarial Task Up-sampling for Meta-learning

Let Images Give You More: Point Cloud Cross-Modal Training for Shape Analysis

Peer Prediction for Learning Agents

Cache-Augmented Inbatch Importance Resampling for Training Recommender Retriever

Interaction Modeling with Multiplex Attention

Learning to Configure Computer Networks with Neural Algorithmic Reasoning

Can Adversarial Training Be Manipulated By Non-Robust Features?

Uncertainty-Aware Hierarchical Refinement for Incremental Implicitly-Refined Classification

MGNNI: Multiscale Graph Neural Networks with Implicit Layers

Discrete Compositional Representations as an Abstraction for Goal Conditioned Reinforcement Learning

MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generation

Learning Mixed Multinomial Logits with Provable Guarantees

Relational Reasoning via Set Transformers: Provable Efficiency and Applications to MARL

Hilbert Distillation for Cross-Dimensionality Networks

Recurrent Video Restoration Transformer with Guided Deformable Attention

Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone

Unified Optimal Transport Framework for Universal Domain Adaptation

Learning Deep Input-Output Stable Dynamics

Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernel

Neural Topological Ordering for Computation Graphs

Memory Efficient Continual Learning with Transformers

Efficient Knowledge Distillation from Model Checkpoints

EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks

SelecMix: Debiased Learning by Contradicting-pair Sampling

Coordinate Linear Variance Reduction for Generalized Linear Programming

Local Latent Space Bayesian Optimization over Structured Inputs

Memorization and Optimization in Deep Neural Networks with Minimum Over-parameterization

Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure

Learning Robust Dynamics through Variational Sparse Gating

VER: Scaling On-Policy RL Leads to the Emergence of Navigation in Embodied Rearrangement

A Unified Framework for Deep Symbolic Regression

[Re] A Cluster-based Approach for Improving Isotropy in Contextual Embedding Space

Is Sortition Both Representative and Fair?

All Politics is Local: Redistricting via Local Fairness

Learning Interface Conditions in Domain Decomposition Solvers

Off-Policy Evaluation for Action-Dependent Non-stationary Environments

Factored DRO: Factored Distributionally Robust Policies for Contextual Bandits

Causal Discovery in Linear Latent Variable Models Subject to Measurement Error

Human-AI Collaborative Bayesian Optimisation

SNN-RAT: Robustness-enhanced Spiking Neural Network through Regularized Adversarial Training

OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs

Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems

GAR: Generalized Autoregression for Multi-Fidelity Fusion

Learning Representations via a Robust Behavioral Metric for Deep Reinforcement Learning

Environment Diversification with Multi-head Neural Network for Invariant Learning

MetaTeacher: Coordinating Multi-Model Domain Adaptation for Medical Image Classification

Collaborative Learning by Detecting Collaboration Partners

DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection

Set-based Meta-Interpolation for Few-Task Meta-Learning

Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold

Error Analysis of Tensor-Train Cross Approximation

Trading off Utility, Informativeness, and Complexity in Emergent Communication

Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights

Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation

A Damped Newton Method Achieves Global $\mathcal O \left(\frac{1}{k^2}\right)$ and Local Quadratic Convergence Rate

Finding Second-Order Stationary Points in Nonconvex-Strongly-Concave Minimax Optimization

Private Set Generation with Discriminative Information

Robust Semi-Supervised Learning when Not All Classes have Labels

Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization

"Lossless" Compression of Deep Neural Networks: A High-dimensional Neural Tangent Kernel Approach

GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks

Finding and Listing Front-door Adjustment Sets

Bridging the Gap from Asymmetry Tricks to Decorrelation Principles in Non-contrastive Self-supervised Learning

Logical Credal Networks

Sharp Analysis of Stochastic Optimization under Global Kurdyka-Lojasiewicz Inequality

SInGE: Sparsity via Integrated Gradients Estimation of Neuron Relevance

A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits

Rethinking the compositionality of point clouds through regularization in the hyperbolic space

Identifiability of deep generative models without auxiliary information

Sub-exponential time Sum-of-Squares lower bounds for Principal Components Analysis

Robust Anytime Learning of Markov Decision Processes

COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics

Simultaneous Missing Value Imputation and Structure Learning with Groups

Provably Efficient Model-Free Constrained RL with Linear Function Approximation

Private Estimation with Public Data

Friendly Noise against Adversarial Noise: A Powerful Defense against Data Poisoning Attack

Multi-Fidelity Best-Arm Identification

Off-Policy Evaluation with Deficient Support Using Side Information

Challenging Common Assumptions in Convex Reinforcement Learning

Decision Trees with Short Explainable Rules

List-Decodable Sparse Mean Estimation

Stochastic Adaptive Activation Function

Rethinking Knowledge Graph Evaluation Under the Open-World Assumption

A Theoretical Framework for Inference Learning

OPEN: Orthogonal Propagation with Ego-Network Modeling

On the Frequency-bias of Coordinate-MLPs

Generalization Properties of NAS under Activation and Skip Connection Search

Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization)

Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study

A Rotated Hyperbolic Wrapped Normal Distribution for Hierarchical Representation Learning

Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields

A general approximation lower bound in $L^p$ norm, with applications to feed-forward neural networks

CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP

Communication Efficient Distributed Learning for Kernelized Contextual Bandits

Communication Efficient Federated Learning for Generalized Linear Bandits

Versatile Multi-stage Graph Neural Network for Circuit Representation

Cost-Sensitive Self-Training for Optimizing Non-Decomposable Metrics

Understanding Square Loss in Training Overparametrized Neural Network Classifiers

The Gyro-Structure of Some Matrix Manifolds

Multi-view Subspace Clustering on Topological Manifold

HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks

S$^3$-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint

PaCo: Parameter-Compositional Multi-task Reinforcement Learning

IALE: Imitating Active Learner Ensembles

Score-based Generative Modeling Secretly Minimizes the Wasserstein Distance

Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning

Momentum Aggregation for Private Non-convex ERM

SCL-WC: Cross-Slide Contrastive Learning for Weakly-Supervised Whole-Slide Image Classification

Differentially Private Online-to-batch for Smooth Losses

Distributionally Robust Optimization with Data Geometry

Decentralized Training of Foundation Models in Heterogeneous Environments

On the convergence of policy gradient methods to Nash equilibria in general stochastic games

Sample Complexity of Learning Heuristic Functions for Greedy-Best-First and A* Search

Rank Diminishing in Deep Neural Networks

Don't Pour Cereal into Coffee: Differentiable Temporal Logic for Temporal Action Segmentation

Lethal Dose Conjecture on Data Poisoning

Learning Substructure Invariance for Out-of-Distribution Molecular Representations

NeuPhysics: Editable Neural Geometry and Physics from Monocular Videos

Understanding the Evolution of Linear Regions in Deep Reinforcement Learning

RecursiveMix: Mixed Learning with History

DeepTOP: Deep Threshold-Optimal Policy for MDPs and RMABs

Fairness Reprogramming

S-Prompts Learning with Pre-trained Transformers: An Occam’s Razor for Domain Incremental Learning

Coded Residual Transform for Generalizable Deep Metric Learning

Embodied Scene-aware Human Pose Estimation

Generative Status Estimation and Information Decoupling for Image Rain Removal

Subsidiary Prototype Alignment for Universal Domain Adaptation

Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences

DOMINO: Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement Learning

EcoFormer: Energy-Saving Attention with Linear Complexity

Machine Learning on Graphs: A Model and Comprehensive Taxonomy

DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization

Self-Supervised Visual Representation Learning with Semantic Grouping

Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning

Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models

Active Labeling: Streaming Stochastic Gradients

SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

Polynomial Neural Fields for Subband Decomposition and Manipulation

Visual Concepts Tokenization

Phase Transition from Clean Training to Adversarial Training

HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces

Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation

Natural Color Fool: Towards Boosting Black-box Unrestricted Attacks

Cross-Image Context for Single Image Inpainting

TOIST: Task Oriented Instance Segmentation Transformer with Noun-Pronoun Distillation

Is Out-of-Distribution Detection Learnable?

Masked Autoencoders As Spatiotemporal Learners

Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork

PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds

Prototypical VoteNet for Few-Shot 3D Point Cloud Object Detection

Optimistic Tree Searches for Combinatorial Black-Box Optimization

Tensor Wheel Decomposition and Its Tensor Completion Application

PALBERT: Teaching ALBERT to Ponder

Towards Efficient 3D Object Detection with Knowledge Distillation

Towards Lightweight Black-Box Attack Against Deep Neural Networks

HumanLiker: A Human-like Object Detector to Model the Manual Labeling Process

Learn what matters: cross-domain imitation learning with task-relevant embeddings

Whitening Convergence Rate of Coupling-based Normalizing Flows

Hierarchical Normalization for Robust Monocular Depth Estimation

Unsupervised Multi-Object Segmentation by Predicting Probable Motion Patterns

On the Strong Correlation Between Model Invariance and Generalization

Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer

Fully Sparse 3D Object Detection

Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matching

A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning

Towards Robust Blind Face Restoration with Codebook Lookup Transformer

Improved Fine-Tuning by Better Leveraging Pre-Training Data

TotalSelfScan: Learning Full-body Avatars from Self-Portrait Videos of Faces, Hands, and Bodies

Cross Aggregation Transformer for Image Restoration

Behavior Transformers: Cloning $k$ modes with one stone

What Makes a "Good" Data Augmentation in Knowledge Distillation - A Statistical Perspective

Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection

Discrete-Convex-Analysis-Based Framework for Warm-Starting Algorithms with Predictions

Divert More Attention to Vision-Language Tracking

Trajectory Inference via Mean-field Langevin in Path Space

ElasticMVS: Learning elastic part representation for self-supervised multi-view stereopsis

A2: Efficient Automated Attacker for Boosting Adversarial Training

PerfectDou: Dominating DouDizhu with Perfect Information Distillation

MsSVT: Mixed-scale Sparse Voxel Transformer for 3D Object Detection on Point Clouds

Towards Versatile Embodied Navigation

Product Ranking for Revenue Maximization with Multiple Purchases

Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks

ResT V2: Simpler, Faster and Stronger

In the Eye of the Beholder: Robust Prediction with Causal User Modeling

Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification

Multi-modal Grouping Network for Weakly-Supervised Audio-Visual Video Parsing

Mining Unseen Classes via Regional Objectness: A Simple Baseline for Incremental Segmentation

Panchromatic and Multispectral Image Fusion via Alternating Reverse Filtering Network

Pay attention to your loss : understanding misconceptions about Lipschitz neural networks

End-to-end Symbolic Regression with Transformers

SPoVT: Semantic-Prototype Variational Transformer for Dense Point Cloud Semantic Completion

Unsupervised Representation Learning from Pre-trained Diffusion Probabilistic Models

What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods

Stochastic Window Transformer for Image Restoration

A Closer Look at Weakly-Supervised Audio-Visual Source Localization

Semi-Discrete Normalizing Flows through Differentiable Tessellation

Blackbox Attacks via Surrogate Ensemble Search

Saliency-Aware Neural Architecture Search

ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation

Learning Best Combination for Efficient N:M Sparsity

Predicting Label Distribution from Multi-label Ranking

Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees

Semantic Diffusion Network for Semantic Segmentation

Regret Bounds for Information-Directed Reinforcement Learning

A Spectral Approach to Item Response Theory

UDC: Unified DNAS for Compressible TinyML Models for Neural Processing Units

AutoLink: Self-supervised Learning of Human Skeletons and Object Outlines by Linking Keypoints

Optimistic Mirror Descent Either Converges to Nash or to Strong Coarse Correlated Equilibria in Bimatrix Games

Parameter-Efficient Masking Networks

Learning Distinct and Representative Modes for Image Captioning

Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images

HUMANISE: Language-conditioned Human Motion Generation in 3D Scenes

VCT: A Video Compression Transformer

Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting

VITA: Video Instance Segmentation via Object Token Association

A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective

Geometry-aware Two-scale PIFu Representation for Human Reconstruction

Causally motivated multi-shortcut identification and removal

SegViT: Semantic Segmentation with Plain Vision Transformers

Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?

Masked Autoencoders that Listen

Semi-Supervised Semantic Segmentation via Gentle Teaching Assistant

Video-based Human-Object Interaction Detection from Tubelet Tokens

Learning Equivariant Segmentation with Instance-Unique Querying

Enhanced Latent Space Blind Model for Real Image Denoising via Alternative Optimization

High-dimensional Additive Gaussian Processes under Monotonicity Constraints

Learning Generalizable Part-based Feature Representation for 3D Point Clouds

Constants of motion network

Asymptotically Unbiased Instance-wise Regularized Partial AUC Optimization: Theory and Algorithm

Rethinking Alignment in Video Super-Resolution Transformers

Robust Testing in High-Dimensional Sparse Models

INRAS: Implicit Neural Representation for Audio Scenes

BMU-MoCo: Bidirectional Momentum Update for Continual Video-Language Modeling

DropCov: A Simple yet Effective Method for Improving Deep Architectures

Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models for Protein Structures

Monocular Dynamic View Synthesis: A Reality Check

A Mixture Of Surprises for Unsupervised Reinforcement Learning

QueryPose: Sparse Multi-Person Pose Regression via Spatial-Aware Part-Level Query

Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning

Misspecified Phase Retrieval with Generative Priors

Watermarking for Out-of-distribution Detection

Error Correction Code Transformer

Maximum Class Separation as Inductive Bias in One Matrix

Sequencer: Deep LSTM for Image Classification

Self-Supervised Learning via Maximum Entropy Coding

Giga-scale Kernel Matrix-Vector Multiplication on GPU

Scalable Infomin Learning

Multi-dataset Training of Transformers for Robust Action Recognition

ZARTS: On Zero-order Optimization for Neural Architecture Search

Online Training Through Time for Spiking Neural Networks

Multi-Instance Causal Representation Learning for Instance Label Prediction and Out-of-Distribution Generalization

P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting

Towards Theoretically Inspired Neural Initialization Optimization

Vision GNN: An Image is Worth Graph of Nodes

Rotation-Equivariant Conditional Spherical Neural Fields for Learning a Natural Illumination Prior

Supported Policy Optimization for Offline Reinforcement Learning

AutoMS: Automatic Model Selection for Novelty Detection with Error Rate Control

Increasing Confidence in Adversarial Robustness Evaluations

Generalization Bounds for Estimating Causal Effects of Continuous Treatments

Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning

Learning Consistency-Aware Unsigned Distance Functions Progressively from Raw Point Clouds

Why Do Artificially Generated Data Help Adversarial Robustness

Learning Infinite-Horizon Average-Reward Restless Multi-Action Bandits via Index Awareness

Theory and Approximate Solvers for Branched Optimal Transport with Multiple Sources

New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma

PointTAD: Multi-Label Temporal Action Detection with Learnable Query Points

On the Generalizability and Predictability of Recommender Systems

Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks

Generative Visual Prompt: Unifying Distributional Control of Pre-Trained Generative Models

Stability and Generalization Analysis of Gradient Methods for Shallow Neural Networks

Physically-Based Face Rendering for NIR-VIS Face Recognition

Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks

Few-Shot Continual Active Learning by a Robot

MultiScan: Scalable RGBD scanning for 3D environments with articulated objects

Transformer-based Working Memory for Multiagent Reinforcement Learning with Action Parsing

Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport

Biologically Inspired Dynamic Thresholds for Spiking Neural Networks

Don't Roll the Dice, Ask Twice: The Two-Query Distortion of Matching Problems and Beyond

A Unified Model for Multi-class Anomaly Detection

A framework for bilevel optimization that enables stochastic and global variance reduction algorithms

SAViT: Structure-Aware Vision Transformer Pruning via Collaborative Optimization

Masked Generative Adversarial Networks are Data-Efficient Generation Learners

Training Spiking Neural Networks with Event-driven Backpropagation

MCMAE: Masked Convolution Meets Masked Autoencoders

Learning Physical Dynamics with Subequivariant Graph Neural Networks

Online PAC-Bayes Learning

Implicit Warping for Animation with Image Sets

Rethinking Resolution in the Context of Efficient Video Recognition

RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning

CEBaB: Estimating the Causal Effects of Real-World Concepts on NLP Model Behavior

Natural gradient enables fast sampling in spiking neural networks

MultiGuard: Provably Robust Multi-label Classification against Adversarial Examples

Efficient and Effective Multi-task Grouping via Meta Learning on Task Combinations

Robust Calibration with Multi-domain Temperature Scaling

Exploration via Planning for Information about the Optimal Trajectory

Mean Estimation in High-Dimensional Binary Markov Gaussian Mixture Models

BiT: Robustly Binarized Multi-distilled Transformer

PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits

On the Effect of Pre-training for Transformer in Different Modality on Offline Reinforcement Learning

On-Device Training Under 256KB Memory

Geo-SIC: Learning Deformable Geometric Shapes in Deep Image Classifiers

An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning

Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning

Finite-Time Analysis of Adaptive Temporal Difference Learning with Deep Neural Networks

Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Camera

Mutual Information Divergence: A Unified Metric for Multimodal Generative Models

Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding

GLIPv2: Unifying Localization and Vision-Language Understanding

A Unified Diversity Measure for Multiagent Reinforcement Learning

Plan To Predict: Learning an Uncertainty-Foreseeing Model For Model-Based Reinforcement Learning

Learning to Accelerate Partial Differential Equations via Latent Global Evolution

Active Learning for Multiple Target Models

Alignment-guided Temporal Attention for Video Action Recognition

Open-Ended Reinforcement Learning with Neural Reward Functions

On Margins and Generalisation for Voting Classifiers

Contrastive Neural Ratio Estimation

Mildly Conservative Q-Learning for Offline Reinforcement Learning

Self-Supervised Image Restoration with Blurry and Noisy Pairs

Recommender Forest for Efficient Retrieval

Retrieval-Augmented Diffusion Models

PatchComplete: Learning Multi-Resolution Patch Priors for 3D Shape Completion on Unseen Categories

Generalized Laplacian Eigenmaps

SAPA: Similarity-Aware Point Affiliation for Feature Upsampling

Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning

Random Sharpness-Aware Minimization

Generalized One-shot Domain Adaptation of Generative Adversarial Networks

SCONE: Surface Coverage Optimization in Unknown Environments by Volumetric Integration

A Quantitative Geometric Approach to Neural-Network Smoothness

Is this the Right Neighborhood? Accurate and Query Efficient Model Agnostic Explanations

Parametrically Retargetable Decision-Makers Tend To Seek Power

Learning Individualized Treatment Rules with Many Treatments: A Supervised Clustering Approach Using Adaptive Fusion

Differentially Private Model Compression

Is a Modular Architecture Enough?

Learning General World Models in a Handful of Reward-Free Deployments

Revisiting Heterophily For Graph Neural Networks

Recipe for a General, Powerful, Scalable Graph Transformer

CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations

GhostNetV2: Enhance Cheap Operation with Long-Range Attention

Elucidating the Design Space of Diffusion-Based Generative Models

Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation

Robust Models are less Over-Confident

OST: Improving Generalization of DeepFake Detection via One-Shot Test-Time Training

KSD Aggregated Goodness-of-fit Test

Distributional Reward Estimation for Effective Multi-agent Deep Reinforcement Learning

A Near-Optimal Primal-Dual Method for Off-Policy Learning in CMDP

ZIN: When and How to Learn Invariance Without Environment Partition?

Enhance the Visual Representation via Discrete Adversarial Training

Frank-Wolfe-based Algorithms for Approximating Tyler's M-estimator

Spending Thinking Time Wisely: Accelerating MCTS with Virtual Expansions

Efficient and Effective Optimal Transport-Based Biclustering

SageMix: Saliency-Guided Mixup for Point Clouds

Heatmap Distribution Matching for Human Pose Estimation

Autoregressive Search Engines: Generating Substrings as Document Identifiers

Mirror Descent with Relative Smoothness in Measure Spaces, with application to Sinkhorn and EM

Deconfounded Representation Similarity for Comparison of Neural Networks

Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective

Fine-Grained Analysis of Stability and Generalization for Modern Meta Learning Algorithms

Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need?

Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition

Out-of-Distribution Detection with An Adaptive Likelihood Ratio on Informative Hierarchical VAE

Relational Proxies: Emergent Relationships as Fine-Grained Discriminators

Sampling without Replacement Leads to Faster Rates in Finite-Sum Minimax Optimization

Unsupervised Cross-Task Generalization via Retrieval Augmentation

coVariance Neural Networks

On the inability of Gaussian process regression to optimally learn compositional functions

Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees

When to Update Your Model: Constrained Model-based Reinforcement Learning

Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization

Constrained Langevin Algorithms with L-mixing External Random Variables

Practical Adversarial Multivalid Conformal Prediction

Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources

Deep Generalized Schrödinger Bridge

Deep Generative Model for Periodic Graphs

Optimal Comparator Adaptive Online Learning with Switching Cost

Enhanced Bilevel Optimization via Bregman Distance

Learning State-Aware Visual Representations from Audible Interactions

Near-Optimal Multi-Agent Learning for Safe Coverage Control

Probabilistic Missing Value Imputation for Mixed Categorical and Ordered Data

Exploration via Elliptical Episodic Bonuses

GAUDI: A Neural Architect for Immersive 3D Scene Generation

Periodic Graph Transformers for Crystal Material Property Prediction

Parallel Tempering With a Variational Reference

On the consistent estimation of optimal Receiver Operating Characteristic (ROC) curve

NS3: Neuro-symbolic Semantic Code Search

A Deep Learning Dataloader with Shared Data Preparation

Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies

Improving Variational Autoencoders with Density Gap-based Regularization

Fused Orthogonal Alternating Least Squares for Tensor Clustering

Representing Spatial Trajectories as Distributions

Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief

CLEAR: Generative Counterfactual Explanations on Graphs

Wasserstein $K$-means for clustering probability distributions

Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators

Cost-efficient Gaussian tensor network embeddings for tensor-structured inputs

Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models

Green Hierarchical Vision Transformer for Masked Image Modeling

Beyond the Best: Distribution Functional Estimation in Infinite-Armed Bandits

An Investigation into Whitening Loss for Self-supervised Learning

Fixed-Distance Hamiltonian Monte Carlo

SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning

Category-Level 6D Object Pose Estimation in the Wild: A Semi-Supervised Learning Approach and A New Dataset

Deep Attentive Belief Propagation: Integrating Reasoning and Learning for Solving Constraint Optimization Problems

Amortized Mixing Coupling Processes for Clustering

HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions

Weakly supervised causal representation learning

Less-forgetting Multi-lingual Fine-tuning

Online Convex Optimization with Hard Constraints: Towards the Best of Two Worlds and Beyond

Rethinking Variational Inference for Probabilistic Programs with Stochastic Support

Retrieve, Reason, and Refine: Generating Accurate and Faithful Patient Instructions

Cross-modal Learning for Image-Guided Point Cloud Shape Completion

TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels

FNeVR: Neural Volume Rendering for Face Animation

Bessel Equivariant Networks for Inversion of Transmission Effects in Multi-Mode Optical Fibres

Bidirectional Learning for Offline Infinite-width Model-based Optimization

TREC: Transient Redundancy Elimination-based Convolution

DivBO: Diversity-aware CASH for Ensemble Learning

Forecasting Human Trajectory from Scene History

Wasserstein Logistic Regression with Mixed Features

Contextual Bandits with Knapsacks for a Conversion Model

Diagnosing failures of fairness transfer across distribution shift in real-world medical settings

Adaptation Accelerating Sampling-based Bayesian Inference in Attractor Neural Networks

ELASTIC: Numerical Reasoning with Adaptive Symbolic Compiler

Oscillatory Tracking of Continuous Attractor Neural Networks Account for Phase Precession and Procession of Hippocampal Place Cells

UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs

Make Sharpness-Aware Minimization Stronger: A Sparsified Perturbation Approach

Contrastive Learning as Goal-Conditioned Reinforcement Learning

Learning Viewpoint-Agnostic Visual Representations by Recovering Tokens in 3D Space

When are Local Queries Useful for Robust Learning?

Shield Decentralization for Safe Multi-Agent Reinforcement Learning

Extracting computational mechanisms from neural data using low-rank RNNs

Data Distributional Properties Drive Emergent In-Context Learning in Transformers

A Quadrature Rule combining Control Variates and Adaptive Importance Sampling

Dynamic Fair Division with Partial Information

Improved Imaging by Invex Regularizers with Global Optima Guarantees

Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients

Change-point Detection for Sparse and Dense Functional Data in General Dimensions

Sample-Efficient Learning of Correlated Equilibria in Extensive-Form Games

Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis

Parameter tuning and model selection in Optimal Transport with semi-dual Brenier formulation

Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems

Generating multivariate time series with COmmon Source CoordInated GAN (COSCI-GAN)

Unsupervised Multi-View Object Segmentation Using Radiance Field Propagation

Online Deep Equilibrium Learning for Regularization by Denoising

When does return-conditioned supervised learning work for offline reinforcement learning?

Inductive Logical Query Answering in Knowledge Graphs

The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning

Biological Learning of Irreducible Representations of Commuting Transformations

The price of ignorance: how much does it cost to forget noise structure in low-rank matrix estimation?

MCVD - Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation

Bayesian Clustering of Neural Spiking Activity Using a Mixture of Dynamic Poisson Factor Analyzers

Non-convex online learning via algorithmic equivalence

Decomposing NeRF for Editing via Feature Field Distillation

Approximate Value Equivalence

Neur2SP: Neural Two-Stage Stochastic Programming

Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models

SparCL: Sparse Continual Learning on the Edge

Visual Prompting via Image Inpainting

Test-Time Training with Masked Autoencoders

Visual Clues: Bridging Vision and Language Foundations for Image Paragraph Captioning

Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization

BILCO: An Efficient Algorithm for Joint Alignment of Time Series

Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks

Falsification before Extrapolation in Causal Effect Estimation

LION: Latent Point Diffusion Models for 3D Shape Generation

FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction

Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions

Sharpness-Aware Training for Free

CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesis

3DILG: Irregular Latent Grids for 3D Generative Modeling

Translation-equivariant Representation in Recurrent Networks with a Continuous Manifold of Attractors

Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition

Towards Learning Universal Hyperparameter Optimizers with Transformers

OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression

ComGAN: Unsupervised Disentanglement and Segmentation via Image Composition

Non-Linear Coordination Graphs

Towards Hard-pose Virtual Try-on via 3D-aware Global Correspondence Learning

Fast Distance Oracles for Any Symmetric Norm

Low-rank Optimal Transport: Approximation, Statistics and Debiasing

Iterative Scene Graph Generation

Eliciting Thinking Hierarchy without a Prior

Learning Robust Rule Representations for Abstract Reasoning via Internal Inferences

Multi-layer State Evolution Under Random Convolutional Design

Latency-aware Spatial-wise Dynamic Networks

Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation

Relation-Constrained Decoding for Text Generation

Searching for Better Spatio-temporal Alignment in Few-Shot Action Recognition

Could Giant Pre-trained Image Models Extract Universal Representations?

IM-Loss: Information Maximization Loss for Spiking Neural Networks

TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers

Hyperbolic Feature Augmentation via Distribution Estimation and Infinite Sampling on Manifolds

Verification and search algorithms for causal DAGs

AD-DROP: Attribution-Driven Dropout for Robust Language Model Fine-Tuning

Where to Pay Attention in Sparse Training for Feature Selection?

TA-MoE: Topology-Aware Large Scale Mixture-of-Expert Training

Understanding the Failure of Batch Normalization for Transformers in NLP

Transformers meet Stochastic Block Models: Attention with Data-Adaptive Sparsity and Cost

Theoretically Provable Spiking Neural Networks

Deep Combinatorial Aggregation

Transcormer: Transformer for Sentence Scoring with Sliding Language Modeling

Self-Supervised Learning with an Information Maximization Criterion

Improved Utility Analysis of Private CountSketch

A Classification of $G$-invariant Shallow Neural Networks

Module-Aware Optimization for Auxiliary Learning

Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering

Riemannian Score-Based Generative Modelling

Out-of-Distribution Detection via Conditional Kernel Independence Model

Towards Effective Multi-Modal Interchanges in Zero-Resource Sounding Object Localization

Policy Gradient With Serial Markov Chain Reasoning

Estimating graphical models for count data with applications to single-cell gene network

Improving 3D-aware Image Synthesis with A Geometry-aware Discriminator

Egocentric Video-Language Pretraining

Efficient Submodular Optimization under Noise: Local Search is Robust

Conservative Dual Policy Optimization for Efficient Model-Based Reinforcement Learning

DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotations

Does Momentum Change the Implicit Regularization on Separable Data?

VRL3: A Data-Driven Framework for Visual Deep Reinforcement Learning

Exact Solutions of a Deep Linear Network

ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning

Masked Prediction: A Parameter Identifiability View

Direct Advantage Estimation

Depth is More Powerful than Width with Prediction Concatenation in Deep Forest

AniFaceGAN: Animatable 3D-Aware Face Image Generation for Video Avatars

Instance-based Learning for Knowledge Base Completion

Efficient and Effective Augmentation Strategy for Adversarial Training

u-HuBERT: Unified Mixed-Modal Speech Pretraining And Zero-Shot Transfer to Unlabeled Modality

First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces

GENIE: Higher-Order Denoising Diffusion Solvers

Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy

Structured Recognition for Generative Models with Explaining Away

UniCLIP: Unified Framework for Contrastive Language-Image Pre-training

InsNet: An Efficient, Flexible, and Performant Insertion-based Text Generation Model

Local-Global MCMC kernels: the best of both worlds

Manifold Interpolating Optimal-Transport Flows for Trajectory Inference

Doubly Robust Counterfactual Classification

Uncertainty-Aware Reinforcement Learning for Risk-Sensitive Player Evaluation in Sports Game

Smooth Fictitious Play in Stochastic Games with Perturbed Payoffs and Unknown Transitions

SKFlow: Learning Optical Flow with Super Kernels

Non-stationary Bandits with Knapsacks

Weighted Mutual Learning with Diversity-Driven Model Compression

Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework

Improving Zero-Shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions

Procedural Image Programs for Representation Learning

Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation

High-Order Pooling for Graph Neural Networks with Tensor Decomposition

TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learning

SALSA: Attacking Lattice Cryptography with Transformers

Class-Aware Adversarial Transformers for Medical Image Segmentation

A Single-timescale Analysis for Stochastic Approximation with Multiple Coupled Sequences

You Only Live Once: Single-Life Reinforcement Learning

Semi-Supervised Learning with Decision Trees: Graph Laplacian Tree Alternating Optimization

When does dough become a bagel? Analyzing the remaining mistakes on ImageNet

Learning from Stochastically Revealed Preference

A Best-of-Both-Worlds Algorithm for Bandits with Delayed Feedback

Online Minimax Multiobjective Optimization: Multicalibeating and Other Applications

Algorithms that Approximate Data Removal: New Results and Limitations

Annihilation of Spurious Minima in Two-Layer ReLU Networks

Unsupervised Image-to-Image Translation with Density Changing Regularization

Reproducibility in Optimization: Theoretical Framework and Limits

Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering

Systematic improvement of neural network quantum states using Lanczos

Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss

Diagonal State Spaces are as Effective as Structured State Spaces

Why neural networks find simple solutions: The many regularizers of geometric complexity

Zero-Shot 3D Drug Design by Sketching and Generating

Adaptive Oracle-Efficient Online Learning

Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints

Efficient Active Learning with Abstention

Unsupervised Learning of Shape Programs with Repeatable Implicit Parts

Moderate-fitting as a Natural Backdoor Defender for Pre-trained Language Models

Controllable Text Generation with Neurally-Decomposed Oracle

A Fast Post-Training Pruning Framework for Transformers

ConfounderGAN: Protecting Image Data Privacy with Causal Confounder

Improved Feature Distillation via Projector Ensemble

Neuron with Steady Response Leads to Better Generalization

Mirror Descent Maximizes Generalized Margin and Can Be Implemented Efficiently

Self-Organized Group for Cooperative Multi-agent Reinforcement Learning

APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction

Learning Manifold Dimensions with Conditional Variational Autoencoders

Discovering Design Concepts for CAD Sketches

Reconstruction on Trees and Low-Degree Polynomials

Test Time Adaptation via Conjugate Pseudo-labels

Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning

GenerSpeech: Towards Style Transfer for Generalizable Out-Of-Domain Text-to-Speech

Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation

FreGAN: Exploiting Frequency Components for Training GANs under Limited Data

FasterRisk: Fast and Accurate Interpretable Risk Scores

When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning

Generalization Bounds for Stochastic Gradient Descent via Localized $\varepsilon$-Covers

Symbolic Distillation for Learned TCP Congestion Control

Proximal Learning With Opponent-Learning Awareness

Accelerated Linearized Laplace Approximation for Bayesian Deep Learning

GAGA: Deciphering Age-path of Generalized Self-paced Regularizer

Provable Benefit of Multitask Representation Learning in Reinforcement Learning

Follow-the-Perturbed-Leader for Adversarial Markov Decision Processes with Bandit Feedback

Why do We Need Large Batchsizes in Contrastive Learning? A Gradient-Bias Perspective

Globally Convergent Policy Search for Output Estimation

To update or not to update? Neurons at equilibrium in deep models

Grow and Merge: A Unified Framework for Continuous Categories Discovery

OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds

Learning a Condensed Frame for Memory-Efficient Video Class-Incremental Learning

Factorized-FL: Personalized Federated Learning with Parameter Factorization & Similarity Matching

Autoinverse: Uncertainty Aware Inversion of Neural Networks

Bootstrapped Transformer for Offline Reinforcement Learning

Double Check Your State Before Trusting It: Confidence-Aware Bidirectional Offline Model-Based Imagination

Fair Wrapping for Black-box Predictions

GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks

Generic bounds on the approximation error for physics-informed (and) operator learning

Debiased, Longitudinal and Coordinated Drug Recommendation through Multi-Visit Clinic Records

Most Activation Functions Can Win the Lottery Without Excessive Depth

VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training

Truncated Matrix Power Iteration for Differentiable DAG Learning

Robust Rent Division

Temporally Disentangled Representation Learning

Improving Transformer with an Admixture of Attention Heads

Para-CFlows: $C^k$-universal diffeomorphism approximators as superior neural surrogates

TA-GATES: An Encoding Scheme for Neural Network Architectures

Gradient Methods Provably Converge to Non-Robust Networks

Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited

Mask-based Latent Reconstruction for Reinforcement Learning

MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learning

SwinTrack: A Simple and Strong Baseline for Transformer Tracking

Self-supervised Amodal Video Object Segmentation

Improving Generative Adversarial Networks via Adversarial Learning in Latent Space

EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization

First is Better Than Last for Language Data Influence

Molecule Generation by Principal Subgraph Mining and Assembling

Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discovery

Equivariant Graph Hierarchy-Based Neural Networks

Semi-infinitely Constrained Markov Decision Processes

One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement

Bridge the Gap Between Architecture Spaces via A Cross-Domain Predictor

Mingling Foresight with Imagination: Model-Based Cooperative Multi-Agent Reinforcement Learning

Top Two Algorithms Revisited

Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum

A Probabilistic Graph Coupling View of Dimension Reduction

Knowledge Distillation Improves Graph Structure Augmentation for Graph Neural Networks

LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning

Stimulative Training of Residual Networks: A Social Psychology Perspective of Loafing

MExMI: Pool-based Active Model Extraction Crossover Membership Inference

S3GC: Scalable Self-Supervised Graph Clustering

Parameter-free Dynamic Graph Embedding for Link Prediction

Federated Submodel Optimization for Hot and Cold Data Features

Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?

Causality Preserving Chaotic Transformation and Classification using Neurochaos Learning

On Margin Maximization in Linear and ReLU Networks

Optimal Binary Classification Beyond Accuracy

Active Learning of Classifiers with Label and Seed Queries

AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition

FedPop: A Bayesian Approach for Personalised Federated Learning

Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs

Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning

Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving

Large Language Models are Zero-Shot Reasoners

Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks

Amplifying Membership Exposure via Data Poisoning

Robust Graph Structure Learning via Multiple Statistical Tests

Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks

Learning to Constrain Policy Optimization with Virtual Trust Region

Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation

NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification

Efficient Architecture Search for Diverse Tasks

GRASP: Navigating Retrosynthetic Planning with Goal-driven Policy

Multi-Agent Reinforcement Learning is a Sequence Modeling Problem

Distributed Learning of Conditional Quantiles in the Reproducing Kernel Hilbert Space

The Policy-gradient Placement and Generative Routing Neural Networks for Chip Design

Pruning Neural Networks via Coresets and Convex Geometry: Towards No Assumptions

Revisiting Injective Attacks on Recommender Systems

On the Convergence of Stochastic Multi-Objective Gradient Manipulation and Beyond

Learning to Generate Inversion-Resistant Model Explanations

Semi-Supervised Generative Models for Multiagent Trajectories

Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation

Distributionally Robust Optimization via Ball Oracle Acceleration

DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learning

Domain Adaptation under Open Set Label Shift

Generalization Bounds with Minimal Dependency on Hypothesis Class via Distributionally Robust Optimization

NeMF: Neural Motion Fields for Kinematic Animation

On Robust Multiclass Learnability

Moment Distributionally Robust Tree Structured Prediction

Alleviating "Posterior Collapse'' in Deep Topic Models via Policy Gradient

Grounding Aleatoric Uncertainty for Unsupervised Environment Design

Conditional Meta-Learning of Linear Representations

AZ-whiteness test: a test for signal uncorrelation on spatio-temporal graphs

Sample-Then-Optimize Batch Neural Thompson Sampling

Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning

Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination

EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations

Blessing of Depth in Linear Regression: Deeper Models Have Flatter Landscape Around the True Solution

PKD: General Distillation Framework for Object Detectors via Pearson Correlation Coefficient

Star Temporal Classification: Sequence Modeling with Partially Labeled Data

Neural Stochastic Control

Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor

Adaptive Sampling for Discovery

Diverse Weight Averaging for Out-of-Distribution Generalization

Counterfactual Temporal Point Processes

Sparse Winning Tickets are Data-Efficient Image Recognizers

Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs

Reduction Algorithms for Persistence Diagrams of Networks: CoralTDA and PrunIT

Approximation with CNNs in Sobolev Space: with Applications to Classification

Tracking Functional Changes in Nonstationary Signals with Evolutionary Ensemble Bayesian Model for Robust Neural Decoding

A Unified Convergence Theorem for Stochastic Optimization Methods

On Embeddings for Numerical Features in Tabular Deep Learning

Near-Optimal Collaborative Learning in Bandits

Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces

Iron: Private Inference on Transformers

Towards Disentangling Information Paths with Coded ResNeXt

Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model

Flamingo: a Visual Language Model for Few-Shot Learning

Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification

ZooD: Exploiting Model Zoo for Out-of-Distribution Generalization

Torsional Diffusion for Molecular Conformer Generation

Beyond Time-Average Convergence: Near-Optimal Uncoupled Online Learning via Clairvoyant Multiplicative Weights Update

Approximate Euclidean lengths and distances beyond Johnson-Lindenstrauss

A consistently adaptive trust-region method

Order-Invariant Cardinality Estimators Are Differentially Private

Spectral Bias in Practice: The Role of Function Frequency in Generalization

Task-level Differentially Private Meta Learning

Distributed Inverse Constrained Reinforcement Learning for Multi-agent Systems

WaveBound: Dynamic Error Bounds for Stable Time Series Forecasting

Self-Supervised Learning of Brain Dynamics from Broad Neuroimaging Data

Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs

Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization

Log-Polar Space Convolution Layers

Efficient Training of Low-Curvature Neural Networks

Self-Supervised Fair Representation Learning without Demographics

Nonlinear MCMC for Bayesian Machine Learning

Scale-invariant Learning by Physics Inversion

On Non-Linear operators for Geometric Deep Learning

A Geometric Perspective on Variational Autoencoders

Contrastive Graph Structure Learning via Information Bottleneck for Recommendation

Iterative Structural Inference of Directed Graphs

PDSketch: Integrated Domain Programming, Learning, and Planning

Off-Policy Evaluation with Policy-Dependent Optimization Response

Interpolation and Regularization for Causal Learning

Confidence-based Reliable Learning under Dual Noises

Dynamic Inverse Reinforcement Learning for Characterizing Animal Behavior

DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing

Black-Box Generalization: Stability of Zeroth-Order Learning

Label-Aware Global Consistency for Multi-Label Learning with Single Positive Labels

Emergent Communication: Generalization and Overfitting in Lewis Games

Latent Planning via Expansive Tree Search

Near-Optimal Regret for Adversarial MDP with Delayed Bandit Feedback

RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer

The Phenomenon of Policy Churn

Optimal-er Auctions through Attention

Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space

Defending Against Adversarial Attacks via Neural Dynamic System

Association Graph Learning for Multi-Task Classification with Category Shifts

Weakly Supervised Representation Learning with Sparse Perturbations

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Learning Superpoint Graph Cut for 3D Instance Segmentation

First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization

CyCLIP: Cyclic Contrastive Language-Image Pretraining

Amortized Inference for Heterogeneous Reconstruction in Cryo-EM

Neural Stochastic PDEs: Resolution-Invariant Learning of Continuous Spatiotemporal Dynamics

Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent

Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF

An $\alpha$-No-Regret Algorithm For Graphical Bilinear Bandits

Perfect Sampling from Pairwise Comparisons

Value Function Decomposition for Iterative Design of Reinforcement Learning Agents

Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations

VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming

Conformal Off-Policy Prediction in Contextual Bandits

Constrained Update Projection Approach to Safe Policy Optimization

Global Linear and Local Superlinear Convergence of IRLS for Non-Smooth Robust Regression

A Fourier Approach to Mixture Learning

LECO: Learnable Episodic Count for Task-Specific Intrinsic Reward

Domain Generalization without Excess Empirical Risk

Navigating Memory Construction by Global Pseudo-Task Simulation for Continual Learning

Optimal Transport of Classifiers to Fairness

FedSR: A Simple and Effective Domain Generalization Method for Federated Learning

Using Partial Monotonicity in Submodular Maximization

When Do Flat Minima Optimizers Work?

Revisiting Non-Parametric Matching Cost Volumes for Robust and Generalizable Stereo Matching

Large-scale Optimization of Partial AUC in a Range of False Positive Rates

Learning in Congestion Games with Bandit Feedback

TreeMoCo: Contrastive Neuron Morphology Representation Learning

Near-Optimal Sample Complexity Bounds for Constrained MDPs

Fairness Transferability Subject to Bounded Distribution Shift

The Burer-Monteiro SDP method can fail even above the Barvinok-Pataki bound

WeightedSHAP: analyzing and improving Shapley based feature attributions

How to talk so AI will learn: Instructions, descriptions, and autonomy

Improved Algorithms for Neural Active Learning

Global Convergence of Direct Policy Search for State-Feedback $\mathcal{H}_\infty$ Robust Control: A Revisit of Nonsmooth Synthesis with Goldstein Subdifferential

Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network

Bayesian inference via sparse Hamiltonian flows

On Batch Teaching with Sample Complexity Bounded by VCD

AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments

Model-based Lifelong Reinforcement Learning with Bayesian Exploration

projUNN: efficient method for training deep networks with unitary matrices

Staggered Rollout Designs Enable Causal Inference Under Interference Without Network Knowledge

KERPLE: Kernelized Relative Positional Embedding for Length Extrapolation

An Information-Theoretic Framework for Deep Learning

ORIENT: Submodular Mutual Information Measures for Data Subset Selection under Distribution Shift

Insights into Pre-training via Simpler Synthetic Tasks

Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation

Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions

Density-driven Regularization for Out-of-distribution Detection

Rapid Model Architecture Adaption for Meta-Learning

Finding Correlated Equilibrium of Constrained Markov Game: A Primal-Dual Approach

Hyperbolic Embedding Inference for Structured Multi-Label Prediction

AutoMTL: A Programming Framework for Automating Efficient Multi-Task Learning

Wavelet Feature Maps Compression for Image-to-Image CNNs

CoPur: Certifiably Robust Collaborative Inference via Feature Purification

Interventions, Where and How? Experimental Design for Causal Models at Scale

Efficient Non-Parametric Optimizer Search for Diverse Tasks

Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers

Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis

Scaling Multimodal Pre-Training via Cross-Modality Gradient Harmonization

A Character-Level Length-Control Algorithm for Non-Autoregressive Sentence Summarization

The Privacy Onion Effect: Memorization is Relative

Recursive Reasoning in Minimax Games: A Level $k$ Gradient Play Method

Deep Ensembles Work, But Are They Necessary?

Tight Lower Bounds on Worst-Case Guarantees for Zero-Shot Learning with Attributes

Variational Model Perturbation for Source-Free Domain Adaptation

Generative multitask learning mitigates target-causing confounding

Draft-and-Revise: Effective Image Generation with Contextual RQ-Transformer

Acceleration in Distributed Sparse Regression

Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium

Repairing Neural Networks by Leaving the Right Past Behind

[Re] Privacy-preserving collaborative learning with automatic transformation search

Sequence Model Imitation Learning with Unobserved Contexts

GULP: a prediction-based metric between representations

Efficient Frameworks for Generalized Low-Rank Matrix Bandit Problems

Composition Theorems for Interactive Differential Privacy

On the Global Convergence Rates of Decentralized Softmax Gradient Play in Markov Potential Games

Robust Generalized Method of Moments: A Finite Sample Viewpoint

Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness

Regret Bounds for Risk-Sensitive Reinforcement Learning

Semi-supervised Active Linear Regression

Near-Isometric Properties of Kronecker-Structured Random Tensor Embeddings

Riemannian Diffusion Models

Towards Safe Reinforcement Learning with a Safety Editor Policy

On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach

The Implicit Delta Method

Bellman Residual Orthogonalization for Offline Reinforcement Learning

Meta-Learning Dynamics Forecasting Using Task Inference

On Scrambling Phenomena for Randomly Initialized Recurrent Networks

Data-Efficient Augmentation for Training Neural Networks

Beyond black box densities: Parameter learning for the deviated components

Robust Learning against Relational Adversaries

Policy Optimization for Markov Games: Unified Framework and Faster Convergence

Continuously Tempered PDMP samplers

Uncalibrated Models Can Improve Human-AI Collaboration

Few-Shot Non-Parametric Learning with Deep Latent Variable Model

Emergent Graphical Conventions in a Visual Communication Game

Chain of Thought Imitation with Procedure Cloning

Conformalized Fairness via Quantile Regression

Improving Self-Supervised Learning by Characterizing Idealized Representations

Learning Options via Compression

Rapidly Mixing Multiple-try Metropolis Algorithms for Model Selection Problems

Understanding Hyperdimensional Computing for Parallel Single-Pass Learning

Functional Indirection Neural Estimator for Better Out-of-distribution Generalization

Few-shot Learning for Feature Selection with Hilbert-Schmidt Independence Criterion

Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty

Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity

Active Learning Polynomial Threshold Functions

Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions

Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse

An Analysis of Ensemble Sampling

Towards Understanding the Condensation of Neural Networks at Initial Training

Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting

Optimal Scaling for Locally Balanced Proposals in Discrete Spaces

End-to-end Algorithm Synthesis with Recurrent Networks: Extrapolation without Overthinking

Domain Adaptation meets Individual Fairness. And they get along.

Free Probability for predicting the performance of feed-forward fully connected neural networks

Conformal Prediction with Temporal Quantile Adjustments

Using natural language and program abstractions to instill human inductive biases in machines

Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning

Neurosymbolic Deep Generative Models for Sequence Data with Relational Constraints

Polynomial time guarantees for the Burer-Monteiro method

Scalable design of Error-Correcting Output Codes using Discrete Optimization with Graph Coloring

On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds

Nest Your Adaptive Algorithm for Parameter-Agnostic Nonconvex Minimax Optimization

Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videos

Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization

Physics-Informed Implicit Representations of Equilibrium Network Flows

Learning Generalized Policy Automata for Relational Stochastic Shortest Path Problems

Simplified Graph Convolution with Heterophily

DMAP: a Distributed Morphological Attention Policy for learning to locomote with a changing body

Byzantine-tolerant federated Gaussian process regression for streaming data

Distributionally Adaptive Meta Reinforcement Learning

Submodular Maximization in Clean Linear Time

Amortized Proximal Optimization

On Learning Fairness and Accuracy on Multiple Subgroups

HUMUS-Net: Hybrid Unrolled Multi-scale Network Architecture for Accelerated MRI Reconstruction

On the Symmetries of Deep Learning Models and their Internal Representations

Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees

Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits

Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal

FourierFormer: Transformer Meets Generalized Fourier Integral Theorem

In What Ways Are Deep Neural Networks Invariant and How Should We Measure This?

Faster and Scalable Algorithms for Densest Subgraph and Decomposition

Co-Modality Graph Contrastive Learning for Imbalanced Node Classification

ACIL: Analytic Class-Incremental Learning with Absolute Memorization and Privacy Protection

Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity

Non-Linguistic Supervision for Contrastive Learning of Sentence Embeddings

A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models

A General Framework for Auditing Differentially Private Machine Learning

Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification

Pruning’s Effect on Generalization Through the Lens of Training and Regularization

Kernel similarity matching with Hebbian networks

QC-StyleGAN - Quality Controllable Image Generation and Manipulation

Human-AI Shared Control via Policy Dissection

Label-invariant Augmentation for Semi-Supervised Graph Classification

Preservation of the Global Knowledge by Not-True Distillation in Federated Learning

Using Embeddings for Causal Estimation of Peer Influence in Social Networks

A Unifying Framework of Off-Policy General Value Function Evaluation

TaSIL: Taylor Series Imitation Learning

Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion Perspective

VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely?

LISA: Learning Interpretable Skill Abstractions from Language

NSNet: A General Neural Probabilistic Framework for Satisfiability Problems

Model Preserving Compression for Neural Networks

Effects of Data Geometry in Early Deep Learning

Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model

Tight Mutual Information Estimation With Contrastive Fenchel-Legendre Optimization

Pre-Trained Model Reusability Evaluation for Small-Data Transfer Learning

Graph Few-shot Learning with Task-specific Structures

Offline Goal-Conditioned Reinforcement Learning via $f$-Advantage Regression

Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great Again

Faster Deep Reinforcement Learning with Slower Online Network

An Asymptotically Optimal Batched Algorithm for the Dueling Bandit Problem

Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer

Distributed Distributionally Robust Optimization with Non-Convex Objectives

Continuous Deep Q-Learning in Optimal Control Problems: Normalized Advantage Functions Analysis

Merging Models with Fisher-Weighted Averaging

Path Independent Equilibrium Models Can Better Exploit Test-Time Computation

Private Graph All-Pairwise-Shortest-Path Distance Release with Improved Error Rate

A Theory of PAC Learnability under Transformation Invariances

Global Convergence of Federated Learning for Mixed Regression

Segmenting Moving Objects via an Object-Centric Layered Representation

Invariance Learning based on Label Hierarchy

Online Algorithms for the Santa Claus Problem

Federated Learning from Pre-Trained Models: A Contrastive Learning Approach

When are Offline Two-Player Zero-Sum Markov Games Solvable?

Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks

Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus

Implicit Bias of Gradient Descent on Reparametrized Models: On Equivalence to Mirror Descent

Few-Shot Audio-Visual Learning of Environment Acoustics

Redundancy-Free Message Passing for Graph Neural Networks

SemMAE: Semantic-Guided Masking for Learning Masked Autoencoders

Weighted Distillation with Unlabeled Examples

Mixture-of-Experts with Expert Choice Routing

The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models

Diffusion-LM Improves Controllable Text Generation

Self-Supervised Pretraining for Large-Scale Point Clouds

Invariant and Transportable Representations for Anti-Causal Domain Shifts

Sparsity in Continuous-Depth Neural Networks

A Variational Edge Partition Model for Supervised Graph Representation Learning

A Simple Approach to Automated Spectral Clustering

Point Transformer V2: Grouped Vector Attention and Partition-based Pooling

Accelerated Training of Physics-Informed Neural Networks (PINNs) using Meshless Discretizations

Fault-Aware Neural Code Rankers

PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization

A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal

Off-Policy Evaluation for Episodic Partially Observable Markov Decision Processes under Non-Parametric Models

Learning Symmetric Rules with SATNet

Consistent Interpolating Ensembles via the Manifold-Hilbert Kernel

Get More at Once: Alternating Sparse Training with Gradient Correction

Learning Fractional White Noises in Neural Stochastic Differential Equations

“Why Not Other Classes?”: Towards Class-Contrastive Back-Propagation Explanations

Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power

Training with More Confidence: Mitigating Injected and Natural Backdoors During Training

Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments

Batch Multi-Fidelity Active Learning with Budget Constraints

Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness

Time-Conditioned Dances with Simplicial Complexes: Zigzag Filtration Curve based Supra-Hodge Convolution Networks for Time-series Forecasting

Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data

Integral Probability Metrics PAC-Bayes Bounds

Skills Regularized Task Decomposition for Multi-task Offline Reinforcement Learning

M2N: Mesh Movement Networks for PDE Solvers

Exploiting the Relationship Between Kendall's Rank Correlation and Cosine Similarity for Attribution Protection

Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation

Gaussian Copula Embeddings

Transferring Pre-trained Multimodal Representations with Cross-modal Similarity Matching

CoNSoLe: Convex Neural Symbolic Learning

Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees

Meta-Auto-Decoder for Solving Parametric Partial Differential Equations

Non-Stationary Bandits under Recharging Payoffs: Improved Planning with Sublinear Regret

Long-Form Video-Language Pre-Training with Multimodal Temporal Contrastive Learning

PlasticityNet: Learning to Simulate Metal, Sand, and Snow for Optimization Time Integration

[Re] Value Alignment Verification

Nearly-Tight Bounds for Testing Histogram Distributions

Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination

Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture

Reinforcement Learning with Automated Auxiliary Loss Search

Tractable Function-Space Variational Inference in Bayesian Neural Networks

Are all Frames Equal? Active Sparse Labeling for Video Action Detection

Unsupervised Learning under Latent Label Shift

You Can’t Count on Luck: Why Decision Transformers and RvS Fail in Stochastic Environments

Provable Subspace Identification Under Post-Nonlinear Mixtures

Truly Deterministic Policy Optimization

Active Learning Helps Pretrained Models Learn the Intended Task

A Consolidated Cross-Validation Algorithm for Support Vector Machines via Data Reduction

Giving Feedback on Interactive Student Programs with Meta-Exploration

On Leave-One-Out Conditional Mutual Information For Generalization

High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation

Global Optimal K-Medoids Clustering of One Million Samples

A Scalable Deterministic Global Optimization Algorithm for Training Optimal Decision Tree

What Can Transformers Learn In-Context? A Case Study of Simple Function Classes

GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis

Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning

Data-Driven Offline Decision-Making via Invariant Representation Learning

When Does Group Invariant Learning Survive Spurious Correlations?

On Elimination Strategies for Bandit Fixed-Confidence Identification

So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems

Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels

DHRL: A Graph-Based Approach for Long-Horizon and Sparse Hierarchical Reinforcement Learning

Improving Diffusion Models for Inverse Problems using Manifold Constraints

DARE: Disentanglement-Augmented Rationale Extraction

Symmetry-induced Disentanglement on Graphs

Learning in Observable POMDPs, without Computationally Intractable Oracles

When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning

Spherization Layer: Representation Using Only Angles

Grounded Reinforcement Learning: Learning to Win the Game under Human Commands

How Powerful are K-hop Message Passing Graph Neural Networks

MEMO: Test Time Robustness via Adaptation and Augmentation

Redundant representations help generalization in wide neural networks

Dynamic Learning in Large Matching Markets

Near-Optimal Goal-Oriented Reinforcement Learning in Non-Stationary Environments

Towards Understanding the Mixture-of-Experts Layer in Deep Learning

A time-resolved theory of information encoding in recurrent neural networks

Coresets for Relational Data and The Applications

Coresets for Wasserstein Distributionally Robust Optimization Problems

Lazy and Fast Greedy MAP Inference for Determinantal Point Process

FlowHMM: Flow-based continuous hidden Markov models

Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification

An Adaptive Deep RL Method for Non-Stationary Environments with Piecewise Stable Context

Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spaces

Imbalance Trouble: Revisiting Neural-Collapse Geometry

Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties

How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders

WT-MVSNet: Window-based Transformers for Multi-view Stereo

Models Out of Line: A Fourier Lens on Distribution Shift Robustness

SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction

Chromatic Correlation Clustering, Revisited

A Reduction to Binary Approach for Debiasing Multiclass Datasets

MetricFormer: A Unified Perspective of Correlation Exploring in Similarity Learning

Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays

Revisiting Neural Scaling Laws in Language and Vision

Towards Consistency in Adversarial Classification

Last-Iterate Convergence of Optimistic Gradient Method for Monotone Variational Inequalities

Graph Convolution Network based Recommender Systems: Learning Guarantee and Item Mixture Powered Strategy

Revisit last-iterate convergence of mSGD under milder requirement on step size

Non-Monotonic Latent Alignments for CTC-Based Non-Autoregressive Machine Translation

Joint Learning of 2D-3D Weakly Supervised Semantic Segmentation

Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction

Optimal Positive Generation via Latent Transformation for Contrastive Learning

Neural-Symbolic Entangled Framework for Complex Query Answering

Multiagent Q-learning with Sub-Team Coordination

Sound and Complete Verification of Polynomial Networks

Laplacian Autoencoders for Learning Stochastic Representations

Oracle Inequalities for Model Selection in Offline Reinforcement Learning

Revisiting Active Sets for Gaussian Process Decoders

Bounding and Approximating Intersectional Fairness through Marginal Fairness

MAtt: A Manifold Attention Network for EEG Decoding

BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis

Collaborative Decision Making Using Action Suggestions

Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution

A gradient estimator via L1-randomization for online zero-order optimization with two point feedback

Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization

Selective compression learning of latent representations for variable-rate image compression

Neural Network Architecture Beyond Width and Depth

On the relationship between variational inference and auto-associative memory

Sparse Probabilistic Circuits via Pruning and Growing

When to Intervene: Learning Optimal Intervention Policies for Critical Events

Smoothed Embeddings for Certified Few-Shot Learning

An Analytical Theory of Curriculum Learning in Teacher-Student Networks

Black-box coreset variational inference

Distilling Representations from GAN Generator via Squeeze and Span

Generalization Analysis of Message Passing Neural Networks on Large Random Graphs

Meta-Learning with Self-Improving Momentum Target

Reinforcement Learning in a Birth and Death Process: Breaking the Dependence on the State Space

Sequence-to-Set Generative Models

What Makes Graph Neural Networks Miscalibrated?

A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models

Consistency of Constrained Spectral Clustering under Graph Induced Fair Planted Partitions

A Regret-Variance Trade-Off in Online Learning

Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning

Not too little, not too much: a theoretical analysis of graph (over)smoothing

On A Mallows-type Model For (Ranked) Choices

Inverse Design for Fluid-Structure Interactions using Graph Network Simulators

Towards a Standardised Performance Evaluation Protocol for Cooperative MARL

On the Learning Mechanisms in Physical Reasoning

Joint Entropy Search For Maximally-Informed Bayesian Optimization

Benign Overfitting in Two-layer Convolutional Neural Networks

Proximal Point Imitation Learning

On the Robustness of Graph Neural Diffusion to Topology Perturbations

Power and limitations of single-qubit native quantum neural networks

A Characterization of Semi-Supervised Adversarially Robust PAC Learnability

Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix Completion

Accelerated Primal-Dual Gradient Method for Smooth and Convex-Concave Saddle-Point Problems with Bilinear Coupling

Distilled Gradient Aggregation: Purify Features for Input Attribution in the Deep Neural Network

Sequential Information Design: Learning to Persuade in the Dark

Optimal Weak to Strong Learning

Unsupervised Learning of Group Invariant and Equivariant Representations

On the Approximation of Cooperative Heterogeneous Multi-Agent Reinforcement Learning (MARL) using Mean Field Control (MFC)

Estimating the Arc Length of the Optimal ROC Curve and Lower Bounding the Maximal AUC

Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders

A Reparametrization-Invariant Sharpness Measure Based on Information Geometry

Bayesian Active Learning with Fully Bayesian Gaussian Processes

Log-Concave and Multivariate Canonical Noise Distributions for Differential Privacy

On Measuring Excess Capacity in Neural Networks

General Cutting Planes for Bound-Propagation-Based Neural Network Verification

Unsupervised Adaptation from Repeated Traversals for Autonomous Driving

Fine-Grained Semantically Aligned Vision-Language Pre-Training

On Sample Optimality in Personalized Collaborative and Federated Learning

Using Mixup as a Regularizer Can Surprisingly Improve Accuracy & Out-of-Distribution Robustness

Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning

A Variant of Anderson Mixing with Minimal Memory Size

Augmented RBMLE-UCB Approach for Adaptive Control of Linear Quadratic Systems

Improved techniques for deterministic l2 robustness

Real-Valued Backpropagation is Unsuitable for Complex-Valued Neural Networks

Anonymized Histograms in Intermediate Privacy Models

Relaxing Equivariance Constraints with Non-stationary Continuous Filters

MCL-GAN: Generative Adversarial Networks with Multiple Specialized Discriminators

Sparse Gaussian Process Hyperparameters: Optimize or Integrate?

Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch

Learning to Branch with Tree MDPs

Fine-tuning Language Models over Slow Networks using Activation Quantization with Guarantees

Probing Classifiers are Unreliable for Concept Removal and Detection

Graph Learning Assisted Multi-Objective Integer Programming

Randomized Sketches for Clustering: Fast and Optimal Kernel $k$-Means

Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data

Certifying Robust Graph Classification under Orthogonal Gromov-Wasserstein Threats

On the Representation Collapse of Sparse Mixture of Experts

Information bottleneck theory of high-dimensional regression: relevancy, efficiency and optimality

Statistically Meaningful Approximation: a Case Study on Approximating Turing Machines with Transformers

A Data-Augmentation Is Worth A Thousand Samples: Analytical Moments And Sampling-Free Training

Partial Identification of Treatment Effects with Implicit Generative Models

Learning Neural Acoustic Fields

Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning

Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions

A contrastive rule for meta-learning

Meta Reinforcement Learning with Finite Training Tasks - a Density Estimation Approach

A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions

Regularized Molecular Conformation Fields

You Never Stop Dancing: Non-freezing Dance Generation via Bank-constrained Manifold Projection

Risk-Driven Design of Perception Systems

Langevin Autoencoders for Learning Deep Latent Variable Models

Neural Estimation of Submodular Functions with Applications to Differentiable Subset Selection

Learning on Arbitrary Graph Topologies via Predictive Coding

Multi-Lingual Acquisition on Multimodal Pre-training for Cross-modal Retrieval

Semantic Exploration from Language Abstractions and Pretrained Representations

A Unified Sequence Interface for Vision Tasks

Is Integer Arithmetic Enough for Deep Learning Training?

Confident Adaptive Language Modeling

Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces

3D Concept Grounding on Neural Fields

A Solver-free Framework for Scalable Learning in Neural ILP Architectures

Unsupervised Object Representation Learning using Translation and Rotation Group Equivariant VAE

Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis

Luckiness in Multiscale Online Learning

Effective Dimension in Bandit Problems under Censorship

In Defense of the Unitary Scalarization for Deep Multi-Task Learning

Beyond IID: data-driven decision-making in heterogeneous environments

Scalable Multi-agent Covering Option Discovery based on Kronecker Graphs

Private Multiparty Perception for Navigation

Group Meritocratic Fairness in Linear Contextual Bandits

Deep Equilibrium Approaches to Diffusion Models

Addressing Leakage in Concept Bottleneck Models

Evolution of Neural Tangent Kernels under Benign and Adversarial Training

The least-control principle for local learning at equilibrium

Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps

Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers

PhysGNN: A Physics--Driven Graph Neural Network Based Model for Predicting Soft Tissue Deformation in Image--Guided Neurosurgery

Archimedes Meets Privacy: On Privately Estimating Quantiles in High Dimensions Under Minimal Assumptions

Better SGD using Second-order Momentum

Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales

DevFly: Bio-Inspired Development of Binary Connections for Locality Preserving Sparse Codes

Multi-agent Dynamic Algorithm Configuration

Predictive Coding beyond Gaussian Distributions

Jump Self-attention: Capturing High-order Statistics in Transformers

Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations

RISE: Robust Individualized Decision Learning with Sensitive Variables

Efficient and Stable Fully Dynamic Facility Location

Envy-free Policy Teaching to Multiple Agents

VaiPhy: a Variational Inference Based Algorithm for Phylogeny

Active Learning with Safety Constraints

Trustworthy Monte Carlo

Learning-Augmented Algorithms for Online Linear and Semidefinite Programming

Near-Optimal Correlation Clustering with Privacy

Neural Attentive Circuits

Intra-agent speech permits zero-shot task acquisition

MACK: Multimodal Aligned Conceptual Knowledge for Unpaired Image-text Matching

Robustness to Label Noise Depends on the Shape of the Noise Distribution

A Theoretical Study on Solving Continual Learning

Anytime-Valid Inference For Multinomial Count Data

Scalable and Efficient Non-adaptive Deterministic Group Testing

Hierarchical Agglomerative Graph Clustering in Poly-Logarithmic Depth

Variable-rate hierarchical CPC leads to acoustic unit discovery in speech

SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression

Contextual Dynamic Pricing with Unknown Noise: Explore-then-UCB Strategy and Improved Regrets

Distributed Online Convex Optimization with Compressed Communication

GlanceNets: Interpretable, Leak-proof Concept-based Models

BEER: Fast $O(1/T)$ Rate for Decentralized Nonconvex Optimization with Communication Compression

On the Effectiveness of Persistent Homology

The Effects of Regularization and Data Augmentation are Class Dependent

On the Stability and Scalability of Node Perturbation Learning

Trimmed Maximum Likelihood Estimation for Robust Generalized Linear Model

Benefits of Additive Noise in Composing Classes with Bounded Capacity

EZNAS: Evolving Zero-Cost Proxies For Neural Architecture Scoring

Proppo: a Message Passing Framework for Customizable and Composable Learning Algorithms

Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees

Tempo: Accelerating Transformer-Based Model Training through Memory Footprint Reduction

CS-Shapley: Class-wise Shapley Values for Data Valuation in Classification

A New Family of Generalization Bounds Using Samplewise Evaluated CMI

Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations

On the Adversarial Robustness of Mixture of Experts

Graph Neural Networks are Dynamic Programmers

K-LITE: Learning Transferable Visual Models with External Knowledge

Mesoscopic modeling of hidden spiking neurons

Self-Supervised Learning Through Efference Copies

Self-Explaining Deviations for Coordination

Multi-Objective Deep Learning with Adaptive Reference Vectors

Overparameterization from Computational Constraints

AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning

Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning

On the Generalization Power of the Overfitted Three-Layer Neural Tangent Kernel Model

Provably Adversarially Robust Detection of Out-of-Distribution Data (Almost) for Free

Modular Flows: Differential Molecular Generation

Bridging Central and Local Differential Privacy in Data Acquisition Mechanisms

PAC Prediction Sets for Meta-Learning

Diffusion Models as Plug-and-Play Priors

MorphTE: Injecting Morphology in Tensorized Embeddings

Trajectory balance: Improved credit assignment in GFlowNets

On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond

Task-Free Continual Learning via Online Discrepancy Distance Learning

Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams

Evaluation beyond Task Performance: Analyzing Concepts in AlphaZero in Hex

Benchopt: Reproducible, efficient and collaborative optimization benchmarks

RNNs of RNNs: Recursive Construction of Stable Assemblies of Recurrent Neural Networks

Nonparametric Uncertainty Quantification for Single Deterministic Neural Network

Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints

Discovering and Overcoming Limitations of Noise-engineered Data-free Knowledge Distillation

Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation

SQ Lower Bounds for Learning Single Neurons with Massart Noise

Meta-Reward-Net: Implicitly Differentiable Reward Learning for Preference-based Reinforcement Learning

Average Sensitivity of Euclidean k-Clustering

A theory of weight distribution-constrained learning

Data augmentation for efficient learning from parametric experts

Active Bayesian Causal Inference

Template based Graph Neural Network with Optimal Transport Distances

Outlier-Robust Sparse Estimation via Non-Convex Optimization

Toward Understanding Privileged Features Distillation in Learning-to-Rank

The Neural Covariance SDE: Shaped Infinite Depth-and-Width Networks at Initialization

FP8 Quantization: The Power of the Exponent

Maximizing Revenue under Market Shrinkage and Market Uncertainty

UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification

Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts

DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning

Structure-Aware Image Segmentation with Homotopy Warping

Deep Learning Methods for Proximal Inference via Maximum Moment Restriction

On global convergence of ResNets: From finite to infinite width using linear parameterization

Residual Multiplicative Filter Networks for Multiscale Reconstruction

Reinforcement Learning with Non-Exponential Discounting

Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors' Reasoning with Deep Reinforcement Learning

On the symmetries of the synchronization problem in Cryo-EM: Multi-Frequency Vector Diffusion Maps on the Projective Plane

A Theoretical View on Sparsely Activated Networks

Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors

Implications of Model Indeterminacy for Explanations of Automated Decisions

NOMAD: Nonlinear Manifold Decoders for Operator Learning

Characterizing the Ventral Visual Stream with Response-Optimized Neural Encoding Models

How Sampling Impacts the Robustness of Stochastic Neural Networks

Forward-Backward Latent State Inference for Hidden Continuous-Time semi-Markov Chains

Shape And Structure Preserving Differential Privacy

On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning

Dynamic Pricing with Monotonicity Constraint under Unknown Parametric Demand Model

Cross-Linked Unified Embedding for cross-modality representation learning

Active Ranking without Strong Stochastic Transitivity

ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model

The Mechanism of Prediction Head in Non-contrastive Self-supervised Learning

Task Discovery: Finding the Tasks that Neural Networks Generalize on

Chaotic Regularization and Heavy-Tailed Limits for Deterministic Gradient Descent

LOT: Layer-wise Orthogonal Training on Improving l2 Certified Robustness

Few-Shot Fast-Adaptive Anomaly Detection

Learning dynamics of deep linear networks with multiple pathways

Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers

Multi-fidelity Monte Carlo: a pseudo-marginal approach

Learning sparse features can lead to overfitting in neural networks

Pushing the limits of fairness impossibility: Who's the fairest of them all?

Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions

Zonotope Domains for Lagrangian Neural Network Verification

Safety Guarantees for Neural Network Dynamic Systems via Stochastic Barrier Functions

Online Bipartite Matching with Advice: Tight Robustness-Consistency Tradeoffs for the Two-Stage Model

Improving Multi-Task Generalization via Regularizing Spurious Correlation

Operative dimensions in unconstrained connectivity of recurrent neural networks

Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules

Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Bounds

Generating Training Data with Language Models: Towards Zero-Shot Language Understanding

Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank

Towards Practical Few-shot Query Sets: Transductive Minimum Description Length Inference

Randomized Channel Shuffling: Minimal-Overhead Backdoor Attack Detection without Clean Datasets

MAgNet: Mesh Agnostic Neural PDE Solver

Online Learning and Pricing for Network Revenue Management with Reusable Resources

Learning Modular Simulations for Homogeneous Systems

Instability and Local Minima in GAN Training with Kernel Discriminators

On Computing Probabilistic Explanations for Decision Trees

Distributed Optimization for Overparameterized Problems: Achieving Optimal Dimension Independent Communication Complexity

Lost in Latent Space: Examining failures of disentangled models at combinatorial generalisation

When Combinatorial Thompson Sampling meets Approximation Regret

Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models

Detecting Abrupt Changes in Sequential Pairwise Comparison Data

Sparse Fourier Backpropagation in Cryo-EM Reconstruction

When Does Differentially Private Learning Not Suffer in High Dimensions?

A Fast Scale-Invariant Algorithm for Non-negative Least Squares with Non-negative Data

(Optimal) Online Bipartite Matching with Degree Information

Learning from a Sample in Online Algorithms

Data-Driven Conditional Robust Optimization

Linear Label Ranking with Bounded Noise

Estimation of Entropy in Constant Space with Improved Sample Complexity

Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuits

Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning

Robust Neural Posterior Estimation and Statistical Model Criticism

CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference

The Missing Invariance Principle found -- the Reciprocal Twin of Invariant Risk Minimization

MABSplit: Faster Forest Training Using Multi-Armed Bandits

Marksman Backdoor: Backdoor Attacks with Arbitrary Target Class

Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints

Sample-Efficient Reinforcement Learning of Partially Observable Markov Games

Phase transitions in when feedback is useful

The Role of Baselines in Policy Gradient Optimization

Autoformalization with Large Language Models

Differentially Private Generalized Linear Models Revisited

Learning to Follow Instructions in Text-Based Games

On Learning and Refutation in Noninteractive Local Differential Privacy

Cryptographic Hardness of Learning Halfspaces with Massart Noise

Instance-optimal PAC Algorithms for Contextual Bandits

Do Current Multi-Task Optimization Methods in Deep Learning Even Help?

Prompt Certified Machine Unlearning with Randomized Gradient Smoothing and Quantization

Unsupervised Reinforcement Learning with Contrastive Intrinsic Control

Exact learning dynamics of deep linear networks with prior knowledge

Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency

Regret Bounds for Multilabel Classification in Sparse Label Regimes

Characterizing Datapoints via Second-Split Forgetting

Training language models to follow instructions with human feedback

S4ND: Modeling Images and Videos as Multidimensional Signals with State Spaces

Defining and Characterizing Reward Gaming

Adversarial training for high-stakes reliability

Semantic Probabilistic Layers for Neuro-Symbolic Learning

WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents

Maximizing and Satisficing in Multi-armed Bandits with Graph Information

Learning Probabilistic Models from Generator Latent Spaces with Hat EBM

Spherical Channels for Modeling Atomic Interactions

HyperTree Proof Search for Neural Theorem Proving

Exploring the Latent Space of Autoencoders with Interventional Assays

Root Cause Analysis of Failures in Microservices through Causal Discovery

Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks

Finite-Sample Maximum Likelihood Estimation of Location

BayesPCN: A Continually Learnable Predictive Coding Associative Memory

On the detrimental effect of invariances in the likelihood for variational inference

Learning to Compare Nodes in Branch and Bound with Graph Neural Networks

Parameter-free Regret in High Probability with Heavy Tails

Multi-Game Decision Transformers

Structural Pruning via Latency-Saliency Knapsack

The Query Complexity of Cake Cutting

Best of Both Worlds Model Selection

Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation

New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound

Memory safe computations with XLA compiler

Learning NP-Hard Multi-Agent Assignment Planning using GNN: Inference on a Random Graph and Provable Auction-Fitted Q-learning

Fairness in Federated Learning via Core-Stability

Accelerating Certified Robustness Training via Knowledge Transfer

Certifying Some Distributional Fairness with Subpopulation Decomposition

A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation

Sublinear Algorithms for Hierarchical Clustering

A Deep Reinforcement Learning Framework for Column Generation

Logical Activation Functions: Logit-space equivalents of Probabilistic Boolean Operators

EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RL

End-to-end Stochastic Optimization with Energy-based Model

ReCo: Retrieve and Co-segment for Zero-shot Transfer

Human-Robotic Prosthesis as Collaborating Agents for Symmetrical Walking

Adaptive Interest for Emphatic Reinforcement Learning

Chaotic Dynamics are Intrinsic to Neural Network Training with SGD

Local Bayesian optimization via maximizing probability of descent

Learning the Structure of Large Networked Systems Obeying Conservation Laws

Near-Optimal No-Regret Learning Dynamics for General Convex Games

The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning

A Practical, Progressively-Expressive GNN

ELIGN: Expectation Alignment as a Multi-Agent Intrinsic Reward

Provably tuning the ElasticNet across instances

Fast Neural Kernel Embeddings for General Activations

Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts

Simple and Optimal Greedy Online Contention Resolution Schemes

Modeling Transitivity and Cyclicity in Directed Graphs via Binary Code Box Embeddings

Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction

Decoupled Context Processing for Context Augmented Language Modeling

Efficiency Ordering of Stochastic Gradient Descent

Robust Streaming PCA

Learning Partial Equivariances From Data

[Re] Lifting 2D StyleGAN for 3D-Aware Face Generation

FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting

Unsupervised Causal Generative Understanding of Images

Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation

Fair Ranking with Noisy Protected Attributes

Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models

Zero-Sum Stochastic Stackelberg Games

Can Hybrid Geometric Scattering Networks Help Solve the Maximum Clique Problem?

NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation

Mining Multi-Label Samples from Single Positive Labels

Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters

Efficient Phi-Regret Minimization in Extensive-Form Games via Online Mirror Descent

Exponential Family Model-Based Reinforcement Learning via Score Matching

Object Scene Representation Transformer

Geometric Order Learning for Rank Estimation

Learning with convolution and pooling operations in kernel methods

Dataset Distillation using Neural Feature Regression

Influencing Long-Term Behavior in Multiagent Reinforcement Learning

Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithm

Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search

Learning Contrastive Embedding in Low-Dimensional Space

Exploring Example Influence in Continual Learning

JAWS: Auditing Predictive Uncertainty Under Covariate Shift

One for All: Simultaneous Metric and Preference Learning over Multiple Users

Paraphrasing Is All You Need for Novel Object Captioning

Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative

Multiview Human Body Reconstruction from Uncalibrated Cameras

FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning

Empirical Gateaux Derivatives for Causal Inference

AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators

Benefits of Permutation-Equivariance in Auction Mechanisms

Learning Active Camera for Multi-Object Navigation

Toward Efficient Robust Training against Union of $\ell_p$ Threat Models

Mask Matching Transformer for Few-Shot Segmentation

A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs

Symplectic Spectrum Gaussian Processes: Learning Hamiltonians from Noisy and Sparse Data

GREED: A Neural Framework for Learning Graph Distance Functions

Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty

Consistent Sufficient Explanations and Minimal Local Rules for explaining the decision of any classifier or regressor

DaDA: Distortion-aware Domain Adaptation for Unsupervised Semantic Segmentation

Learning Optical Flow from Continuous Spike Streams

Retrospective Adversarial Replay for Continual Learning

Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach

On Feature Learning in the Presence of Spurious Correlations

Explaining Preferences with Shapley Values

Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation

ReFactor GNNs: Revisiting Factorisation-based Models from a Message-Passing Perspective

Block-Recurrent Transformers

Hamiltonian Latent Operators for content and motion disentanglement in image sequences

Learning (Very) Simple Generative Models Is Hard

Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction

A Non-asymptotic Analysis of Non-parametric Temporal-Difference Learning

Rethinking Generalization in Few-Shot Classification

VectorAdam for Rotation Equivariant Geometry Optimization

Keypoint-Guided Optimal Transport with Applications in Heterogeneous Domain Adaptation

Supervising the Multi-Fidelity Race of Hyperparameter Configurations

Trajectory of Mini-Batch Momentum: Batch Size Saturation and Convergence in High Dimensions

Single Model Uncertainty Estimation via Stochastic Data Centering

CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers

Graph Neural Networks with Adaptive Readouts

Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport

Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation

Beyond Mahalanobis Distance for Textual OOD Detection

Tensor Program Optimization with Probabilistic Programs

VICE: Variational Interpretable Concept Embeddings

Learning single-index models with shallow neural networks

Near-Optimal Randomized Exploration for Tabular Markov Decision Processes

Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective

LOG: Active Model Adaptation for Label-Efficient OOD Generalization

Structural Knowledge Distillation for Object Detection

Semantic uncertainty intervals for disentangled latent spaces

Uni[MASK]: Unified Inference in Sequential Decision Problems

Bayesian Optimistic Optimization: Optimistic Exploration for Model-based Reinforcement Learning

Invertible Monotone Operators for Normalizing Flows

A Transformer-Based Object Detector with Coarse-Fine Crossing Representations

Distinguishing Learning Rules with Brain Machine Interfaces

Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning

Expected Improvement for Contextual Bandits

BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework

Graph Neural Network Bandits

Mean Estimation with User-level Privacy under Data Heterogeneity

Precise Regret Bounds for Log-loss via a Truncated Bayesian Algorithm

ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints

LobsDICE: Offline Learning from Observation via Stationary Distribution Correction Estimation

360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter Tuning

Reduced Representation of Deformation Fields for Effective Non-rigid Shape Matching

Explain My Surprise: Learning Efficient Long-Term Memory by predicting uncertain outcomes

A Simple and Optimal Policy Design for Online Learning with Safety against Heavy-tailed Risk

Policy Optimization with Linear Temporal Logic Constraints

Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning

Decomposed Knowledge Distillation for Class-Incremental Semantic Segmentation

On the Theoretical Properties of Noise Correlation in Stochastic Optimization

NCP: Neural Correspondence Prior for Effective Unsupervised Shape Matching

ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs

On Divergence Measures for Bayesian Pseudocoresets

Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid

Can Push-forward Generative Models Fit Multimodal Distributions?

Posterior and Computational Uncertainty in Gaussian Processes

MORA: Improving Ensemble Robustness Evaluation with Model Reweighing Attack

Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphs

Advancing Model Pruning via Bi-level Optimization

An Algorithm for Learning Switched Linear Dynamics from Data

Batch Bayesian optimisation via density-ratio estimation with guarantees

An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects

Structuring Uncertainty for Fine-Grained Sampling in Stochastic Segmentation Networks

Multi-objective Deep Data Generation with Correlated Property Control

Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations

Learning Debiased Classifier with Biased Committee

Surprising Instabilities in Training Deep Networks and a Theoretical Analysis

Capturing Failures of Large Language Models via Human Cognitive Biases

Nonnegative Tensor Completion via Integer Optimization

Equivariant Networks for Crystal Structures

LieGG: Studying Learned Lie Group Generators

On-Demand Sampling: Learning Optimally from Multiple Distributions

A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning

Robust Model Selection and Nearly-Proper Learning for GMMs

Combining Explicit and Implicit Regularization for Efficient Learning in Deep Networks

A Unified Framework for Alternating Offline Model Training and Policy Learning

Automatic Differentiation of Programs with Discrete Randomness

Thinned random measures for sparse graphs with overlapping communities

Hybrid Neural Autoencoders for Stimulus Encoding in Visual and Other Sensory Neuroprostheses

Tight Analysis of Extra-gradient and Optimistic Gradient Methods For Nonconvex Minimax Problems

Amortized Inference for Causal Structure Learning

Staircase Attention for Recurrent Processing of Sequences

A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs

Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off

Biologically plausible solutions for spiking networks with efficient coding

Algorithms with Prediction Portfolios

SAGDA: Achieving $\mathcal{O}(\epsilon^{-2})$ Communication Complexity in Federated Min-Max Learning

Deep Compression of Pre-trained Transformer Models

Beyond neural scaling laws: beating power law scaling via data pruning

Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation

Learning Optimal Flows for Non-Equilibrium Importance Sampling

Incentivizing Combinatorial Bandit Exploration

A Simple Decentralized Cross-Entropy Method

Neural Abstractions

Learning Dense Object Descriptors from Multiple Views for Low-shot Category Generalization

Flowification: Everything is a normalizing flow

Non-monotonic Resource Utilization in the Bandits with Knapsacks Problem

Evaluating Robustness to Dataset Shift via Parametric Robustness Sets

Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement

Private and Communication-Efficient Algorithms for Entropy Estimation

Kernel Multimodal Continuous Attention

Stars: Tera-Scale Graph Building for Clustering and Learning

Anonymous Bandits for Multi-User Systems

Understanding Deep Contrastive Learning via Coordinate-wise Optimization

Stochastic Halpern Iteration with Variance Reduction for Stochastic Monotone Inclusions

PALMER: Perception - Action Loop with Memory for Long-Horizon Planning

Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation

Finite Sample Analysis Of Dynamic Regression Parameter Learning

Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization

Improved Coresets for Euclidean $k$-Means

Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data

Distributed Influence-Augmented Local Simulators for Parallel MARL in Large Networked Systems

Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs

Grounded Video Situation Recognition

Learning to Scaffold: Optimizing Model Explanations for Teaching

Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social Text Classification

Efficient Methods for Non-stationary Online Learning

Sustainable Online Reinforcement Learning for Auto-bidding

Effectiveness of Vision Transformer for Fast and Accurate Single-Stage Pedestrian Detection

On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models

Isometric 3D Adversarial Examples in the Physical World

The Hessian Screening Rule

Measuring Data Reconstruction Defenses in Collaborative Inference Systems

A Stochastic Linearized Augmented Lagrangian Method for Decentralized Bilevel Optimization

Kernel Memory Networks: A Unifying Framework for Memory Modeling

A Neural Pre-Conditioning Active Learning Algorithm to Reduce Label Complexity

Flexible Diffusion Modeling of Long Videos

Learning Structure from the Ground up---Hierarchical Representation Learning by Chunking

Meta-Complementing the Semantics of Short Texts in Neural Topic Models

Robust Feature-Level Adversaries are Interpretability Tools

Knowledge-Aware Bayesian Deep Topic Model

GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games

Quantum Algorithms for Sampling Log-Concave Distributions and Estimating Normalizing Constants

Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs

FourierNets enable the design of highly non-local optical encoders for computational imaging

TVLT: Textless Vision-Language Transformer

No Free Lunch from Deep Learning in Neuroscience: A Case Study through Models of the Entorhinal-Hippocampal Circuit

Retaining Knowledge for Learning with Dynamic Definition

XTC: Extreme Compression for Pre-trained Transformers Made Simple and Efficient

PAC: Assisted Value Factorization with Counterfactual Predictions in Multi-Agent Reinforcement Learning

Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts

Compositional Generalization in Unsupervised Compositional Representation Learning: A Study on Disentanglement and Emergent Language

Fairness without Demographics through Knowledge Distillation

Deep Bidirectional Language-Knowledge Graph Pretraining

Rethinking Value Function Learning for Generalization in Reinforcement Learning

Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design

Exposing and Exploiting Fine-Grained Block Structures for Fast and Accurate Sparse Training

Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference

Efficient Dataset Distillation using Random Feature Approximation

Locally Hierarchical Auto-Regressive Modeling for Image Generation

Interaction-Grounded Learning with Action-Inclusive Feedback

AdaFocal: Calibration-aware Adaptive Focal Loss

Convergence for score-based generative modeling with polynomial complexity

Toward Robust Spiking Neural Network Against Adversarial Perturbation

Layer Freezing & Data Sieving: Missing Pieces of a Generic Framework for Sparse Training

Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation

$\alpha$-ReQ : Assessing Representation Quality in Self-Supervised Learning by measuring eigenspectrum decay

Bounded-Regret MPC via Perturbation Analysis: Prediction Error, Constraints, and Nonlinearity

NaturalProver: Grounded Mathematical Proof Generation with Language Models

Predictive Querying for Autoregressive Neural Sequence Models

Differentially Private Linear Sketches: Efficient Implementations and Applications

Probable Domain Generalization via Quantile Risk Minimization

Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantification

Minimax Optimal Online Imitation Learning via Replay Estimation

Subspace Recovery from Heterogeneous Data with Non-isotropic Noise

Transferring Fairness under Distribution Shifts via Fair Consistency Regularization

Exploring the Whole Rashomon Set of Sparse Decision Trees

On Image Segmentation With Noisy Labels: Characterization and Volume Properties of the Optimal Solutions to Accuracy and Dice

AutoML Two-Sample Test

Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning

Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations

Sampling from Log-Concave Distributions with Infinity-Distance Guarantees

Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks

Distributional Reinforcement Learning for Risk-Sensitive Policies

Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis

Promising or Elusive? Unsupervised Object Segmentation from Real-world Single Images

Unsupervised Domain Adaptation for Semantic Segmentation using Depth Distribution

Data-Efficient Structured Pruning via Submodular Optimization

Structured Energy Network As a Loss

Iso-Dream: Isolating and Leveraging Noncontrollable Visual Dynamics in World Models

Improving Barely Supervised Learning by Discriminating Unlabeled Samples with Super-Class

Rethinking and Improving Robustness of Convolutional Neural Networks: a Shapley Value-based Approach in Frequency Domain

Exploring evolution-aware & -free protein language models as protein function predictors

Boosting the Performance of Generic Deep Neural Network Frameworks with Log-supermodular CRFs

On the Tradeoff Between Robustness and Fairness

Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures

Causality-driven Hierarchical Structure Discovery for Reinforcement Learning

Are AlphaZero-like Agents Robust to Adversarial Perturbations?

Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization

Pluralistic Image Completion with Gaussian Mixture Models

Generalization Analysis on Learning with a Concurrent Verifier

Receding Horizon Inverse Reinforcement Learning

Learning to Share in Networked Multi-Agent Reinforcement Learning

FIRE: Semantic Field of Words Represented as Non-Linear Functions

Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop

ProtoX: Explaining a Reinforcement Learning Agent via Prototyping

Pyramid Attention For Source Code Summarization

Taming Fat-Tailed (“Heavier-Tailed” with Potentially Infinite Variance) Noise in Federated Learning

Maximum a posteriori natural scene reconstruction from retinal ganglion cells with deep denoiser priors

DigGAN: Discriminator gradIent Gap Regularization for GAN Training with Limited Data

DNA: Proximal Policy Optimization with a Dual Network Architecture

Will Bilevel Optimizers Benefit from Loops

Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders

Redeeming intrinsic rewards via constrained optimization

Target alignment in truncated kernel ridge regression

Queue Up Your Regrets: Achieving the Dynamic Capacity Region of Multiplayer Bandits

Mismatched No More: Joint Model-Policy Optimization for Model-Based RL

Dynamic Sparse Network for Time Series Classification: Learning What to “See”

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Functions

Constrained Predictive Coding as a Biologically Plausible Model of the Cortical Hierarchy

Perturbation Learning Based Anomaly Detection

Hierarchical Graph Transformer with Adaptive Node Sampling

LogiGAN: Learning Logical Reasoning via Adversarial Pre-training

Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs

Structure-Preserving 3D Garment Modeling with Neural Sewing Machines

Improved Bounds on Neural Complexity for Representing Piecewise Linear Functions

On the Limitations of Stochastic Pre-processing Defenses

ResQ: A Residual Q Function-based Approach for Multi-Agent Reinforcement Learning Value Factorization

Global Convergence and Stability of Stochastic Gradient Descent

Delving into Out-of-Distribution Detection with Vision-Language Representations

Recruitment Strategies That Take a Chance

Inference and Sampling for Archimax Copulas

Text Classification with Born's Rule

Cluster and Aggregate: Face Recognition with Large Probe Set

VTC-LFC: Vision Transformer Compression with Low-Frequency Components

Lipschitz Bandits with Batched Feedback

Formulating Robustness Against Unforeseen Attacks

Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks

Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap

CageNeRF: Cage-based Neural Radiance Field for Generalized 3D Deformation and Animation

Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection

Non-Gaussian Tensor Programs

Understanding the Eluder Dimension

Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization

Local Linear Convergence of Gradient Methods for Subspace Optimization via Strict Complementarity

Simulation-guided Beam Search for Neural Combinatorial Optimization

Quo Vadis: Is Trajectory Forecasting the Key Towards Long-Term Multi-Object Tracking?

Meta-Reinforcement Learning with Self-Modifying Networks

Respecting Transfer Gap in Knowledge Distillation

What is Where by Looking: Weakly-Supervised Open-World Phrase-Grounding without Text Inputs

TarGF: Learning Target Gradient Field to Rearrange Objects without Explicit Goal Specification

One-Inlier is First: Towards Efficient Position Encoding for Point Cloud Registration

I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification

Sharing Knowledge for Meta-learning with Feature Descriptions

Large-batch Optimization for Dense Visual Predictions: Training Faster R-CNN in 4.2 Minutes

Continual Learning with Evolving Class Ontologies

Quasi-Newton Methods for Saddle Point Problems

TANGO: Text-driven Photorealistic and Robust 3D Stylization via Lighting Decomposition

Asymptotic Properties for Bayesian Neural Network in Besov Space

Planning for Sample Efficient Imitation Learning

Peripheral Vision Transformer

Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization

HSDF: Hybrid Sign and Distance Field for Modeling Surfaces with Arbitrary Topologies

Approximate Secular Equations for the Cubic Regularization Subproblem

Faster Stochastic Algorithms for Minimax Optimization under Polyak-{\L}ojasiewicz Condition

Unsupervised Learning of Equivariant Structure from Sequences

Inception Transformer

Signal Recovery with Non-Expansive Generative Network Priors

Counterfactual harm

Posterior Collapse of a Linear Latent Variable Model

Harmonizing the object recognition strategies of deep neural networks with humans

When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment

Exploit Reward Shifting in Value-Based Deep-RL: Optimistic Curiosity-Based Exploration and Conservative Exploitation via Linear Reward Shaping

Model-Based Imitation Learning for Urban Driving

OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Models

ELIAS: End-to-End Learning to Index and Search in Large Output Spaces

QUARK: Controllable Text Generation with Reinforced Unlearning

Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition

Finding Differences Between Transformers and ConvNets Using Counterfactual Simulation Testing

Anticipating Performativity by Predicting from Predictions

Fast Vision Transformers with HiLo Attention

OpenAUC: Towards AUC-Oriented Open-Set Recognition

Exploring the Algorithm-Dependent Generalization of AUPRC Optimization with List Stability

Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models

Differentiable Analog Quantum Computing for Optimization and Control

Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing

Monte Carlo Tree Descent for Black-Box Optimization

On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting

Robust Imitation of a Few Demonstrations with a Backwards Model

AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness

Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox

Performative Power

SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery

Benign, Tempered, or Catastrophic: Toward a Refined Taxonomy of Overfitting

The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift

Confident Approximate Policy Iteration for Efficient Local Planning in $q^\pi$-realizable MDPs

Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity

Society of Agents: Regret Bounds of Concurrent Thompson Sampling

Exploring Length Generalization in Large Language Models

Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxation

GPT3.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks

Revisiting Sparse Convolutional Model for Visual Recognition

Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning

MoCoDA: Model-based Counterfactual Data Augmentation

Beyond Adult and COMPAS: Fair Multi-Class Prediction via Information Projection

On the generalization of learning algorithms that do not converge

Capturing Graphs with Hypo-Elliptic Diffusions

Hypothesis Testing for Differentially Private Linear Regression

Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms

AutoST: Towards the Universal Modeling of Spatio-temporal Sequences

SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning

ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicine

Explicit Tradeoffs between Adversarial and Natural Distributional Robustness

Generalization Bounds for Gradient Methods via Discrete and Continuous Prior

CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification

BYOL-Explore: Exploration by Bootstrapped Prediction

Ordered Subgraph Aggregation Networks

Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability

Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfitting

Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime

Text-Adaptive Multiple Visual Prototype Matching for Video-Text Retrieval

An In-depth Study of Stochastic Backpropagation

Tractable Optimality in Episodic Latent MABs

Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning

Improving Certified Robustness via Statistical Learning with Logical Reasoning

Online Decision Mediation

Deep Differentiable Logic Gate Networks

Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity

Associating Objects and Their Effects in Video through Coordination Games

Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits

Precise Learning Curves and Higher-Order Scalings for Dot-product Kernel Regression

Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inference

Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization

Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift

Neural Conservation Laws: A Divergence-Free Perspective

Sparse Hypergraph Community Detection Thresholds in Stochastic Block Model

Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries

Latent Hierarchical Causal Structure Discovery with Rank Constraints

Task-Agnostic Graph Explanations

ZSON: Zero-Shot Object-Goal Navigation using Multimodal Goal Embeddings

Towards Optimal Communication Complexity in Distributed Non-Convex Optimization

Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement

Optimal Rates for Regularized Conditional Mean Embedding Learning

Are All Losses Created Equal: A Neural Collapse Perspective

Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees

What You See is What You Get: Principled Deep Learning via Distributional Generalization

Knowledge Distillation: Bad Models Can Be Good Role Models

Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively

Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting

Rare Gems: Finding Lottery Tickets at Initialization

Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit

Neural Approximation of Graph Topological Features

Near-Optimal Regret Bounds for Multi-batch Reinforcement Learning

Surprise Minimizing Multi-Agent Learning with Energy-based Models

Sparse Structure Search for Delta Tuning

Stability and Generalization for Markov Chain Stochastic Gradient Methods

Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare

Discovery of Single Independent Latent Variable

MoGDE: Boosting Mobile Monocular 3D Object Detection with Ground Depth Estimation

Compressible-composable NeRF via Rank-residual Decomposition

Asymmetric Temperature Scaling Makes Larger Networks Teach Well Again

DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps

Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs

Neural Shape Deformation Priors

Hierarchical Channel-spatial Encoding for Communication-efficient Collaborative Learning

Debugging and Explaining Metric Learning Approaches: An Influence Function Based Perspective

Factuality Enhanced Language Models for Open-Ended Text Generation

Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets

A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits

MaskTune: Mitigating Spurious Correlations by Forcing to Explore

Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions

Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology

Reconstructing Training Data From Trained Neural Networks

Use-Case-Grounded Simulations for Explanation Evaluation

Differentiable hierarchical and surrogate gradient search for spiking neural networks

CalFAT: Calibrated Federated Adversarial Training with Label Skewness

Cluster Randomized Designs for One-Sided Bipartite Experiments

Multi-Sample Training for Neural Image Compression

On the Parameterization and Initialization of Diagonal State Space Models

Solving Quantitative Reasoning Problems with Language Models

Learnable Polyphase Sampling for Shift Invariant and Equivariant Convolutional Networks

D^2NeRF: Self-Supervised Decoupling of Dynamic and Static Objects from a Monocular Video

Semi-Supervised Video Salient Object Detection Based on Uncertainty-Guided Pseudo Labels

C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic Forecasting

SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks

Squeezeformer: An Efficient Transformer for Automatic Speech Recognition

Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks

Generalizing Bayesian Optimization with Decision-theoretic Entropies

Dict-TTS: Learning to Pronounce with Prior Dictionary Knowledge for Text-to-Speech

The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes

Unsupervised Object Detection Pretraining with Joint Object Priors Generation and Detector Learning

Learning Chaotic Dynamics in Dissipative Systems

MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction

SeqPATE: Differentially Private Text Generation via Knowledge Distillation

DENSE: Data-Free One-Shot Federated Learning

Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization

Is $L^2$ Physics Informed Loss Always Suitable for Training Physics Informed Neural Network?

Hiding Images in Deep Probabilistic Models

Factored Adaptation for Non-Stationary Reinforcement Learning

Optimal Algorithms for Decentralized Stochastic Variational Inequalities

Semi-supervised Vision Transformers at Scale

Deep Model Reassembly

Your Transformer May Not be as Powerful as You Expect

InsPro: Propagating Instance Query and Proposal for Online Video Instance Segmentation

Linear tree shap

Delving into Sequential Patches for Deepfake Detection

Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection

ClimbQ: Class Imbalanced Quantization Enabling Robustness on Efficient Inferences

Learning Latent Seasonal-Trend Representations for Time Series Forecasting

Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks

Back Razor: Memory-Efficient Transfer Learning by Self-Sparsified Backpropagation

DreamShard: Generalizable Embedding Table Placement for Recommender Systems

Dataset Distillation via Factorization

Video Diffusion Models

Theseus: A Library for Differentiable Nonlinear Optimization

Decoupling Features in Hierarchical Propagation for Video Object Segmentation

RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection

Explainable Reinforcement Learning via Model Transforms

Matryoshka Representation Learning

VoxGRAF: Fast 3D-Aware Image Synthesis with Sparse Voxel Grids

Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation

MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning

LGDN: Language-Guided Denoising Network for Video-Language Modeling

PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining

Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning

Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking Consistency

Flexible Neural Image Compression via Code Editing

Learning Physics Constrained Dynamics Using Autoencoders

Active Learning with Neural Networks: Insights from Nonparametric Statistics

Understanding Robust Learning through the Lens of Representation Similarities

Beyond Separability: Analyzing the Linear Transferability of Contrastive Representations to Related Subpopulations

Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models

Zero-Shot Video Question Answering via Frozen Bidirectional Language Models

Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling

Measuring and Reducing Model Update Regression in Structured Prediction for NLP

Coordinates Are NOT Lonely - Codebook Prior Helps Implicit Neural 3D representations

Multitasking Models are Robust to Structural Failure: A Neural Model for Bilingual Cognitive Reserve

A Policy-Guided Imitation Approach for Offline Reinforcement Learning

Asymptotics of smoothed Wasserstein distances in the small noise regime

Finite-Time Last-Iterate Convergence for Learning in Multi-Player Games

CARD: Classification and Regression Diffusion Models

GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs

Unlabelled Sample Compression Schemes for Intersection-Closed Classes and Extremal Classes

Concentration of Data Encoding in Parameterized Quantum Circuits

Learning Efficient Vision Transformers via Fine-Grained Manifold Distillation

M$^4$I: Multi-modal Models Membership Inference

Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules

VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts

Pre-Trained Language Models for Interactive Decision-Making

Learning from Label Proportions by Learning with Label Noise

A Closer Look at Offline RL Agents

Beyond spectral gap: the role of the topology in decentralized learning

A permutation-free kernel two-sample test

C-Mixup: Improving Generalization in Regression

Generalizing Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses

Efficient Multi-agent Communication via Self-supervised Information Aggregation

EfficientFormer: Vision Transformers at MobileNet Speed

Pseudo-Riemannian Graph Convolutional Networks

Fast Algorithms for Packing Proportional Fairness and its Dual

Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees

Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes

Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress

Active Exploration for Inverse Reinforcement Learning

UniGAN: Reducing Mode Collapse in GANs using a Uniform Generator

Diffusion Curvature for Estimating Local Curvature in High Dimensional Data

Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms

On Enforcing Better Conditioned Meta-Learning for Rapid Few-Shot Adaptation

Efficient learning of nonlinear prediction models with time-series privileged information

Training and Inference on Any-Order Autoregressive Models the Right Way

SPD: Synergy Pattern Diversifying Oriented Unsupervised Multi-agent Reinforcement Learning

GAPX: Generalized Autoregressive Paraphrase-Identification X

CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks

Reinforcement Learning with a Terminator

Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens

Class-Dependent Label-Noise Learning with Cycle-Consistency Regularization

CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds

Sparse Interaction Additive Networks via Feature Interaction Detection and Sparse Selection

Object-Category Aware Reinforcement Learning

Decision-Focused Learning without Decision-Making: Learning Locally Optimized Decision Losses

Universally Expressive Communication in Multi-Agent Reinforcement Learning

Are GANs overkill for NLP?

Simple Mechanisms for Welfare Maximization in Rich Advertising Auctions

Scalable Interpretability via Polynomials

NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation

Learning Audio-Visual Dynamics Using Scene Graphs for Audio Source Separation

Symmetry Teleportation for Accelerated Optimization

The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning

Truncated proposals for scalable and hassle-free simulation-based inference

Large-Scale Retrieval for Reinforcement Learning

Decoupled Self-supervised Learning for Graphs

In Differential Privacy, There is Truth: on Vote-Histogram Leakage in Ensemble Private Learning

Handcrafted Backdoors in Deep Neural Networks

Structuring Representations Using Group Invariants

A sharp NMF result with applications in network modeling

Improving Policy Learning via Language Dynamics Distillation

Pure Transformers are Powerful Graph Learners

Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification

Few-shot Image Generation via Adaptation-Aware Kernel Modulation

Towards Understanding Grokking: An Effective Theory of Representation Learning

Online Agnostic Multiclass Boosting

Adversarial Unlearning: Reducing Confidence Along Adversarial Directions

Robust Imitation via Mirror Descent Inverse Reinforcement Learning

HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding

Oracle-Efficient Online Learning for Smoothed Adversaries

Multiclass Learnability Beyond the PAC Framework: Universal Rates and Partial Concept Classes

Lower Bounds on Randomly Preconditioned Lasso via Robust Sparse Designs

Learning from Distributed Users in Contextual Linear Bandits Without Sharing the Context

Accelerating Sparse Convolution with Column Vector-Wise Sparsity

Fast Instrument Learning with Faster Rates

LTMD: Learning Improvement of Spiking Neural Networks with Learnable Thresholding Neurons and Moderate Dropout

Improving Neural Ordinary Differential Equations with Nesterov's Accelerated Gradient Method

Learning Neural Set Functions Under the Optimal Subset Oracle

Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics

Universality of Group Convolutional Neural Networks Based on Ridgelet Analysis on Groups

On the Spectral Bias of Convolutional Neural Tangent and Gaussian Process Kernels

Beyond L1: Faster and Better Sparse Models with skglm

Improving GANs with A Dynamic Discriminator

Streaming Radiance Fields for 3D Video Synthesis

On the non-universality of deep learning: quantifying the cost of symmetry

GraB: Finding Provably Better Data Permutations than Random Reshuffling

Enhancing Safe Exploration Using Safety State Augmentation

Robust Binary Models by Pruning Randomly-initialized Networks

Optimal and Adaptive Monteiro-Svaiter Acceleration

Reinforcement Learning with Logarithmic Regret and Policy Switches

HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences

Temporally-Consistent Survival Analysis

Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data

Learning and Covering Sums of Independent Random Variables with Unbounded Support

Learning to Discover and Detect Objects

UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes

BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach

DeepFoids: Adaptive Bio-Inspired Fish Simulation with Deep Reinforcement Learning

Improving Intrinsic Exploration with Language Abstractions

MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

Monte Carlo Augmented Actor-Critic for Sparse Reward Deep Reinforcement Learning from Suboptimal Demonstrations

ShapeCrafter: A Recursive Text-Conditioned 3D Shape Generation Model

Non-identifiability and the Blessings of Misspecification in Models of Molecular Fitness

VoiceBlock: Privacy through Real-Time Adversarial Attacks with Audio-to-Audio Models

Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm

Outlier-Robust Sparse Mean Estimation for Heavy-Tailed Distributions

Invariance-Aware Randomized Smoothing Certificates

Beyond the Return: Off-policy Function Estimation under User-specified Error-measuring Distributions

On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL

Energy-Based Contrastive Learning of Visual Representations

Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials

Deep Surrogate Assisted Generation of Environments

Hierarchical Lattice Layer for Partially Monotone Neural Networks

SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training

Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations

Sample Constrained Treatment Effect Estimation

What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment

Empirical Phase Diagram for Three-layer Neural Networks with Infinite Width

FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation

Maximum Likelihood Training of Implicit Nonlinear Diffusion Model

Single Loop Gaussian Homotopy Method for Non-convex Optimization

GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations

CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning

Inducing Equilibria via Incentives: Simultaneous Design-and-Play Ensures Global Convergence

Reinforcement Learning with Neural Radiance Fields

Multi-agent Performative Prediction with Greedy Deployment and Consensus Seeking Agents

A Differentially Private Linear-Time fPTAS for the Minimum Enclosing Ball Problem

Debiased Causal Tree: Heterogeneous Treatment Effects Estimation with Unmeasured Confounding

Assistive Teaching of Motor Control Tasks to Humans

Learning interacting dynamical systems with latent Gaussian process ODEs

Provably expressive temporal graph networks

A Universal Error Measure for Input Predictions Applied to Online Graph Problems

On the difficulty of learning chaotic dynamics with RNNs

Learning on the Edge: Online Learning with Stochastic Feedback Graphs

Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks

Adjoint-aided inference of Gaussian process driven differential equations

Subquadratic Kronecker Regression with Applications to Tensor Decomposition

Post-hoc estimators for learning to defer to an expert

Polynomial-Time Optimal Equilibria with a Mediator in Extensive-Form Games

Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods

Discovered Policy Optimisation

Decomposable Non-Smooth Convex Optimization with Nearly-Linear Gradient Oracle Complexity

SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG

Convexity Certificates from Hessians

Holomorphic Equilibrium Propagation Computes Exact Gradients Through Finite Size Oscillations

Log-Linear-Time Gaussian Processes Using Binary Tree Kernels

Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples

Continual Learning In Environments With Polynomial Mixing Times

VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives

Algorithms and Hardness for Learning Linear Thresholds from Label Proportions

Enhanced Meta Reinforcement Learning via Demonstrations in Sparse Reward Environments

Make Some Noise: Reliable and Efficient Single-Step Adversarial Training

Transformer Memory as a Differentiable Search Index

(De-)Randomized Smoothing for Decision Stump Ensembles

Global Normalization for Streaming Speech Recognition in a Modular Framework

Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques

Learning Tractable Probabilistic Models from Inconsistent Local Estimates

List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering

Normalizing Flows for Knockoff-free Controlled Feature Selection

Debiased Machine Learning without Sample-Splitting for Stable Estimators

Explicable Policy Search

Robustness to Unbounded Smoothness of Generalized SignSGD

Subgame Solving in Adversarial Team Games

Autoregressive Perturbations for Data Poisoning

Trust Region Policy Optimization with Optimal Transport Discrepancies: Duality and Algorithm for Continuous Actions

Statistical Learning and Inverse Problems: A Stochastic Gradient Approach

TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s

Self-Aware Personalized Federated Learning

Unsupervised Visual Representation Learning via Mutual Information Regularized Assignment

LiteTransformerSearch: Training-free Neural Architecture Search for Efficient Language Models

Nonstationary Dual Averaging and Online Fair Allocation

Leveraging Inter-Layer Dependency for Post -Training Quantization

FOF: Learning Fourier Occupancy Field for Monocular Real-time Human Reconstruction

Learning Expressive Meta-Representations with Mixture of Expert Neural Processes

REVIVE: Regional Visual Representation Matters in Knowledge-Based Visual Question Answering

Online Neural Sequence Detection with Hierarchical Dirichlet Point Process

Exploring Figure-Ground Assignment Mechanism in Perceptual Organization

DTG-SSOD: Dense Teacher Guidance for Semi-Supervised Object Detection

Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation

Dual-Curriculum Contrastive Multi-Instance Learning for Cancer Prognosis Analysis with Whole Slide Images

BadPrompt: Backdoor Attacks on Continuous Prompts

Geodesic Self-Attention for 3D Point Clouds

Learning Enhanced Representation for Tabular Data via Neighborhood Propagation

Spectrum Random Masking for Generalization in Image-based Reinforcement Learning

3DB: A Framework for Debugging Computer Vision Models

High-dimensional limit theorems for SGD: Effective dynamics and critical scaling

Provable Generalization of Overparameterized Meta-learning Trained with SGD

MinVIS: A Minimal Video Instance Segmentation Framework without Video-based Training

Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation

Reinforced Genetic Algorithm for Structure-based Drug Design

Motion Transformer with Global Intention Localization and Local Movement Refinement

Deep Fourier Up-Sampling

FR: Folded Rationalization with a Unified Encoder

Measures of Information Reflect Memorization Patterns

Trading off Image Quality for Robustness is not Necessary with Regularized Deterministic Autoencoders

CASA: Category-agnostic Skeletal Animal Reconstruction

Learning Energy Networks with Generalized Fenchel-Young Losses

Regularized Gradient Descent Ascent for Two-Player Zero-Sum Markov Games

Rethinking Image Restoration for Object Detection

GBA: A Tuning-free Approach to Switch between Synchronous and Asynchronous Training for Recommendation Models

Modeling Human Exploration Through Resource-Rational Reinforcement Learning

SignRFF: Sign Random Fourier Features

Gradient Estimation with Discrete Stein Operators

Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems

Single-phase deep learning in cortico-cortical networks

GraphQNTK: Quantum Neural Tangent Kernel for Graph Data

BiMLP: Compact Binary Architectures for Vision Multi-Layer Perceptrons

Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent

Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models

LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model

An $\alpha$-regret analysis of Adversarial Bilateral Trade

Intrinsic dimensionality estimation using Normalizing Flows

Supervised Training of Conditional Monge Maps

Drawing out of Distribution with Neuro-Symbolic Generative Models

Sketching based Representations for Robust Image Classification with Provable Guarantees

Learning low-dimensional generalizable natural features from retina using a U-net

Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome

VisCo Grids: Surface Reconstruction with Viscosity and Coarea Grids

Synergy-of-Experts: Collaborate to Improve Adversarial Robustness

Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence

Fast Bayesian Estimation of Point Process Intensity as Function of Covariates

MOVE: Unsupervised Movable Object Segmentation and Detection

Not All Bits have Equal Value: Heterogeneous Precisions via Trainable Noise

Differentially Private Learning Needs Hidden State (Or Much Faster Convergence)

Training Spiking Neural Networks with Local Tandem Learning

Unsupervised Skill Discovery via Recurrent Skill Training

Interpreting Operation Selection in Differentiable Architecture Search: A Perspective from Influence-Directed Explanations

Fair Rank Aggregation

Optimal Gradient Sliding and its Application to Optimal Distributed Optimization Under Similarity

Contact-aware Human Motion Forecasting

Non-rigid Point Cloud Registration with Neural Deformation Pyramid

Make an Omelette with Breaking Eggs: Zero-Shot Learning for Novel Attribute Synthesis

The First Optimal Algorithm for Smooth and Strongly-Convex-Strongly-Concave Minimax Optimization

Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias

Stability and Generalization of Kernel Clustering: from Single Kernel to Multiple Kernel

Few-shot Relational Reasoning via Connection Subgraph Pretraining

Alleviating Adversarial Attacks on Variational Autoencoders with MCMC

Coreset for Line-Sets Clustering

Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization

FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness

HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details

On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning

Spatial Pruned Sparse Convolution for Efficient 3D Object Detection

Byzantine Spectral Ranking

What Can the Neural Tangent Kernel Tell Us About Adversarial Robustness?

On Translation and Reconstruction Guarantees of the Cycle-Consistent Generative Adversarial Networks

Evaluated CMI Bounds for Meta Learning: Tightness and Expressiveness

SnAKe: Bayesian Optimization with Pathwise Exploration

Random Rank: The One and Only Strategyproof and Proportionally Fair Randomized Facility Location Mechanism

Resolving the data ambiguity for periodic crystals

CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion

Coresets for Vertical Federated Learning: Regularized Linear Regression and $K$-Means Clustering

Learning Predictions for Algorithms with Predictions

Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solution

DASCO: Dual-Generator Adversarial Support Constrained Offline Reinforcement Learning

Exploring through Random Curiosity with General Value Functions

Equivariant Networks for Zero-Shot Coordination

A PAC-Bayesian Generalization Bound for Equivariant Networks

Split-kl and PAC-Bayes-split-kl Inequalities for Ternary Random Variables

Pareto Set Learning for Expensive Multi-Objective Optimization

Formalizing Consistency and Coherence of Representation Learning

Compositional generalization through abstract representations in human and artificial neural networks

The Sample Complexity of One-Hidden-Layer Neural Networks

Diffusion Visual Counterfactual Explanations

Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget

Pessimism for Offline Linear Contextual Bandits using $\ell_p$ Confidence Sets

Assaying Out-Of-Distribution Generalization in Transfer Learning

What are the best Systems? New Perspectives on NLP Benchmarking

Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise

Hardness in Markov Decision Processes: Theory and Practice

Generalization Error Bounds on Deep Learning with Markov Datasets

Information-Theoretic Safe Exploration with Gaussian Processes

M³ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design

HierSpeech: Bridging the Gap between Text and Speech by Hierarchical Variational Inference using Self-supervised Representations for Speech Synthesis

[Re] Replication Study of "Fairness and Bias in Online Selection"

Triangulation candidates for Bayesian optimization

Non-asymptotic and Accurate Learning of Nonlinear Dynamical Systems

Washing The Unwashable : On The (Im)possibility of Fairwashing Detection

No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation

Adaptive Data Debiasing through Bounded Exploration

Positive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimization

Toward a realistic model of speech processing in the brain with self-supervised learning

TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets

CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders

Attention-based Neural Cellular Automata

Sparse Additive Gaussian Process Regression

Attraction-Repulsion Spectrum in Neighbor Embeddings

Online Nonnegative CP-dictionary Learning for Markovian Data

Decimated Framelet System on Graphs and Fast G-Framelet Transforms

Multi-Agent Multi-Armed Bandits with Limited Communication

Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization

Optimality and Stability in Non-Convex Smooth Games

Deep Limits and a Cut-Off Phenomenon for Neural Networks

Robust and scalable manifold learning via landmark diffusion for long-term medical signal processing

[Re] Differentiable Spatial Planning using Transformers

All You Need is a Good Functional Prior for Bayesian Deep Learning

Recovery and Generalization in Over-Realized Dictionary Learning

Truncated Emphatic Temporal Difference Methods for Prediction and Control

[Re] AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients

When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint

[Re] Replication study of 'Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling'

Learning Operators with Coupled Attention

[Re] Solving Phase Retrieval With a Learned Reference

[Re] Explaining in Style: Training a GAN to explain a classifier in StyleSpace

DeepInteraction: 3D Object Detection via Modality Interaction

Mix and Reason: Reasoning over Semantic Topology with Data Mixing for Domain Generalization

SGAM: Building a Virtual 3D World through Simultaneous Generation and Mapping

RLIP: Relational Language-Image Pre-training for Human-Object Interaction Detection

Dense Interspecies Face Embedding

Orthogonal Transformer: An Efficient Vision Transformer Backbone with Token Orthogonalization

UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup

Rethinking Individual Global Max in Cooperative Multi-Agent Reinforcement Learning

Resource-Adaptive Federated Learning with All-In-One Neural Composition

One Model to Edit Them All: Free-Form Text-Driven Image Manipulation with Semantic Modulations

Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation

On the Robustness of Deep Clustering Models: Adversarial Attacks and Defenses

Uncoupled Learning Dynamics with $O(\log T)$ Swap Regret in Multiplayer Games

Weak-shot Semantic Segmentation via Dual Similarity Transfer

Effective Backdoor Defense by Exploiting Sensitivity of Poisoned Samples

Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks

OTKGE: Multi-modal Knowledge Graph Embeddings via Optimal Transport

Positively Weighted Kernel Quadrature via Subsampling

LASSIE: Learning Articulated Shapes from Sparse Image Ensemble via 3D Part Discovery

A Kernelised Stein Statistic for Assessing Implicit Generative Models

E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance

EpiGRAF: Rethinking training of 3D GANs

Bridging the Gap Between Vision Transformers and Convolutional Neural Networks on Small Datasets

Optimal Efficiency-Envy Trade-Off via Optimal Transport

Generating Long Videos of Dynamic Scenes

Private Synthetic Data for Multitask Learning and Marginal Queries

Graph Self-supervised Learning with Accurate Discrepancy Learning

Independence Testing for Bounded Degree Bayesian Networks

Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints

ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Time

SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections

Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models

Bayesian Persuasion for Algorithmic Recourse

Deep Hierarchical Planning from Pixels

Noise Attention Learning: Enhancing Noise Robustness by Gradient Scaling

Neural Basis Models for Interpretability

Hierarchical classification at multiple operating points

Information-Theoretic GAN Compression with Variational Energy-based Model

Redistribution of Weights and Activations for AdderNet Quantization

Deep invariant networks with differentiable augmentation layers

Convergence beyond the over-parameterized regime using Rayleigh quotients

Robust $\phi$-Divergence MDPs

ToDD: Topological Compound Fingerprinting in Computer-Aided Drug Discovery

On Privacy and Personalization in Cross-Silo Federated Learning

Differentially Private Covariance Revisited

Learning Graph-embedded Key-event Back-tracing for Object Tracking in Event Clouds

Distributional Convergence of the Sliced Wasserstein Process

Homomorphic Matrix Completion

Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation

On the Identifiability of Nonlinear ICA: Sparsity and Beyond

Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation

Towards Diverse and Faithful One-shot Adaption of Generative Adversarial Networks

Dance of SNN and ANN: Solving binding problem by combining spike timing and reconstructive attention

Efficient Sampling on Riemannian Manifolds via Langevin MCMC

ATD: Augmenting CP Tensor Decomposition by Self Supervision

Imitating Past Successes can be Very Suboptimal

RKHS-SHAP: Shapley Values for Kernel Methods

SAPD+: An Accelerated Stochastic Method for Nonconvex-Concave Minimax Problems

On Scalable Testing of Samplers

Markovian Interference in Experiments

DP-PCA: Statistically Optimal and Differentially Private PCA

Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Continual learning: a feature extraction formalization, an efficient algorithm, and fundamental obstructions

Functional Ensemble Distillation

Self-explaining deep models with logic rule reasoning

Benign Underfitting of Stochastic Gradient Descent

Modeling the Machine Learning Multiverse

Stability Analysis and Generalization Bounds of Adversarial Training

Exact Shape Correspondence via 2D graph convolution

A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning

How and Why to Manipulate Your Own Agent: On the Incentives of Users of Learning Agents

MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models

Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks

Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality

Spectral Bias Outside the Training Set for Deep Networks in the Kernel Regime

First Hitting Diffusion Models for Generating Manifold, Graph and Categorical Data

Universal Rates for Interactive Learning

DGD^2: A Linearly Convergent Distributed Algorithm For High-dimensional Statistical Recovery

Single-Stage Visual Relationship Learning using Conditional Queries

Pruning has a disparate impact on model accuracy

Teacher Forcing Recovers Reward Functions for Text Generation

Model-based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity

Optimal Dynamic Regret in LQR Control

Generalization Gap in Amortized Inference

Near-Optimal Private and Scalable $k$-Clustering

Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners

Hedging as Reward Augmentation in Probabilistic Graphical Models

Training Subset Selection for Weak Supervision

Online Reinforcement Learning for Mixed Policy Scopes

Branch & Learn for Recursively and Iteratively Solvable Problems in Predict+Optimize

Your Out-of-Distribution Detection Method is Not Robust!

An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries

Communication-Efficient Topologies for Decentralized Learning with $O(1)$ Consensus Rate

Rethinking the Reverse-engineering of Trojan Triggers

Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets

On the Epistemic Limits of Personalized Prediction

Learning to Mitigate AI Collusion on Economic Platforms

STNDT: Modeling Neural Population Activity with Spatiotemporal Transformers

Masked Autoencoding for Scalable and Generalizable Decision Making

Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs

DiSC: Differential Spectral Clustering of Features

Personalized Online Federated Learning with Multiple Kernels

Patching open-vocabulary models by interpolating weights

Concrete Score Matching: Generalized Score Matching for Discrete Data

LBD: Decouple Relevance and Observation for Individual-Level Unbiased Learning to Rank

Palm up: Playing in the Latent Manifold for Unsupervised Pretraining

Focal Modulation Networks

S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning

Exploitability Minimization in Games and Beyond

FeLMi : Few shot Learning with hard Mixup

The First Optimal Acceleration of High-Order Methods in Smooth Convex Optimization

On Optimal Learning Under Targeted Data Poisoning

The computational and learning benefits of Daleian neural networks

Support Recovery in Sparse PCA with Incomplete Data

Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo

Private Isotonic Regression

Do Residual Neural Networks discretize Neural Ordinary Differential Equations?

Continuous MDP Homomorphisms and Homomorphic Policy Gradient

Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks

Look where you look! Saliency-guided Q-networks for generalization in visual Reinforcement Learning

Constrained GPI for Zero-Shot Transfer in Reinforcement Learning

Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution

A Boosting Approach to Reinforcement Learning

DataMUX: Data Multiplexing for Neural Networks

Are Defenses for Graph Neural Networks Robust?

Adversarial Robustness is at Odds with Lazy Training

Robust Reinforcement Learning using Offline Data

Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits

Fine-tuning language models to find agreement among humans with diverse preferences

Tsetlin Machine for Solving Contextual Bandit Problems

Multi-Class $H$-Consistency Bounds

Statistical, Robustness, and Computational Guarantees for Sliced Wasserstein Distances

Lifting Weak Supervision To Structured Prediction

Learning Concept Credible Models for Mitigating Shortcuts

LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning

Disentangling Transfer in Continual Reinforcement Learning

Distinguishing discrete and continuous behavioral variability using warped autoregressive HMMs

Off-Team Learning

LAMP: Extracting Text from Gradients with Language Model Priors

4D Unsupervised Object Discovery

Bayesian subset selection and variable importance for interpretable prediction and classification

Unifying Voxel-based Representation with Transformer for 3D Object Detection

Multi-Scale Adaptive Network for Single Image Denoising

Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors

Efficient Graph Similarity Computation with Alignment Regularization

Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering

Falconn++: A Locality-sensitive Filtering Approach for Approximate Nearest Neighbor Search

Natural image synthesis for the retina with variational information bottleneck representation

A Lower Bound of Hash Codes' Performance

I2Q: A Fully Decentralized Q-Learning Algorithm

Shadow Knowledge Distillation: Bridging Offline and Online Knowledge Transfer

A Differentiable Semantic Metric Approximation in Probabilistic Embedding for Cross-Modal Retrieval

Heterogeneous Skill Learning for Multi-agent Tasks

Are You Stealing My Model? Sample Correlation for Fingerprinting Deep Neural Networks

Model-Based Opponent Modeling

When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture

Learning Invariant Graph Representations for Out-of-Distribution Generalization

Learning from Future: A Novel Self-Training Framework for Semantic Segmentation

Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction

Wasserstein Iterative Networks for Barycenter Estimation

Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness

Analyzing Sharpness along GD Trajectory: Progressive Sharpening and Edge of Stability

Rashomon Capacity: A Metric for Predictive Multiplicity in Classification

Pre-trained Adversarial Perturbations

Conformal Frequency Estimation with Sketched Data

Convergent Representations of Computer Programs in Human and Artificial Neural Networks

tntorch: Tensor Network Learning with PyTorch

Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization

Joint Entropy Search for Multi-Objective Bayesian Optimization

Embracing Consistency: A One-Stage Approach for Spatio-Temporal Video Grounding

A Closer Look at the Adversarial Robustness of Deep Equilibrium Models

Language Conditioned Spatial Relation Reasoning for 3D Object Grounding

[Re] Projection-based Algorithm for Updating the TruncatedSVD of Evolving Matrices

Unsupervised Point Cloud Completion and Segmentation by Generative Adversarial Autoencoding Network

Audio-Driven Co-Speech Gesture Video Generation

Dynamic Graph Neural Networks Under Spatio-Temporal Distribution Shift

On the role of overparameterization in off-policy Temporal Difference learning with linear function approximation

Quantized Training of Gradient Boosting Decision Trees

InterpretDL: Explaining Deep Models in PaddlePaddle

Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits

A Unified Statistical Learning Model for Rankings and Scores with Application to Grant Panel Review

LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data

D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data

Supervised Dimensionality Reduction and Visualization using Centroid-Encoder

Foolish Crowds Support Benign Overfitting

Rethinking Nonlinear Instrumental Variable Models through Prediction Validity

[Re] Reproduction and Extension of "Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation"

[Re] An Implementation of Fair Robust Learning

GAMA: Generative Adversarial Multi-Object Scene Attacks

Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training

An Empirical Study on Disentanglement of Negative-free Contrastive Learning

Social-Inverse: Inverse Decision-making of Social Contagion Management with Task Migrations

Bayesian Risk Markov Decision Processes

SHINE: SubHypergraph Inductive Neural nEtwork

Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning

Non-deep Networks

Feature-Proxy Transformer for Few-Shot Segmentation

Estimating and Explaining Model Performance When Both Covariates and Labels Shift

The alignment property of SGD noise and how it helps select flat minima: A stability analysis

Thompson Sampling Efficiently Learns to Control Diffusion Processes

Fair and Efficient Allocations Without Obvious Manipulations

Fuzzy Learning Machine

ASPiRe: Adaptive Skill Priors for Reinforcement Learning

Sound and Complete Causal Identification with Latent Variables Given Local Background Knowledge

Explainability Via Causal Self-Talk

ALIFE: Adaptive Logit Regularizer and Feature Replay for Incremental Semantic Segmentation

Causal Identification under Markov equivalence: Calculus, Algorithm, and Completeness

Random Normalization Aggregation for Adversarial Defense

Size and depth of monotone neural networks: interpolation and approximation

Spartan: Differentiable Sparsity via Regularized Transportation

On Gap-dependent Bounds for Offline Reinforcement Learning

Revisiting Sliced Wasserstein on Images: From Vectorization to Convolution

Accelerated Projected Gradient Algorithms for Sparsity Constrained Optimization Problems

Context-Based Dynamic Pricing with Partially Linear Demand Model

S-PIFu: Integrating Parametric Human Models with PIFu for Single-view Clothed Human Reconstruction

Action-modulated midbrain dopamine activity arises from distributed control policies

NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis

Bezier Gaussian Processes for Tall and Wide Data

Minimax Regret for Cascading Bandits

Pre-activation Distributions Expose Backdoor Neurons

Posterior Matching for Arbitrary Conditioning

Alternating Mirror Descent for Constrained Min-Max Games

Early Stage Convergence and Global Convergence of Training Mildly Parameterized Neural Networks

Foundation Posteriors for Approximate Probabilistic Inference

Entropy-Driven Mixed-Precision Quantization for Deep Network Design

Denoising Diffusion Restoration Models

Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms

A Combinatorial Perspective on the Optimization of Shallow ReLU Networks

A Lagrangian Duality Approach to Active Learning

A Statistical Online Inference Approach in Averaged Stochastic Approximation

Theoretical analysis of deep neural networks for temporally dependent observations

On the Complexity of Adversarial Decision Making

Scalable Distributional Robustness in a Class of Non-Convex Optimization with Guarantees

Uncovering the Structural Fairness in Graph Contrastive Learning

ULNeF: Untangled Layered Neural Fields for Mix-and-Match Virtual Try-On

Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces

Better Uncertainty Calibration via Proper Scores for Classification and Beyond

Augmenting Online Algorithms with $\varepsilon$-Accurate Predictions

Asymptotics of $\ell_2$ Regularized Network Embeddings

Deep Counterfactual Estimation with Categorical Background Variables

What is a Good Metric to Study Generalization of Minimax Learners?

Non-Convex Bilevel Games with Critical Point Selection Maps

The Franz-Parisi Criterion and Computational Trade-offs in High Dimensional Statistics

Provable Defense against Backdoor Policies in Reinforcement Learning

IMED-RL: Regret optimal learning of ergodic Markov decision processes

Leveraging the Hints: Adaptive Bidding in Repeated First-Price Auctions

A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases

Distributed Learning of Finite Gaussian Mixtures

[Re] Reproducibility Report: Contrastive Learning of Socially-aware Motion Representations

[Re] GANSpace: Discovering Interpretable GAN Controls

[Re] Reproducibility Study of “Counterfactual Generative Networks”

[Re] Does Self-Supervision Always Improve Few-Shot Learning?

On Kernelized Multi-Armed Bandits with Constraints

A Nonconvex Framework for Structured Dynamic Covariance Recovery

A Mean-Field Game Approach to Cloud Resource Management with Function Approximation

CoupAlign: Coupling Word-Pixel with Sentence-Mask Alignments for Referring Image Segmentation

Fast and Robust Rank Aggregation against Model Misspecification

Poisson Flow Generative Models

Boosting Out-of-distribution Detection with Typical Features

Joint Estimation and Inference for Data Integration Problems based on Multiple Multi-layered Gaussian Graphical Models

Conditional Diffusion Process for Inverse Halftoning

Knowledge Distillation from A Stronger Teacher

Dynamic pricing and assortment under a contextual MNL demand

Temporal Effective Batch Normalization in Spiking Neural Networks

[Re] Replication Study of "Fairness and Bias in Online Selection"

The trade-offs of model size in large recommendation models : 100GB to 10MB Criteo-tb DLRM model

Neural Transmitted Radiance Fields

🏘️ ProcTHOR: Large-Scale Embodied AI Using Procedural Generation

[Re] Exacerbating Algorithmic Bias through Fairness Attacks

Transfer Learning in Information Criteria-based Feature Selection

Learning with little mixing

Fairness-Aware PAC Learning from Corrupted Data

Online Frank-Wolfe with Arbitrary Delays

Online Allocation and Learning in the Presence of Strategic Agents

Weisfeiler and Leman Go Walking: Random Walk Kernels Revisited

Sufficient reductions in regression with mixed predictors

Optimal Query Complexities for Dynamic Trace Estimation

Understanding Benign Overfitting in Gradient-Based Meta Learning

Generalised Mutual Information for Discriminative Clustering

Prune and distill: similar reformatting of image information along rat visual cortex and deep neural networks

ShuffleMixer: An Efficient ConvNet for Image Super-Resolution

Minimax Optimal Fixed-Budget Best Arm Identification in Linear Bandits

[Re] Strategic classification made practical: reproduction

Communication-efficient distributed eigenspace estimation with arbitrary node failures

Generalised Implicit Neural Representations

AttCAT: Explaining Transformers via Attentive Class Activation Tokens

[Re] Explaining in Style: Training a GAN to explain a classifier in StyleSpace

Score-Based Generative Models Detect Manifolds

Geodesic Graph Neural Network for Efficient Graph Representation Learning

[Re] Nondeterminism and Instability in Neural Network Optimization

Diffusion-based Molecule Generation with Informative Prior Bridges

[Re] Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction

The Importance of Being Correlated: Implications of Dependence in Joint Spectral Inference across Multiple Networks

Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networks

Provably sample-efficient RL with side information about latent dynamics

Signal Processing for Implicit Neural Representations

Embrace the Gap: VAEs Perform Independent Mechanism Analysis

Exponential Separations in Symmetric Neural Networks

[Re] Understanding Self-Supervised Learning Dynamics without Contrastive Pairs

[Re] Exacerbating Algorithmic Bias through Fairness Attacks

Transformers from an Optimization Perspective

(f,Gamma)-Divergences: Interpolating between f-Divergences and Integral Probability Metrics

PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies

[Re] Transparent Object Tracking Benchmark

Neural Lyapunov Control of Unknown Nonlinear Systems with Stability Guarantees

[Re] Graph Edit Networks

[Re] Reproduction Study of Variational Fair Clustering

BR-SNIS: Bias Reduced Self-Normalized Importance Sampling

Fair and Optimal Decision Trees: A Dynamic Programming Approach

Brain Network Transformer

Learning to Navigate Wikipedia by Taking Random Walks

The Neural Testbed: Evaluating Joint Predictions

A Bregman Learning Framework for Sparse Neural Networks

[Re] Learning to count everything

On the Double Descent of Random Features Models Trained with SGD