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Gaussian Mixture Solvers for Diffusion Models
Dense and Aligned Captions (DAC) Promote Compositional Reasoning in VL Models
Efficiently incorporating quintuple interactions into geometric deep learning force fields
Geometric Transformer with Interatomic Positional Encoding
PTADisc: A Cross-Course Dataset Supporting Personalized Learning in Cold-Start Scenarios
How to Turn Your Knowledge Graph Embeddings into Generative Models
CLeAR: Continual Learning on Algorithmic Reasoning for Human-like Intelligence
Trial matching: capturing variability with data-constrained spiking neural networks
Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model
Multi-task Graph Neural Architecture Search with Task-aware Collaboration and Curriculum
Why Did This Model Forecast This Future? Information-Theoretic Saliency for Counterfactual Explanations of Probabilistic Regression Models
Discovering Intrinsic Spatial-Temporal Logic Rules to Explain Human Actions
Self-Refine: Iterative Refinement with Self-Feedback
Flow-Attention-based Spatio-Temporal Aggregation Network for 3D Mask Detection
Massively Multilingual Corpus of Sentiment Datasets and Multi-faceted Sentiment Classification Benchmark
Bandit Task Assignment with Unknown Processing Time
Wasserstein Gradient Flows for Optimizing Gaussian Mixture Policies
Learning to Reason and Memorize with Self-Notes
AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking
MeCo: Zero-Shot NAS with One Data and Single Forward Pass via Minimum Eigenvalue of Correlation
Semantic Image Synthesis with Unconditional Generator
Scaling Data-Constrained Language Models
Joint Attribute and Model Generalization Learning for Privacy-Preserving Action Recognition
ImageBrush: Learning Visual In-Context Instructions for Exemplar-Based Image Manipulation
POMDP Planning for Object Search in Partially Unknown Environment
VillanDiffusion: A Unified Backdoor Attack Framework for Diffusion Models
LuminAIRe: Illumination-Aware Conditional Image Repainting for Lighting-Realistic Generation
LeanDojo: Theorem Proving with Retrieval-Augmented Language Models
Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks
Multiply Robust Federated Estimation of Targeted Average Treatment Effects
Learning Time-Invariant Representations for Individual Neurons from Population Dynamics
Two Sides of The Same Coin: Bridging Deep Equilibrium Models and Neural ODEs via Homotopy Continuation
Reduced Policy Optimization for Continuous Control with Hard Constraints
CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy Labels
Towards Semi-Structured Automatic ICD Coding via Tree-based Contrastive Learning
All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation
Adversarial Examples Are Not Real Features
StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners
Improving CLIP Training with Language Rewrites
Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning
Effective Targeted Attacks for Adversarial Self-Supervised Learning
Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels
Stable Bias: Evaluating Societal Representations in Diffusion Models
Frequency Domain-Based Dataset Distillation
NeuroEvoBench: Benchmarking Evolutionary Optimizers for Deep Learning Applications
Are GATs Out of Balance?
Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
An Iterative Self-Learning Framework for Medical Domain Generalization
Temporal Dynamic Quantization for Diffusion Models
RevColV2: Exploring Disentangled Representations in Masked Image Modeling
On the Relationship Between Relevance and Conflict in Online Social Link Recommendations
PUCA: Patch-Unshuffle and Channel Attention for Enhanced Self-Supervised Image Denoising
STXD: Structural and Temporal Cross-Modal Distillation for Multi-View 3D Object Detection
Spectral Co-Distillation for Personalized Federated Learning
Efficient Low-rank Backpropagation for Vision Transformer Adaptation
A Path to Simpler Models Starts With Noise
A Recurrent Neural Circuit Mechanism of Temporal-scaling Equivariant Representation
Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation
Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data
Exploiting Correlated Auxiliary Feedback in Parameterized Bandits
Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning
A Simple Solution for Offline Imitation from Observations and Examples with Possibly Incomplete Trajectories
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture
New Bounds for Hyperparameter Tuning of Regression Problems Across Instances
Efficient Adversarial Attacks on Online Multi-agent Reinforcement Learning
DAMEX: Dataset-aware Mixture-of-Experts for visual understanding of mixture-of-datasets
Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded Rewards
Estimating and Controlling for Equalized Odds via Sensitive Attribute Predictors
Optimal Exploration for Model-Based RL in Nonlinear Systems
Accelerating Exploration with Unlabeled Prior Data
HIQL: Offline Goal-Conditioned RL with Latent States as Actions
Provably Efficient Algorithm for Nonstationary Low-Rank MDPs
Searching for Optimal Per-Coordinate Step-sizes with Multidimensional Backtracking
A Unified Approach to Count-Based Weakly Supervised Learning
How Re-sampling Helps for Long-Tail Learning?
A General Framework for Robust G-Invariance in G-Equivariant Networks
Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents
Lovász Principle for Unsupervised Graph Representation Learning
SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling
Reusing Pretrained Models by Multi-linear Operators for Efficient Training
Tame a Wild Camera: In-the-Wild Monocular Camera Calibration
Learning Domain-Aware Detection Head with Prompt Tuning
Learning Rule-Induced Subgraph Representations for Inductive Relation Prediction
OpenIllumination: A Multi-Illumination Dataset for Inverse Rendering Evaluation on Real Objects
How Does Adaptive Optimization Impact Local Neural Network Geometry?
HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution
EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models
Reference-Based POMDPs
Large Language Models as Commonsense Knowledge for Large-Scale Task Planning
Strategyproof Voting under Correlated Beliefs
Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer
No Change, No Gain: Empowering Graph Neural Networks with Expected Model Change Maximization for Active Learning
Optimal Block-wise Asymmetric Graph Construction for Graph-based Semi-supervised Learning
Mitigating the Popularity Bias of Graph Collaborative Filtering: A Dimensional Collapse Perspective
Survival Permanental Processes for Survival Analysis with Time-Varying Covariates
Functional Renyi Differential Privacy for Generative Modeling
CoLLAT: On Adding Fine-grained Audio Understanding to Language Models using Token-Level Locked-Language Tuning
Fine-Grained Human Feedback Gives Better Rewards for Language Model Training
Continual Learning for Instruction Following from Realtime Feedback
Approximate inference of marginals using the IBIA framework
Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks
Analysis of Variance of Multiple Causal Networks
SOC: Semantic-Assisted Object Cluster for Referring Video Object Segmentation
Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection
Provably Bounding Neural Network Preimages
On the Power of SVD in the Stochastic Block Model
ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling
ExPT: Synthetic Pretraining for Few-Shot Experimental Design
HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point Clouds
On the Exploitability of Instruction Tuning
Black-box Backdoor Defense via Zero-shot Image Purification
Efficient Learning of Linear Graph Neural Networks via Node Subsampling
Data-Driven Network Neuroscience: On Data Collection and Benchmark
Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration
Posterior Sampling for Competitive RL: Function Approximation and Partial Observation
Learning Regularized Monotone Graphon Mean-Field Games
Online Performative Gradient Descent for Learning Nash Equilibria in Decision-Dependent Games
Ignorance is Bliss: Robust Control via Information Gating
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning
UP-DP: Unsupervised Prompt Learning for Data Pre-Selection with Vision-Language Models
GradOrth: A Simple yet Efficient Out-of-Distribution Detection with Orthogonal Projection of Gradients
RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability
Invariant Anomaly Detection under Distribution Shifts: A Causal Perspective
A Holistic Approach to Unifying Automatic Concept Extraction and Concept Importance Estimation
Spuriosity Rankings: Sorting Data to Measure and Mitigate Biases
Quilt-1M: One Million Image-Text Pairs for Histopathology
Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research
A generative model of the hippocampal formation trained with theta driven local learning rules
Egocentric Planning for Scalable Embodied Task Achievement
Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models
Guide Your Agent with Adaptive Multimodal Rewards
Language Quantized AutoEncoders: Towards Unsupervised Text-Image Alignment
Blockwise Parallel Transformers for Large Context Models
Robust Lipschitz Bandits to Adversarial Corruptions
Towards Label-free Scene Understanding by Vision Foundation Models
Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective
RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions
ZipLM: Inference-Aware Structured Pruning of Language Models
Estimating Noise Correlations Across Continuous Conditions With Wishart Processes
LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching
Kernelized Cumulants: Beyond Kernel Mean Embeddings
Global Optimality in Bivariate Gradient-based DAG Learning
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models
Geometric Analysis of Matrix Sensing over Graphs
Don’t Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learner
SoundCam: A Dataset for Finding Humans Using Room Acoustics
SPRING: Studying Papers and Reasoning to play Games
Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization
Explainable Brain Age Prediction using coVariance Neural Networks
Epidemic Learning: Boosting Decentralized Learning with Randomized Communication
Beyond Average Return in Markov Decision Processes
CBD: A Certified Backdoor Detector Based on Local Dominant Probability
Harnessing the power of choices in decision tree learning
FIND: A Function Description Benchmark for Evaluating Interpretability Methods
Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learning
Multi-Agent Meta-Reinforcement Learning: Sharper Convergence Rates with Task Similarity
A Privacy-Friendly Approach to Data Valuation
Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies
DataPerf: Benchmarks for Data-Centric AI Development
Sparse Modular Activation for Efficient Sequence Modeling
ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design
Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach
COCO-Counterfactuals: Automatically Constructed Counterfactual Examples for Image-Text Pairs
Learning Robust Statistics for Simulation-based Inference under Model Misspecification
Smoothed Online Learning for Prediction in Piecewise Affine Systems
Revisiting Evaluation Metrics for Semantic Segmentation: Optimization and Evaluation of Fine-grained Intersection over Union
Convolution Monge Mapping Normalization for learning on sleep data
L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inference
Learning to Taste: A Multimodal Wine Dataset
Balancing Risk and Reward: A Batched-Bandit Strategy for Automated Phased Release
Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting
Online Nonstochastic Model-Free Reinforcement Learning
Recurrent Hypernetworks are Surprisingly Strong in Meta-RL
Conformalized matrix completion
A case for reframing automated medical image classification as segmentation
Embroid: Unsupervised Prediction Smoothing Can Improve Few-Shot Classification
Skill-it! A data-driven skills framework for understanding and training language models
Combinatorial Optimization with Policy Adaptation using Latent Space Search
Collaborative Learning via Prediction Consensus
Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior
Hyper-HMM: aligning human brains and semantic features in a common latent event space
Emergence of Shape Bias in Convolutional Neural Networks through Activation Sparsity
CoPriv: Network/Protocol Co-Optimization for Communication-Efficient Private Inference
Robust Bayesian Satisficing
Embracing the chaos: analysis and diagnosis of numerical instability in variational flows
Red Teaming Deep Neural Networks with Feature Synthesis Tools
Sampling from Structured Log-Concave Distributions via a Soft-Threshold Dikin Walk
Riemannian stochastic optimization methods avoid strict saddle points
Exploiting hidden structures in non-convex games for convergence to Nash equilibrium
The Equivalence of Dynamic and Strategic Stability under Regularized Learning in Games
Payoff-based Learning with Matrix Multiplicative Weights in Quantum Games
A Computationally Efficient Sparsified Online Newton Method
Block Low-Rank Preconditioner with Shared Basis for Stochastic Optimization
Accelerated Zeroth-order Method for Non-Smooth Stochastic Convex Optimization Problem with Infinite Variance
Byzantine-Tolerant Methods for Distributed Variational Inequalities
Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition
Normalizing flow neural networks by JKO scheme
Bicriteria Approximation Algorithms for the Submodular Cover Problem
Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint
DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology
Guiding The Last Layer in Federated Learning with Pre-Trained Models
NAP: Neural 3D Articulated Object Prior
Banana: Banach Fixed-Point Network for Pointcloud Segmentation with Inter-Part Equivariance
$SE(3)$ Equivariant Convolution and Transformer in Ray Space
Efficient Activation Function Optimization through Surrogate Modeling
Polyhedron Attention Module: Learning Adaptive-order Interactions
CAST: Cross-Attention in Space and Time for Video Action Recognition
On Separate Normalization in Self-supervised Transformers
Fast and Regret Optimal Best Arm Identification: Fundamental Limits and Low-Complexity Algorithms
Human spatiotemporal pattern learning as probabilistic program synthesis
BioMassters: A Benchmark Dataset for Forest Biomass Estimation using Multi-modal Satellite Time-series
Adaptive recurrent vision performs zero-shot computation scaling to unseen difficulty levels
Reproducibility Study of ”Label-Free Explainability for Unsupervised Models”
Understanding the Limitations of Deep Models for Molecular property prediction: Insights and Solutions
IDRNet: Intervention-Driven Relation Network for Semantic Segmentation
Machine learning detects terminal singularities
From ViT Features to Training-free Video Object Segmentation via Streaming-data Mixture Models
Transitivity Recovering Decompositions: Interpretable and Robust Fine-Grained Relationships
A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond
Improving Graph Matching with Positional Reconstruction Encoder-Decoder Network
Learning Reliable Logical Rules with SATNet
From Distribution Learning in Training to Gradient Search in Testing for Combinatorial Optimization
Utilitarian Algorithm Configuration
The Tunnel Effect: Building Data Representations in Deep Neural Networks
Effective Bayesian Heteroscedastic Regression with Deep Neural Networks
GloptiNets: Scalable Non-Convex Optimization with Certificates
Empowering Convolutional Neural Nets with MetaSin Activation
Multi-resolution Spectral Coherence for Graph Generation with Score-based Diffusion
Learning Topology-Agnostic EEG Representations with Geometry-Aware Modeling
ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling
Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data
[Re] Fairness Guarantees under Demographic Shift
FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models
Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans
Leveraging Vision-Centric Multi-Modal Expertise for 3D Object Detection
MultiFusion: Fusing Pre-Trained Models for Multi-Lingual, Multi-Modal Image Generation
SEGA: Instructing Text-to-Image Models using Semantic Guidance
Contrastive Modules with Temporal Attention for Multi-Task Reinforcement Learning
Leveraging Early-Stage Robustness in Diffusion Models for Efficient and High-Quality Image Synthesis
Understanding, Predicting and Better Resolving Q-Value Divergence in Offline-RL
A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability
Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization
Imitation Learning from Vague Feedback
Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection
Learning Adaptive Tensorial Density Fields for Clean Cryo-ET Reconstruction
Fantastic Robustness Measures: The Secrets of Robust Generalization
Iteratively Learn Diverse Strategies with State Distance Information
Variational Weighting for Kernel Density Ratios
Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation
A Guide Through the Zoo of Biased SGD
Momentum Provably Improves Error Feedback!
Optimal Time Complexities of Parallel Stochastic Optimization Methods Under a Fixed Computation Model
2Direction: Theoretically Faster Distributed Training with Bidirectional Communication Compression
Outlier-Robust Gromov-Wasserstein for Graph Data
GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection
Multi-Agent First Order Constrained Optimization in Policy Space
Online learning of long-range dependencies
Hierarchical Multi-Agent Skill Discovery
Act As You Wish: Fine-Grained Control of Motion Diffusion Model with Hierarchical Semantic Graphs
The Geometry of Neural Nets' Parameter Spaces Under Reparametrization
Distributionally Robust Linear Quadratic Control
Speculative Decoding with Big Little Decoder
Towards Optimal Effective Resistance Estimation
Fixing the NTK: From Neural Network Linearizations to Exact Convex Programs
A Logic for Expressing Log-Precision Transformers
Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design
Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics
Exponential Lower Bounds for Fictitious Play in Potential Games
Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression
GPEX, A Framework For Interpreting Artificial Neural Networks
Disentangled Wasserstein Autoencoder for T-Cell Receptor Engineering
Parsel🐍: Algorithmic Reasoning with Language Models by Composing Decompositions
Lexinvariant Language Models
PRODIGY: Enabling In-context Learning Over Graphs
Refined Mechanism Design for Approximately Structured Priors via Active Regression
Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management
DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data
Federated Learning via Meta-Variational Dropout
HyP-NeRF: Learning Improved NeRF Priors using a HyperNetwork
Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes
SOAR: Improved Quantization for Approximate Nearest Neighbor Search
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures
Extracting Reward Functions from Diffusion Models
Anytime Model Selection in Linear Bandits
Counterfactually Comparing Abstaining Classifiers
DISCOVER: Making Vision Networks Interpretable via Competition and Dissection
Counting Distinct Elements in the Turnstile Model with Differential Privacy under Continual Observation
ASL Citizen: A Community-Sourced Dataset for Advancing Isolated Sign Language Recognition
Cross-Episodic Curriculum for Transformer Agents
This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations
FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout
Mitigating the Effect of Incidental Correlations on Part-based Learning
Experimental Designs for Heteroskedastic Variance
Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior
GAUCHE: A Library for Gaussian Processes in Chemistry
Mathematical Capabilities of ChatGPT
AbDiffuser: full-atom generation of in-vitro functioning antibodies
Construction of Hierarchical Neural Architecture Search Spaces based on Context-free Grammars
Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition
Energy Discrepancies: A Score-Independent Loss for Energy-Based Models
Active Bipartite Ranking
DynPoint: Dynamic Neural Point For View Synthesis
Multi-body SE(3) Equivariance for Unsupervised Rigid Segmentation and Motion Estimation
Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Events
A Theory of Link Prediction via Relational Weisfeiler-Leman on Knowledge Graphs
A Robust Exact Algorithm for the Euclidean Bipartite Matching Problem
Effectively Learning Initiation Sets in Hierarchical Reinforcement Learning
Accelerating Molecular Graph Neural Networks via Knowledge Distillation
Follow-ups Also Matter: Improving Contextual Bandits via Post-serving Contexts
Guarantees for Self-Play in Multiplayer Games via Polymatrix Decomposability
SPA: A Graph Spectral Alignment Perspective for Domain Adaptation
Pengi: An Audio Language Model for Audio Tasks
Block-State Transformers
A Trichotomy for Transductive Online Learning
Efficient Data Subset Selection to Generalize Training Across Models: Transductive and Inductive Networks
Neural (Tangent Kernel) Collapse
The ToMCAT Dataset
Brain Diffusion for Visual Exploration: Cortical Discovery using Large Scale Generative Models
A Combinatorial Algorithm for Approximating the Optimal Transport in the Parallel and MPC Settings
Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation
Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization
Generalised f-Mean Aggregation for Graph Neural Networks
AdaVAE: Bayesian Structural Adaptation for Variational Autoencoders
ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning
Data-Centric Learning from Unlabeled Graphs with Diffusion Model
Reliable Off-Policy Learning for Dosage Combinations
xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data
Diverse Community Data for Benchmarking Data Privacy Algorithms
Pseudo-Likelihood Inference
On kernel-based statistical learning theory in the mean field limit
On Occlusions in Video Action Detection: Benchmark Datasets And Training Recipes
The Distortion of Binomial Voting Defies Expectation
Energy Transformer
COOM: A Game Benchmark for Continual Reinforcement Learning
Pareto Frontiers in Deep Feature Learning: Data, Compute, Width, and Luck
CAT-Walk: Inductive Hypergraph Learning via Set Walks
Can Language Models Teach? Teacher Explanations Improve Student Performance via Personalization
LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models
Beyond Invariance: Test-Time Label-Shift Adaptation for Addressing "Spurious" Correlations
Practical Contextual Bandits with Feedback Graphs
Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal
SE(3) Equivariant Augmented Coupling Flows
Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures
Generalization in the Face of Adaptivity: A Bayesian Perspective
Small batch deep reinforcement learning
Max-Sliced Mutual Information
Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RL
A Unified, Scalable Framework for Neural Population Decoding
Learning better with Dale’s Law: A Spectral Perspective
Formalizing locality for normative synaptic plasticity models
Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding
Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing
FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning
Quantification of Uncertainty with Adversarial Models
AndroidInTheWild: A Large-Scale Dataset For Android Device Control
Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization
On Certified Generalization in Structured Prediction
Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning
Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing
Reproducibility Study of “Quantifying Societal Bias Amplification in Image Captioning”
T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional Text-to-image Generation
Mr. HiSum: A Large-scale Dataset for Video Highlight Detection and Summarization
Oracle Complexity of Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Functional Constrained Optimization
Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models
SHAP-IQ: Unified Approximation of any-order Shapley Interactions
Add and Thin: Diffusion for Temporal Point Processes
Vocabulary-free Image Classification
Human-Guided Complexity-Controlled Abstractions
Accelerating Motion Planning via Optimal Transport
Conditional Matrix Flows for Gaussian Graphical Models
The Bayesian Stability Zoo
Multi-Head Adapter Routing for Cross-Task Generalization
Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees
Auditing for Human Expertise
MARBLE: Music Audio Representation Benchmark for Universal Evaluation
Implicit Variational Inference for High-Dimensional Posteriors
LD2: Scalable Heterophilous Graph Neural Network with Decoupled Embeddings
POP-3D: Open-Vocabulary 3D Occupancy Prediction from Images
An Empirical Study Towards Prompt-Tuning for Graph Contrastive Pre-Training in Recommendations
New Complexity-Theoretic Frontiers of Tractability for Neural Network Training
Scalarization for Multi-Task and Multi-Domain Learning at Scale
Window-Based Distribution Shift Detection for Deep Neural Networks
Multi Time Scale World Models
Online Inventory Problems: Beyond the i.i.d. Setting with Online Convex Optimization
Provable convergence guarantees for black-box variational inference
Neural Frailty Machine: Beyond proportional hazard assumption in neural survival regressions
WildfireSpreadTS: A dataset of multi-modal time series for wildfire spread prediction
Learning to Discover Skills through Guidance
PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning
Double Gumbel Q-Learning
Holistic Evaluation of Text-to-Image Models
A Cross-Moment Approach for Causal Effect Estimation
A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning
Joint Training of Deep Ensembles Fails Due to Learner Collusion
One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models
Provably Efficient Offline Reinforcement Learning in Regular Decision Processes
Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction
Multiplication-Free Transformer Training via Piecewise Affine Operations
[Re] On the Reproducibility of “FairCal: Fairness Calibration for Face Verification”
Compression with Bayesian Implicit Neural Representations
Greedy Poisson Rejection Sampling
Faster Relative Entropy Coding with Greedy Rejection Coding
On Transfer of Adversarial Robustness from Pretraining to Downstream Tasks
Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift
RenderMe-360: A Large Digital Asset Library and Benchmarks Towards High-fidelity Head Avatars
LaFTer: Label-Free Tuning of Zero-shot Classifier using Language and Unlabeled Image Collections
On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective
IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL
Connecting Certified and Adversarial Training
Classification of Heavy-tailed Features in High Dimensions: a Superstatistical Approach
Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance Assessment
GMSF: Global Matching Scene Flow
Knowledge Distillation Performs Partial Variance Reduction
CL-NeRF: Continual Learning of Neural Radiance Fields for Evolving Scene Representation
Sequential Preference Ranking for Efficient Reinforcement Learning from Human Feedback
Zero-shot causal learning
Swarm Reinforcement Learning for Adaptive Mesh Refinement
DSR: Dynamical Surface Representation as Implicit Neural Networks for Protein
Fully Dynamic $k$-Clustering in $\tilde O(k)$ Update Time
Hierarchical clustering with dot products recovers hidden tree structure
The expressive power of pooling in Graph Neural Networks
Sample Complexity of Goal-Conditioned Hierarchical Reinforcement Learning
ScaleLong: Towards More Stable Training of Diffusion Model via Scaling Network Long Skip Connection
Find What You Want: Learning Demand-conditioned Object Attribute Space for Demand-driven Navigation
Beyond Normal: On the Evaluation of Mutual Information Estimators
Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models
Dynamics Generalisation in Reinforcement Learning via Adaptive Context-Aware Policies
Implicit Manifold Gaussian Process Regression
Conditional Mutual Information for Disentangled Representations in Reinforcement Learning
End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes
Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization
Thought Cloning: Learning to Think while Acting by Imitating Human Thinking
Understanding Neural Network Binarization with Forward and Backward Proximal Quantizers
How to Scale Your EMA
Hardness of Low Rank Approximation of Entrywise Transformed Matrix Products
On Differentially Private Sampling from Gaussian and Product Distributions
Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data
Counterfactual Evaluation of Peer-Review Assignment Policies
Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicine
Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysis
A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference
Patch n’ Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution
Scaling Open-Vocabulary Object Detection
(Provable) Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More
Hierarchical Randomized Smoothing
WordScape: a Pipeline to extract multilingual, visually rich Documents with Layout Annotations from Web Crawl Data
Learning Provably Robust Estimators for Inverse Problems via Jittering
Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data
Sampling weights of deep neural networks
Parts of Speech–Grounded Subspaces in Vision-Language Models
Direct Training of SNN using Local Zeroth Order Method
Optimal Convergence Rate for Exact Policy Mirror Descent in Discounted Markov Decision Processes
SiT Dataset: Socially Interactive Pedestrian Trajectory Dataset for Social Navigation Robots
PromptIR: Prompting for All-in-One Image Restoration
Faster Query Times for Fully Dynamic $k$-Center Clustering with Outliers
Goal-conditioned Offline Planning from Curious Exploration
Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models
Latent exploration for Reinforcement Learning
A benchmark of categorical encoders for binary classification
Don't be so Monotone: Relaxing Stochastic Line Search in Over-Parameterized Models
Can semi-supervised learning use all the data effectively? A lower bound perspective
Exploring Loss Functions for Time-based Training Strategy in Spiking Neural Networks
ProteinShake: Building datasets and benchmarks for deep learning on protein structures
BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization
VisoGender: A dataset for benchmarking gender bias in image-text pronoun resolution
Principled Weight Initialisation for Input-Convex Neural Networks
Automated Classification of Model Errors on ImageNet
Activity Grammars for Temporal Action Segmentation
Normalization-Equivariant Neural Networks with Application to Image Denoising
Implicit variance regularization in non-contrastive SSL
Stable Vectorization of Multiparameter Persistent Homology using Signed Barcodes as Measures
A Framework for Fast and Stable Representations of Multiparameter Persistent Homology Decompositions
Optimal approximation using complex-valued neural networks
Scale Alone Does not Improve Mechanistic Interpretability in Vision Models
A Bayesian Take on Gaussian Process Networks
Robust covariance estimation with missing values and cell-wise contamination
On permutation symmetries in Bayesian neural network posteriors: a variational perspective
Continuous-Time Functional Diffusion Processes
On Generalization Bounds for Projective Clustering
Expert load matters: operating networks at high accuracy and low manual effort
Fast Online Changepoint Detection via Functional Pruning CUSUM Statistics
Reusable Slotwise Mechanisms
Representation Learning via Consistent Assignment of Views over Random Partitions
SEEDS: Exponential SDE Solvers for Fast High-Quality Sampling from Diffusion Models
Policy Optimization for Continuous Reinforcement Learning
SAME: Uncovering GNN Black Box with Structure-aware Shapley-based Multipiece Explanations
Bifurcations and loss jumps in RNN training
Latent Space Translation via Semantic Alignment
Leveraging sparse and shared feature activations for disentangled representation learning
ASIF: Coupled Data Turns Unimodal Models to Multimodal without Training
Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning
Make Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning
On the Role of Randomization in Adversarially Robust Classification
On student-teacher deviations in distillation: does it pay to disobey?
Sharpness-Aware Minimization Leads to Low-Rank Features
Truncating Trajectories in Monte Carlo Policy Evaluation: an Adaptive Approach
Distributional Policy Evaluation: a Maximum Entropy approach to Representation Learning
Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift
[Re] End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking
[Re] Bandit Theory and Thompson Sampling-guided Directed Evolution for Sequence Optimization
Easy Bayesian Transfer Learning with Informative Priors
[Re] On the Reproducibility of CartoonX
[Re] FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles
[Re] Numerical influence of ReLU'(0) on backpropagation
[Re] Hierarchical Shrinkage: Improving the Accuracy and Interpretability of Tree-Based Methods
Reproducibility study of 'Proto2Proto: Can you recognise the car, the way I do?'
[Re] Variational Neural Cellular Automata
[Re] Exploring the Role of Grammar and Word Choice in Bias Toward African American English (AAE) in Hate Speech Classification
RELIC: Reproducibility and Extension on LIC metric in quantifying bias in captioning models
[Re] VAE Approximation Error: ELBO and Exponential Families
[Re] Pure Noise to the Rescue of Insufficient Data
MMD Aggregated Two-Sample Test
Global Optimality and Finite Sample Analysis of Softmax Off-Policy Actor Critic under State Distribution Mismatch
Sparse Graph Learning from Spatiotemporal Time Series
Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start
Large sample spectral analysis of graph-based multi-manifold clustering
Euler-Lagrange Analysis of Generative Adversarial Networks
Semantic HELM: A Human-Readable Memory for Reinforcement Learning
RoboHive: A Unified Framework for Robot Learning
OFCOURSE: A Multi-Agent Reinforcement Learning Environment for Order Fulfillment
Decoding the Enigma: Benchmarking Humans and AIs on the Many Facets of Working Memory
MVDoppler: Unleashing the Power of Multi-View Doppler for MicroMotion-based Gait Classification
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks
MultiVENT: Multilingual Videos of Events and Aligned Natural Text
Creating Multi-Level Skill Hierarchies in Reinforcement Learning
SyncDiffusion: Coherent Montage via Synchronized Joint Diffusions
FourierHandFlow: Neural 4D Hand Representation Using Fourier Query Flow
ATMAN: Understanding Transformer Predictions Through Memory Efficient Attention Manipulation
Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering
GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization
ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners
Practical Differentially Private Hyperparameter Tuning with Subsampling
On the Exploration of Local Significant Differences For Two-Sample Test
Optimizing over trained GNNs via symmetry breaking
KAKURENBO: Adaptively Hiding Samples in Deep Neural Network Training
Mixture Weight Estimation and Model Prediction in Multi-source Multi-target Domain Adaptation
Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage
A Reduction-based Framework for Sequential Decision Making with Delayed Feedback
Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds
A Theoretical Analysis of Optimistic Proximal Policy Optimization in Linear Markov Decision Processes
RDumb: A simple approach that questions our progress in continual test-time adaptation
Paxion: Patching Action Knowledge in Video-Language Foundation Models
Any-to-Any Generation via Composable Diffusion
Low Tensor Rank Learning of Neural Dynamics
Deep Recurrent Optimal Stopping
SoTTA: Robust Test-Time Adaptation on Noisy Data Streams
Humans in Kitchens: A Dataset for Multi-Person Human Motion Forecasting with Scene Context
Detecting hidden confounding in observational data using multiple environments
A Theory of Unsupervised Translation Motivated by Understanding Animal Communication
Partial Matrix Completion
Polynomially Over-Parameterized Convolutional Neural Networks Contain Structured Strong Winning Lottery Tickets
Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyond
The Drunkard’s Odometry: Estimating Camera Motion in Deforming Scenes
DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning
Does Continual Learning Meet Compositionality? New Benchmarks and An Evaluation Framework
ChimpACT: A Longitudinal Dataset for Understanding Chimpanzee Behaviors
$\mathbf{\mathbb{E}^{FWI}}$: Multiparameter Benchmark Datasets for Elastic Full Waveform Inversion of Geophysical Properties
Ego4D Goal-Step: Toward Hierarchical Understanding of Procedural Activities
INSPECT: A Multimodal Dataset for Patient Outcome Prediction of Pulmonary Embolisms
Benchmarking Robustness of Adaptation Methods on Pre-trained Vision-Language Models
WCLD: Curated Large Dataset of Criminal Cases from Wisconsin Circuit Courts
The Waymo Open Sim Agents Challenge
GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning
Into the Single Cell Multiverse: an End-to-End Dataset for Procedural Knowledge Extraction in Biomedical Texts
$\mathcal{M}^4$: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities and Models
QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules
Benchmarking and Analyzing 3D-aware Image Synthesis with a Modularized Codebase
Alexa Arena: A User-Centric Interactive Platform for Embodied AI
AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs
XES3G5M: A Knowledge Tracing Benchmark Dataset with Auxiliary Information
Benchmarking Encoder-Decoder Architectures for Biplanar X-ray to 3D Bone Shape Reconstruction
Evaluating and Improving Tool-Augmented Computation-Intensive Math Reasoning
Imagine the Unseen World: A Benchmark for Systematic Generalization in Visual World Models
Building the Bridge of Schrödinger: A Continuous Entropic Optimal Transport Benchmark
OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping
A Dataset for Analyzing Streaming Media Performance over HTTP/3 Browsers
CAPP-130: A Corpus of Chinese Application Privacy Policy Summarization and Interpretation
On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets
Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks
EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Images
Ethical Considerations for Responsible Data Curation
RL-ViGen: A Reinforcement Learning Benchmark for Visual Generalization
Intelligent Knee Sleeves: A Real-time Multimodal Dataset for 3D Lower Body Motion Estimation Using Smart Textile
Classical Simulation of Quantum Circuits: Parallel Environments and Benchmark
Learning Human Action Recognition Representations Without Real Humans
Evaluating Open-QA Evaluation
Gigastep - One Billion Steps per Second Multi-agent Reinforcement Learning
Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean
Improving multimodal datasets with image captioning
SatBird: a Dataset for Bird Species Distribution Modeling using Remote Sensing and Citizen Science Data
WBCAtt: A White Blood Cell Dataset Annotated with Detailed Morphological Attributes
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation
UDC-SIT: A Real-World Dataset for Under-Display Cameras
QATCH: Benchmarking SQL-centric tasks with Table Representation Learning Models on Your Data
Realistic Synthetic Financial Transactions for Anti-Money Laundering Models
Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Images
VisIT-Bench: A Dynamic Benchmark for Evaluating Instruction-Following Vision-and-Language Models
ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogram
PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change
SEVA: Leveraging sketches to evaluate alignment between human and machine visual abstraction
Uncovering Neural Scaling Laws in Molecular Representation Learning
A Step Towards Worldwide Biodiversity Assessment: The BIOSCAN-1M Insect Dataset
Large Language Models are Fixated by Red Herrings: Exploring Creative Problem Solving and Einstellung Effect using the Only Connect Wall Dataset
CSMeD: Bridging the Dataset Gap in Automated Citation Screening for Systematic Literature Reviews
VidChapters-7M: Video Chapters at Scale
Data Portraits: Recording Foundation Model Training Data
MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image Editing
LoRA: A Logical Reasoning Augmented Dataset for Visual Question Answering
How to Data in Datathons
EV-Eye: Rethinking High-frequency Eye Tracking through the Lenses of Event Cameras
Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs
KuaiSim: A Comprehensive Simulator for Recommender Systems
Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark
SG×P : A Sorghum Genotype × Phenotype Prediction Dataset and Benchmark
DataComp: In search of the next generation of multimodal datasets
Hyper-Skin: A Hyperspectral Dataset for Reconstructing Facial Skin-Spectra from RGB Images
URL: A Representation Learning Benchmark for Transferable Uncertainty Estimates
OpenDataVal: a Unified Benchmark for Data Valuation
LAMM: Language-Assisted Multi-Modal Instruction-Tuning Dataset, Framework, and Benchmark
AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web
C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models
InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback
BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset
Auslan-Daily: Australian Sign Language Translation for Daily Communication and News
OpenAGI: When LLM Meets Domain Experts
M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models
Benchmark of Machine Learning Force Fields for Semiconductor Simulations: Datasets, Metrics, and Comparative Analysis
MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning
Cola: A Benchmark for Compositional Text-to-image Retrieval
FELM: Benchmarking Factuality Evaluation of Large Language Models
A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning
BenchCLAMP: A Benchmark for Evaluating Language Models on Syntactic and Semantic Parsing
The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data Only
Interactive Visual Reasoning under Uncertainty
CMMA: Benchmarking Multi-Affection Detection in Chinese Multi-Modal Conversations
LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting
A Comprehensive Study on Text-attributed Graphs: Benchmarking and Rethinking
SUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physics
AirDelhi: Fine-Grained Spatio-Temporal Particulate Matter Dataset From Delhi For ML based Modeling
SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation
LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios
DISCS: A Benchmark for Discrete Sampling
SynMob: Creating High-Fidelity Synthetic GPS Trajectory Dataset for Urban Mobility Analysis
DynaDojo: An Extensible Platform for Benchmarking Scaling in Dynamical System Identification
SubseasonalClimateUSA: A Dataset for Subseasonal Forecasting and Benchmarking
TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs
Exploring Why Object Recognition Performance Degrades Across Income Levels and Geographies with Factor Annotations
Temporal Graph Benchmark for Machine Learning on Temporal Graphs
StressID: a Multimodal Dataset for Stress Identification
RD-Suite: A Benchmark for Ranking Distillation
OceanBench: The Sea Surface Height Edition
NurViD: A Large Expert-Level Video Database for Nursing Procedure Activity Understanding
The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications
UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal Prediction
Building Socio-culturally Inclusive Stereotype Resources with Community Engagement
M5HisDoc: A Large-scale Multi-style Chinese Historical Document Analysis Benchmark
Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data
Automatic Grouping for Efficient Cooperative Multi-Agent Reinforcement Learning
On the Complexity of Differentially Private Best-Arm Identification with Fixed Confidence
An $\varepsilon$-Best-Arm Identification Algorithm for Fixed-Confidence and Beyond
Non-Asymptotic Analysis of a UCB-based Top Two Algorithm
Score-based Source Separation with Applications to Digital Communication Signals
Optimistic Active Exploration of Dynamical Systems
Efficient Exploration in Continuous-time Model-based Reinforcement Learning
Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning
Contextual Stochastic Bilevel Optimization
Likelihood Ratio Confidence Sets for Sequential Decision Making
Stochastic Approximation Algorithms for Systems of Interacting Particles
A Dynamical System View of Langevin-Based Non-Convex Sampling
Learning To Dive In Branch And Bound
Logarithmic-Regret Quantum Learning Algorithms for Zero-Sum Games
Towards Unbounded Machine Unlearning
What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic Segmentation
$p$-value Adjustment for Monotonous, Unbiased, and Fast Clustering Comparison
Approximate Allocation Matching for Structural Causal Bandits with Unobserved Confounders
Two Heads are Better Than One: A Simple Exploration Framework for Efficient Multi-Agent Reinforcement Learning
A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning
PIXIU: A Comprehensive Benchmark, Instruction Dataset and Large Language Model for Finance
SpokenWOZ: A Large-Scale Speech-Text Benchmark for Spoken Task-Oriented Dialogue Agents
HT-Step: Aligning Instructional Articles with How-To Videos
How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources
CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completion
Validated Image Caption Rating Dataset
GSLB: The Graph Structure Learning Benchmark
A Multi-modal Global Instance Tracking Benchmark (MGIT): Better Locating Target in Complex Spatio-temporal and Causal Relationship
LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning
FETV: A Benchmark for Fine-Grained Evaluation of Open-Domain Text-to-Video Generation
OpenGSL: A Comprehensive Benchmark for Graph Structure Learning
Diplomat: A Dialogue Dataset for Situated PragMATic Reasoning
A High-Resolution Dataset for Instance Detection with Multi-View Object Capture
Stanford-ORB: A Real-World 3D Object Inverse Rendering Benchmark
Online Convex Optimization with Unbounded Memory
Alternating Updates for Efficient Transformers
Explaining V1 Properties with a Biologically Constrained Deep Learning Architecture
Unexpected Improvements to Expected Improvement for Bayesian Optimization
Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions
DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models
CAPro: Webly Supervised Learning with Cross-modality Aligned Prototypes
Solving Inverse Physics Problems with Score Matching
ISP: Multi-Layered Garment Draping with Implicit Sewing Patterns
DOSE: Diffusion Dropout with Adaptive Prior for Speech Enhancement
Characterizing the Optimal $0-1$ Loss for Multi-class Classification with a Test-time Attacker
Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Approach for Object Detection
Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning
A General Theory of Correct, Incorrect, and Extrinsic Equivariance
Constructing Non-isotropic Gaussian Diffusion Model Using Isotropic Gaussian Diffusion Model for Image Editing
Gaussian Membership Inference Privacy
Segment Anything in 3D with NeRFs
Group Robust Classification Without Any Group Information
Structured Federated Learning through Clustered Additive Modeling
Towards Accelerated Model Training via Bayesian Data Selection
Setting the Trap: Capturing and Defeating Backdoors in Pretrained Language Models through Honeypots
Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing
Efficient Online Clustering with Moving Costs
Mode Connectivity in Auction Design
Estimating Propensity for Causality-based Recommendation without Exposure Data
Density of States Prediction of Crystalline Materials via Prompt-guided Multi-Modal Transformer
Double Auctions with Two-sided Bandit Feedback
What Do Deep Saliency Models Learn about Visual Attention?
Compressed Video Prompt Tuning
Mutual Information Regularized Offline Reinforcement Learning
Evolving Connectivity for Recurrent Spiking Neural Networks
Idempotent Learned Image Compression with Right-Inverse
Equivariant Spatio-Temporal Attentive Graph Networks to Simulate Physical Dynamics
Fairness Aware Counterfactuals for Subgroups
Im-Promptu: In-Context Composition from Image Prompts
Time-uniform confidence bands for the CDF under nonstationarity
Addressing Negative Transfer in Diffusion Models
The Pursuit of Human Labeling: A New Perspective on Unsupervised Learning
Post Hoc Explanations of Language Models Can Improve Language Models
Undirected Probabilistic Model for Tensor Decomposition
From Pixels to UI Actions: Learning to Follow Instructions via Graphical User Interfaces
DP-HyPO: An Adaptive Private Framework for Hyperparameter Optimization
Why Does Sharpness-Aware Minimization Generalize Better Than SGD?
Learning Large Graph Property Prediction via Graph Segment Training
ReTR: Modeling Rendering Via Transformer for Generalizable Neural Surface Reconstruction
Deep learning with kernels through RKHM and the Perron-Frobenius operator
A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks
Mitigating Over-smoothing in Transformers via Regularized Nonlocal Functionals
Differentiable and Stable Long-Range Tracking of Multiple Posterior Modes
Learning Interpretable Low-dimensional Representation via Physical Symmetry
PERFOGRAPH: A Numerical Aware Program Graph Representation for Performance Optimization and Program Analysis
Beyond probability partitions: Calibrating neural networks with semantic aware grouping
ReHLine: Regularized Composite ReLU-ReHU Loss Minimization with Linear Computation and Linear Convergence
The Quantization Model of Neural Scaling
Uncoupled and Convergent Learning in Two-Player Zero-Sum Markov Games with Bandit Feedback
Accelerated On-Device Forward Neural Network Training with Module-Wise Descending Asynchronism
Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation
Zeroth-Order Methods for Nonsmooth, Nonconvex, and Hierarchical Federated Optimization
Lightweight Vision Transformer with Bidirectional Interaction
Block-Coordinate Methods and Restarting for Solving Extensive-Form Games
On the Variance, Admissibility, and Stability of Empirical Risk Minimization
When Does Confidence-Based Cascade Deferral Suffice?
Direction-oriented Multi-objective Learning: Simple and Provable Stochastic Algorithms
Object-centric Learning with Cyclic Walks between Parts and Whole
High Precision Causal Model Evaluation with Conditional Randomization
Binary Classification with Confidence Difference
Reversible and irreversible bracket-based dynamics for deep graph neural networks
MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data
Training-free Diffusion Model Adaptation for Variable-Sized Text-to-Image Synthesis
Adaptive Selective Sampling for Online Prediction with Experts
Learning Sample Difficulty from Pre-trained Models for Reliable Prediction
Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning
Recovering from Out-of-sample States via Inverse Dynamics in Offline Reinforcement Learning
Data-Dependent Bounds for Online Portfolio Selection Without Lipschitzness and Smoothness
Multi-Modal Inverse Constrained Reinforcement Learning from a Mixture of Demonstrations
Online Pricing for Multi-User Multi-Item Markets
Censored Sampling of Diffusion Models Using 3 Minutes of Human Feedback
HiBug: On Human-Interpretable Model Debug
A Unified Detection Framework for Inference-Stage Backdoor Defenses
Towards a fuller understanding of neurons with Clustered Compositional Explanations
Quantifying the Cost of Learning in Queueing Systems
Language Models are Weak Learners
Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement
Common Ground in Cooperative Communication
A Spectral Theory of Neural Prediction and Alignment
Adaptive Normalization for Non-stationary Time Series Forecasting: A Temporal Slice Perspective
Provable Guarantees for Neural Networks via Gradient Feature Learning
Diffusion Model for Graph Inverse Problems: Towards Effective Source Localization on Complex Networks
Strong and Precise Modulation of Human Percepts via Robustified ANNs
PAC Learning Linear Thresholds from Label Proportions
Computational Guarantees for Doubly Entropic Wasserstein Barycenters
GeoTMI: Predicting Quantum Chemical Property with Easy-to-Obtain Geometry via Positional Denoising
Fast Bellman Updates for Wasserstein Distributionally Robust MDPs
Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models
Inconsistency, Instability, and Generalization Gap of Deep Neural Network Training
Replicable Clustering
Characteristic Circuits
Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Understanding Contrastive Learning via Distributionally Robust Optimization
Self-Adaptive Motion Tracking against On-body Displacement of Flexible Sensors
HOH: Markerless Multimodal Human-Object-Human Handover Dataset with Large Object Count
A Comprehensive Benchmark for Neural Human Radiance Fields
ProBio: A Protocol-guided Multimodal Dataset for Molecular Biology Lab
NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and Pose Annotations
LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite
Pairwise GUI Dataset Construction Between Android Phones and Tablets
EMBERSim: A Large-Scale Databank for Boosting Similarity Search in Malware Analysis
Generating QM1B with PySCF$_{\text{IPU}}$
Digital Typhoon: Long-term Satellite Image Dataset for the Spatio-Temporal Modeling of Tropical Cyclones
OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning
Estimating Generic 3D Room Structures from 2D Annotations
BoardgameQA: A Dataset for Natural Language Reasoning with Contradictory Information
AVIDa-hIL6: A Large-Scale VHH Dataset Produced from an Immunized Alpaca for Predicting Antigen-Antibody Interactions
Revisiting the Evaluation of Image Synthesis with GANs
Benchmarking Robustness to Adversarial Image Obfuscations
When Do Neural Nets Outperform Boosted Trees on Tabular Data?
CARE-MI: Chinese Benchmark for Misinformation Evaluation in Maternity and Infant Care
HA-ViD: A Human Assembly Video Dataset for Comprehensive Assembly Knowledge Understanding
EgoTracks: A Long-term Egocentric Visual Object Tracking Dataset
Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning
Video Timeline Modeling For News Story Understanding
Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning
MiliPoint: A Point Cloud Dataset for mmWave Radar
An NLP Benchmark Dataset for Assessing Corporate Climate Policy Engagement
GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image
RealTime QA: What's the Answer Right Now?
Benchmarking Large Language Models on CMExam - A comprehensive Chinese Medical Exam Dataset
NIS3D: A Completely Annotated Benchmark for Dense 3D Nuclei Image Segmentation
GeoDE: a Geographically Diverse Evaluation Dataset for Object Recognition
LithoBench: Benchmarking AI Computational Lithography for Semiconductor Manufacturing
SugarCrepe: Fixing Hackable Benchmarks for Vision-Language Compositionality
PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning
FLAIR : a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical Imagery
CHAMMI: A benchmark for channel-adaptive models in microscopy imaging
NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics
LOVM: Language-Only Vision Model Selection
Approximately Equivariant Graph Networks
Replicability in Reinforcement Learning
Evolving Standardization for Continual Domain Generalization over Temporal Drift
LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference
Cross-Scale MAE: A Tale of Multiscale Exploitation in Remote Sensing
Pairwise Causality Guided Transformers for Event Sequences
Composing Parameter-Efficient Modules with Arithmetic Operation
Module-wise Training of Neural Networks via the Minimizing Movement Scheme
StreamNet: Memory-Efficient Streaming Tiny Deep Learning Inference on the Microcontroller
Many-body Approximation for Non-negative Tensors
No Representation Rules Them All in Category Discovery
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning
No-Regret Learning with Unbounded Losses: The Case of Logarithmic Pooling
A Competitive Algorithm for Agnostic Active Learning
Learning the Efficient Frontier
Bayesian Optimization with Cost-varying Variable Subsets
Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?
Explaining Predictive Uncertainty with Information Theoretic Shapley Values
Causes and Effects of Unanticipated Numerical Deviations in Neural Network Inference Frameworks
VPGTrans: Transfer Visual Prompt Generator across LLMs
No-Regret Learning in Dynamic Competition with Reference Effects Under Logit Demand
IDEA: An Invariant Perspective for Efficient Domain Adaptive Image Retrieval
Language Model Tokenizers Introduce Unfairness Between Languages
Defending against Data-Free Model Extraction by Distributionally Robust Defensive Training
AVIS: Autonomous Visual Information Seeking with Large Language Model Agent
Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation
Adaptive Contextual Perception: How To Generalize To New Backgrounds and Ambiguous Objects
Dual Self-Awareness Value Decomposition Framework without Individual Global Max for Cooperative MARL
Statistical and Computational Trade-off in Multi-Agent Multi-Armed Bandits
Tempo Adaptation in Non-stationary Reinforcement Learning
Order Matters in the Presence of Dataset Imbalance for Multilingual Learning
A State Representation for Diminishing Rewards
Masked Image Residual Learning for Scaling Deeper Vision Transformers
Joint processing of linguistic properties in brains and language models
Bias in Evaluation Processes: An Optimization-Based Model
Random-Access Infinite Context Length for Transformers
Information Maximizing Curriculum: A Curriculum-Based Approach for Learning Versatile Skills
Multi-Agent Learning with Heterogeneous Linear Contextual Bandits
Unsupervised Optical Flow Estimation with Dynamic Timing Representation for Spike Camera
Physics-Driven ML-Based Modelling for Correcting Inverse Estimation
A Sublinear-Time Spectral Clustering Oracle with Improved Preprocessing Time
AMAG: Additive, Multiplicative and Adaptive Graph Neural Network For Forecasting Neuron Activity
Energy-Efficient Scheduling with Predictions
Dis-inhibitory neuronal circuits can control the sign of synaptic plasticity
Federated Learning with Manifold Regularization and Normalized Update Reaggregation
HyTrel: Hypergraph-enhanced Tabular Data Representation Learning
The Target-Charging Technique for Privacy Analysis across Interactive Computations
Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow Shrink Trees
Expanding Small-Scale Datasets with Guided Imagination
Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP
Nearest Neighbour with Bandit Feedback
Geometry-Informed Neural Operator for Large-Scale 3D PDEs
Towards Automated Circuit Discovery for Mechanistic Interpretability
Label-efficient Segmentation via Affinity Propagation
Analyzing Generalization of Neural Networks through Loss Path Kernels
Exploiting Connections between Lipschitz Structures for Certifiably Robust Deep Equilibrium Models
Conservative State Value Estimation for Offline Reinforcement Learning
Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning
Efficient Subgame Refinement for Extensive-form Games
Systematic Visual Reasoning through Object-Centric Relational Abstraction
DAC-DETR: Divide the Attention Layers and Conquer
Bootstrapping Vision-Language Learning with Decoupled Language Pre-training
Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration
Data-driven Optimal Filtering for Linear Systems with Unknown Noise Covariances
Real-World Image Super-Resolution as Multi-Task Learning
Semantic segmentation of sparse irregular point clouds for leaf/wood discrimination
Learning Shared Safety Constraints from Multi-task Demonstrations
MixFormerV2: Efficient Fully Transformer Tracking
A Unified Discretization Framework for Differential Equation Approach with Lyapunov Arguments for Convex Optimization
On the Convergence of Encoder-only Shallow Transformers
Convex and Non-convex Optimization Under Generalized Smoothness
Accurate Interpolation for Scattered Data through Hierarchical Residual Refinement
Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent
A Riemannian Exponential Augmented Lagrangian Method for Computing the Projection Robust Wasserstein Distance
Closing the Computational-Statistical Gap in Best Arm Identification for Combinatorial Semi-bandits
When Does Optimizing a Proper Loss Yield Calibration?
Self-Supervised Learning of Representations for Space Generates Multi-Modular Grid Cells
FLSL: Feature-level Self-supervised Learning
One-for-All: Bridge the Gap Between Heterogeneous Architectures in Knowledge Distillation
Constant Approximation for Individual Preference Stable Clustering
Intrinsic Dimension Estimation for Robust Detection of AI-Generated Texts
OpenMask3D: Open-Vocabulary 3D Instance Segmentation
Training neural operators to preserve invariant measures of chaotic attractors
GUST: Combinatorial Generalization by Unsupervised Grouping with Neuronal Coherence
Distribution-Free Statistical Dispersion Control for Societal Applications
Self-supervised Object-Centric Learning for Videos
UltraRE: Enhancing RecEraser for Recommendation Unlearning via Error Decomposition
Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Power
Generalizable One-shot 3D Neural Head Avatar
Towards Data-Algorithm Dependent Generalization: a Case Study on Overparameterized Linear Regression
Accelerating Reinforcement Learning with Value-Conditional State Entropy Exploration
Generalization bounds for neural ordinary differential equations and deep residual networks
Scenario Diffusion: Controllable Driving Scenario Generation With Diffusion
Scale-teaching: Robust Multi-scale Training for Time Series Classification with Noisy Labels
Should We Learn Most Likely Functions or Parameters?
Provable Advantage of Curriculum Learning on Parity Targets with Mixed Inputs
The Behavior and Convergence of Local Bayesian Optimization
Optimal Transport-Guided Conditional Score-Based Diffusion Model
Cross-modal Prompts: Adapting Large Pre-trained Models for Audio-Visual Downstream Tasks
Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization
Robust Learning with Progressive Data Expansion Against Spurious Correlation
Diffused Task-Agnostic Milestone Planner
Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality
Learning threshold neurons via edge of stability
Diffusion Representation for Asymmetric Kernels via Magnetic Transform
Reinforcement-Enhanced Autoregressive Feature Transformation: Gradient-steered Search in Continuous Space for Postfix Expressions
(Almost) Provable Error Bounds Under Distribution Shift via Disagreement Discrepancy
Rewrite Caption Semantics: Bridging Semantic Gaps for Language-Supervised Semantic Segmentation
RangePerception: Taming LiDAR Range View for Efficient and Accurate 3D Object Detection
Unbiased Compression Saves Communication in Distributed Optimization: When and How Much?
Diversify Your Vision Datasets with Automatic Diffusion-based Augmentation
Tuning Multi-mode Token-level Prompt Alignment across Modalities
Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors
Generalized Semi-Supervised Learning via Self-Supervised Feature Adaptation
One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization
Image Captioners Are Scalable Vision Learners Too
AGD: an Auto-switchable Optimizer using Stepwise Gradient Difference for Preconditioning Matrix
Universal Online Learning with Gradient Variations: A Multi-layer Online Ensemble Approach
DIFFER:Decomposing Individual Reward for Fair Experience Replay in Multi-Agent Reinforcement Learning
Linguistic Binding in Diffusion Models: Enhancing Attribute Correspondence through Attention Map Alignment
Differentiable Sampling of Categorical Distributions Using the CatLog-Derivative Trick
Live Graph Lab: Towards Open, Dynamic and Real Transaction Graphs with NFT
Finite Population Regression Adjustment and Non-asymptotic Guarantees for Treatment Effect Estimation
Complexity Matters: Rethinking the Latent Space for Generative Modeling
On the Powerfulness of Textual Outlier Exposure for Visual OoD Detection
Gaussian Process Probes (GPP) for Uncertainty-Aware Probing
Adaptive Uncertainty Estimation via High-Dimensional Testing on Latent Representations
AR-Diffusion: Auto-Regressive Diffusion Model for Text Generation
Dynamic Non-monotone Submodular Maximization
Universal Prompt Tuning for Graph Neural Networks
Pruning vs Quantization: Which is Better?
Efficient Diffusion Policies For Offline Reinforcement Learning
A Fast and Accurate Estimator for Large Scale Linear Model via Data Averaging
Improved Frequency Estimation Algorithms with and without Predictions
No-Regret Online Reinforcement Learning with Adversarial Losses and Transitions
Score-based Generative Models with Lévy Processes
Model Sparsity Can Simplify Machine Unlearning
Transformer-based Planning for Symbolic Regression
Collaborative Score Distillation for Consistent Visual Editing
The Adversarial Consistency of Surrogate Risks for Binary Classification
On the Convergence of No-Regret Learning Dynamics in Time-Varying Games
Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization
Accelerating Monte Carlo Tree Search with Probability Tree State Abstraction
Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent
Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment
Instructing Goal-Conditioned Reinforcement Learning Agents with Temporal Logic Objectives
De novo Drug Design using Reinforcement Learning with Multiple GPT Agents
Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations
Cascading Contextual Assortment Bandits
How a Student becomes a Teacher: learning and forgetting through Spectral methods
On Computing Pairwise Statistics with Local Differential Privacy
Blurred-Dilated Method for Adversarial Attacks
Single-Stage Visual Query Localization in Egocentric Videos
Adaptive Topological Feature via Persistent Homology: Filtration Learning for Point Clouds
Robust Knowledge Transfer in Tiered Reinforcement Learning
Tanh Works Better with Asymmetry
Learning Unseen Modality Interaction
Exact Verification of ReLU Neural Control Barrier Functions
Reliable learning in challenging environments
Towards Efficient Image Compression Without Autoregressive Models
Neural Sculpting: Uncovering hierarchically modular task structure in neural networks through pruning and network analysis
Exploring the Optimal Choice for Generative Processes in Diffusion Models: Ordinary vs Stochastic Differential Equations
Adjustable Robust Reinforcement Learning for Online 3D Bin Packing
Enhancing User Intent Capture in Session-Based Recommendation with Attribute Patterns
Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction
A Unified Solution for Privacy and Communication Efficiency in Vertical Federated Learning
To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis
Multi-task Representation Learning for Pure Exploration in Bilinear Bandits
Strategic Data Sharing between Competitors
Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity
Phase diagram of early training dynamics in deep neural networks: effect of the learning rate, depth, and width
Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness
Resetting the Optimizer in Deep RL: An Empirical Study
Extraction and Recovery of Spatio-Temporal Structure in Latent Dynamics Alignment with Diffusion Model
MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under nonparametrized geometrical variability
PAPR: Proximity Attention Point Rendering
When Visual Prompt Tuning Meets Source-Free Domain Adaptive Semantic Segmentation
Newton–Cotes Graph Neural Networks: On the Time Evolution of Dynamic Systems
Finding Local Minima Efficiently in Decentralized Optimization
List and Certificate Complexities in Replicable Learning
Saving 100x Storage: Prototype Replay for Reconstructing Training Sample Distribution in Class-Incremental Semantic Segmentation
V-InFoR: A Robust Graph Neural Networks Explainer for Structurally Corrupted Graphs
Online POMDP Planning with Anytime Deterministic Guarantees
Align Your Prompts: Test-Time Prompting with Distribution Alignment for Zero-Shot Generalization
Diffusion Probabilistic Models for Structured Node Classification
Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb
Exploring and Interacting with the Set of Good Sparse Generalized Additive Models
Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning
Flow Matching for Scalable Simulation-Based Inference
Optimal Transport Model Distributional Robustness
Fairness-guided Few-shot Prompting for Large Language Models
A One-Size-Fits-All Approach to Improving Randomness in Paper Assignment
A Smooth Binary Mechanism for Efficient Private Continual Observation
Optimal Transport for Treatment Effect Estimation
ParaFuzz: An Interpretability-Driven Technique for Detecting Poisoned Samples in NLP
Brant: Foundation Model for Intracranial Neural Signal
Discrete-Smoothness in Online Algorithms with Predictions
Coupled Reconstruction of Cortical Surfaces by Diffeomorphic Mesh Deformation
Propagating Knowledge Updates to LMs Through Distillation
Zero-One Laws of Graph Neural Networks
First Order Stochastic Optimization with Oblivious Noise
On the Stability-Plasticity Dilemma in Continual Meta-Learning: Theory and Algorithm
Weakly Coupled Deep Q-Networks
ZoomTrack: Target-aware Non-uniform Resizing for Efficient Visual Tracking
R-divergence for Estimating Model-oriented Distribution Discrepancy
Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach
AiluRus: A Scalable ViT Framework for Dense Prediction
How2comm: Communication-Efficient and Collaboration-Pragmatic Multi-Agent Perception
Uncovering Prototypical Knowledge for Weakly Open-Vocabulary Semantic Segmentation
NAR-Former V2: Rethinking Transformer for Universal Neural Network Representation Learning
Sharpness Minimization Algorithms Do Not Only Minimize Sharpness To Achieve Better Generalization
Approximation-Generalization Trade-offs under (Approximate) Group Equivariance
Decentralized Randomly Distributed Multi-agent Multi-armed Bandit with Heterogeneous Rewards
Gaussian Differential Privacy on Riemannian Manifolds
The Impact of Positional Encoding on Length Generalization in Transformers
Curvature Filtrations for Graph Generative Model Evaluation
Convergence Analysis of Sequential Federated Learning on Heterogeneous Data
Federated Conditional Stochastic Optimization
Reward Imputation with Sketching for Contextual Batched Bandits
Learning from Visual Observation via Offline Pretrained State-to-Go Transformer
Zero-Regret Performative Prediction Under Inequality Constraints
Prototype-based Aleatoric Uncertainty Quantification for Cross-modal Retrieval
Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution
LayoutPrompter: Awaken the Design Ability of Large Language Models
A Long $N$-step Surrogate Stage Reward for Deep Reinforcement Learning
Neural Graph Generation from Graph Statistics
AUDIT: Audio Editing by Following Instructions with Latent Diffusion Models
H3T: Efficient Integration of Memory Optimization and Parallelism for Large-scale Transformer Training
PrimDiffusion: Volumetric Primitives Diffusion for 3D Human Generation
SlotDiffusion: Object-Centric Generative Modeling with Diffusion Models
Generative Neural Fields by Mixtures of Neural Implicit Functions
Riemannian Laplace approximations for Bayesian neural networks
Reward Scale Robustness for Proximal Policy Optimization via DreamerV3 Tricks
Optimal Treatment Allocation for Efficient Policy Evaluation in Sequential Decision Making
Isometric Quotient Variational Auto-Encoders for Structure-Preserving Representation Learning
STREAMER: Streaming Representation Learning and Event Segmentation in a Hierarchical Manner
Training Private Models That Know What They Don’t Know
SwapPrompt: Test-Time Prompt Adaptation for Vision-Language Models
Information-guided Planning: An Online Approach for Partially Observable Problems
Equivariant Neural Operator Learning with Graphon Convolution
Recaptured Raw Screen Image and Video Demoiréing via Channel and Spatial Modulations
Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models
Transfer learning for atomistic simulations using GNNs and kernel mean embeddings
Reconciling Competing Sampling Strategies of Network Embedding
Does Invariant Graph Learning via Environment Augmentation Learn Invariance?
GlucoSynth: Generating Differentially-Private Synthetic Glucose Traces
GRAND-SLAMIN’ Interpretable Additive Modeling with Structural Constraints
Decision Tree for Locally Private Estimation with Public Data
Debiased and Denoised Entity Recognition from Distant Supervision
Variational Monte Carlo on a Budget — Fine-tuning pre-trained Neural Wavefunctions
Anytime-Competitive Reinforcement Learning with Policy Prior
C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder
Core-sets for Fair and Diverse Data Summarization
Hyperbolic VAE via Latent Gaussian Distributions
Streaming PCA for Markovian Data
Token-Scaled Logit Distillation for Ternary Weight Generative Language Models
Episodic Multi-Task Learning with Heterogeneous Neural Processes
StyleGAN knows Normal, Depth, Albedo, and More
Towards In-context Scene Understanding
Mnemosyne: Learning to Train Transformers with Transformers
PaintSeg: Painting Pixels for Training-free Segmentation
On the Trade-off of Intra-/Inter-class Diversity for Supervised Pre-training
Gradient-Based Feature Learning under Structured Data
A Metadata-Driven Approach to Understand Graph Neural Networks
A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithm
Direct Preference-based Policy Optimization without Reward Modeling
Faith and Fate: Limits of Transformers on Compositionality
RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorization
Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming Approach
Revisit the Power of Vanilla Knowledge Distillation: from Small Scale to Large Scale
Counterfactual Memorization in Neural Language Models
In-Context Learning Unlocked for Diffusion Models
DDF-HO: Hand-Held Object Reconstruction via Conditional Directed Distance Field
LinkerNet: Fragment Poses and Linker Co-Design with 3D Equivariant Diffusion
Certified Minimax Unlearning with Generalization Rates and Deletion Capacity
Learning World Models with Identifiable Factorization
TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery
ComSL: A Composite Speech-Language Model for End-to-End Speech-to-Text Translation
DELTA: Diverse Client Sampling for Fasting Federated Learning
HAP: Structure-Aware Masked Image Modeling for Human-Centric Perception
Strategic Classification under Unknown Personalized Manipulation
Globally injective and bijective neural operators
Offline Imitation Learning with Variational Counterfactual Reasoning
An Efficient End-to-End Training Approach for Zero-Shot Human-AI Coordination
Optimal and Fair Encouragement Policy Evaluation and Learning
When is Agnostic Reinforcement Learning Statistically Tractable?
Synthetic Experience Replay
Adversarially Robust Distributed Count Tracking via Partial Differential Privacy
Language Model Alignment with Elastic Reset
Mask Propagation for Efficient Video Semantic Segmentation
Balancing memorization and generalization in RNNs for high performance brain-machine Interfaces
Data Quality in Imitation Learning
Neural Image Compression: Generalization, Robustness, and Spectral Biases
A Dual-Stream Neural Network Explains the Functional Segregation of Dorsal and Ventral Visual Pathways in Human Brains
Evolutionary Neural Architecture Search for Transformer in Knowledge Tracing
Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation
$L_2$-Uniform Stability of Randomized Learning Algorithms: Sharper Generalization Bounds and Confidence Boosting
Domain Adaptive Imitation Learning with Visual Observation
High dimensional, tabular deep learning with an auxiliary knowledge graph
Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions
ChatGPT-Powered Hierarchical Comparisons for Image Classification
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning
Human-Aligned Calibration for AI-Assisted Decision Making
Goal Driven Discovery of Distributional Differences via Language Descriptions
The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit
Contextually Affinitive Neighborhood Refinery for Deep Clustering
Interpretable Graph Networks Formulate Universal Algebra Conjectures
Understanding Few-Shot Learning: Measuring Task Relatedness and Adaptation Difficulty via Attributes
Learning Exponential Families from Truncated Samples
Private estimation algorithms for stochastic block models and mixture models
Vulnerabilities in Video Quality Assessment Models: The Challenge of Adversarial Attacks
Near-optimal learning with average Hölder smoothness
OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling
Fast and Simple Spectral Clustering in Theory and Practice
Unified 3D Segmenter As Prototypical Classifiers
Nearly Tight Bounds For Differentially Private Multiway Cut
Learning Large-scale Neural Fields via Context Pruned Meta-Learning
RADAR: Robust AI-Text Detection via Adversarial Learning
FaceDNeRF: Semantics-Driven Face Reconstruction, Prompt Editing and Relighting with Diffusion Models
CoDA: Collaborative Novel Box Discovery and Cross-modal Alignment for Open-vocabulary 3D Object Detection
Counterfactually Fair Representation
Adaptive Data Analysis in a Balanced Adversarial Model
ProPILE: Probing Privacy Leakage in Large Language Models
Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo Labeling and Multi-scale Feature Grouping
Convergence of Actor-Critic with Multi-Layer Neural Networks
Towards a Unified Framework of Contrastive Learning for Disentangled Representations
Streaming Factor Trajectory Learning for Temporal Tensor Decomposition
PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers
Sorting with Predictions
Is Distance Matrix Enough for Geometric Deep Learning?
Beyond Myopia: Learning from Positive and Unlabeled Data through Holistic Predictive Trends
Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation
The Utility of “Even if” Semifactual Explanation to Optimise Positive Outcomes
Projection-Free Methods for Stochastic Simple Bilevel Optimization with Convex Lower-level Problem
PROTES: Probabilistic Optimization with Tensor Sampling
Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictions
Cheaply Estimating Inference Efficiency Metrics for Autoregressive Transformer Models
Symbol-LLM: Leverage Language Models for Symbolic System in Visual Human Activity Reasoning
On Imitation in Mean-field Games
ANPL: Towards Natural Programming with Interactive Decomposition
Promises and Pitfalls of Threshold-based Auto-labeling
Modality-Agnostic Self-Supervised Learning with Meta-Learned Masked Auto-Encoder
GenS: Generalizable Neural Surface Reconstruction from Multi-View Images
DeepPCR: Parallelizing Sequential Operations in Neural Networks
Agnostic Multi-Group Active Learning
Cluster-aware Semi-supervised Learning: Relational Knowledge Distillation Provably Learns Clustering
Dynamic Personalized Federated Learning with Adaptive Differential Privacy
Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation
SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks
The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning
Efficient Equivariant Transfer Learning from Pretrained Models
The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks
A3FL: Adversarially Adaptive Backdoor Attacks to Federated Learning
$p$-Poisson surface reconstruction in curl-free flow from point clouds
Towards Self-Interpretable Graph-Level Anomaly Detection
Nearly Optimal VC-Dimension and Pseudo-Dimension Bounds for Deep Neural Network Derivatives
Graph Convolutional Kernel Machine versus Graph Convolutional Networks
Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach
Unlocking Deterministic Robustness Certification on ImageNet
CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra
PLANNER: Generating Diversified Paragraph via Latent Language Diffusion Model
Leveraging Locality and Robustness to Achieve Massively Scalable Gaussian Process Regression
Plug-and-Play Stability for Intracortical Brain-Computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication
Language Models Meet World Models: Embodied Experiences Enhance Language Models
DiffKendall: A Novel Approach for Few-Shot Learning with Differentiable Kendall's Rank Correlation
Optimistic Exploration in Reinforcement Learning Using Symbolic Model Estimates
Enhancing Sharpness-Aware Optimization Through Variance Suppression
An Optimal Structured Zeroth-order Algorithm for Non-smooth Optimization
ARTree: A Deep Autoregressive Model for Phylogenetic Inference
Robust and Actively Secure Serverless Collaborative Learning
Solving a Class of Non-Convex Minimax Optimization in Federated Learning
Cappy: Outperforming and Boosting Large Multi-Task LMs with a Small Scorer
Dataset Diffusion: Diffusion-based Synthetic Data Generation for Pixel-Level Semantic Segmentation
Topological Parallax: A Geometric Specification for Deep Perception Models
DeepSimHO: Stable Pose Estimation for Hand-Object Interaction via Physics Simulation
Precision-Recall Divergence Optimization for Generative Modeling with GANs and Normalizing Flows
REx: Data-Free Residual Quantization Error Expansion
Uniform Convergence with Square-Root Lipschitz Loss
CoDet: Co-occurrence Guided Region-Word Alignment for Open-Vocabulary Object Detection
Learning Rate Free Bayesian Inference in Constrained Domains
Information Geometry of the Retinal Representation Manifold
Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained Models
GLOBER: Coherent Non-autoregressive Video Generation via GLOBal Guided Video DecodER
BayesTune: Bayesian Sparse Deep Model Fine-tuning
Context Shift Reduction for Offline Meta-Reinforcement Learning
4M: Massively Multimodal Masked Modeling
Loss Dynamics of Temporal Difference Reinforcement Learning
Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization
Predict-then-Calibrate: A New Perspective of Robust Contextual LP
GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning
Tools for Verifying Neural Models' Training Data
An Efficient Doubly-Robust Test for the Kernel Treatment Effect
Long Sequence Hopfield Memory
Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning
Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs
A Hierarchical Spatial Transformer for Massive Point Samples in Continuous Space
A Unified Generalization Analysis of Re-Weighting and Logit-Adjustment for Imbalanced Learning
Cross-modal Active Complementary Learning with Self-refining Correspondence
Can Language Models Solve Graph Problems in Natural Language?
MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detection
Asynchrony-Robust Collaborative Perception via Bird's Eye View Flow
Fast Attention Over Long Sequences With Dynamic Sparse Flash Attention
Provably Safe Reinforcement Learning with Step-wise Violation Constraints
Learning to Tokenize for Generative Retrieval
The s-value: evaluating stability with respect to distributional shifts
Fast Asymptotically Optimal Algorithms for Non-Parametric Stochastic Bandits
Horospherical Decision Boundaries for Large Margin Classification in Hyperbolic Space
Expressive Sign Equivariant Networks for Spectral Geometric Learning
Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks
Model-enhanced Vector Index
Active Negative Loss Functions for Learning with Noisy Labels
Identification of Nonlinear Latent Hierarchical Models
Model-Free Reinforcement Learning with the Decision-Estimation Coefficient
Fast Approximation of Similarity Graphs with Kernel Density Estimation
An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradient
CamoPatch: An Evolutionary Strategy for Generating Camoflauged Adversarial Patches
Selective Amnesia: A Continual Learning Approach to Forgetting in Deep Generative Models
Multi-Object Representation Learning via Feature Connectivity and Object-Centric Regularization
TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials
Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning
Regularity as Intrinsic Reward for Free Play
ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings
An Improved Relaxation for Oracle-Efficient Adversarial Contextual Bandits
OKRidge: Scalable Optimal k-Sparse Ridge Regression
Flow: Per-instance Personalized Federated Learning
Recommender Systems with Generative Retrieval
Retaining Beneficial Information from Detrimental Data for Neural Network Repair
Fast Model DeBias with Machine Unlearning
Uncovering and Quantifying Social Biases in Code Generation
Identifiable Contrastive Learning with Automatic Feature Importance Discovery
Optimal privacy guarantees for a relaxed threat model: Addressing sub-optimal adversaries in differentially private machine learning
Passive learning of active causal strategies in agents and language models
From Cloze to Comprehension: Retrofitting Pre-trained Masked Language Models to Pre-trained Machine Reader
Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization
Waypoint Transformer: Reinforcement Learning via Supervised Learning with Intermediate Targets
LANCE: Stress-testing Visual Models by Generating Language-guided Counterfactual Images
The Contextual Lasso: Sparse Linear Models via Deep Neural Networks
Koopman Kernel Regression
Designing Robust Transformers using Robust Kernel Density Estimation
SANFlow: Semantic-Aware Normalizing Flow for Anomaly Detection
Augmenting Language Models with Long-Term Memory
Incomplete Multimodality-Diffused Emotion Recognition
Self-Evaluation Guided Beam Search for Reasoning
Self-Consistent Velocity Matching of Probability Flows
Conservative Offline Policy Adaptation in Multi-Agent Games
Fast Projected Newton-like Method for Precision Matrix Estimation under Total Positivity
Causal discovery from observational and interventional data across multiple environments
Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior
Information Maximization Perspective of Orthogonal Matching Pursuit with Applications to Explainable AI
Neural Data Transformer 2: Multi-context Pretraining for Neural Spiking Activity
Video Dynamics Prior: An Internal Learning Approach for Robust Video Enhancements
Meet in the Middle: A New Pre-training Paradigm
Dual Mean-Teacher: An Unbiased Semi-Supervised Framework for Audio-Visual Source Localization
Differentiable Random Partition Models
Divide, Evaluate, and Refine: Evaluating and Improving Text-to-Image Alignment with Iterative VQA Feedback
Unbiased learning of deep generative models with structured discrete representations
SPAE: Semantic Pyramid AutoEncoder for Multimodal Generation with Frozen LLMs
DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models
Generating Images with Multimodal Language Models
MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment Tasks
When can Regression-Adjusted Control Variate Help? Rare Events, Sobolev Embedding and Minimax Optimality
LoCoOp: Few-Shot Out-of-Distribution Detection via Prompt Learning
Inner-Outer Aware Reconstruction Model for Monocular 3D Scene Reconstruction
Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent
Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman
Bayesian Optimisation of Functions on Graphs
UP-NeRF: Unconstrained Pose Prior-Free Neural Radiance Field
Guiding Large Language Models via Directional Stimulus Prompting
$k$-Means Clustering with Distance-Based Privacy
Effective Human-AI Teams via Learned Natural Language Rules and Onboarding
Perceptual Kalman Filters: Online State Estimation under a Perfect Perceptual-Quality Constraint
Causal Effect Regularization: Automated Detection and Removal of Spurious Correlations
Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training
Toward Understanding Generative Data Augmentation
NCDL: A Framework for Deep Learning on non-Cartesian Lattices
Top-Ambiguity Samples Matter: Understanding Why Deep Ensemble Works in Selective Classification
Rank-N-Contrast: Learning Continuous Representations for Regression
Unsupervised Anomaly Detection with Rejection
Projection-Free Methods for Solving Nonconvex-Concave Saddle Point Problems
Full-Atom Protein Pocket Design via Iterative Refinement
DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting
Max-Margin Token Selection in Attention Mechanism
Necessary and Sufficient Conditions for Optimal Decision Trees using Dynamic Programming
Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense
Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization
Differentiable Registration of Images and LiDAR Point Clouds with VoxelPoint-to-Pixel Matching
Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models
Contextual Bandits and Imitation Learning with Preference-Based Active Queries
Regularizing Neural Networks with Meta-Learning Generative Models
STORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning
Causal Imitability Under Context-Specific Independence Relations
Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex
Towards Evaluating Transfer-based Attacks Systematically, Practically, and Fairly
Latent SDEs on Homogeneous Spaces
On the Planning Abilities of Large Language Models - A Critical Investigation
Self-Weighted Contrastive Learning among Multiple Views for Mitigating Representation Degeneration
Binarized Neural Machine Translation
An Inductive Bias for Tabular Deep Learning
Convergence analysis of ODE models for accelerated first-order methods via positive semidefinite kernels
Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models
Incentives in Private Collaborative Machine Learning
DiffUTE: Universal Text Editing Diffusion Model
CosNet: A Generalized Spectral Kernel Network
TD Convergence: An Optimization Perspective
Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions
A Bayesian Approach To Analysing Training Data Attribution In Deep Learning
On the Size and Approximation Error of Distilled Datasets
Have it your way: Individualized Privacy Assignment for DP-SGD
Making Scalable Meta Learning Practical
Holistic Transfer: Towards Non-Disruptive Fine-Tuning with Partial Target Data
On Calibrating Diffusion Probabilistic Models
On the Minimax Regret for Online Learning with Feedback Graphs
Deep Insights into Noisy Pseudo Labeling on Graph Data
Batch Bayesian Optimization For Replicable Experimental Design
Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data
Multi-Prompt Alignment for Multi-Source Unsupervised Domain Adaptation
Locality-Aware Generalizable Implicit Neural Representation
Self-supervised Graph Neural Networks via Low-Rank Decomposition
CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models
Minimax Risks and Optimal Procedures for Estimation under Functional Local Differential Privacy
Towards Higher Ranks via Adversarial Weight Pruning
Beyond NTK with Vanilla Gradient Descent: A Mean-Field Analysis of Neural Networks with Polynomial Width, Samples, and Time
Detection Based Part-level Articulated Object Reconstruction from Single RGBD Image
PTQD: Accurate Post-Training Quantization for Diffusion Models
Quantum Bayesian Optimization
Model Shapley: Equitable Model Valuation with Black-box Access
Finding Counterfactually Optimal Action Sequences in Continuous State Spaces
Model-Free Active Exploration in Reinforcement Learning
NICE: NoIse-modulated Consistency rEgularization for Data-Efficient GANs
Connecting Pre-trained Language Model and Downstream Task via Properties of Representation
Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness
On Measuring Fairness in Generative Models
Spatio-Angular Convolutions for Super-resolution in Diffusion MRI
Harnessing Hard Mixed Samples with Decoupled Regularizer
Implicit Regularization in Over-Parameterized Support Vector Machine
CELLE-2: Translating Proteins to Pictures and Back with a Bidirectional Text-to-Image Transformer
Offline Reinforcement Learning with Differential Privacy
Predicting a Protein's Stability under a Million Mutations
GAN You See Me? Enhanced Data Reconstruction Attacks against Split Inference
Toolformer: Language Models Can Teach Themselves to Use Tools
Real-Time Motion Prediction via Heterogeneous Polyline Transformer with Relative Pose Encoding
Learning Trajectories are Generalization Indicators
Autodecoding Latent 3D Diffusion Models
RayDF: Neural Ray-surface Distance Fields with Multi-view Consistency
Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise
STEVE-1: A Generative Model for Text-to-Behavior in Minecraft
GraphAdapter: Tuning Vision-Language Models With Dual Knowledge Graph
Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object Detection
Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds
Text Promptable Surgical Instrument Segmentation with Vision-Language Models
Compositional Policy Learning in Stochastic Control Systems with Formal Guarantees
Path Regularization: A Convexity and Sparsity Inducing Regularization for Parallel ReLU Networks
Meta-Learning with Neural Bandit Scheduler
Wide Neural Networks as Gaussian Processes: Lessons from Deep Equilibrium Models
Tree-Rings Watermarks: Invisible Fingerprints for Diffusion Images
Topology-Aware Uncertainty for Image Segmentation
Puzzlefusion: Unleashing the Power of Diffusion Models for Spatial Puzzle Solving
NEO-KD: Knowledge-Distillation-Based Adversarial Training for Robust Multi-Exit Neural Networks
A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning
Sparse Parameterization for Epitomic Dataset Distillation
ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting
Depth-discriminative Metric Learning for Monocular 3D Object Detection
Encoding Human Behavior in Information Design through Deep Learning
PyNeRF: Pyramidal Neural Radiance Fields
Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback
Self-Supervised Learning with Lie Symmetries for Partial Differential Equations
Structured Semidefinite Programming for Recovering Structured Preconditioners
A Definition of Continual Reinforcement Learning
Continuous Parametric Optical Flow
Large Language Models Are Semi-Parametric Reinforcement Learning Agents
Local Convergence of Gradient Methods for Min-Max Games: Partial Curvature Generically Suffices
Leveraging the two-timescale regime to demonstrate convergence of neural networks
A Unified Conditional Framework for Diffusion-based Image Restoration
Collapsed Inference for Bayesian Deep Learning
Optimistic Meta-Gradients
Time Series as Images: Vision Transformer for Irregularly Sampled Time Series
Bypass Exponential Time Preprocessing: Fast Neural Network Training via Weight-Data Correlation Preprocessing
WalkLM: A Uniform Language Model Fine-tuning Framework for Attributed Graph Embedding
Differentially Private Approximate Near Neighbor Counting in High Dimensions
Rehearsal Learning for Avoiding Undesired Future
Students Parrot Their Teachers: Membership Inference on Model Distillation
On the Convergence of CART under Sufficient Impurity Decrease Condition
Polynomial-Time Linear-Swap Regret Minimization in Imperfect-Information Sequential Games
Spectral Entry-wise Matrix Estimation for Low-Rank Reinforcement Learning
Optimal Unbiased Randomizers for Regression with Label Differential Privacy
Enhancing Adaptive History Reserving by Spiking Convolutional Block Attention Module in Recurrent Neural Networks
CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions
Feature Adaptation for Sparse Linear Regression
Learning to Modulate pre-trained Models in RL
Inference-Time Intervention: Eliciting Truthful Answers from a Language Model
The emergence of clusters in self-attention dynamics
On quantum backpropagation, information reuse, and cheating measurement collapse
Learning Dense Flow Field for Highly-accurate Cross-view Camera Localization
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Online RL in Linearly $q^\pi$-Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore
On the Convergence and Sample Complexity Analysis of Deep Q-Networks with $\epsilon$-Greedy Exploration
Diffusion-Based Probabilistic Uncertainty Estimation for Active Domain Adaptation
Assessor360: Multi-sequence Network for Blind Omnidirectional Image Quality Assessment
Causal Component Analysis
Understanding and Mitigating Copying in Diffusion Models
Randomized and Deterministic Maximin-share Approximations for Fractionally Subadditive Valuations
On Robust Streaming for Learning with Experts: Algorithms and Lower Bounds
Robust Concept Erasure via Kernelized Rate-Distortion Maximization
State Regularized Policy Optimization on Data with Dynamics Shift
Formulating Discrete Probability Flow Through Optimal Transport
Evaluating and Inducing Personality in Pre-trained Language Models
Connecting Multi-modal Contrastive Representations
Goal-Conditioned Predictive Coding for Offline Reinforcement Learning
QuACK: Accelerating Gradient-Based Quantum Optimization with Koopman Operator Learning
Towards Label Position Bias in Graph Neural Networks
EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought
HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text
Towards Characterizing the First-order Query Complexity of Learning (Approximate) Nash Equilibria in Zero-sum Matrix Games
Mutual-Information Regularized Multi-Agent Policy Iteration
Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability
Are Emergent Abilities of Large Language Models a Mirage?
Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question Answering
Optimization or Architecture: How to Hack Kalman Filtering
Intriguing Properties of Quantization at Scale
Efficient Test-Time Adaptation for Super-Resolution with Second-Order Degradation and Reconstruction
Stochastic Approximation Approaches to Group Distributionally Robust Optimization
Conditional independence testing under misspecified inductive biases
Bayes beats Cross Validation: Efficient and Accurate Ridge Regression via Expectation Maximization
Calibration by Distribution Matching: Trainable Kernel Calibration Metrics
StableFDG: Style and Attention Based Learning for Federated Domain Generalization
Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition
Conformal Meta-learners for Predictive Inference of Individual Treatment Effects
Perceptual adjustment queries and an inverted measurement paradigm for low-rank metric learning
Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference
SUBP: Soft Uniform Block Pruning for 1$\times$N Sparse CNNs Multithreading Acceleration
Kernel Quadrature with Randomly Pivoted Cholesky
Glance and Focus: Memory Prompting for Multi-Event Video Question Answering
LLM-Pruner: On the Structural Pruning of Large Language Models
Grounded Decoding: Guiding Text Generation with Grounded Models for Embodied Agents
Deep Non-line-of-sight Imaging from Under-scanning Measurements
Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness
DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models
Simple, Scalable and Effective Clustering via One-Dimensional Projections
Global Identifiability of $\ell_1$-based Dictionary Learning via Matrix Volume Optimization
On Slicing Optimality for Mutual Information
ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation
Parameterizing Non-Parametric Meta-Reinforcement Learning Tasks via Subtask Decomposition
Switching Temporary Teachers for Semi-Supervised Semantic Segmentation
Scissorhands: Exploiting the Persistence of Importance Hypothesis for LLM KV Cache Compression at Test Time
Module-wise Adaptive Distillation for Multimodality Foundation Models
Towards Symmetry-Aware Generation of Periodic Materials
ALGO: Synthesizing Algorithmic Programs with Generated Oracle Verifiers
Attentive Transfer Entropy to Exploit Transient Emergence of Coupling Effect
Train Hard, Fight Easy: Robust Meta Reinforcement Learning
DASpeech: Directed Acyclic Transformer for Fast and High-quality Speech-to-Speech Translation
MAG-GNN: Reinforcement Learning Boosted Graph Neural Network
Neural Modulation for Flash Memory: An Unsupervised Learning Framework for Improved Reliability
Regularization properties of adversarially-trained linear regression
Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling
DropCompute: simple and more robust distributed synchronous training via compute variance reduction
LIMA: Less Is More for Alignment
Towards Free Data Selection with General-Purpose Models
The probability flow ODE is provably fast
Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments
Conformal Prediction for Time Series with Modern Hopfield Networks
On the Constrained Time-Series Generation Problem
Reinforcement Learning with Fast and Forgetful Memory
Trading-off price for data quality to achieve fair online allocation
ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction
Wasserstein distributional robustness of neural networks
Demystifying Oversmoothing in Attention-Based Graph Neural Networks
VoxDet: Voxel Learning for Novel Instance Detection
Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings
An Information Theory Perspective on Variance-Invariance-Covariance Regularization
Disentangling Voice and Content with Self-Supervision for Speaker Recognition
Distributed Personalized Empirical Risk Minimization
StyleDrop: Text-to-Image Synthesis of Any Style
Time-Independent Information-Theoretic Generalization Bounds for SGLD
Tester-Learners for Halfspaces: Universal Algorithms
Spatial-frequency channels, shape bias, and adversarial robustness
Learning Descriptive Image Captioning via Semipermeable Maximum Likelihood Estimation
Truly Scale-Equivariant Deep Nets with Fourier Layers
PrObeD: Proactive Object Detection Wrapper
Efficient Batched Algorithm for Contextual Linear Bandits with Large Action Space via Soft Elimination
Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models
SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding
Cascading Bandits: Optimizing Recommendation Frequency in Delayed Feedback Environments
Certified Robustness via Dynamic Margin Maximization and Improved Lipschitz Regularization
Dynamic Tensor Decomposition via Neural Diffusion-Reaction Processes
An Inverse Scaling Law for CLIP Training
Sample Efficient Reinforcement Learning in Mixed Systems through Augmented Samples and Its Applications to Queueing Networks
Feature-Learning Networks Are Consistent Across Widths At Realistic Scales
Learning Visual Prior via Generative Pre-Training
Exploring Question Decomposition for Zero-Shot VQA
Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels
Large Language Models can Implement Policy Iteration
Echoes Beyond Points: Unleashing the Power of Raw Radar Data in Multi-modality Fusion
Residual Q-Learning: Offline and Online Policy Customization without Value
On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes
AlberDICE: Addressing Out-Of-Distribution Joint Actions in Offline Multi-Agent RL via Alternating Stationary Distribution Correction Estimation
When Can We Track Significant Preference Shifts in Dueling Bandits?
CS-Isolate: Extracting Hard Confident Examples by Content and Style Isolation
Flow Factorized Representation Learning
Compositional Generalization from First Principles
Stabilizing the Optimization of Neural Signed Distance Functions and Finer Shape Representation
Prompt-augmented Temporal Point Process for Streaming Event Sequence
Diffused Redundancy in Pre-trained Representations
Transformers learn to implement preconditioned gradient descent for in-context learning
H-nobs: Achieving Certified Fairness and Robustness in Distributed Learning on Heterogeneous Datasets
Language Models can Solve Computer Tasks
Enhancing Minority Classes by Mixing: An Adaptative Optimal Transport Approach for Long-tailed Classification
Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning
Fast Rank-1 Lattice Targeted Sampling for Black-box Optimization
Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples
Imbalanced Mixed Linear Regression
Random Cuts are Optimal for Explainable k-Medians
Should Under-parameterized Student Networks Copy or Average Teacher Weights?
Debiasing Conditional Stochastic Optimization
Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation Models
Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models
Enhancing Adversarial Robustness via Score-Based Optimization
Restless Bandits with Average Reward: Breaking the Uniform Global Attractor Assumption
Explainable and Efficient Randomized Voting Rules
Efficient Training of Energy-Based Models Using Jarzynski Equality
Optimization of Inter-group criteria for clustering with minimum size constraints
Delayed Algorithms for Distributed Stochastic Weakly Convex Optimization
Diverse Conventions for Human-AI Collaboration
Birder: Communication-Efficient 1-bit Adaptive Optimizer for Practical Distributed DNN Training
Minimum norm interpolation by perceptra: Explicit regularization and implicit bias
Trade-off Between Efficiency and Consistency for Removal-based Explanations
Convolutional Neural Operators for robust and accurate learning of PDEs
State Sequences Prediction via Fourier Transform for Representation Learning
Mitigating Test-Time Bias for Fair Image Retrieval
Active Learning-Based Species Range Estimation
Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization
Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement
Online Learning under Adversarial Nonlinear Constraints
SLaM: Student-Label Mixing for Distillation with Unlabeled Examples
Alternation makes the adversary weaker in two-player games
Monte Carlo Tree Search with Boltzmann Exploration
Zero-sum Polymatrix Markov Games: Equilibrium Collapse and Efficient Computation of Nash Equilibria
Boundary Guided Learning-Free Semantic Control with Diffusion Models
Robust low-rank training via approximate orthonormal constraints
Latent Diffusion for Language Generation
To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning
Fairly Recommending with Social Attributes: A Flexible and Controllable Optimization Approach
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits
Prefix-Tree Decoding for Predicting Mass Spectra from Molecules
Provably Robust Temporal Difference Learning for Heavy-Tailed Rewards
Minimum Description Length and Generalization Guarantees for Representation Learning
Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning
Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders
Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation
Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control
Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models
IPMix: Label-Preserving Data Augmentation Method for Training Robust Classifiers
Distributionally Robust Skeleton Learning of Discrete Bayesian Networks
On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms
Reverse Engineering Self-Supervised Learning
Label-Only Model Inversion Attacks via Knowledge Transfer
Evaluating the Moral Beliefs Encoded in LLMs
Training biologically plausible recurrent neural networks on cognitive tasks with long-term dependencies
Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities
Inserting Anybody in Diffusion Models via Celeb Basis
EICIL: Joint Excitatory Inhibitory Cycle Iteration Learning for Deep Spiking Neural Networks
Robust Contrastive Language-Image Pretraining against Data Poisoning and Backdoor Attacks
Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions
Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors
The Transient Nature of Emergent In-Context Learning in Transformers
Quantum speedups for stochastic optimization
Augmentation-Aware Self-Supervision for Data-Efficient GAN Training
Transformers are uninterpretable with myopic methods: a case study with bounded Dyck grammars
Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization
KD-Zero: Evolving Knowledge Distiller for Any Teacher-Student Pairs
Federated Multi-Objective Learning
Reading Relevant Feature from Global Representation Memory for Visual Object Tracking
Modulated Neural ODEs
Learning Multi-agent Behaviors from Distributed and Streaming Demonstrations
Importance-aware Co-teaching for Offline Model-based Optimization
VCC: Scaling Transformers to 128K Tokens or More by Prioritizing Important Tokens
Achieving $\mathcal{O}(\epsilon^{-1.5})$ Complexity in Hessian/Jacobian-free Stochastic Bilevel Optimization
Differentially Private Image Classification by Learning Priors from Random Processes
Temporal Robustness against Data poisoning
Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing
Type-to-Track: Retrieve Any Object via Prompt-based Tracking
Effective Robustness against Natural Distribution Shifts for Models with Different Training Data
Contrastive Moments: Unsupervised Halfspace Learning in Polynomial Time
User-Level Differential Privacy With Few Examples Per User
Fair Adaptive Experiments
Policy Space Diversity for Non-Transitive Games
Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence
Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback
Feature Learning for Interpretable, Performant Decision Trees
ALIM: Adjusting Label Importance Mechanism for Noisy Partial Label Learning
Performance Bounds for Policy-Based Average Reward Reinforcement Learning Algorithms
On the Statistical Consistency of Risk-Sensitive Bayesian Decision-Making
Responsible AI (RAI) Games and Ensembles
Learning Transformer Programs
Behavior Alignment via Reward Function Optimization
Overcoming Recency Bias of Normalization Statistics in Continual Learning: Balance and Adaptation
Learning Fine-grained View-Invariant Representations from Unpaired Ego-Exo Videos via Temporal Alignment
Implicit Bias of (Stochastic) Gradient Descent for Rank-1 Linear Neural Network
Optimal Preconditioning and Fisher Adaptive Langevin Sampling
Taking the neural sampling code very seriously: A data-driven approach for evaluating generative models of the visual system
FABind: Fast and Accurate Protein-Ligand Binding
Consistent Diffusion Models: Mitigating Sampling Drift by Learning to be Consistent
CoDrug: Conformal Drug Property Prediction with Density Estimation under Covariate Shift
Multi-Player Zero-Sum Markov Games with Networked Separable Interactions
Score-based Generative Modeling through Stochastic Evolution Equations in Hilbert Spaces
Demographic Parity Constrained Minimax Optimal Regression under Linear Model
REFINE: A Fine-Grained Medication Recommendation System Using Deep Learning and Personalized Drug Interaction Modeling
Unified Segment-to-Segment Framework for Simultaneous Sequence Generation
Training Fully Connected Neural Networks is $\exists\mathbb{R}$-Complete
Accelerated Training via Incrementally Growing Neural Networks using Variance Transfer and Learning Rate Adaptation
Facilitating Graph Neural Networks with Random Walk on Simplicial Complexes
Failure-Aware Gaussian Process Optimization with Regret Bounds
Drift doesn't Matter: Dynamic Decomposition with Diffusion Reconstruction for Unstable Multivariate Time Series Anomaly Detection
Anchor Data Augmentation
Adapting to Continuous Covariate Shift via Online Density Ratio Estimation
Tree Variational Autoencoders
RanPAC: Random Projections and Pre-trained Models for Continual Learning
Learning Mixtures of Gaussians Using the DDPM Objective
Decompose a Task into Generalizable Subtasks in Multi-Agent Reinforcement Learning
Factorized Contrastive Learning: Going Beyond Multi-view Redundancy
Risk-Averse Active Sensing for Timely Outcome Prediction under Cost Pressure
On the Ability of Graph Neural Networks to Model Interactions Between Vertices
Stable Nonconvex-Nonconcave Training via Linear Interpolation
LightSpeed: Light and Fast Neural Light Fields on Mobile Devices
Convergence of Alternating Gradient Descent for Matrix Factorization
One-Step Diffusion Distillation via Deep Equilibrium Models
Marich: A Query-efficient Distributionally Equivalent Model Extraction Attack
Chatting Makes Perfect: Chat-based Image Retrieval
Global Structure-Aware Diffusion Process for Low-light Image Enhancement
Federated Linear Bandits with Finite Adversarial Actions
BIOT: Biosignal Transformer for Cross-data Learning in the Wild
Tight Bounds for Volumetric Spanners and Applications
Maximum Average Randomly Sampled: A Scale Free and Non-parametric Algorithm for Stochastic Bandits
Estimating the Rate-Distortion Function by Wasserstein Gradient Descent
The geometry of hidden representations of large transformer models
NeuroGF: A Neural Representation for Fast Geodesic Distance and Path Queries
On the Role of Entanglement and Statistics in Learning
Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing
Distributional Pareto-Optimal Multi-Objective Reinforcement Learning
On the Generalization Properties of Diffusion Models
A Scalable Neural Network for DSIC Affine Maximizer Auction Design
Riemannian Residual Neural Networks
Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive Learning
Bayesian Extensive-Rank Matrix Factorization with Rotational Invariant Priors
Augmentation-free Dense Contrastive Distillation for Efficient Semantic Segmentation
Towards Distribution-Agnostic Generalized Category Discovery
DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization
Learning non-Markovian Decision-Making from State-only Sequences
Semi-Supervised Domain Generalization with Known and Unknown Classes
IBA: Towards Irreversible Backdoor Attacks in Federated Learning
Fine-Grained Cross-View Geo-Localization Using a Correlation-Aware Homography Estimator
Convergent Bregman Plug-and-Play Image Restoration for Poisson Inverse Problems
Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection
Bayesian Learning of Optimal Policies in Markov Decision Processes with Countably Infinite State-Space
Context-PIPs: Persistent Independent Particles Demands Context Features
Back-Modality: Leveraging Modal Transformation for Data Augmentation
Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations
Counterfactual Generation with Identifiability Guarantees
Demystifying Softmax Gating Function in Gaussian Mixture of Experts
Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception?
Exploring Diverse In-Context Configurations for Image Captioning
Zero-Shot Anomaly Detection via Batch Normalization
Unbalanced Low-rank Optimal Transport Solvers
On the Gini-impurity Preservation For Privacy Random Forests
Towards Test-Time Refusals via Concept Negation
Unsupervised Image Denoising with Score Function
Meta-AdaM: An Meta-Learned Adaptive Optimizer with Momentum for Few-Shot Learning
SmooSeg: Smoothness Prior for Unsupervised Semantic Segmentation
Fed-CO$_{2}$: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning
CP-SLAM: Collaborative Neural Point-based SLAM System
Cookie Consent Has Disparate Impact on Estimation Accuracy
LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object Recognition
On the Convergence of Black-Box Variational Inference
Causal-structure Driven Augmentations for Text OOD Generalization
Keypoint-Augmented Self-Supervised Learning for Medical Image Segmentation with Limited Annotation
Double and Single Descent in Causal Inference with an Application to High-Dimensional Synthetic Control
Constrained Policy Optimization with Explicit Behavior Density For Offline Reinforcement Learning
From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusion
Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms
Slow and Weak Attractor Computation Embedded in Fast and Strong E-I Balanced Neural Dynamics
Does a sparse ReLU network training problem always admit an optimum ?
RECKONING: Reasoning through Dynamic Knowledge Encoding
Human-like Few-Shot Learning via Bayesian Reasoning over Natural Language
Causal Discovery in Semi-Stationary Time Series
Epistemic Neural Networks
Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems
Functional Equivalence and Path Connectivity of Reducible Hyperbolic Tangent Networks
Improving Adversarial Transferability via Intermediate-level Perturbation Decay
Scalable Transformer for PDE Surrogate Modeling
Sample Complexity of Forecast Aggregation
Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery
The Memory-Perturbation Equation: Understanding Model's Sensitivity to Data
SmoothHess: ReLU Network Feature Interactions via Stein's Lemma
Deep Patch Visual Odometry
Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural Networks
Aligning Gradient and Hessian for Neural Signed Distance Function
Large Language Models of Code Fail at Completing Code with Potential Bugs
CLadder: A Benchmark to Assess Causal Reasoning Capabilities of Language Models
DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets
Using Imperfect Surrogates for Downstream Inference: Design-based Supervised Learning for Social Science Applications of Large Language Models
How to Select Which Active Learning Strategy is Best Suited for Your Specific Problem and Budget
Revisiting Visual Model Robustness: A Frequency Long-Tailed Distribution View
Frequency-Enhanced Data Augmentation for Vision-and-Language Navigation
Equivariant flow matching
Diversify \& Conquer: Outcome-directed Curriculum RL via Out-of-Distribution Disagreement
TRIAGE: Characterizing and auditing training data for improved regression
Combating Bilateral Edge Noise for Robust Link Prediction
Spatially Resolved Gene Expression Prediction from Histology Images via Bi-modal Contrastive Learning
Double Randomized Underdamped Langevin with Dimension-Independent Convergence Guarantee
ASPEN: Breaking Operator Barriers for Efficient Parallelization of Deep Neural Networks
Algorithmic Regularization in Tensor Optimization: Towards a Lifted Approach in Matrix Sensing
Inferring the Future by Imagining the Past
Flat Seeking Bayesian Neural Networks
Unleash the Potential of Image Branch for Cross-modal 3D Object Detection
Graph-Structured Gaussian Processes for Transferable Graph Learning
Learning and processing the ordinal information of temporal sequences in recurrent neural circuits
Simultaneous embedding of multiple attractor manifolds in a recurrent neural network using constrained gradient optimization
Train Once and Explain Everywhere: Pre-training Interpretable Graph Neural Networks
Black-Box Differential Privacy for Interactive ML
Understanding and Improving Feature Learning for Out-of-Distribution Generalization
Supply-Side Equilibria in Recommender Systems
Geometry-Aware Adaptation for Pretrained Models
Model Spider: Learning to Rank Pre-Trained Models Efficiently
CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders
HubRouter: Learning Global Routing via Hub Generation and Pin-hub Connection
Iterative Reachability Estimation for Safe Reinforcement Learning
Privacy Auditing with One (1) Training Run
Collaborative Alignment of NLP Models
Norm-guided latent space exploration for text-to-image generation
SA-Solver: Stochastic Adams Solver for Fast Sampling of Diffusion Models
Learning Re-sampling Methods with Parameter Attribution for Image Super-resolution
Sequential Memory with Temporal Predictive Coding
Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents
Entropic Neural Optimal Transport via Diffusion Processes
Learning Energy-Based Prior Model with Diffusion-Amortized MCMC
Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks
In Defense of Softmax Parametrization for Calibrated and Consistent Learning to Defer
Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks
Curriculum Learning for Graph Neural Networks: Which Edges Should We Learn First
Two Sides of One Coin: the Limits of Untuned SGD and the Power of Adaptive Methods
A fast heuristic to optimize time-space tradeoff for large models
RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion
Neural Foundations of Mental Simulation: Future Prediction of Latent Representations on Dynamic Scenes
Non-autoregressive Machine Translation with Probabilistic Context-free Grammar
From Tempered to Benign Overfitting in ReLU Neural Networks
Nonparametric Identifiability of Causal Representations from Unknown Interventions
Temporally Disentangled Representation Learning under Unknown Nonstationarity
Video-Mined Task Graphs for Keystep Recognition in Instructional Videos
Training Neural Networks is NP-Hard in Fixed Dimension
Mixed-Initiative Multiagent Apprenticeship Learning for Human Training of Robot Teams
Neural Polarizer: A Lightweight and Effective Backdoor Defense via Purifying Poisoned Features
Delegated Classification
Interactive Multi-fidelity Learning for Cost-effective Adaptation of Language Model with Sparse Human Supervision
Test-Time Distribution Normalization for Contrastively Learned Visual-language Models
Regret Minimization via Saddle Point Optimization
Hyperbolic Space with Hierarchical Margin Boosts Fine-Grained Learning from Coarse Labels
Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery
LogSpecT: Feasible Graph Learning Model from Stationary Signals with Recovery Guarantees
Batchnorm Allows Unsupervised Radial Attacks
Understanding the Latent Space of Diffusion Models through the Lens of Riemannian Geometry
Correlation Aware Sparsified Mean Estimation Using Random Projection
Learning Large-Scale MTP$_2$ Gaussian Graphical Models via Bridge-Block Decomposition
DropPos: Pre-Training Vision Transformers by Reconstructing Dropped Positions
Fine-Tuning Language Models with Just Forward Passes
Chanakya: Learning Runtime Decisions for Adaptive Real-Time Perception
Evaluating Cognitive Maps and Planning in Large Language Models with CogEval
PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion Models
Fair Canonical Correlation Analysis
Rotating Features for Object Discovery
Prototypical Variational Autoencoder for 3D Few-shot Object Detection
Agnostically Learning Single-Index Models using Omnipredictors
Demo2Code: From Summarizing Demonstrations to Synthesizing Code via Extended Chain-of-Thought
Sensitivity in Translation Averaging
Learning Score-based Grasping Primitive for Human-assisting Dexterous Grasping
Template-free Articulated Neural Point Clouds for Reposable View Synthesis
Supported Value Regularization for Offline Reinforcement Learning
MIMEx: Intrinsic Rewards from Masked Input Modeling
LART: Neural Correspondence Learning with Latent Regularization Transformer for 3D Motion Transfer
Transformers over Directed Acyclic Graphs
Inverse Preference Learning: Preference-based RL without a Reward Function
SOL: Sampling-based Optimal Linear bounding of arbitrary scalar functions
Parallel Submodular Function Minimization
Uncertainty-Aware Alignment Network for Cross-Domain Video-Text Retrieval
On the Identifiability and Interpretability of Gaussian Process Models
Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond
Interpretable Prototype-based Graph Information Bottleneck
EvoPrompting: Language Models for Code-Level Neural Architecture Search
Maximum Independent Set: Self-Training through Dynamic Programming
GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels
Generalized Belief Transport
Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models
Active Vision Reinforcement Learning under Limited Visual Observability
Resolving the Tug-of-War: A Separation of Communication and Learning in Federated Learning
Learning from Both Structural and Textual Knowledge for Inductive Knowledge Graph Completion
On Masked Pre-training and the Marginal Likelihood
Jailbroken: How Does LLM Safety Training Fail?
Hierarchical Adaptive Value Estimation for Multi-modal Visual Reinforcement Learning
Learning Repeatable Speech Embeddings Using An Intra-class Correlation Regularizer
The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation
Fast Partitioned Learned Bloom Filter
IEBins: Iterative Elastic Bins for Monocular Depth Estimation
When are ensembles really effective?
RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths
Moral Responsibility for AI Systems
Design from Policies: Conservative Test-Time Adaptation for Offline Policy Optimization
Debiasing Scores and Prompts of 2D Diffusion for View-consistent Text-to-3D Generation
Finite-Time Analysis of Single-Timescale Actor-Critic
Adaptive Online Replanning with Diffusion Models
D-CIPHER: Discovery of Closed-form Partial Differential Equations
Implicit Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis
Domain Re-Modulation for Few-Shot Generative Domain Adaptation
Fine-grained Expressivity of Graph Neural Networks
Simple and Controllable Music Generation
Improving Language Plasticity via Pretraining with Active Forgetting
Generalized test utilities for long-tail performance in extreme multi-label classification
Lookaround Optimizer: $k$ steps around, 1 step average
L2T-DLN: Learning to Teach with Dynamic Loss Network
Open Compound Domain Adaptation with Object Style Compensation for Semantic Segmentation
Coherent Soft Imitation Learning
Can Pre-Trained Text-to-Image Models Generate Visual Goals for Reinforcement Learning?
Safety Verification of Decision-Tree Policies in Continuous Time
When Do Graph Neural Networks Help with Node Classification? Investigating the Homophily Principle on Node Distinguishability
On Learning Necessary and Sufficient Causal Graphs
ConDaFormer: Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding
On the choice of Perception Loss Function for Learned Video Compression
Point Cloud Completion with Pretrained Text-to-Image Diffusion Models
Online Ad Allocation with Predictions
Subclass-Dominant Label Noise: A Counterexample for the Success of Early Stopping
NuTrea: Neural Tree Search for Context-guided Multi-hop KGQA
Bayesian Active Causal Discovery with Multi-Fidelity Experiments
Towards Efficient Pre-Trained Language Model via Feature Correlation Distillation
DaTaSeg: Taming a Universal Multi-Dataset Multi-Task Segmentation Model
DESSERT: An Efficient Algorithm for Vector Set Search with Vector Set Queries
Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition
SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data
Multi-task learning with summary statistics
DDCoT: Duty-Distinct Chain-of-Thought Prompting for Multimodal Reasoning in Language Models
Computing Optimal Nash Equilibria in Multiplayer Games
Feature Likelihood Score: Evaluating the Generalization of Generative Models Using Samples
Scan and Snap: Understanding Training Dynamics and Token Composition in 1-layer Transformer
BIRD: Generalizable Backdoor Detection and Removal for Deep Reinforcement Learning
FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space
Visual Instruction Inversion: Image Editing via Image Prompting
Globally solving the Gromov-Wasserstein problem for point clouds in low dimensional Euclidean spaces
Unsupervised Learning for Solving the Travelling Salesman Problem
Normalization Layers Are All That Sharpness-Aware Minimization Needs
AutoGO: Automated Computation Graph Optimization for Neural Network Evolution
Unlimiformer: Long-Range Transformers with Unlimited Length Input
On the Interplay between Social Welfare and Tractability of Equilibria
Hypothesis Selection with Memory Constraints
Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards
Entropy-based Training Methods for Scalable Neural Implicit Samplers
A Single 2D Pose with Context is Worth Hundreds for 3D Human Pose Estimation
A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence
Toward Re-Identifying Any Animal
Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstruction
Single-Pass Pivot Algorithm for Correlation Clustering. Keep it simple!
Robust Learning for Smoothed Online Convex Optimization with Feedback Delay
A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting
ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided Diffusion
K-Nearest-Neighbor Local Sampling Based Conditional Independence Testing
CARE: Modeling Interacting Dynamics Under Temporal Environmental Variation
StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models
Efficient Hyper-parameter Optimization with Cubic Regularization
Learning List-Level Domain-Invariant Representations for Ranking
Provably Fast Convergence of Independent Natural Policy Gradient for Markov Potential Games
Theoretically Guaranteed Bidirectional Data Rectification for Robust Sequential Recommendation
Slimmed Asymmetrical Contrastive Learning and Cross Distillation for Lightweight Model Training
Hierarchical Gaussian Mixture based Task Generative Model for Robust Meta-Learning
Reining Generalization in Offline Reinforcement Learning via Representation Distinction
Class-Conditional Conformal Prediction with Many Classes
Towards the Difficulty for a Deep Neural Network to Learn Concepts of Different Complexities
Bridging Discrete and Backpropagation: Straight-Through and Beyond
SEENN: Towards Temporal Spiking Early Exit Neural Networks
Neural Lighting Simulation for Urban Scenes
Memory-Constrained Algorithms for Convex Optimization
Unleashing the Power of Randomization in Auditing Differentially Private ML
A Causal Framework for Decomposing Spurious Variations
Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Method
Transient Neural Radiance Fields for Lidar View Synthesis and 3D Reconstruction
Fast Optimal Transport through Sliced Generalized Wasserstein Geodesics
DRAUC: An Instance-wise Distributionally Robust AUC Optimization Framework
Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity
Learning with Explanation Constraints
Clifford Group Equivariant Neural Networks
Training Transitive and Commutative Multimodal Transformers with LoReTTa
Asymmetric Certified Robustness via Feature-Convex Neural Networks
Neural Lad: A Neural Latent Dynamics Framework for Times Series Modeling
Uncovering the Hidden Dynamics of Video Self-supervised Learning under Distribution Shifts
Greatness in Simplicity: Unified Self-Cycle Consistency for Parser-Free Virtual Try-On
Sequential Predictive Two-Sample and Independence Testing
Distributionally Robust Bayesian Optimization with $\varphi$-divergences
Sample-Efficient and Safe Deep Reinforcement Learning via Reset Deep Ensemble Agents
Kissing to Find a Match: Efficient Low-Rank Permutation Representation
Hybrid Search for Efficient Planning with Completeness Guarantees
Anonymous and Copy-Robust Delegations for Liquid Democracy
Adaptive SGD with Polyak stepsize and Line-search: Robust Convergence and Variance Reduction
Bringing regularized optimal transport to lightspeed: a splitting method adapted for GPUs
Active Reasoning in an Open-World Environment
Parallel Sampling of Diffusion Models
Label Correction of Crowdsourced Noisy Annotations with an Instance-Dependent Noise Transition Model
What Truly Matters in Trajectory Prediction for Autonomous Driving?
PromptRestorer: A Prompting Image Restoration Method with Degradation Perception
Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts
Generative Pre-Training of Spatio-Temporal Graph Neural Networks
Memory Efficient Optimizers with 4-bit States
Eliminating Domain Bias for Federated Learning in Representation Space
Differentiable Clustering with Perturbed Spanning Forests
Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices
Mitigating Source Bias for Fairer Weak Supervision
Regret Matching+: (In)Stability and Fast Convergence in Games
Uncertainty Quantification via Neural Posterior Principal Components
Generating Behaviorally Diverse Policies with Latent Diffusion Models
Learning Curves for Deep Structured Gaussian Feature Models
Symbolic Discovery of Optimization Algorithms
Post-processing Private Synthetic Data for Improving Utility on Selected Measures
Practical Sharpness-Aware Minimization Cannot Converge All the Way to Optima
A Novel Framework for Policy Mirror Descent with General Parameterization and Linear Convergence
Joint Feature and Differentiable $ k $-NN Graph Learning using Dirichlet Energy
From Trainable Negative Depth to Edge Heterophily in Graphs
Demystifying the Optimal Performance of Multi-Class Classification
Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Linear Subspaces
PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks
Non-Smooth Weakly-Convex Finite-sum Coupled Compositional Optimization
Statistical Knowledge Assessment for Large Language Models
ESSEN: Improving Evolution State Estimation for Temporal Networks using Von Neumann Entropy
Parameter-efficient Tuning of Large-scale Multimodal Foundation Model
The Best of Both Worlds in Network Population Games: Reaching Consensus and Convergence to Equilibrium
Kernel-Based Tests for Likelihood-Free Hypothesis Testing
Free-Bloom: Zero-Shot Text-to-Video Generator with LLM Director and LDM Animator
Learning Generalizable Agents via Saliency-guided Features Decorrelation
Binarized Spectral Compressive Imaging
Initialization-Dependent Sample Complexity of Linear Predictors and Neural Networks
Kernelized Reinforcement Learning with Order Optimal Regret Bounds
Most Neural Networks Are Almost Learnable
StateMask: Explaining Deep Reinforcement Learning through State Mask
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing
Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt
Discriminative Feature Attributions: Bridging Post Hoc Explainability and Inherent Interpretability
Diff-Foley: Synchronized Video-to-Audio Synthesis with Latent Diffusion Models
Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting
Deep Contract Design via Discontinuous Networks
Trust Region-Based Safe Distributional Reinforcement Learning for Multiple Constraints
Complex-valued Neurons Can Learn More but Slower than Real-valued Neurons via Gradient Descent
Towards Revealing the Mystery behind Chain of Thought: A Theoretical Perspective
Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming
Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning
VaRT: Variational Regression Trees
Statistical Insights into HSIC in High Dimensions
Provably Efficient Offline Goal-Conditioned Reinforcement Learning with General Function Approximation and Single-Policy Concentrability
Alleviating the Semantic Gap for Generalized fMRI-to-Image Reconstruction
LambdaBeam: Neural Program Search with Higher-Order Functions and Lambdas
One Risk to Rule Them All: A Risk-Sensitive Perspective on Model-Based Offline Reinforcement Learning
Cross-Domain Policy Adaptation via Value-Guided Data Filtering
ANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulation
Convolutional Visual Prompt for Robust Visual Perception
Stability and Generalization of the Decentralized Stochastic Gradient Descent Ascent Algorithm
Bayesian nonparametric (non-)renewal processes for analyzing neural spike train variability
PHOTOSWAP: Personalized Subject Swapping in Images
TabMT: Generating tabular data with masked transformers
Reinforcement Learning with Simple Sequence Priors
Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information
TexQ: Zero-shot Network Quantization with Texture Feature Distribution Calibration
Grounding Neural Inference with Satisfiability Modulo Theories
ARTIC3D: Learning Robust Articulated 3D Shapes from Noisy Web Image Collections
Preconditioning Matters: Fast Global Convergence of Non-convex Matrix Factorization via Scaled Gradient Descent
Continuous-time Analysis of Anchor Acceleration
Homotopy-based training of NeuralODEs for accurate dynamics discovery
Locality Sensitive Hashing in Fourier Frequency Domain For Soft Set Containment Search
On the impact of activation and normalization in obtaining isometric embeddings at initialization
Adaptive Test-Time Personalization for Federated Learning
Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependencies
Transformed Low-Rank Parameterization Can Help Robust Generalization for Tensor Neural Networks
False Discovery Proportion control for aggregated Knockoffs
Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off
AdaPlanner: Adaptive Planning from Feedback with Language Models
Break It Down: Evidence for Structural Compositionality in Neural Networks
Matrix Compression via Randomized Low Rank and Low Precision Factorization
EgoEnv: Human-centric environment representations from egocentric video
Private Federated Frequency Estimation: Adapting to the Hardness of the Instance
GALOPA: Graph Transport Learning with Optimal Plan Alignment
RH-BrainFS: Regional Heterogeneous Multimodal Brain Networks Fusion Strategy
SutraNets: Sub-series Autoregressive Networks for Long-Sequence, Probabilistic Forecasting
Cross-links Matter for Link Prediction: Rethinking the Debiased GNN from a Data Perspective
ELDEN: Exploration via Local Dependencies
On skip connections and normalisation layers in deep optimisation
Error Bounds for Learning with Vector-Valued Random Features
A Measure-Theoretic Axiomatisation of Causality
Understanding and Addressing the Pitfalls of Bisimulation-based Representations in Offline Reinforcement Learning
Ordering-based Conditions for Global Convergence of Policy Gradient Methods
DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing
VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset
Retrieval-Augmented Multiple Instance Learning
On Sample-Efficient Offline Reinforcement Learning: Data Diversity, Posterior Sampling and Beyond
Data Market Design through Deep Learning
Survival Instinct in Offline Reinforcement Learning
AV-NeRF: Learning Neural Fields for Real-World Audio-Visual Scene Synthesis
What’s Left? Concept Grounding with Logic-Enhanced Foundation Models
Sparsity-Preserving Differentially Private Training of Large Embedding Models
HASSOD: Hierarchical Adaptive Self-Supervised Object Detection
CEIL: Generalized Contextual Imitation Learning
Meta-in-context learning in large language models
Riemannian Projection-free Online Learning
Tailoring Self-Attention for Graph via Rooted Subtrees
Trans-Dimensional Generative Modeling via Jump Diffusion Models
Achieving Cross Modal Generalization with Multimodal Unified Representation
Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models
Uncertainty Estimation for Safety-critical Scene Segmentation via Fine-grained Reward Maximization
Replicable Reinforcement Learning
A Robust and Opponent-Aware League Training Method for StarCraft II
PPi: Pretraining Brain Signal Model for Patient-independent Seizure Detection
Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditions
Maximum State Entropy Exploration using Predecessor and Successor Representations
Distribution-Free Model-Agnostic Regression Calibration via Nonparametric Methods
Pre-Training Protein Encoder via Siamese Sequence-Structure Diffusion Trajectory Prediction
Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data
Offline RL with Discrete Proxy Representations for Generalizability in POMDPs
Modeling Human Visual Motion Processing with Trainable Motion Energy Sensing and a Self-attention Network
ForecastPFN: Synthetically-Trained Zero-Shot Forecasting
Tracr: Compiled Transformers as a Laboratory for Interpretability
FACE: Evaluating Natural Language Generation with Fourier Analysis of Cross-Entropy
SPACE: Single-round Participant Amalgamation for Contribution Evaluation in Federated Learning
Dream the Impossible: Outlier Imagination with Diffusion Models
SafeDICE: Offline Safe Imitation Learning with Non-Preferred Demonstrations
Derandomized novelty detection with FDR control via conformal e-values
DinoSR: Self-Distillation and Online Clustering for Self-supervised Speech Representation Learning
InstanT: Semi-supervised Learning with Instance-dependent Thresholds
PlanE: Representation Learning over Planar Graphs
Theoretical and Practical Perspectives on what Influence Functions Do
Labeling Neural Representations with Inverse Recognition
Content-based Unrestricted Adversarial Attack
Rethinking Conditional Diffusion Sampling with Progressive Guidance
Test-time Adaptation of Discriminative Models via Diffusion Generative Feedback
Global Convergence Analysis of Local SGD for Two-layer Neural Network without Overparameterization
Differentiable sorting for censored time-to-event data.
Best Arm Identification with Fixed Budget: A Large Deviation Perspective
On Private and Robust Bandits
Object-Centric Slot Diffusion
Finding Safe Zones of Markov Decision Processes Policies
How do Minimum-Norm Shallow Denoisers Look in Function Space?
Learning to Configure Separators in Branch-and-Cut
Reducing Shape-Radiance Ambiguity in Radiance Fields with a Closed-Form Color Estimation Method
Hierarchical Semi-Implicit Variational Inference with Application to Diffusion Model Acceleration
Scaling Laws for Hyperparameter Optimization
Coop: Memory is not a Commodity
Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data
Flow-Based Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object Detection
NVFi: Neural Velocity Fields for 3D Physics Learning from Dynamic Videos
Composable Coresets for Determinant Maximization: Greedy is Almost Optimal
Emergent Communication for Rules Reasoning
Query-based Temporal Fusion with Explicit Motion for 3D Object Detection
Resilient Constrained Learning
Improved Convergence in High Probability of Clipped Gradient Methods with Heavy Tailed Noise
Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledge
VideoComposer: Compositional Video Synthesis with Motion Controllability
Towards robust and generalizable representations of extracellular data using contrastive learning
Online Adaptive Policy Selection in Time-Varying Systems: No-Regret via Contractive Perturbations
Creating a Public Repository for Joining Private Data
Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems
Rubik's Cube: High-Order Channel Interactions with a Hierarchical Receptive Field
A Hierarchical Training Paradigm for Antibody Structure-sequence Co-design
Hardware Resilience Properties of Text-Guided Image Classifiers
Exact Generalization Guarantees for (Regularized) Wasserstein Distributionally Robust Models
Taylor TD-learning
Path following algorithms for $\ell_2$-regularized $M$-estimation with approximation guarantee
Test-Time Amendment with a Coarse Classifier for Fine-Grained Classification
Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training
Macro Placement by Wire-Mask-Guided Black-Box Optimization
AMDP: An Adaptive Detection Procedure for False Discovery Rate Control in High-Dimensional Mediation Analysis
Focus on Query: Adversarial Mining Transformer for Few-Shot Segmentation
State-space models with layer-wise nonlinearity are universal approximators with exponential decaying memory
Decentralized Matrix Sensing: Statistical Guarantees and Fast Convergence
ShiftAddViT: Mixture of Multiplication Primitives Towards Efficient Vision Transformer
Cinematic Mindscapes: High-quality Video Reconstruction from Brain Activity
Geodesic Multi-Modal Mixup for Robust Fine-Tuning
Sharp Bounds for Generalized Causal Sensitivity Analysis
MultiMoDN—Multimodal, Multi-Task, Interpretable Modular Networks
Stochastic Collapse: How Gradient Noise Attracts SGD Dynamics Towards Simpler Subnetworks
Simplifying Neural Network Training Under Class Imbalance
Graph Denoising Diffusion for Inverse Protein Folding
MADG: Margin-based Adversarial Learning for Domain Generalization
Real-World Image Variation by Aligning Diffusion Inversion Chain
Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models
ReDS: Offline RL With Heteroskedastic Datasets via Support Constraints
(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability
Efficient Robust Bayesian Optimization for Arbitrary Uncertain inputs
Static and Sequential Malicious Attacks in the Context of Selective Forgetting
Focus Your Attention when Few-Shot Classification
FairLISA: Fair User Modeling with Limited Sensitive Attributes Information
Exploring Geometry of Blind Spots in Vision models
The Graph Pencil Method: Mapping Subgraph Densities to Stochastic Block Models
Adaptive Linear Estimating Equations
Data Selection for Language Models via Importance Resampling
Self-Supervised Reinforcement Learning that Transfers using Random Features
Learning Neural Implicit through Volume Rendering with Attentive Depth Fusion Priors
TIES-Merging: Resolving Interference When Merging Models
Personalized Dictionary Learning for Heterogeneous Datasets
BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis
Towards Hybrid-grained Feature Interaction Selection for Deep Sparse Network
Private Everlasting Prediction
SNEkhorn: Dimension Reduction with Symmetric Entropic Affinities
Operation-Level Early Stopping for Robustifying Differentiable NAS
Siamese Masked Autoencoders
Advancing Bayesian Optimization via Learning Correlated Latent Space
Convergence of Adam Under Relaxed Assumptions
Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency
What Can We Learn from Unlearnable Datasets?
HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
PGDiff: Guiding Diffusion Models for Versatile Face Restoration via Partial Guidance
Complexity of Derivative-Free Policy Optimization for Structured $\mathcal{H}_\infty$ Control
Understanding the detrimental class-level effects of data augmentation
Finite-Time Analysis of Whittle Index based Q-Learning for Restless Multi-Armed Bandits with Neural Network Function Approximation
DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model
TOA: Task-oriented Active VQA
Emergent Correspondence from Image Diffusion
A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learning
Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form Games
Revisiting Adversarial Robustness Distillation from the Perspective of Robust Fairness
Pre-RMSNorm and Pre-CRMSNorm Transformers: Equivalent and Efficient Pre-LN Transformers
Thin and deep Gaussian processes
Online List Labeling with Predictions
A Regularized Conditional GAN for Posterior Sampling in Image Recovery Problems
CycleNet: Rethinking Cycle Consistency in Text-Guided Diffusion for Image Manipulation
Quasi-Monte Carlo Graph Random Features
Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning
Optimal Treatment Regimes for Proximal Causal Learning
FAMO: Fast Adaptive Multitask Optimization
P-Flow: A Fast and Data-Efficient Zero-Shot TTS through Speech Prompting
Multi-scale Diffusion Denoised Smoothing
Optimized Covariance Design for AB Test on Social Network under Interference
Sounding Bodies: Modeling 3D Spatial Sound of Humans Using Body Pose and Audio
$S^3$: Increasing GPU Utilization during Generative Inference for Higher Throughput
Statistical Limits of Adaptive Linear Models: Low-Dimensional Estimation and Inference
Counting Distinct Elements Under Person-Level Differential Privacy
Comparing Apples to Oranges: Learning Similarity Functions for Data Produced by Different Distributions
Curriculum Learning With Infant Egocentric Videos
Time-Reversed Dissipation Induces Duality Between Minimizing Gradient Norm and Function Value
Towards Personalized Federated Learning via Heterogeneous Model Reassembly
Shared Adversarial Unlearning: Backdoor Mitigation by Unlearning Shared Adversarial Examples
Learning Invariant Representations of Graph Neural Networks via Cluster Generalization
Generator Identification for Linear SDEs with Additive and Multiplicative Noise
An Optimization-based Approach To Node Role Discovery in Networks: Approximating Equitable Partitions
History Filtering in Imperfect Information Games: Algorithms and Complexity
The Separation Capacity of Random Neural Networks
Fast Conditional Mixing of MCMC Algorithms for Non-log-concave Distributions
Closing the gap between the upper bound and lower bound of Adam's iteration complexity
Statistically Valid Variable Importance Assessment through Conditional Permutations
Discover and Align Taxonomic Context Priors for Open-world Semi-Supervised Learning
Tight Risk Bounds for Gradient Descent on Separable Data
Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection
CLIP4HOI: Towards Adapting CLIP for Practical Zero-Shot HOI Detection
State-Action Similarity-Based Representations for Off-Policy Evaluation
Variational Gaussian Processes with Decoupled Conditionals
Individual Arbitrariness and Group Fairness
Active Observing in Continuous-time Control
Arbitrarily Scalable Environment Generators via Neural Cellular Automata
Recursion in Recursion: Two-Level Nested Recursion for Length Generalization with Scalability
Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective
UniT: A Unified Look at Certified Robust Training against Text Adversarial Perturbation
Worst-case Performance of Popular Approximate Nearest Neighbor Search Implementations: Guarantees and Limitations
Unbiased constrained sampling with Self-Concordant Barrier Hamiltonian Monte Carlo
Particle-based Variational Inference with Generalized Wasserstein Gradient Flow
Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods
Softmax Output Approximation for Activation Memory-Efficient Training of Attention-based Networks
FGPrompt: Fine-grained Goal Prompting for Image-goal Navigation
Disentangling Cognitive Diagnosis with Limited Exercise Labels
Successor-Predecessor Intrinsic Exploration
Multi-Fidelity Multi-Armed Bandits Revisited
Explore In-Context Learning for 3D Point Cloud Understanding
Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning
Linear Time Algorithms for k-means with Multi-Swap Local Search
On the Properties of Kullback-Leibler Divergence Between Multivariate Gaussian Distributions
MIMONets: Multiple-Input-Multiple-Output Neural Networks Exploiting Computation in Superposition
Temperature Balancing, Layer-wise Weight Analysis, and Neural Network Training
FlowPG: Action-constrained Policy Gradient with Normalizing Flows
Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision
How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model
DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction
Modality-Independent Teachers Meet Weakly-Supervised Audio-Visual Event Parser
Active representation learning for general task space with applications in robotics
f-Policy Gradients: A General Framework for Goal-Conditioned RL using f-Divergences
OV-PARTS: Towards Open-Vocabulary Part Segmentation
White-Box Transformers via Sparse Rate Reduction
Domain Watermark: Effective and Harmless Dataset Copyright Protection is Closed at Hand
Optimization and Bayes: A Trade-off for Overparameterized Neural Networks
One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning
Differentiable Neuro-Symbolic Reasoning on Large-Scale Knowledge Graphs
Understanding Social Reasoning in Language Models with Language Models
Certifiably Robust Graph Contrastive Learning
Simple and Asymmetric Graph Contrastive Learning without Augmentations
FlowCam: Training Generalizable 3D Radiance Fields without Camera Poses via Pixel-Aligned Scene Flow
Doubly-Robust Self-Training
Towards Optimal Caching and Model Selection for Large Model Inference
On Convergence of Polynomial Approximations to the Gaussian Mixture Entropy
On the Generalization Error of Stochastic Mirror Descent for Quadratically-Bounded Losses: an Improved Analysis
Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models
VRA: Variational Rectified Activation for Out-of-distribution Detection
Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale
Grammar Prompting for Domain-Specific Language Generation with Large Language Models
DreamSparse: Escaping from Plato’s Cave with 2D Diffusion Model Given Sparse Views
PreDiff: Precipitation Nowcasting with Latent Diffusion Models
RGMIL: Guide Your Multiple-Instance Learning Model with Regressor
NPCL: Neural Processes for Uncertainty-Aware Continual Learning
Deductive Verification of Chain-of-Thought Reasoning
DiffVL: Scaling Up Soft Body Manipulation using Vision-Language Driven Differentiable Physics
Recasting Continual Learning as Sequence Modeling
On Proper Learnability between Average- and Worst-case Robustness
Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations
Small Total-Cost Constraints in Contextual Bandits with Knapsacks, with Application to Fairness
Sample-efficient Multi-objective Molecular Optimization with GFlowNets
PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning
Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function
Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers
Improving Adversarial Robustness via Information Bottleneck Distillation
Deep Equilibrium Based Neural Operators for Steady-State PDEs
Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model
MVDiffusion: Enabling Holistic Multi-view Image Generation with Correspondence-Aware Diffusion
Neural Algorithmic Reasoning Without Intermediate Supervision
Generalized Weighted Path Consistency for Mastering Atari Games
Accelerated Quasi-Newton Proximal Extragradient: Faster Rate for Smooth Convex Optimization
Transformer as a hippocampal memory consolidation model based on NMDAR-inspired nonlinearity
Suggesting Variable Order for Cylindrical Algebraic Decomposition via Reinforcement Learning
Imitation Learning from Imperfection: Theoretical Justifications and Algorithms
Going Beyond Linear Mode Connectivity: The Layerwise Linear Feature Connectivity
Faster Margin Maximization Rates for Generic Optimization Methods
Structured Voronoi Sampling
A Novel Approach for Effective Multi-View Clustering with Information-Theoretic Perspective
Conditional score-based diffusion models for Bayesian inference in infinite dimensions
TransHP: Image Classification with Hierarchical Prompting
Online PCA in Converging Self-consistent Field Equations
Revisiting the Minimalist Approach to Offline Reinforcement Learning
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
Visual Instruction Tuning
Optimal Learners for Realizable Regression: PAC Learning and Online Learning
Balanced Training for Sparse GANs
Minimax Optimal Rate for Parameter Estimation in Multivariate Deviated Models
Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach
Ambient Diffusion: Learning Clean Distributions from Corrupted Data
Restart Sampling for Improving Generative Processes
An Efficient and Robust Framework for Approximate Nearest Neighbor Search with Attribute Constraint
Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability
A Closer Look at the Robustness of Contrastive Language-Image Pre-Training (CLIP)
Improving Diffusion-Based Image Synthesis with Context Prediction
What can a Single Attention Layer Learn? A Study Through the Random Features Lens
Language-driven Scene Synthesis using Multi-conditional Diffusion Model
Provable benefits of score matching
Improved Best-of-Both-Worlds Guarantees for Multi-Armed Bandits: FTRL with General Regularizers and Multiple Optimal Arms
Zero-shot Visual Relation Detection via Composite Visual Cues from Large Language Models
Maximization of Average Precision for Deep Learning with Adversarial Ranking Robustness
BayesDAG: Gradient-Based Posterior Inference for Causal Discovery
Granger Components Analysis: Unsupervised learning of latent temporal dependencies
Subject-driven Text-to-Image Generation via Apprenticeship Learning
Federated Learning with Bilateral Curation for Partially Class-Disjoint Data
Taming Local Effects in Graph-based Spatiotemporal Forecasting
Online Constrained Meta-Learning: Provable Guarantees for Generalization
Improving *day-ahead* Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context
Thrust: Adaptively Propels Large Language Models with External Knowledge
Do Not Marginalize Mechanisms, Rather Consolidate!
SODA: Robust Training of Test-Time Data Adaptors
Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costs
State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding
Towards Better Dynamic Graph Learning: New Architecture and Unified Library
Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Tasks
Optimal Algorithms for the Inhomogeneous Spiked Wigner Model
Proximity-Informed Calibration for Deep Neural Networks
Robust Model Reasoning and Fitting via Dual Sparsity Pursuit
Rethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial?
Practical Equivariances via Relational Conditional Neural Processes
On Evaluating Adversarial Robustness of Large Vision-Language Models
Adversarial Resilience in Sequential Prediction via Abstention
Towards Data-Agnostic Pruning At Initialization: What Makes a Good Sparse Mask?
Physics-Informed Bayesian Optimization of Variational Quantum Circuits
Learning a Neuron by a Shallow ReLU Network: Dynamics and Implicit Bias for Correlated Inputs
Asymptotics of Bayesian Uncertainty Estimation in Random Features Regression
Text Alignment Is An Efficient Unified Model for Massive NLP Tasks
Fitting trees to $\ell_1$-hyperbolic distances
Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimation
FaceComposer: A Unified Model for Versatile Facial Content Creation
QuIP: 2-Bit Quantization of Large Language Models With Guarantees
LayoutGPT: Compositional Visual Planning and Generation with Large Language Models
Bayesian target optimisation for high-precision holographic optogenetics
A polar prediction model for learning to represent visual transformations
Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning
Selective Sampling and Imitation Learning via Online Regression
D4: Improving LLM Pretraining via Document De-Duplication and Diversification
Efficient Model-Free Exploration in Low-Rank MDPs
IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AI
An Efficient Dataset Condensation Plugin and Its Application to Continual Learning
Corruption-Robust Offline Reinforcement Learning with General Function Approximation
ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation
A Unified Framework for Rank-based Loss Minimization
Learning Invariant Representations with a Nonparametric Nadaraya-Watson Head
SyncTREE: Fast Timing Analysis for Integrated Circuit Design through a Physics-informed Tree-based Graph Neural Network
Hokoff: Real Game Dataset from Honor of Kings and its Offline Reinforcement Learning Benchmarks
Calibrate and Boost Logical Expressiveness of GNN Over Multi-Relational and Temporal Graphs
ToolQA: A Dataset for LLM Question Answering with External Tools
Revisiting Scalarization in Multi-Task Learning: A Theoretical Perspective
Is RLHF More Difficult than Standard RL? A Theoretical Perspective
M$^2$Hub: Unlocking the Potential of Machine Learning for Materials Discovery
Bilevel Coreset Selection in Continual Learning: A New Formulation and Algorithm
QLoRA: Efficient Finetuning of Quantized LLMs
Test-time Training for Matching-based Video Object Segmentation
Joint Data-Task Generation for Auxiliary Learning
Aligning Synthetic Medical Images with Clinical Knowledge using Human Feedback
AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weeks
CLIP-OGD: An Experimental Design for Adaptive Neyman Allocation in Sequential Experiments
Switching Autoregressive Low-rank Tensor Models
Fractal Landscapes in Policy Optimization
DPM-Solver-v3: Improved Diffusion ODE Solver with Empirical Model Statistics
Lossy Image Compression with Conditional Diffusion Models
Training Energy-Based Normalizing Flow with Score-Matching Objectives
Nash Regret Guarantees for Linear Bandits
Optimal Excess Risk Bounds for Empirical Risk Minimization on $p$-Norm Linear Regression
Django: Detecting Trojans in Object Detection Models via Gaussian Focus Calibration
Where Did I Come From? Origin Attribution of AI-Generated Images
Auditing Fairness by Betting
ChessGPT: Bridging Policy Learning and Language Modeling
Lending Interaction Wings to Recommender Systems with Conversational Agents
Invariant Learning via Probability of Sufficient and Necessary Causes
Towards Last-Layer Retraining for Group Robustness with Fewer Annotations
Metis: Understanding and Enhancing In-Network Regular Expressions
Why think step by step? Reasoning emerges from the locality of experience
A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs
Adversarial Learning for Feature Shift Detection and Correction
Efficient Algorithms for Generalized Linear Bandits with Heavy-tailed Rewards
Hypervolume Maximization: A Geometric View of Pareto Set Learning
ProteinNPT: Improving protein property prediction and design with non-parametric transformers
Cognitive Model Discovery via Disentangled RNNs
Brain Dissection: fMRI-trained Networks Reveal Spatial Selectivity in the Processing of Natural Images
LEACE: Perfect linear concept erasure in closed form
Training Chain-of-Thought via Latent-Variable Inference
What Planning Problems Can A Relational Neural Network Solve?
Improving Compositional Generalization using Iterated Learning and Simplicial Embeddings
Objaverse-XL: A Universe of 10M+ 3D Objects
Neural Priming for Sample-Efficient Adaptation
Actively Testing Your Model While It Learns: Realizing Label-Efficient Learning in Practice
Intervention Generalization: A View from Factor Graph Models
Schema-learning and rebinding as mechanisms of in-context learning and emergence
Two-Stage Learning to Defer with Multiple Experts
Structured Prediction with Stronger Consistency Guarantees
Rethinking the Role of Token Retrieval in Multi-Vector Retrieval
Conformal Prediction for Uncertainty-Aware Planning with Diffusion Dynamics Model
Towards a Comprehensive Benchmark for High-Level Synthesis Targeted to FPGAs
Learning Efficient Coding of Natural Images with Maximum Manifold Capacity Representations
Adaptive whitening with fast gain modulation and slow synaptic plasticity
Doubly Constrained Fair Clustering
A Dataset of Relighted 3D Interacting Hands
Self-Chained Image-Language Model for Video Localization and Question Answering
Visual Programming for Step-by-Step Text-to-Image Generation and Evaluation
Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters
Adversarial Training from Mean Field Perspective
Implicit Bias of Gradient Descent for Two-layer ReLU and Leaky ReLU Networks on Nearly-orthogonal Data
Context-lumpable stochastic bandits
Squared Neural Families: A New Class of Tractable Density Models
Mirror Diffusion Models for Constrained and Watermarked Generation
Nonparametric Teaching for Multiple Learners
Mechanism Design for Collaborative Normal Mean Estimation
ResMem: Learn what you can and memorize the rest
Representational Strengths and Limitations of Transformers
Networks are Slacking Off: Understanding Generalization Problem in Image Deraining
A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks
Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks
A Unified Algorithm Framework for Unsupervised Discovery of Skills based on Determinantal Point Process
Reflexion: language agents with verbal reinforcement learning
trajdata: A Unified Interface to Multiple Human Trajectory Datasets
DeWave: Discrete Encoding of EEG Waves for EEG to Text Translation
On Sparse Modern Hopfield Model
Coneheads: Hierarchy Aware Attention
Non-Convex Bilevel Optimization with Time-Varying Objective Functions
Self-Supervised Motion Magnification by Backpropagating Through Optical Flow
BEDD: The MineRL BASALT Evaluation and Demonstrations Dataset for Training and Benchmarking Agents that Solve Fuzzy Tasks
Segment Everything Everywhere All at Once
May the Force be with You: Unified Force-Centric Pre-Training for 3D Molecular Conformations
Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation
Critical Initialization of Wide and Deep Neural Networks using Partial Jacobians: General Theory and Applications
Dynamic Sparsity Is Channel-Level Sparsity Learner
Sharp Calibrated Gaussian Processes
What Distributions are Robust to Indiscriminate Poisoning Attacks for Linear Learners?
Probabilistic Invariant Learning with Randomized Linear Classifiers
Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits with Dynamical Similarity Analysis
Graph Clustering with Graph Neural Networks
Cal-DETR: Calibrated Detection Transformer
The Pick-to-Learn Algorithm: Empowering Compression for Tight Generalization Bounds and Improved Post-training Performance
Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds
Safety Gymnasium: A Unified Safe Reinforcement Learning Benchmark
Hierarchical Open-vocabulary Universal Image Segmentation
Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials
Graph of Circuits with GNN for Exploring the Optimal Design Space
Credal Marginal MAP
Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task
NeuralGF: Unsupervised Point Normal Estimation by Learning Neural Gradient Function
Open-Vocabulary Semantic Segmentation via Attribute Decomposition-Aggregation
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound Framework
Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks
Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds
Game Solving with Online Fine-Tuning
Provably (More) Sample-Efficient Offline RL with Options
The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions
Posthoc privacy guarantees for collaborative inference with modified Propose-Test-Release
Clustering the Sketch: Dynamic Compression for Embedding Tables
Emergent Communication in Interactive Sketch Question Answering
Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models
Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources
Knowledge Diffusion for Distillation
SwiFT: Swin 4D fMRI Transformer
Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking
Expressive probabilistic sampling in recurrent neural networks
Mind2Web: Towards a Generalist Agent for the Web
Inverse Reinforcement Learning with the Average Reward Criterion
GraphMP: Graph Neural Network-based Motion Planning with Efficient Graph Search
Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy
Compositional Foundation Models for Hierarchical Planning
$H$-Consistency Bounds: Characterization and Extensions
Equal Opportunity of Coverage in Fair Regression
Supervised Pretraining Can Learn In-Context Reinforcement Learning
Open Visual Knowledge Extraction via Relation-Oriented Multimodality Model Prompting
Complex Query Answering on Eventuality Knowledge Graph with Implicit Logical Constraints
Beyond Confidence: Reliable Models Should Also Consider Atypicality
SSL4EO-L: Datasets and Foundation Models for Landsat Imagery
Meta-Learning Adversarial Bandit Algorithms
Adaptive Principal Component Regression with Applications to Panel Data
Strategic Apple Tasting
Contrast, Attend and Diffuse to Decode High-Resolution Images from Brain Activities
Spontaneous symmetry breaking in generative diffusion models
Autonomous Capability Assessment of Sequential Decision-Making Systems in Stochastic Settings
Variational Inference with Gaussian Score Matching
Discriminative Calibration: Check Bayesian Computation from Simulations and Flexible Classifier
Neural Lyapunov Control for Discrete-Time Systems
Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure
Deep Momentum Multi-Marginal Schrödinger Bridge
Causal Fairness for Outcome Control
rPPG-Toolbox: Deep Remote PPG Toolbox
Relax, it doesn’t matter how you get there: A new self-supervised approach for multi-timescale behavior analysis
NetHack is Hard to Hack
Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability
Nearly Optimal Bounds for Cyclic Forgetting
Diffusion Self-Guidance for Controllable Image Generation
Boosting Learning for LDPC Codes to Improve the Error-Floor Performance
Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms
STARSS23: An Audio-Visual Dataset of Spatial Recordings of Real Scenes with Spatiotemporal Annotations of Sound Events
Generalized Information-theoretic Multi-view Clustering
DAW: Exploring the Better Weighting Function for Semi-supervised Semantic Segmentation
TopP&R: Robust Support Estimation Approach for Evaluating Fidelity and Diversity in Generative Models
E2PNet: Event to Point Cloud Registration with Spatio-Temporal Representation Learning
Circuit as Set of Points
Identifiability Guarantees for Causal Disentanglement from Soft Interventions
Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints
Diverse Shape Completion via Style Modulated Generative Adversarial Networks
End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics
AND: Adversarial Neural Degradation for Learning Blind Image Super-Resolution
Decorate3D: Text-Driven High-Quality Texture Generation for Mesh Decoration in the Wild
Accelerating Value Iteration with Anchoring
Learning to Compress Prompts with Gist Tokens
An Adaptive Algorithm for Learning with Unknown Distribution Drift
A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games
Strategic Distribution Shift of Interacting Agents via Coupled Gradient Flows
AmadeusGPT: a natural language interface for interactive animal behavioral analysis
GEO-Bench: Toward Foundation Models for Earth Monitoring
Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network
Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets
Errors-in-variables Fr\'echet Regression with Low-rank Covariate Approximation
Bayesian Learning via Q-Exponential Process
Future-Dependent Value-Based Off-Policy Evaluation in POMDPs
Marginal Density Ratio for Off-Policy Evaluation in Contextual Bandits
Faster Differentially Private Convex Optimization via Second-Order Methods
Concept Distillation: Leveraging Human-Centered Explanations for Model Improvement
Concept Algebra for (Score-Based) Text-Controlled Generative Models
Uncovering Meanings of Embeddings via Partial Orthogonality
Self-Predictive Universal AI
Bounded rationality in structured density estimation
Quantifying & Modeling Multimodal Interactions: An Information Decomposition Framework
Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning
Spectral Evolution and Invariance in Linear-width Neural Networks
Energy-based learning algorithms for analog computing: a comparative study
Riemannian SAM: Sharpness-Aware Minimization on Riemannian Manifolds
FAST: a Fused and Accurate Shrinkage Tree for Heterogeneous Treatment Effects Estimation
Dense-Exponential Random Features: Sharp Positive Estimators of the Gaussian Kernel
GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction
Differentiable Blocks World: Qualitative 3D Decomposition by Rendering Primitives
Learning from Rich Semantics and Coarse Locations for Long-tailed Object Detection
Evaluating Self-Supervised Learning for Molecular Graph Embeddings
NU-MCC: Multiview Compressive Coding with Neighborhood Decoder and Repulsive UDF
Learning to Group Auxiliary Datasets for Molecule
OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents
RRHF: Rank Responses to Align Language Models with Human Feedback
S-CLIP: Semi-supervised Vision-Language Learning using Few Specialist Captions
Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules
Not All Out-of-Distribution Data Are Harmful to Open-Set Active Learning
RoboCLIP: One Demonstration is Enough to Learn Robot Policies
Neural Functional Transformers
Permutation Equivariant Neural Functionals
Disentanglement via Latent Quantization
ProteinInvBench: Benchmarking Protein Inverse Folding on Diverse Tasks, Models, and Metrics
On the Connection between Pre-training Data Diversity and Fine-tuning Robustness
Adaptive Algorithms for Relaxed Pareto Set Identification
A Pseudo-Semantic Loss for Autoregressive Models with Logical Constraints
Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis
Sample-Conditioned Hypothesis Stability Sharpens Information-Theoretic Generalization Bounds
Prioritizing Samples in Reinforcement Learning with Reducible Loss
Structured Neural Networks for Density Estimation and Causal Inference
On the Robustness of Mechanism Design under Total Variation Distance
TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph
Understanding Deep Gradient Leakage via Inversion Influence Functions
Training Transformers with 4-bit Integers
Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion
Is This Loss Informative? Faster Text-to-Image Customization by Tracking Objective Dynamics
Distributed Inference and Fine-tuning of Large Language Models Over The Internet
SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning
OBJECT 3DIT: Language-guided 3D-aware Image Editing
Improving Robustness with Adaptive Weight Decay
Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generation
Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models
Bootstrapped Training of Score-Conditioned Generator for Offline Design of Biological Sequences
Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design
3D-LLM: Injecting the 3D World into Large Language Models
Relative Entropic Optimal Transport: a (Prior-aware) Matching Perspective to (Unbalanced) Classification
Ensemble-based Deep Reinforcement Learning for Vehicle Routing Problems under Distribution Shift
Equivariant Flow Matching with Hybrid Probability Transport for 3D Molecule Generation
Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models
Motion-X: A Large-scale 3D Expressive Whole-body Human Motion Dataset
AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback
Likelihood-Based Diffusion Language Models
Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game Perspective
Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning
Boosting Spectral Clustering on Incomplete Data via Kernel Correction and Affinity Learning
Extensible Prompts for Language Models on Zero-shot Language Style Customization
Fundamental Limits and Tradeoffs in Invariant Representation Learning
Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning
Describe, Explain, Plan and Select: Interactive Planning with LLMs Enables Open-World Multi-Task Agents
Computing a human-like reaction time metric from stable recurrent vision models
SpecTr: Fast Speculative Decoding via Optimal Transport
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets
Emergent and Predictable Memorization in Large Language Models
Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets
Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations
PDF: Point Diffusion Implicit Function for Large-scale Scene Neural Representation
Towards Efficient and Accurate Winograd Convolution via Full Quantization
How to Fine-tune the Model: Unified Model Shift and Model Bias Policy Optimization
Described Object Detection: Liberating Object Detection with Flexible Expressions
SnapFusion: Text-to-Image Diffusion Model on Mobile Devices within Two Seconds
Low-shot Object Learning with Mutual Exclusivity Bias
PopSign ASL v1.0: An Isolated American Sign Language Dataset Collected via Smartphones
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
[Re] $\mathcal{G}$-Mixup: Graph Data Augmentation for Graph Classification
An Empirical Investigation of the Role of Pre-training in Lifelong Learning
ADGym: Design Choices for Deep Anomaly Detection
Bullying10K: A Large-Scale Neuromorphic Dataset towards Privacy-Preserving Bullying Recognition
MLFMF: Data Sets for Machine Learning for Mathematical Formalization
OpenProteinSet: Training data for structural biology at scale
Partial Label Learning with Dissimilarity Propagation guided Candidate Label Shrinkage
Efficient Potential-based Exploration in Reinforcement Learning using Inverse Dynamic Bisimulation Metric
ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learning
Learning Efficient Surrogate Dynamic Models with Graph Spline Networks
Investigating how ReLU-networks encode symmetries
Belief Projection-Based Reinforcement Learning for Environments with Delayed Feedback
Textually Pretrained Speech Language Models
Task-Robust Pre-Training for Worst-Case Downstream Adaptation
Gacs-Korner Common Information Variational Autoencoder
Sample based Explanations via Generalized Representers
Implicit Transfer Operator Learning: Multiple Time-Resolution Models for Molecular Dynamics
Topological Obstructions and How to Avoid Them
Enhancing CLIP with CLIP: Exploring Pseudolabeling for Limited-Label Prompt Tuning
Foundation Model is Efficient Multimodal Multitask Model Selector
Learning via Wasserstein-Based High Probability Generalisation Bounds
RECESS Vaccine for Federated Learning: Proactive Defense Against Model Poisoning Attacks
Removing Hidden Confounding in Recommendation: A Unified Multi-Task Learning Approach
Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval
Preference-grounded Token-level Guidance for Language Model Fine-tuning
Structure from Duplicates: Neural Inverse Graphics from a Pile of Objects
3D Indoor Instance Segmentation in an Open-World
Near-Linear Time Algorithm for the Chamfer Distance
Variational Gaussian processes for linear inverse problems
EgoDistill: Egocentric Head Motion Distillation for Efficient Video Understanding
Trajectory Alignment: Understanding the Edge of Stability Phenomenon via Bifurcation Theory
Advice Querying under Budget Constraint for Online Algorithms
Simplifying and Empowering Transformers for Large-Graph Representations
Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization
Public Opinion Field Effect Fusion in Representation Learning for Trending Topics Diffusion
H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models
Transportability for Bandits with Data from Different Environments
Distributional Model Equivalence for Risk-Sensitive Reinforcement Learning
The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance
Exact recovery and Bregman hard clustering of node-attributed Stochastic Block Model
Brain encoding models based on multimodal transformers can transfer across language and vision
Score-based Data Assimilation
Distributionally Robust Ensemble of Lottery Tickets Towards Calibrated Sparse Network Training
Provable Training for Graph Contrastive Learning
A Unified Approach for Maximizing Continuous DR-submodular Functions
Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation
Enhancing Motion Deblurring in High-Speed Scenes with Spike Streams
Train Faster, Perform Better: Modular Adaptive Training in Over-Parameterized Models
XAGen: 3D Expressive Human Avatars Generation
D-Separation for Causal Self-Explanation
Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential Equations
MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers
Stable and low-precision training for large-scale vision-language models
Learning Universal Policies via Text-Guided Video Generation
Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial Defense
Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?
Are Diffusion Models Vision-And-Language Reasoners?
BiSLS/SPS: Auto-tune Step Sizes for Stable Bi-level Optimization
Slot-guided Volumetric Object Radiance Fields
Versatile Energy-Based Probabilistic Models for High Energy Physics
PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning.
Equivariant Adaptation of Large Pretrained Models
InfoPrompt: Information-Theoretic Soft Prompt Tuning for Natural Language Understanding
When Demonstrations meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning
Neural Fields with Hard Constraints of Arbitrary Differential Order
Causal Effect Identification in Uncertain Causal Networks
Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance
Triple Eagle: Simple, Fast and Practical Budget-Feasible Mechanisms
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy
Learning Invariant Molecular Representation in Latent Discrete Space
Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling
ContinuAR: Continuous Autoregression For Infinite-Fidelity Fusion
FedNAR: Federated Optimization with Normalized Annealing Regularization
Learning Causal Models under Independent Changes
Error Discovery By Clustering Influence Embeddings
PAC-Bayesian Spectrally-Normalized Bounds for Adversarially Robust Generalization
Beta Diffusion
Temporal Causal Mediation through a Point Process: Direct and Indirect Effects of Healthcare Interventions
Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces
PanoGen: Text-Conditioned Panoramic Environment Generation for Vision-and-Language Navigation
Dissecting Chain-of-Thought: Compositionality through In-Context Filtering and Learning
Thinker: Learning to Plan and Act
Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization
Analyzing Vision Transformers for Image Classification in Class Embedding Space
Object-Centric Learning for Real-World Videos by Predicting Temporal Feature Similarities
RL-based Stateful Neural Adaptive Sampling and Denoising for Real-Time Path Tracing
Bicriteria Multidimensional Mechanism Design with Side Information
Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics
PETAL: Physics Emulation Through Averaged Linearizations for Solving Inverse Problems
Smooth, exact rotational symmetrization for deep learning on point clouds
Generative Modelling of Stochastic Actions with Arbitrary Constraints in Reinforcement Learning
Spiking PointNet: Spiking Neural Networks for Point Clouds
Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data
Transition-constant Normalization for Image Enhancement
SLIBO-Net: Floorplan Reconstruction via Slicing Box Representation with Local Geometry Regularization
Addressing the speed-accuracy simulation trade-off for adaptive spiking neurons
Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds
VPP: Efficient Conditional 3D Generation via Voxel-Point Progressive Representation
Cocktail: Mixing Multi-Modality Control for Text-Conditional Image Generation
CD-GraB: Coordinating Distributed Example Orders for Provably Accelerated Training
Function Space Bayesian Pseudocoreset for Bayesian Neural Networks
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective
A Bounded Ability Estimation for Computerized Adaptive Testing
Moment Matching Denoising Gibbs Sampling
D4Explainer: In-distribution Explanations of Graph Neural Network via Discrete Denoising Diffusion
Online Clustering of Bandits with Misspecified User Models
Causal Interpretation of Self-Attention in Pre-Trained Transformers
EPIC Fields: Marrying 3D Geometry and Video Understanding
Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry
Optimality of Message-Passing Architectures for Sparse Graphs
[Re] CrossWalk: Fairness-enhanced Node Representation Learning
Markovian Sliced Wasserstein Distances: Beyond Independent Projections
Energy-Based Sliced Wasserstein Distance
StoryBench: A Multifaceted Benchmark for Continuous Story Visualization
Intra-Modal Proxy Learning for Zero-Shot Visual Categorization with CLIP
Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learning
Video Prediction Models as Rewards for Reinforcement Learning
In-Context Impersonation Reveals Large Language Models' Strengths and Biases
On the Robustness of Removal-Based Feature Attributions
Feature Selection in the Contrastive Analysis Setting
Learning to Receive Help: Intervention-Aware Concept Embedding Models
Bitstream-Corrupted Video Recovery: A Novel Benchmark Dataset and Method
Simplicity Bias in 1-Hidden Layer Neural Networks
Training on Foveated Images Improves Robustness to Adversarial Attacks
Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts
Unsupervised Graph Neural Architecture Search with Disentangled Self-Supervision
[Re] On Explainability of Graph Neural Networks via Subgraph Explorations
SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement Learning
General Munchausen Reinforcement Learning with Tsallis Kullback-Leibler Divergence
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis
SARAMIS: Simulation Assets for Robotic Assisted and Minimally Invasive Surgery
What is the Inductive Bias of Flatness Regularization? A Study of Deep Matrix Factorization Models
The Exact Sample Complexity Gain from Invariances for Kernel Regression
Limits, approximation and size transferability for GNNs on sparse graphs via graphops
Evaluating Neuron Interpretation Methods of NLP Models
SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization
Operator Learning with Neural Fields: Tackling PDEs on General Geometries
Robust Data Valuation with Weighted Banzhaf Values
Latent Graph Inference with Limited Supervision
“Why Not Looking backward?” A Robust Two-Step Method to Automatically Terminate Bayesian Optimization
Compact Neural Volumetric Video Representations with Dynamic Codebooks
Better Correlation and Robustness: A Distribution-Balanced Self-Supervised Learning Framework for Automatic Dialogue Evaluation
Convolutional State Space Models for Long-Range Spatiotemporal Modeling
The Cambridge Law Corpus: A Corpus for Legal AI Research
CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graphs
Provably Fast Finite Particle Variants of SVGD via Virtual Particle Stochastic Approximation
AdANNS: A Framework for Adaptive Semantic Search
Label Robust and Differentially Private Linear Regression: Computational and Statistical Efficiency
Volume Feature Rendering for Fast Neural Radiance Field Reconstruction
Unifying GANs and Score-Based Diffusion as Generative Particle Models
Statistical Analysis of Quantum State Learning Process in Quantum Neural Networks
Partial Multi-Label Learning with Probabilistic Graphical Disambiguation
Online Map Vectorization for Autonomous Driving: A Rasterization Perspective
Geometric Algebra Transformer
EDGI: Equivariant Diffusion for Planning with Embodied Agents
Causal Discovery from Subsampled Time Series with Proxy Variables
Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms
Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift
Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes
MeGraph: Capturing Long-Range Interactions by Alternating Local and Hierarchical Aggregation on Multi-Scaled Graph Hierarchy
Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties
Rank-DETR for High Quality Object Detection
Sketchy: Memory-efficient Adaptive Regularization with Frequent Directions
Online Control for Meta-optimization
Interaction Measures, Partition Lattices and Kernel Tests for High-Order Interactions
Deep Reinforcement Learning with Plasticity Injection
Metropolis Sampling for Constrained Diffusion Models
Going beyond persistent homology using persistent homology
Faster approximate subgraph counts with privacy
Flexible Attention-Based Multi-Policy Fusion for Efficient Deep Reinforcement Learning
Combining Behaviors with the Successor Features Keyboard
A Spectral Algorithm for List-Decodable Covariance Estimation in Relative Frobenius Norm
Near-Optimal Algorithms for Gaussians with Huber Contamination: Mean Estimation and Linear Regression
Self-supervised video pretraining yields robust and more human-aligned visual representations
Interpretability at Scale: Identifying Causal Mechanisms in Alpaca
TWIGMA: A dataset of AI-Generated Images with Metadata From Twitter
DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method
SALSA VERDE: a machine learning attack on LWE with sparse small secrets
On the Identifiability of Sparse ICA without Assuming Non-Gaussianity
A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction
For SALE: State-Action Representation Learning for Deep Reinforcement Learning
Variational Annealing on Graphs for Combinatorial Optimization
Generalizable Lightweight Proxy for Robust NAS against Diverse Perturbations
An active learning framework for multi-group mean estimation
Experiment Planning with Function Approximation
Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond
Mean-field Langevin dynamics: Time-space discretization, stochastic gradient, and variance reduction
On Class Distributions Induced by Nearest Neighbor Graphs for Node Classification of Tabular Data
CityRefer: Geography-aware 3D Visual Grounding Dataset on City-scale Point Cloud Data
Multi-Objective Intrinsic Reward Learning for Conversational Recommender Systems
Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision
3D-IntPhys: Towards More Generalized 3D-grounded Visual Intuitive Physics under Challenging Scenes
Imagine That! Abstract-to-Intricate Text-to-Image Synthesis with Scene Graph Hallucination Diffusion
Enhancing Knowledge Transfer for Task Incremental Learning with Data-free Subnetwork
Faster Discrete Convex Function Minimization with Predictions: The M-Convex Case
MotionGPT: Human Motion as a Foreign Language
Don’t blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy
Active Learning for Semantic Segmentation with Multi-class Label Query
Recurrent Temporal Revision Graph Networks
DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection
Noether Embedding: Efficient Learning of Temporal Regularities
An Alternating Optimization Method for Bilevel Problems under the Polyak-Łojasiewicz Condition
Off-Policy Evaluation for Human Feedback
Fast Exact Leverage Score Sampling from Khatri-Rao Products with Applications to Tensor Decomposition
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day
Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective
BubbleML: A Multiphase Multiphysics Dataset and Benchmarks for Machine Learning
Distribution Learnability and Robustness
ResoNet: a Physics-Informed DL Framework for Off-Resonance Correction in MRI Trained with Noise
Communication-Efficient Federated Bilevel Optimization with Global and Local Lower Level Problems
ConRad: Image Constrained Radiance Fields for 3D Generation from a Single Image
Minimax-Optimal Location Estimation
Optimality in Mean Estimation: Beyond Worst-Case, Beyond Sub-Gaussian, and Beyond $1+\alpha$ Moments
Color Equivariant Convolutional Networks
Unlocking Feature Visualization for Deep Network with MAgnitude Constrained Optimization
Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with Text
Segment Anything in High Quality
Optimizing Prompts for Text-to-Image Generation
Language Is Not All You Need: Aligning Perception with Language Models
What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks
CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths
A new perspective on building efficient and expressive 3D equivariant graph neural networks
Star-Shaped Denoising Diffusion Probabilistic Models
Towards Consistent Video Editing with Text-to-Image Diffusion Models
MAViL: Masked Audio-Video Learners
Inferring Hybrid Neural Fluid Fields from Videos
Rewiring Neurons in Non-Stationary Environments
Alternating Gradient Descent and Mixture-of-Experts for Integrated Multimodal Perception
DICES Dataset: Diversity in Conversational AI Evaluation for Safety
Regression with Cost-based Rejection
On the Importance of Feature Separability in Predicting Out-Of-Distribution Error
Your representations are in the network: composable and parallel adaptation for large scale models
GEX: A flexible method for approximating influence via Geometric Ensemble
Natural Language Instruction-following with Task-related Language Development and Translation
Structured State Space Models for In-Context Reinforcement Learning
Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation
OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding
SustainGym: Reinforcement Learning Environments for Sustainable Energy Systems
MKOR: Momentum-Enabled Kronecker-Factor-Based Optimizer Using Rank-1 Updates
Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory
Scalable Membership Inference Attacks via Quantile Regression
AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning
Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models
InfoCD: A Contrastive Chamfer Distance Loss for Point Cloud Completion
Orthogonal Non-negative Tensor Factorization based Multi-view Clustering
No-regret Algorithms for Fair Resource Allocation
What You See is What You Read? Improving Text-Image Alignment Evaluation
Optimal cross-learning for contextual bandits with unknown context distributions
Revisit Weakly-Supervised Audio-Visual Video Parsing from the Language Perspective
SAMoSSA: Multivariate Singular Spectrum Analysis with Stochastic Autoregressive Noise
SHOT: Suppressing the Hessian along the Optimization Trajectory for Gradient-Based Meta-Learning
YouTubePD: A Multimodal Benchmark for Parkinson’s Disease Analysis
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks
Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes
Grassmann Manifold Flows for Stable Shape Generation
$\texttt{TACO}$: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning
Large-Scale Distributed Learning via Private On-Device LSH
PCF-GAN: generating sequential data via the characteristic function of measures on the path space
Self-Correcting Bayesian Optimization through Bayesian Active Learning
Neural Injective Functions for Multisets, Measures and Graphs via a Finite Witness Theorem
MG-ViT: A Multi-Granularity Method for Compact and Efficient Vision Transformers
GeoPhy: Differentiable Phylogenetic Inference via Geometric Gradients of Tree Topologies
Aiming towards the minimizers: fast convergence of SGD for overparametrized problems
Inner Product-based Neural Network Similarity
Outlier-Robust Wasserstein DRO
Asynchronous Proportional Response Dynamics: Convergence in Markets with Adversarial Scheduling
Triangulation Residual Loss for Data-efficient 3D Pose Estimation
Multi-modal Queried Object Detection in the Wild
Energy-Based Cross Attention for Bayesian Context Update in Text-to-Image Diffusion Models
Algorithm Selection for Deep Active Learning with Imbalanced Datasets
Exponentially Convergent Algorithms for Supervised Matrix Factorization
GlyphControl: Glyph Conditional Controllable Visual Text Generation
DisDiff: Unsupervised Disentanglement of Diffusion Probabilistic Models
Streaming Algorithms and Lower Bounds for Estimating Correlation Clustering Cost
A Fractional Graph Laplacian Approach to Oversmoothing
Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples
Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration
RETVec: Resilient and Efficient Text Vectorizer
Conformal PID Control for Time Series Prediction
CluB: Cluster Meets BEV for LiDAR-Based 3D Object Detection
Equivariant Single View Pose Prediction Via Induced and Restriction Representations
Modeling Dynamics over Meshes with Gauge Equivariant Nonlinear Message Passing
Concentration analysis of multivariate elliptic diffusions
CORL: Research-oriented Deep Offline Reinforcement Learning Library
Katakomba: Tools and Benchmarks for Data-Driven NetHack
Projection-Free Online Convex Optimization via Efficient Newton Iterations
Into the LAION’s Den: Investigating Hate in Multimodal Datasets
Norm-based Generalization Bounds for Sparse Neural Networks
Unpaired Multi-Domain Causal Representation Learning
What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization
What Makes Data Suitable for a Locally Connected Neural Network? A Necessary and Sufficient Condition Based on Quantum Entanglement.
DäRF: Boosting Radiance Fields from Sparse Input Views with Monocular Depth Adaptation
Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization
Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design
FiGURe: Simple and Efficient Unsupervised Node Representations with Filter Augmentations
FD-Align: Feature Discrimination Alignment for Fine-tuning Pre-Trained Models in Few-Shot Learning
PointGPT: Auto-regressively Generative Pre-training from Point Clouds
Data-Informed Geometric Space Selection
Efficient Symbolic Policy Learning with Differentiable Symbolic Expression
Task-aware world model learning with meta weighting via bi-level optimization
Adversarial Robustness through Random Weight Sampling
UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild
BERT Lost Patience Won't Be Robust to Adversarial Slowdown
Convex-Concave Zero-Sum Stochastic Stackelberg Games
DiffComplete: Diffusion-based Generative 3D Shape Completion
Implicit Convolutional Kernels for Steerable CNNs
Global-correlated 3D-decoupling Transformer for Clothed Avatar Reconstruction
Neural-Logic Human-Object Interaction Detection
Implicit Contrastive Representation Learning with Guided Stop-gradient
Adapting Neural Link Predictors for Data-Efficient Complex Query Answering
Attacks on Online Learners: a Teacher-Student Analysis
Individualized Dosing Dynamics via Neural Eigen Decomposition
Doubly Robust Augmented Transfer for Meta-Reinforcement Learning
Non-Stationary Bandits with Auto-Regressive Temporal Dependency
Higher-Order Uncoupled Dynamics Do Not Lead to Nash Equilibrium - Except When They Do
Calibrating “Cheap Signals” in Peer Review without a Prior
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension
CaMP: Causal Multi-policy Planning for Interactive Navigation in Multi-room Scenes
Expressivity-Preserving GNN Simulation
Decision Stacks: Flexible Reinforcement Learning via Modular Generative Models
Multi-Swap k-Means++
Interpreting Unsupervised Anomaly Detection in Security via Rule Extraction
NeRF Revisited: Fixing Quadrature Instability in Volume Rendering
Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance
Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy
Look Ma, No Hands! Agent-Environment Factorization of Egocentric Videos
Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot Classification
Adversarial Model for Offline Reinforcement Learning
VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models
Near-Optimal $k$-Clustering in the Sliding Window Model
Layer-Neighbor Sampling --- Defusing Neighborhood Explosion in GNNs
First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities
Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities
MathNAS: If Blocks Have a Role in Mathematical Architecture Design
Human-in-the-Loop Optimization for Deep Stimulus Encoding in Visual Prostheses
Multimodal Deep Learning Model Unveils Behavioral Dynamics of V1 Activity in Freely Moving Mice
Are These the Same Apple? Comparing Images Based on Object Intrinsics
Learning in the Presence of Low-dimensional Structure: A Spiked Random Matrix Perspective
Neural Oscillators are Universal
Learning Nonparametric Latent Causal Graphs with Unknown Interventions
PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction
Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism
Learning Cuts via Enumeration Oracles
Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation
VOCE: Variational Optimization with Conservative Estimation for Offline Safe Reinforcement Learning
Crystal Structure Prediction by Joint Equivariant Diffusion
Would I have gotten that reward? Long-term credit assignment by counterfactual contribution analysis
Attention as Implicit Structural Inference
Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement Learning
GenEval: An object-focused framework for evaluating text-to-image alignment
BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load Forecasting
AVOIDDS: Aircraft Vision-based Intruder Detection Dataset and Simulator
Object Reprojection Error (ORE): Camera pose benchmarks from lightweight tracking annotations
Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking
Stochastic Multi-armed Bandits: Optimal Trade-off among Optimality, Consistency, and Tail Risk
GEQ: Gaussian Kernel Inspired Equilibrium Models
Boosting with Tempered Exponential Measures
Task-aware Distributed Source Coding under Dynamic Bandwidth
Gaussian Partial Information Decomposition: Bias Correction and Application to High-dimensional Data
A Diffusion-Model of Joint Interactive Navigation
Explore to Generalize in Zero-Shot RL
Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization
Three Iterations of (d − 1)-WL Test Distinguish Non Isometric Clouds of d-dimensional Points
Combinatorial Group Testing with Selfish Agents
3D-Aware Visual Question Answering about Parts, Poses and Occlusions
Universal Gradient Descent Ascent Method for Nonconvex-Nonconcave Minimax Optimization
Adversarially Robust Learning with Uncertain Perturbation Sets
PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification
BCDiff: Bidirectional Consistent Diffusion for Instantaneous Trajectory Prediction
Dynamic Pricing and Learning with Bayesian Persuasion
TaskMet: Task-driven Metric Learning for Model Learning
Structured Neural-PI Control with End-to-End Stability and Output Tracking Guarantees
$\varepsilon$-fractional core stability in Hedonic Games.
Knowledge-based in silico models and dataset for the comparative evaluation of mammography AI for a range of breast characteristics, lesion conspicuities and doses
DVSOD: RGB-D Video Salient Object Detection
EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understanding
Opening the Vocabulary of Egocentric Actions
Multi-Step Generalized Policy Improvement by Leveraging Approximate Models
Prediction and Control in Continual Reinforcement Learning
High-dimensional Contextual Bandit Problem without Sparsity
Sharp Spectral Rates for Koopman Operator Learning
Protein Design with Guided Discrete Diffusion
Understanding and Improving Ensemble Adversarial Defense
Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation
The Gain from Ordering in Online Learning
Jigsaw: Learning to Assemble Multiple Fractured Objects
Efficient Beam Tree Recursion
Universality laws for Gaussian mixtures in generalized linear models
ViCA-NeRF: View-Consistency-Aware 3D Editing of Neural Radiance Fields
Does Visual Pretraining Help End-to-End Reasoning?
VanillaNet: the Power of Minimalism in Deep Learning
Causal normalizing flows: from theory to practice
An Optimal and Scalable Matrix Mechanism for Noisy Marginals under Convex Loss Functions
Long-Term Fairness with Unknown Dynamics
Generative Modeling through the Semi-dual Formulation of Unbalanced Optimal Transport
Towards Stable Backdoor Purification through Feature Shift Tuning
BanditPAM++: Faster $k$-medoids Clustering
Tracking Most Significant Shifts in Nonparametric Contextual Bandits
MomentDiff: Generative Video Moment Retrieval from Random to Real
Learning to Parameterize Visual Attributes for Open-set Fine-grained Retrieval
Strategic Behavior in Two-sided Matching Markets with Prediction-enhanced Preference-formation
Estimating Riemannian Metric with Noise-Contaminated Intrinsic Distance
Exposing Attention Glitches with Flip-Flop Language Modeling
BadTrack: A Poison-Only Backdoor Attack on Visual Object Tracking
GAIA: Delving into Gradient-based Attribution Abnormality for Out-of-distribution Detection
DreamWaltz: Make a Scene with Complex 3D Animatable Avatars
DiViNeT: 3D Reconstruction from Disparate Views using Neural Template Regularization
Towards Robust and Expressive Whole-body Human Pose and Shape Estimation
A Randomized Approach to Tight Privacy Accounting
$\textbf{A}^2\textbf{CiD}^2$: Accelerating Asynchronous Communication in Decentralized Deep Learning
Bandit Social Learning under Myopic Behavior
Deep Fractional Fourier Transform
Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes
Characterizing the Impacts of Semi-supervised Learning for Weak Supervision
ATTA: Anomaly-aware Test-Time Adaptation for Out-of-Distribution Detection in Segmentation
Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss
Block Coordinate Plug-and-Play Methods for Blind Inverse Problems
Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning
Conditional Score Guidance for Text-Driven Image-to-Image Translation
Precise asymptotic generalization for multiclass classification with overparameterized linear models
BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing
Generalizing Nonlinear ICA Beyond Structural Sparsity
Optimistic Rates for Multi-Task Representation Learning
Learning From Biased Soft Labels
Scattering Vision Transformer: Spectral Mixing Matters
Knowledge Distillation for High Dimensional Search Index
Face Reconstruction from Facial Templates by Learning Latent Space of a Generator Network
Near Optimal Reconstruction of Spherical Harmonic Expansions
Boosting Verification of Deep Reinforcement Learning via Piece-Wise Linear Decision Neural Networks
CWCL: Cross-Modal Transfer with Continuously Weighted Contrastive Loss
Similarity-based cooperative equilibrium
Semi-Supervised Contrastive Learning for Deep Regression with Ordinal Rankings from Spectral Seriation
Punctuation-level Attack: Single-shot and Single Punctuation Can Fool Text Models
Is Learning in Games Good for the Learners?
Generator Born from Classifier
Auxiliary Losses for Learning Generalizable Concept-based Models
Abide by the law and follow the flow: conservation laws for gradient flows
Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation
Easy Learning from Label Proportions
Minimum-Risk Recalibration of Classifiers
Generative Category-level Object Pose Estimation via Diffusion Models
DrugCLIP: Contrasive Protein-Molecule Representation Learning for Virtual Screening
Unconstrained Dynamic Regret via Sparse Coding
UNSSOR: Unsupervised Neural Speech Separation by Leveraging Over-determined Training Mixtures
Mass-Producing Failures of Multimodal Systems with Language Models
L-CAD: Language-based Colorization with Any-level Descriptions using Diffusion Priors
Efficient Sampling of Stochastic Differential Equations with Positive Semi-Definite Models
Predicting Global Label Relationship Matrix for Graph Neural Networks under Heterophily
Recovering Unbalanced Communities in the Stochastic Block Model with Application to Clustering with a Faulty Oracle
Unleashing the Full Potential of Product Quantization for Large-Scale Image Retrieval
Dynamically Masked Discriminator for GANs
QuadAttac$K$: A Quadratic Programming Approach to Learning Ordered Top-$K$ Adversarial Attacks
Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts
D$^2$CSG: Unsupervised Learning of Compact CSG Trees with Dual Complements and Dropouts
Language Semantic Graph Guided Data-Efficient Learning
CQM: Curriculum Reinforcement Learning with a Quantized World Model
Disentangled Counterfactual Learning for Physical Audiovisual Commonsense Reasoning
Uncovering motifs of concurrent signaling across multiple neuronal populations
Domain Agnostic Fourier Neural Operators
Minimax Forward and Backward Learning of Evolving Tasks with Performance Guarantees
Compositional Sculpting of Iterative Generative Processes
Learning a 1-layer conditional generative model in total variation
Learning DAGs from Data with Few Root Causes
Model-Based Control with Sparse Neural Dynamics
Self-Supervised Visual Acoustic Matching
Characterizing Out-of-Distribution Error via Optimal Transport
Accessing Higher Dimensions for Unsupervised Word Translation
A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods
Team-PSRO for Learning Approximate TMECor in Large Team Games via Cooperative Reinforcement Learning
Participatory Personalization in Classification
PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detection
Degraded Polygons Raise Fundamental Questions of Neural Network Perception
Does Graph Distillation See Like Vision Dataset Counterpart?
On the Last-iterate Convergence in Time-varying Zero-sum Games: Extra Gradient Succeeds where Optimism Fails
Dynamic Regret of Adversarial Linear Mixture MDPs
SceneScape: Text-Driven Consistent Scene Generation
An Information-Theoretic Evaluation of Generative Models in Learning Multi-modal Distributions
Neuro-symbolic Learning Yielding Logical Constraints
A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression
How hard are computer vision datasets? Calibrating dataset difficulty to viewing time
A Unified Framework for Uniform Signal Recovery in Nonlinear Generative Compressed Sensing
Gradient Informed Proximal Policy Optimization
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits
Language-based Action Concept Spaces Improve Video Self-Supervised Learning
Label Poisoning is All You Need
On the Consistency of Maximum Likelihood Estimation of Probabilistic Principal Component Analysis
Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context
DreamHuman: Animatable 3D Avatars from Text
On the Role of Noise in the Sample Complexity of Learning Recurrent Neural Networks: Exponential Gaps for Long Sequences
Focused Transformer: Contrastive Training for Context Scaling
Soft-Unification in Deep Probabilistic Logic
EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning
Stable Diffusion is Unstable
Context-guided Embedding Adaptation for Effective Topic Modeling in Low-Resource Regimes
TriRE: A Multi-Mechanism Learning Paradigm for Continual Knowledge Retention and Promotion
UE4-NeRF:Neural Radiance Field for Real-Time Rendering of Large-Scale Scene
Neural Sampling in Hierarchical Exponential-family Energy-based Models
Don’t just prune by magnitude! Your mask topology is a secret weapon
The Emergence of Essential Sparsity in Large Pre-trained Models: The Weights that Matter
Few-shot Generation via Recalling Brain-Inspired Episodic-Semantic Memory
Train 'n Trade: Foundations of Parameter Markets
Fast Trainable Projection for Robust Fine-tuning
Temporal Conditioning Spiking Latent Variable Models of the Neural Response to Natural Visual Scenes
The CLIP Model is Secretly an Image-to-Prompt Converter
FouriDown: Factoring Down-Sampling into Shuffling and Superposing
Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation
Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy
Unifying Predictions of Deterministic and Stochastic Physics in Mesh-reduced Space with Sequential Flow Generative Model
Keep Various Trajectories: Promoting Exploration of Ensemble Policies in Continuous Control
Connected Superlevel Set in (Deep) Reinforcement Learning and its Application to Minimax Theorems
Improving Self-supervised Molecular Representation Learning using Persistent Homology
Adversarial Attacks on Online Learning to Rank with Click Feedback
Online Corrupted User Detection and Regret Minimization
Latent Field Discovery in Interacting Dynamical Systems with Neural Fields
ReSync: Riemannian Subgradient-based Robust Rotation Synchronization
Extremal Domain Translation with Neural Optimal Transport
Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics
Real3D-AD: A Dataset of Point Cloud Anomaly Detection
Robust Mean Estimation Without Moments for Symmetric Distributions
Neural Latent Geometry Search: Product Manifold Inference via Gromov-Hausdorff-Informed Bayesian Optimization
Ess-InfoGAIL: Semi-supervised Imitation Learning from Imbalanced Demonstrations
[Re] Masked Autoencoders Are Small Scale Vision Learners: A Reproduction Under Resource Constraints
Network Regression with Graph Laplacians
Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM
Bi-Level Offline Policy Optimization with Limited Exploration
DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization
Penalising the biases in norm regularisation enforces sparsity
Learning Mask-aware CLIP Representations for Zero-Shot Segmentation
Fast Attention Requires Bounded Entries
Tanimoto Random Features for Scalable Molecular Machine Learning
SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process
A Partially-Supervised Reinforcement Learning Framework for Visual Active Search
A Variational Perspective on High-Resolution ODEs
Time Series Kernels based on Nonlinear Vector AutoRegressive Delay Embeddings
Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics
Efficient Adaptation of Large Vision Transformer via Adapter Re-Composing
Diversifying Spatial-Temporal Perception for Video Domain Generalization
Mixed Samples as Probes for Unsupervised Model Selection in Domain Adaptation
A Unified Fast Gradient Clipping Framework for DP-SGD
NeRF-IBVS: Visual Servo Based on NeRF for Visual Localization and Navigation
Boosting Adversarial Transferability by Achieving Flat Local Maxima
You Only Condense Once: Two Rules for Pruning Condensed Datasets
A Batch-to-Online Transformation under Random-Order Model
Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors
Unsupervised Semantic Correspondence Using Stable Diffusion
Temporal Continual Learning with Prior Compensation for Human Motion Prediction
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger
Online robust non-stationary estimation
Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series
M$^{2}$SODAI: Multi-Modal Maritime Object Detection Dataset With RGB and Hyperspectral Image Sensors
Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL
Category-Extensible Out-of-Distribution Detection via Hierarchical Context Descriptions
VTaC: A Benchmark Dataset of Ventricular Tachycardia Alarms from ICU Monitors
LICO: Explainable Models with Language-Image COnsistency
Separable Physics-Informed Neural Networks
Penguin: Parallel-Packed Homomorphic Encryption for Fast Graph Convolutional Network Inference
When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment
What Knowledge Gets Distilled in Knowledge Distillation?
Computing Approximate $\ell_p$ Sensitivities
Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage
4D Panoptic Scene Graph Generation
TextDiffuser: Diffusion Models as Text Painters
Contrastive Sampling Chains in Diffusion Models
Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL
Combating Representation Learning Disparity with Geometric Harmonization
Diffusion Hyperfeatures: Searching Through Time and Space for Semantic Correspondence
Rethinking the Backward Propagation for Adversarial Transferability
One Fits All: Power General Time Series Analysis by Pretrained LM
Scaling Riemannian Diffusion Models
RVD: A Handheld Device-Based Fundus Video Dataset for Retinal Vessel Segmentation
RegBN: Batch Normalization of Multimodal Data with Regularization
Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias
Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms
Learning to Influence Human Behavior with Offline Reinforcement Learning
Greedy Pruning with Group Lasso Provably Generalizes for Matrix Sensing
Gradient-Free Kernel Stein Discrepancy
Optimal Rates for Bandit Nonstochastic Control
Spuriosity Didn’t Kill the Classifier: Using Invariant Predictions to Harness Spurious Features
FedL2P: Federated Learning to Personalize
A Single-Loop Accelerated Extra-Gradient Difference Algorithm with Improved Complexity Bounds for Constrained Minimax Optimization
Learning Better with Less: Effective Augmentation for Sample-Efficient Visual Reinforcement Learning
On Single-Index Models beyond Gaussian Data
Hierarchical Integration Diffusion Model for Realistic Image Deblurring
On-the-Fly Adapting Code Summarization on Trainable Cost-Effective Language Models
CrossGNN: Confronting Noisy Multivariate Time Series Via Cross Interaction Refinement
Social Motion Prediction with Cognitive Hierarchies
Fair, Polylog-Approximate Low-Cost Hierarchical Clustering
Revealing the unseen: Benchmarking video action recognition under occlusion
Policy Finetuning in Reinforcement Learning via Design of Experiments using Offline Data
Budgeting Counterfactual for Offline RL
Stability of Random Forests and Coverage of Random-Forest Prediction Intervals
FineMoGen: Fine-Grained Spatio-Temporal Motion Generation and Editing
Automatic Integration for Spatiotemporal Neural Point Processes
Birth of a Transformer: A Memory Viewpoint
CODA: Generalizing to Open and Unseen Domains with Compaction and Disambiguation
Learning from Active Human Involvement through Proxy Value Propagation
Large Language Models are Visual Reasoning Coordinators
Cognitive Steering in Deep Neural Networks via Long-Range Modulatory Feedback Connections
Towards Combinatorial Generalization for Catalysts: A Kohn-Sham Charge-Density Approach
Estimating Koopman operators with sketching to provably learn large scale dynamical systems
Learning Dictionary for Visual Attention
Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses
Probabilistic Inference in Reinforcement Learning Done Right
Scalable Fair Influence Maximization
Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schrödinger Equation
On the Learnability of Multilabel Ranking
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks
Kiki or Bouba? Sound Symbolism in Vision-and-Language Models
Learning Layer-wise Equivariances Automatically using Gradients
MGDD: A Meta Generator for Fast Dataset Distillation
Saddle-to-Saddle Dynamics in Diagonal Linear Networks
Private Distribution Learning with Public Data: The View from Sample Compression
Stein $\Pi$-Importance Sampling
AIMS: All-Inclusive Multi-Level Segmentation for Anything
WITRAN: Water-wave Information Transmission and Recurrent Acceleration Network for Long-range Time Series Forecasting
Training shallow ReLU networks on noisy data using hinge loss: when do we overfit and is it benign?
Adversarial Counterfactual Environment Model Learning
Learning Functional Transduction
AD-PT: Autonomous Driving Pre-Training with Large-scale Point Cloud Dataset
Unified Enhancement of Privacy Bounds for Mixture Mechanisms via $f$-Differential Privacy
MoVie: Visual Model-Based Policy Adaptation for View Generalization
H-InDex: Visual Reinforcement Learning with Hand-Informed Representations for Dexterous Manipulation
Learning Modulated Transformation in GANs
Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance
3D molecule generation by denoising voxel grids
Med-UniC: Unifying Cross-Lingual Medical Vision-Language Pre-Training by Diminishing Bias
Robustifying Generalizable Implicit Shape Networks with a Tunable Non-Parametric Model
Exploiting Contextual Objects and Relations for 3D Visual Grounding
Contrastive Training of Complex-Valued Autoencoders for Object Discovery
Segment Any Point Cloud Sequences by Distilling Vision Foundation Models
Deep Optimal Transport: A Practical Algorithm for Photo-realistic Image Restoration
A Unified Model and Dimension for Interactive Estimation
Alignment with human representations supports robust few-shot learning
SaVeNet: A Scalable Vector Network for Enhanced Molecular Representation Learning
Fair Graph Distillation
Exact Representation of Sparse Networks with Symmetric Nonnegative Embeddings
Towards a Unified Analysis of Kernel-based Methods Under Covariate Shift
QuantSR: Accurate Low-bit Quantization for Efficient Image Super-Resolution
BiMatting: Efficient Video Matting via Binarization
TradeMaster: A Holistic Quantitative Trading Platform Empowered by Reinforcement Learning
Canonical normalizing flows for manifold learning
Scaling MLPs: A Tale of Inductive Bias
EMMA-X: An EM-like Multilingual Pre-training Algorithm for Cross-lingual Representation Learning
DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining
LEPARD: Learning Explicit Part Discovery for 3D Articulated Shape Reconstruction
ScenarioNet: Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling
Nonparametric Boundary Geometry in Physics Informed Deep Learning
Conditional Distribution Function Estimation Using Neural Networks for Censored and Uncensored Data
Characterization and Learning of Causal Graphs with Small Conditioning Sets
Uncertainty Quantification over Graph with Conformalized Graph Neural Networks
Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification
RIO: A Benchmark for Reasoning Intention-Oriented Objects in Open Environments
A Heavy-Tailed Algebra for Probabilistic Programming
Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline
Fed-FA: Theoretically Modeling Client Data Divergence for Federated Language Backdoor Defense
Characterization of Overfitting in Robust Multiclass Classification
HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models
Augmented Memory Replay-based Continual Learning Approaches for Network Intrusion Detection
MosaicBERT: A Bidirectional Encoder Optimized for Fast Pretraining
Covariance-adaptive best arm identification
Rank-1 Matrix Completion with Gradient Descent and Small Random Initialization
Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion
The noise level in linear regression with dependent data
Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction
SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models
Contextual Gaussian Process Bandits with Neural Networks
MIM4DD: Mutual Information Maximization for Dataset Distillation
Localized Symbolic Knowledge Distillation for Visual Commonsense Models
MarioGPT: Open-Ended Text2Level Generation through Large Language Models
Robustness Guarantees for Adversarially Trained Neural Networks
Adaptive Privacy Composition for Accuracy-first Mechanisms
Learning Energy-based Model via Dual-MCMC Teaching
Diffusion Schrödinger Bridge Matching
AllSim: Simulating and Benchmarking Resource Allocation Policies in Multi-User Systems
On the Sublinear Regret of GP-UCB
Debiasing Pretrained Generative Models by Uniformly Sampling Semantic Attributes
Towards A Richer 2D Understanding of Hands at Scale
Large Language Models Are Zero-Shot Time Series Forecasters
SLM: A Smoothed First-Order Lagrangian Method for Structured Constrained Nonconvex Optimization
CorresNeRF: Image Correspondence Priors for Neural Radiance Fields
MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless Sensing
DELIFFAS: Deformable Light Fields for Fast Avatar Synthesis
Reward Finetuning for Faster and More Accurate Unsupervised Object Discovery
One Less Reason for Filter Pruning: Gaining Free Adversarial Robustness with Structured Grouped Kernel Pruning
Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving
American Stories: A Large-Scale Structured Text Dataset of Historical U.S. Newspapers
FORB: A Flat Object Retrieval Benchmark for Universal Image Embedding
Renku: a platform for sustainable data science
JourneyDB: A Benchmark for Generative Image Understanding
A Massive Scale Semantic Similarity Dataset of Historical English
MADLAD-400: A Multilingual And Document-Level Large Audited Dataset
Benchmarking Distribution Shift in Tabular Data with TableShift
OpenAssistant Conversations - Democratizing Large Language Model Alignment
Benchmarking Foundation Models with Language-Model-as-an-Examiner
Perception Test: A Diagnostic Benchmark for Multimodal Video Models
Lo-Hi: Practical ML Drug Discovery Benchmark
Lung250M-4B: A Combined 3D Dataset for CT- and Point Cloud-Based Intra-Patient Lung Registration
Learning to Augment Distributions for Out-of-distribution Detection
Weakly Supervised 3D Open-vocabulary Segmentation
Percentile Criterion Optimization in Offline Reinforcement Learning
PackQViT: Faster Sub-8-bit Vision Transformers via Full and Packed Quantization on the Mobile
MCUFormer: Deploying Vision Tranformers on Microcontrollers with Limited Memory
Asymptotically Optimal Quantile Pure Exploration for Infinite-Armed Bandits
Controlling Text-to-Image Diffusion by Orthogonal Finetuning
Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration
Estimating Causal Effects Identifiable from a Combination of Observations and Experiments
Aligning Language Models with Human Preferences via a Bayesian Approach
Facing Off World Model Backbones: RNNs, Transformers, and S4
Constraint-Conditioned Policy Optimization for Versatile Safe Reinforcement Learning
HiNeRV: Video Compression with Hierarchical Encoding-based Neural Representation
A Theory of Transfer-Based Black-Box Attacks: Explanation and Implications
Lookup Table meets Local Laplacian Filter: Pyramid Reconstruction Network for Tone Mapping
A normative theory of social conflict
k-Median Clustering via Metric Embedding: Towards Better Initialization with Differential Privacy
Generalized Logit Adjustment: Calibrating Fine-tuned Models by Removing Label Bias in Foundation Models
Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources
One-step differentiation of iterative algorithms
A Theory of Multimodal Learning
Certification of Distributional Individual Fairness
Information Theoretic Lower Bounds for Information Theoretic Upper Bounds
Bounding training data reconstruction in DP-SGD
Change point detection and inference in multivariate non-parametric models under mixing conditions
Fragment-based Pretraining and Finetuning on Molecular Graphs
PUe: Biased Positive-Unlabeled Learning Enhancement by Causal Inference
HotBEV: Hardware-oriented Transformer-based Multi-View 3D Detector for BEV Perception
Scaling laws for language encoding models in fMRI
Revisiting Implicit Differentiation for Learning Problems in Optimal Control
Unified Lower Bounds for Interactive High-dimensional Estimation under Information Constraints
HeadSculpt: Crafting 3D Head Avatars with Text
Graph Contrastive Learning with Stable and Scalable Spectral Encoding
Online Ad Procurement in Non-stationary Autobidding Worlds
Brain-like Flexible Visual Inference by Harnessing Feedback Feedforward Alignment
Probabilistic Exponential Integrators
Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label Learning
Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protection
Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation
On the Asymptotic Learning Curves of Kernel Ridge Regression under Power-law Decay
DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial Purification
Online (Multinomial) Logistic Bandit: Improved Regret and Constant Computation Cost
High-Fidelity Audio Compression with Improved RVQGAN
Lift Yourself Up: Retrieval-augmented Text Generation with Self-Memory
Unsupervised Behavior Extraction via Random Intent Priors
Fast Optimal Locally Private Mean Estimation via Random Projections
Differentially Private Statistical Inference through $\beta$-Divergence One Posterior Sampling
TMT-VIS: Taxonomy-aware Multi-dataset Joint Training for Video Instance Segmentation
Affinity-Aware Graph Networks
Probabilistic Weight Fixing: Large-scale training of neural network weight uncertainties for quantisation.
CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image Steganography
A Unified Framework for U-Net Design and Analysis
A General Framework for Equivariant Neural Networks on Reductive Lie Groups
A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm
Perturbation Towards Easy Samples Improves Targeted Adversarial Transferability
Model-free Posterior Sampling via Learning Rate Randomization
Geometric Neural Diffusion Processes
Are aligned neural networks adversarially aligned?
Beyond MLE: Convex Learning for Text Generation
Multiclass Boosting: Simple and Intuitive Weak Learning Criteria
Langevin Quasi-Monte Carlo
PRED: Pre-training via Semantic Rendering on LiDAR Point Clouds
Learning Curves for Noisy Heterogeneous Feature-Subsampled Ridge Ensembles
Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks
Michelangelo: Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representation
How many samples are needed to leverage smoothness?
Molecule Joint Auto-Encoding: Trajectory Pretraining with 2D and 3D Diffusion
On the Importance of Exploration for Generalization in Reinforcement Learning
(Amplified) Banded Matrix Factorization: A unified approach to private training
Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints
Residual Alignment: Uncovering the Mechanisms of Residual Networks
Universality and Limitations of Prompt Tuning
The Crucial Role of Normalization in Sharpness-Aware Minimization
Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model
Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects
Learning Environment-Aware Affordance for 3D Articulated Object Manipulation under Occlusions
Lie Point Symmetry and Physics-Informed Networks
Efficient Neural Music Generation
Evaluating Post-hoc Explanations for Graph Neural Networks via Robustness Analysis
Semi-Implicit Denoising Diffusion Models (SIDDMs)
UniTSFace: Unified Threshold Integrated Sample-to-Sample Loss for Face Recognition
Frequency-domain MLPs are More Effective Learners in Time Series Forecasting
Fine-Grained Visual Prompting
An information-theoretic quantification of the content of communication between brain regions
Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models
Q-DM: An Efficient Low-bit Quantized Diffusion Model
Parallel-mentoring for Offline Model-based Optimization
No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models
Data Pruning via Moving-one-Sample-out
Progressive Ensemble Distillation: Building Ensembles for Efficient Inference
Collaboratively Learning Linear Models with Structured Missing Data
Structural Pruning for Diffusion Models
Are Vision Transformers More Data Hungry Than Newborn Visual Systems?
Assumption violations in causal discovery and the robustness of score matching
Neural Harmonics: Bridging Spectral Embedding and Matrix Completion in Self-Supervised Learning
SparseProp: Efficient Event-Based Simulation and Training of Sparse Recurrent Spiking Neural Networks
Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization
Masked Space-Time Hash Encoding for Efficient Dynamic Scene Reconstruction
Neural Processes with Stability
On the Adversarial Robustness of Out-of-distribution Generalization Models
Adapting Fairness Interventions to Missing Values
Consistent Aggregation of Objectives with Diverse Time Preferences Requires Non-Markovian Rewards
High-dimensional Asymptotics of Denoising Autoencoders
Enhancing Robot Program Synthesis Through Environmental Context
Optimal testing using combined test statistics across independent studies
What Makes Good Examples for Visual In-Context Learning?
SatLM: Satisfiability-Aided Language Models Using Declarative Prompting
Res-Tuning: A Flexible and Efficient Tuning Paradigm via Unbinding Tuner from Backbone
Revisiting Area Convexity: Faster Box-Simplex Games and Spectrahedral Generalizations
TrojLLM: A Black-box Trojan Prompt Attack on Large Language Models
Injecting Multimodal Information into Rigid Protein Docking via Bi-level Optimization
NAS-X: Neural Adaptive Smoothing via Twisting
SQ Lower Bounds for Learning Mixtures of Linear Classifiers
Binary Radiance Fields
On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective
Leave No Stone Unturned: Mine Extra Knowledge for Imbalanced Facial Expression Recognition
Weitzman's Rule for Pandora's Box with Correlations
PoET: A generative model of protein families as sequences-of-sequences
TART: A plug-and-play Transformer module for task-agnostic reasoning
An Exploration-by-Optimization Approach to Best of Both Worlds in Linear Bandits
Regret-Optimal Model-Free Reinforcement Learning for Discounted MDPs with Short Burn-In Time
Sharp Recovery Thresholds of Tensor PCA Spectral Algorithms
MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth Clues
H2RBox-v2: Incorporating Symmetry for Boosting Horizontal Box Supervised Oriented Object Detection
Mechanic: A Learning Rate Tuner
Breadcrumbs to the Goal: Supervised Goal Selection from Human-in-the-Loop Feedback
Fair Allocation of Indivisible Chores: Beyond Additive Costs
The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classification
Better with Less: A Data-Active Perspective on Pre-Training Graph Neural Networks
Transformers learn through gradual rank increase
Weighted ROC Curve in Cost Space: Extending AUC to Cost-Sensitive Learning
Secure Out-of-Distribution Task Generalization with Energy-Based Models
FedFed: Feature Distillation against Data Heterogeneity in Federated Learning
Joint Prompt Optimization of Stacked LLMs using Variational Inference
InsActor: Instruction-driven Physics-based Characters
UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models
Lower Bounds on Adaptive Sensing for Matrix Recovery
Block Broyden's Methods for Solving Nonlinear Equations
Decompose Novel into Known: Part Concept Learning For 3D Novel Class Discovery
Use perturbations when learning from explanations
Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals
Bayesian Risk-Averse Q-Learning with Streaming Observations
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generation
Federated Spectral Clustering via Secure Similarity Reconstruction
Large language models transition from integrating across position-yoked, exponential windows to structure-yoked, power-law windows
Learning and Collusion in Multi-unit Auctions
Hierarchical VAEs provide a normative account of motion processing in the primate brain
CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning
Adversarial Self-Training Improves Robustness and Generalization for Gradual Domain Adaptation
Federated Compositional Deep AUC Maximization
Direct Diffusion Bridge using Data Consistency for Inverse Problems
Make the U in UDA Matter: Invariant Consistency Learning for Unsupervised Domain Adaptation
Structure of universal formulas
Generalized equivalences between subsampling and ridge regularization
A unified framework for information-theoretic generalization bounds
The Simplicity Bias in Multi-Task RNNs: Shared Attractors, Reuse of Dynamics, and Geometric Representation
PDP: Parameter-free Differentiable Pruning is All You Need
Weakly-Supervised Audio-Visual Segmentation
Understanding Multi-phase Optimization Dynamics and Rich Nonlinear Behaviors of ReLU Networks
Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation
Non-adversarial training of Neural SDEs with signature kernel scores
Incentivizing Honesty among Competitors in Collaborative Learning and Optimization
Parameterizing Context: Unleashing the Power of Parameter-Efficient Fine-Tuning and In-Context Tuning for Continual Table Semantic Parsing
Text-to-Image Diffusion Models are Zero Shot Classifiers
Meek Separators and Their Applications in Targeted Causal Discovery
Better Private Linear Regression Through Better Private Feature Selection
The Learnability of In-Context Learning
Honesty Is the Best Policy: Defining and Mitigating AI Deception
Logarithmic Bayes Regret Bounds
On the Pareto Front of Multilingual Neural Machine Translation
Smooth Flipping Probability for Differential Private Sign Random Projection Methods
CORNN: Convex optimization of recurrent neural networks for rapid inference of neural dynamics
Multinomial Logistic Regression: Asymptotic Normality on Null Covariates in High-Dimensions
DesCo: Learning Object Recognition with Rich Language Descriptions
Hybrid Policy Optimization from Imperfect Demonstrations
Improvements on Uncertainty Quantification for Node Classification via Distance Based Regularization
Information Design in Multi-Agent Reinforcement Learning
Group Fairness in Peer Review
GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation
Data Minimization at Inference Time
Uncertainty-Aware Instance Reweighting for Off-Policy Learning
Bottleneck Structure in Learned Features: Low-Dimension vs Regularity Tradeoff
Elastic Decision Transformer
ClusterFomer: Clustering As A Universal Visual Learner
Persuading Farsighted Receivers in MDPs: the Power of Honesty
Meta-Adapter: An Online Few-shot Learner for Vision-Language Model
Optimal Parameter and Neuron Pruning for Out-of-Distribution Detection
Topological RANSAC for instance verification and retrieval without fine-tuning
Structure Learning with Adaptive Random Neighborhood Informed MCMC
Reproducibility in Multiple Instance Learning: A Case For Algorithmic Unit Tests
Scale-Space Hypernetworks for Efficient Biomedical Image Analysis
Repetition In Repetition Out: Towards Understanding Neural Text Degeneration from the Data Perspective
Regularized Behavior Cloning for Blocking the Leakage of Past Action Information
Pitfall of Optimism: Distributional Reinforcement Learning by Randomizing Risk Criterion
Non-Rigid Shape Registration via Deep Functional Maps Prior
On Learning Latent Models with Multi-Instance Weak Supervision
FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective
Explain Any Concept: Segment Anything Meets Concept-Based Explanation
Sequential Subset Matching for Dataset Distillation
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model
SQ Lower Bounds for Non-Gaussian Component Analysis with Weaker Assumptions
VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks
Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions
Customizable Image Synthesis with Multiple Subjects
Large language models implicitly learn to straighten neural sentence trajectories to construct a predictive representation of natural language.
Gradient Flossing: Improving Gradient Descent through Dynamic Control of Jacobians
Learn to Categorize or Categorize to Learn? Self-Coding for Generalized Category Discovery
SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation
PanoGRF: Generalizable Spherical Radiance Fields for Wide-baseline Panoramas
Stability Guarantees for Feature Attributions with Multiplicative Smoothing
Learning Motion Refinement for Unsupervised Face Animation
Efficient Testable Learning of Halfspaces with Adversarial Label Noise
3D Copy-Paste: Physically Plausible Object Insertion for Monocular 3D Detection
FedGame: A Game-Theoretic Defense against Backdoor Attacks in Federated Learning
Directional diffusion models for graph representation learning
FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks
Reducing Blackwell and Average Optimality to Discounted MDPs via the Blackwell Discount Factor
FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning
Conformal Prediction Sets for Ordinal Classification
Shape Non-rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape Matching via Unsupervised Functional Map Regularized Reconstruction
Robust Matrix Sensing in the Semi-Random Model
Small Transformers Compute Universal Metric Embeddings
Reproducibility Study of "Label-Free Explainability for Unsupervised Models"
Hierarchically Gated Recurrent Neural Network for Sequence Modeling
Bridging RL Theory and Practice with the Effective Horizon
What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding
Deep Stochastic Processes via Functional Markov Transition Operators
ODE-based Recurrent Model-free Reinforcement Learning for POMDPs
Three Towers: Flexible Contrastive Learning with Pretrained Image Models
Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks
MMD-Fuse: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting
Embedding Space Interpolation Beyond Mini-Batch, Beyond Pairs and Beyond Examples
On the Overlooked Structure of Stochastic Gradients
CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversion
Entropy-dissipation Informed Neural Network for McKean-Vlasov Type PDEs
Non-stationary Experimental Design under Linear Trends
Meta-learning families of plasticity rules in recurrent spiking networks using simulation-based inference
On the spectral bias of two-layer linear networks
Toward Better PAC-Bayes Bounds for Uniformly Stable Algorithms
Unbounded Differentially Private Quantile and Maximum Estimation
Sub-optimality of the Naive Mean Field approximation for proportional high-dimensional Linear Regression
Rigorous Runtime Analysis of MOEA/D for Solving Multi-Objective Minimum Weight Base Problems
LLMScore: Unveiling the Power of Large Language Models in Text-to-Image Synthesis Evaluation
Jaccard Metric Losses: Optimizing the Jaccard Index with Soft Labels
Sheaf Hypergraph Networks
Improving neural network representations using human similarity judgments
GNeSF: Generalizable Neural Semantic Fields
Policy Gradient for Rectangular Robust Markov Decision Processes
Theoretical Analysis of the Inductive Biases in Deep Convolutional Networks
Improving the Knowledge Gradient Algorithm
Noise-Adaptive Thompson Sampling for Linear Contextual Bandits
Mip-Grid: Anti-aliased Grid Representations for Neural Radiance Fields
Subspace Identification for Multi-Source Domain Adaptation
A graphon-signal analysis of graph neural networks
TopoSRL: Topology preserving self-supervised Simplicial Representation Learning
Synthetic-to-Real Pose Estimation with Geometric Reconstruction
No-Regret Online Prediction with Strategic Experts
Learning Space-Time Continuous Latent Neural PDEs from Partially Observed States
Loss Decoupling for Task-Agnostic Continual Learning
Spike-driven Transformer
Sparse Deep Learning for Time Series Data: Theory and Applications
The Grand Illusion: The Myth of Software Portability and Implications for ML Progress.
Rethinking Gauss-Newton for learning over-parameterized models
Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information
VeriX: Towards Verified Explainability of Deep Neural Networks
Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder
Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning
On the Implicit Bias of Linear Equivariant Steerable Networks
Transfer Learning with Affine Model Transformation
Amortized Reparametrization: Efficient and Scalable Variational Inference for Latent SDEs
The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs
Uni3DETR: Unified 3D Detection Transformer
Smoothed Analysis of Sequential Probability Assignment
Recovering Simultaneously Structured Data via Non-Convex Iteratively Reweighted Least Squares
Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach
FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression
Neural approximation of Wasserstein distance via a universal architecture for symmetric and factorwise group invariant functions
GLIME: General, Stable and Local LIME Explanation
ViSt3D: Video Stylization with 3D CNN
Energy Guided Diffusion for Generating Neurally Exciting Images
Incentivized Communication for Federated Bandits
Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals
Two-Stage Predict+Optimize for MILPs with Unknown Parameters in Constraints
Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition
A Finite-Particle Convergence Rate for Stein Variational Gradient Descent
Revisiting Out-of-distribution Robustness in NLP: Benchmarks, Analysis, and LLMs Evaluations
SituatedGen: Incorporating Geographical and Temporal Contexts into Generative Commonsense Reasoning
AQuA: A Benchmarking Tool for Label Quality Assessment
Scientific Document Retrieval using Multi-level Aspect-based Queries
VisAlign: Dataset for Measuring the Alignment between AI and Humans in Visual Perception
Does progress on ImageNet transfer to real-world datasets?
DISCO-10M: A Large-Scale Music Dataset
Species196: A One-Million Semi-supervised Dataset for Fine-grained Species Recognition
YouTube-ASL: A Large-Scale, Open-Domain American Sign Language-English Parallel Corpus
RaLEs: a Benchmark for Radiology Language Evaluations
Consensus and Subjectivity of Skin Tone Annotation for ML Fairness
Toolbox for Multimodal Learn (scikit-multimodallearn)
Reproducibility study of the Fairness-enhanced Node Representation Learning
Reproducibility Study of ”CartoonX: Cartoon Explanations of Image Classifiers”
Synthcity: a benchmark framework for diverse use cases of tabular synthetic data
REASONER: An Explainable Recommendation Dataset with Comprehensive Labeling Ground Truths
Scalable 3D Captioning with Pretrained Models
SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model
ImageNet-Hard: The Hardest Images Remaining from a Study of the Power of Zoom and Spatial Biases in Image Classification
MedSat: A Public Health Dataset for England Featuring Medical Prescriptions and Satellite Imagery
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