Skip to yearly menu bar
Skip to main content
Main Navigation
ICLR
Help/FAQ
Contact ICLR
Downloads
ICLR Blog
Code of Conduct
Privacy Policy
Create Profile
Reset Password
Journal To Conference Track
Diversity & Inclusion
Proceedings at OpenReview
Future Meetings
Press
Exhibitor Information
ICLR Twitter
About ICLR
My Stuff
Login
Select Year: (2024)
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
Getting Started
Schedule
Main Conference
Invited Talks
Awards
Papers
In-person Orals
Spotlight Posters
Workshops
Community
Socials
Town Hall
Affinity Events
Sponsors
Organizers
Help
Helpdesk
RocketChat Client
Website FAQ
Browse
Visualization
mini
compact
topic
detail
Showing papers for
.
×
×
title
author
topic
session
shuffle
by
serendipity
bookmarked first
visited first
not visited first
bookmarked but not visited
Enable Javascript in your browser to see the papers page.
Robot Fleet Learning via Policy Merging
GNNCert: Deterministic Certification of Graph Neural Networks against Adversarial Perturbations
Eureka: Human-Level Reward Design via Coding Large Language Models
Oracle Efficient Algorithms for Groupwise Regret
Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning
Topic Modeling as Multi-Objective Contrastive Optimization
Principled Architecture-aware Scaling of Hyperparameters
Set Learning for Accurate and Calibrated Models
T-MARS: Improving Visual Representations by Circumventing Text Feature Learning
Diffusion Sampling with Momentum for Mitigating Divergence Artifacts
PROGRAM: PROtotype GRAph Model based Pseudo-Label Learning for Test-Time Adaptation
LOQA: Learning with Opponent Q-Learning Awareness
Online Stabilization of Spiking Neural Networks
Meta Continual Learning Revisited: Implicitly Enhancing Online Hessian Approximation via Variance Reduction
Blending Imitation and Reinforcement Learning for Robust Policy Improvement
In-Context Learning Learns Label Relationships but Is Not Conventional Learning
Latent Trajectory Learning for Limited Timestamps under Distribution Shift over Time
ConR: Contrastive Regularizer for Deep Imbalanced Regression
Label-Noise Robust Diffusion Models
Exploring the cloud of feature interaction scores in a Rashomon set
Unveiling the Unseen: Identifiable Clusters in Trained Depthwise Convolutional Kernels
A Simple Romance Between Multi-Exit Vision Transformer and Token Reduction
Sign2GPT: Leveraging Large Language Models for Gloss-Free Sign Language Translation
Learning to Compose: Improving Object Centric Learning by Injecting Compositionality
Equivariant Matrix Function Neural Networks
Sparsistency for inverse optimal transport
Towards Poisoning Fair Representations
Order-Preserving GFlowNets
Zipformer: A faster and better encoder for automatic speech recognition
Looped Transformers are Better at Learning Learning Algorithms
Boosting Graph Anomaly Detection with Adaptive Message Passing
Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
Rethinking the Benefits of Steerable Features in 3D Equivariant Graph Neural Networks
Forward $\chi^2$ Divergence Based Variational Importance Sampling
NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation
Effective Data Augmentation With Diffusion Models
Incremental Randomized Smoothing Certification
Training Graph Transformers via Curriculum-Enhanced Attention Distillation
FITS: Modeling Time Series with $10k$ Parameters
Robust Training of Federated Models with Extremely Label Deficiency
Towards Green AI in Fine-tuning Large Language Models via Adaptive Backpropagation
Continuous Field Reconstruction from Sparse Observations with Implicit Neural Networks
DiffusionNAG: Predictor-guided Neural Architecture Generation with Diffusion Models
Robust agents learn causal world models
The Effect of Intrinsic Dataset Properties on Generalization: Unraveling Learning Differences Between Natural and Medical Images
Scaling Convex Neural Networks with Burer-Monteiro Factorization
Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach
Robust Model Based Reinforcement Learning Using $\mathcal{L}_1$ Adaptive Control
GeneOH Diffusion: Towards Generalizable Hand-Object Interaction Denoising via Denoising Diffusion
Reward Model Ensembles Help Mitigate Overoptimization
Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks
Near-Optimal Solutions of Constrained Learning Problems
PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedback
Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX
Denoising Diffusion via Image-Based Rendering
MMD Graph Kernel: Effective Metric Learning for Graphs via Maximum Mean Discrepancy
On the Hardness of Online Nonconvex Optimization with Single Oracle Feedback
Weaker MVI Condition: Extragradient Methods with Multi-Step Exploration
Domain Randomization via Entropy Maximization
Constraint-Free Structure Learning with Smooth Acyclic Orientations
SEABO: A Simple Search-Based Method for Offline Imitation Learning
Duolando: Follower GPT with Off-Policy Reinforcement Learning for Dance Accompaniment
Consistent4D: Consistent 360° Dynamic Object Generation from Monocular Video
The Reversal Curse: LLMs trained on “A is B” fail to learn “B is A”
Identifying the Risks of LM Agents with an LM-Emulated Sandbox
On Bias-Variance Alignment in Deep Models
InstructDET: Diversifying Referring Object Detection with Generalized Instructions
Patched Denoising Diffusion Models For High-Resolution Image Synthesis
Teach LLMs to Phish: Stealing Private Information from Language Models
Customizable Combination of Parameter-Efficient Modules for Multi-Task Learning
How Do Transformers Learn In-Context Beyond Simple Functions? A Case Study on Learning with Representations
Noise-free Score Distillation
AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning
Efficient Integrators for Diffusion Generative Models
AttEXplore: Attribution for Explanation with model parameters eXploration
Symmetric Basis Convolutions for Learning Lagrangian Fluid Mechanics
You Only Query Once: An Efficient Label-Only Membership Inference Attack
The Marginal Value of Momentum for Small Learning Rate SGD
Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!
CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?
DreamLLM: Synergistic Multimodal Comprehension and Creation
AffineQuant: Affine Transformation Quantization for Large Language Models
On the Power of the Weisfeiler-Leman Test for Graph Motif Parameters
Multisize Dataset Condensation
MVDream: Multi-view Diffusion for 3D Generation
Graph-based Virtual Sensing from Sparse and Partial Multivariate Observations
Point2SSM: Learning Morphological Variations of Anatomies from Point Clouds
Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation
ZeroFlow: Scalable Scene Flow via Distillation
Leftover Lunch: Advantage-based Offline Reinforcement Learning for Language Models
Gradual Optimization Learning for Conformational Energy Minimization
Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates
Post-hoc bias scoring is optimal for fair classification
Conditional Information Bottleneck Approach for Time Series Imputation
Reasoning with Latent Diffusion in Offline Reinforcement Learning
COSA: Concatenated Sample Pretrained Vision-Language Foundation Model
FedWon: Triumphing Multi-domain Federated Learning Without Normalization
Grokking as the transition from lazy to rich training dynamics
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo
Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing
QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models
InfoCon: Concept Discovery with Generative and Discriminative Informativeness
Sparse Autoencoders Find Highly Interpretable Features in Language Models
Fixed Non-negative Orthogonal Classifier: Inducing Zero-mean Neural Collapse with Feature Dimension Separation
Self-supervised Representation Learning from Random Data Projectors
Dual-Encoders for Extreme Multi-label Classification
Privileged Sensing Scaffolds Reinforcement Learning
Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation
Neural Auto-designer for Enhanced Quantum Kernels
Fully Hyperbolic Convolutional Neural Networks for Computer Vision
Cameras as Rays: Pose Estimation via Ray Diffusion
Open-ended VQA benchmarking of Vision-Language models by exploiting Classification datasets and their semantic hierarchy
Adversarial Supervision Makes Layout-to-Image Diffusion Models Thrive
ResFields: Residual Neural Fields for Spatiotemporal Signals
AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors
Prompt Gradient Projection for Continual Learning
Vision-by-Language for Training-Free Compositional Image Retrieval
Parallelizing non-linear sequential models over the sequence length
Single Motion Diffusion
Group Preference Optimization: Few-Shot Alignment of Large Language Models
DeepSPF: Spherical SO(3)-Equivariant Patches for Scan-to-CAD Estimation
Cleanba: A Reproducible and Efficient Distributed Reinforcement Learning Platform
SuRe: Summarizing Retrievals using Answer Candidates for Open-domain QA of LLMs
Feature Collapse
WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series
HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs
Multi-Resolution Diffusion Models for Time Series Forecasting
In-context Exploration-Exploitation for Reinforcement Learning
Non-negative Contrastive Learning
A Plug-and-Play Image Registration Network
Model Merging by Uncertainty-Based Gradient Matching
Idempotence and Perceptual Image Compression
DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation
Does Progress On Object Recognition Benchmarks Improve Generalization on Crowdsourced, Global Data?
Zero-Shot Robustification of Zero-Shot Models
Uncertainty-aware Graph-based Hyperspectral Image Classification
SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents
Towards image compression with perfect realism at ultra-low bitrates
Learning Optimal Contracts: How to Exploit Small Action Spaces
Transferring Labels to Solve Annotation Mismatches Across Object Detection Datasets
Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs
PAC-FNO: Parallel-Structured All-Component Fourier Neural Operators for Recognizing Low-Quality Images
Self-Supervised High Dynamic Range Imaging with Multi-Exposure Images in Dynamic Scenes
An Efficient Membership Inference Attack for the Diffusion Model by Proximal Initialization
FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets
BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference
Test-Time Training on Nearest Neighbors for Large Language Models
STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction
Unified Projection-Free Algorithms for Adversarial DR-Submodular Optimization
Critical Learning Periods Emerge Even in Deep Linear Networks
Understanding the robustness difference between stochastic gradient descent and adaptive gradient methods
OVOR: OnePrompt with Virtual Outlier Regularization for Rehearsal-Free Class-Incremental Learning
Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise
Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency
Mastering Symbolic Operations: Augmenting Language Models with Compiled Neural Networks
InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning
Str2Str: A Score-based Framework for Zero-shot Protein Conformation Sampling
PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks
The Lipschitz-Variance-Margin Tradeoff for Enhanced Randomized Smoothing
CoRe-GD: A Hierarchical Framework for Scalable Graph Visualization with GNNs
f-FERM: A Scalable Framework for Robust Fair Empirical Risk Minimization
Mitigating the Curse of Dimensionality for Certified Robustness via Dual Randomized Smoothing
Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equations
Elastic Feature Consolidation For Cold Start Exemplar-Free Incremental Learning
BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation
Understanding the Robustness of Multi-modal Contrastive Learning to Distribution Shift
Investigating the Benefits of Projection Head for Representation Learning
LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and Generation
Stochastic Modified Equations and Dynamics of Dropout Algorithm
Implicit Neural Representations and the Algebra of Complex Wavelets
Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature
A Benchmark for Learning to Translate a New Language from One Grammar Book
Conformal Inductive Graph Neural Networks
Continual Momentum Filtering on Parameter Space for Online Test-time Adaptation
Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning
On the Limitations of Temperature Scaling for Distributions with Overlaps
Unveiling and Manipulating Prompt Influence in Large Language Models
FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity
LRM: Large Reconstruction Model for Single Image to 3D
Generative Sliced MMD Flows with Riesz Kernels
Kosmos-G: Generating Images in Context with Multimodal Large Language Models
AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos?
BaDExpert: Extracting Backdoor Functionality for Accurate Backdoor Input Detection
Self-Supervised Dataset Distillation for Transfer Learning
Large-scale Training of Foundation Models for Wearable Biosignals
Provably Robust Conformal Prediction with Improved Efficiency
Accelerating Distributed Stochastic Optimization via Self-Repellent Random Walks
Adaptive Federated Learning with Auto-Tuned Clients
LogicMP: A Neuro-symbolic Approach for Encoding First-order Logic Constraints
DP-SGD Without Clipping: The Lipschitz Neural Network Way
Pre-training Sequence, Structure, and Surface Features for Comprehensive Protein Representation Learning
OmniControl: Control Any Joint at Any Time for Human Motion Generation
Leave-one-out Distinguishability in Machine Learning
In defense of parameter sharing for model-compression
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting
Accelerating Data Generation for Neural Operators via Krylov Subspace Recycling
How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks?
Revisit and Outstrip Entity Alignment: A Perspective of Generative Models
OMNI: Open-endedness via Models of human Notions of Interestingness
Risk Bounds of Accelerated SGD for Overparameterized Linear Regression
The Hidden Language of Diffusion Models
Distinguished In Uniform: Self-Attention Vs. Virtual Nodes
Task Adaptation from Skills: Information Geometry, Disentanglement, and New Objectives for Unsupervised Reinforcement Learning
Consistent Multi-Class Classification from Multiple Unlabeled Datasets
Enhancing Instance-Level Image Classification with Set-Level Labels
Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources
Coeditor: Leveraging Repo-level Diffs for Code Auto-editing
Unifying Feature and Cost Aggregation with Transformers for Semantic and Visual Correspondence
Causality-Inspired Spatial-Temporal Explanations for Dynamic Graph Neural Networks
Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty
MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Images
Semantic Flow: Learning Semantic Fields of Dynamic Scenes from Monocular Videos
Leveraging Generative Models for Unsupervised Alignment of Neural Time Series Data
TD-MPC2: Scalable, Robust World Models for Continuous Control
AnyText: Multilingual Visual Text Generation and Editing
LLM Augmented LLMs: Expanding Capabilities through Composition
On the Provable Advantage of Unsupervised Pretraining
Pooling Image Datasets with Multiple Covariate Shift and Imbalance
Provably Efficient Iterated CVaR Reinforcement Learning with Function Approximation and Human Feedback
Domain-Agnostic Molecular Generation with Chemical Feedback
Towards Optimal Feature-Shaping Methods for Out-of-Distribution Detection
Quality-Diversity through AI Feedback
Relaxing the Additivity Constraints in Decentralized No-Regret High-Dimensional Bayesian Optimization
Bayesian Optimization through Gaussian Cox Process Models for Spatio-temporal Data
Safe and Robust Watermark Injection with a Single OoD Image
TOSS: High-quality Text-guided Novel View Synthesis from a Single Image
Elucidating the design space of classifier-guided diffusion generation
Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer
Periodicity Decoupling Framework for Long-term Series Forecasting
General Stability Analysis for Zeroth-Order Optimization Algorithms
The Cost of Scaling Down Large Language Models: Reducing Model Size Affects Memory before In-context Learning
Think before you speak: Training Language Models With Pause Tokens
DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer
Context is Environment
Class Incremental Learning via Likelihood Ratio Based Task Prediction
Denoising Diffusion Step-aware Models
Noisy Interpolation Learning with Shallow Univariate ReLU Networks
Initializing Models with Larger Ones
HyperHuman: Hyper-Realistic Human Generation with Latent Structural Diffusion
AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection
Benchmarking and Improving Generator-Validator Consistency of Language Models
Language-Interfaced Tabular Oversampling via Progressive Imputation and Self-Authentication
Counterfactual Density Estimation using Kernel Stein Discrepancies
EQA-MX: Embodied Question Answering using Multimodal Expression
Proper Laplacian Representation Learning
Scaling Autoregressive Models for Content-Rich Text-to-Image Generation
Learning Stackable and Skippable LEGO Bricks for Efficient, Reconfigurable, and Variable-Resolution Diffusion Modeling
DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genomes
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Adaptive Regularization of Representation Rank as an Implicit Constraint of Bellman Equation
PanoDiffusion: 360-degree Panorama Outpainting via Diffusion
PerceptionCLIP: Visual Classification by Inferring and Conditioning on Contexts
Making LLaMA SEE and Draw with SEED Tokenizer
Removing Biases from Molecular Representations via Information Maximization
Online Continual Learning for Interactive Instruction Following Agents
Human Motion Diffusion as a Generative Prior
Closing the Curious Case of Neural Text Degeneration
GTA: A Geometry-Aware Attention Mechanism for Multi-View Transformers
Is attention required for ICL? Exploring the Relationship Between Model Architecture and In-Context Learning Ability
Stable Anisotropic Regularization
A Framework for Inference Inspired by Human Memory Mechanisms
SF(DA)$^2$: Source-free Domain Adaptation Through the Lens of Data Augmentation
PBADet: A One-Stage Anchor-Free Approach for Part-Body Association
Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors
A Characterization Theorem for Equivariant Networks with Point-wise Activations
Mirage: Model-agnostic Graph Distillation for Graph Classification
Exploring the Common Appearance-Boundary Adaptation for Nighttime Optical Flow
Faster Approximation of Probabilistic and Distributional Values via Least Squares
Accelerating Sinkhorn algorithm with sparse Newton iterations
Circuit Component Reuse Across Tasks in Transformer Language Models
CORN: Contact-based Object Representation for Nonprehensile Manipulation of General Unseen Objects
Neural-Symbolic Recursive Machine for Systematic Generalization
FlashFFTConv: Efficient Convolutions for Long Sequences with Tensor Cores
Towards Establishing Guaranteed Error for Learned Database Operations
Continuous Invariance Learning
Learning to solve Class-Constrained Bin Packing Problems via Encoder-Decoder Model
EmerDiff: Emerging Pixel-level Semantic Knowledge in Diffusion Models
Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models
Dichotomy of Early and Late Phase Implicit Biases Can Provably Induce Grokking
IceFormer: Accelerated Inference with Long-Sequence Transformers on CPUs
Dynamic Discounted Counterfactual Regret Minimization
Graphical Multioutput Gaussian Process with Attention
RetroBridge: Modeling Retrosynthesis with Markov Bridges
DrS: Learning Reusable Dense Rewards for Multi-Stage Tasks
Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game
Few-Shot Detection of Machine-Generated Text using Style Representations
Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model
Visual Data-Type Understanding does not emerge from scaling Vision-Language Models
Enhancing Tail Performance in Extreme Classifiers by Label Variance Reduction
Reward Design for Justifiable Sequential Decision-Making
Rethinking Complex Queries on Knowledge Graphs with Neural Link Predictors
Generative Pre-training for Speech with Flow Matching
Enhancing Neural Training via a Correlated Dynamics Model
Modeling state-dependent communication between brain regions with switching nonlinear dynamical systems
Some Fundamental Aspects about Lipschitz Continuity of Neural Networks
Structured Video-Language Modeling with Temporal Grouping and Spatial Grounding
Certified Adversarial Robustness for Rate Encoded Spiking Neural Networks
Graph Metanetworks for Processing Diverse Neural Architectures
Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning
TEDDY: Trimming Edges with Degree-based Discrimination Strategy
A differentiable brain simulator bridging brain simulation and brain-inspired computing
From Zero to Turbulence: Generative Modeling for 3D Flow Simulation
RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems
AlignDiff: Aligning Diverse Human Preferences via Behavior-Customisable Diffusion Model
Physics-Regulated Deep Reinforcement Learning: Invariant Embeddings
FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data
Efficient Planning with Latent Diffusion
PAC Prediction Sets Under Label Shift
Structural Fairness-aware Active Learning for Graph Neural Networks
Scalable Neural Network Kernels
Language Model Detectors Are Easily Optimized Against
How I Warped Your Noise: a Temporally-Correlated Noise Prior for Diffusion Models
Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning
Zero-Mean Regularized Spectral Contrastive Learning: Implicitly Mitigating Wrong Connections in Positive-Pair Graphs
Neural Field Classifiers via Target Encoding and Classification Loss
Variance-enlarged Poisson Learning for Graph-based Semi-Supervised Learning with Extremely Sparse Labeled Data
Solving Homogeneous and Heterogeneous Cooperative Tasks with Greedy Sequential Execution
Coordinate-Aware Modulation for Neural Fields
Neuroformer: Multimodal and Multitask Generative Pretraining for Brain Data
Do Generated Data Always Help Contrastive Learning?
Rigid Protein-Protein Docking via Equivariant Elliptic-Paraboloid Interface Prediction
An improved analysis of per-sample and per-update clipping in federated learning
Evoke: Evoking Critical Thinking Abilities in LLMs via Reviewer-Author Prompt Editing
CoVLM: Composing Visual Entities and Relationships in Large Language Models Via Communicative Decoding
WizardCoder: Empowering Code Large Language Models with Evol-Instruct
Numerical Accounting in the Shuffle Model of Differential Privacy
Language Model Decoding as Direct Metrics Optimization
On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes
Provable Benefits of Multi-task RL under Non-Markovian Decision Making Processes
Like Oil and Water: Group Robustness Methods and Poisoning Defenses May Be at Odds
Geometrically Aligned Transfer Encoder for Inductive Transfer in Regression Tasks
Generative Learning for Solving Non-Convex Problem with Multi-Valued Input-Solution Mapping
Knowledge Fusion of Large Language Models
Learning Multi-Agent Communication from Graph Modeling Perspective
Evaluating Language Model Agency Through Negotiations
SCHEMA: State CHangEs MAtter for Procedure Planning in Instructional Videos
Overthinking the Truth: Understanding how Language Models Process False Demonstrations
BTR: Binary Token Representations for Efficient Retrieval Augmented Language Models
The mechanistic basis of data dependence and abrupt learning in an in-context classification task
A Probabilistic Framework for Modular Continual Learning
Uni-O4: Unifying Online and Offline Deep Reinforcement Learning with Multi-Step On-Policy Optimization
Universal Backdoor Attacks
DDMI: Domain-agnostic Latent Diffusion Models for Synthesizing High-Quality Implicit Neural Representations
Sentence-level Prompts Benefit Composed Image Retrieval
Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory
Are Human-generated Demonstrations Necessary for In-context Learning?
Enabling Lanuguage Models to Implicitly Learn Self-Improvement
L2MAC: Large Language Model Automatic Computer for Extensive Code Generation
Light-MILPopt: Solving Large-scale Mixed Integer Linear Programs with Lightweight Optimizer and Small-scale Training Dataset
Deep SE(3)-Equivariant Geometric Reasoning for Precise Placement Tasks
A Restoration Network as an Implicit Prior
SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations
Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration
Scaling for Training Time and Post-hoc Out-of-distribution Detection Enhancement
Test-time Adaptation against Multi-modal Reliability Bias
INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection
GOAt: Explaining Graph Neural Networks via Graph Output Attribution
RA-DIT: Retrieval-Augmented Dual Instruction Tuning
A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis
Directly Fine-Tuning Diffusion Models on Differentiable Rewards
OpenNeRF: Open Set 3D Neural Scene Segmentation with Pixel-Wise Features and Rendered Novel Views
Efficiently Computing Similarities to Private Datasets
Domain constraints improve risk prediction when outcome data is missing
Towards Few-Shot Adaptation of Foundation Models via Multitask Finetuning
Multi-granularity Correspondence Learning from Long-term Noisy Videos
LEAP: Liberate Sparse-View 3D Modeling from Camera Poses
Outliers with Opposing Signals Have an Outsized Effect on Neural Network Optimization
Beyond Stationarity: Convergence Analysis of Stochastic Softmax Policy Gradient Methods
Skill or Luck? Return Decomposition via Advantage Functions
Transport meets Variational Inference: Controlled Monte Carlo Diffusions
Unsupervised Order Learning
Enhancing Transferable Adversarial Attacks on Vision Transformers through Gradient Normalization Scaling and High-Frequency Adaptation
Rethinking the symmetry-preserving circuits for constrained variational quantum algorithms
KITAB: Evaluating LLMs on Constraint Satisfaction for Information Retrieval
Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models
Retrieval-Guided Reinforcement Learning for Boolean Circuit Minimization
Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated Policies
Rethinking Adversarial Policies: A Generalized Attack Formulation and Provable Defense in RL
Compressed Context Memory for Online Language Model Interaction
Revisiting Link Prediction: a data perspective
MINDE: Mutual Information Neural Diffusion Estimation
Realistic Evaluation of Semi-supervised Learning Algorithms in Open Environments
Achieving Human Parity in Content-Grounded Datasets Generation
Contrastive Difference Predictive Coding
Efficient-3Dim: Learning a Generalizable Single-image Novel-view Synthesizer in One Day
Unified Generative Modeling of 3D Molecules with Bayesian Flow Networks
Image2Sentence based Asymmetrical Zero-shot Composed Image Retrieval
Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks
FairerCLIP: Debiasing CLIP's Zero-Shot Predictions using Functions in RKHSs
BarLeRIa: An Efficient Tuning Framework for Referring Image Segmentation
Asymptotically Free Sketched Ridge Ensembles: Risks, Cross-Validation, and Tuning
CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling
Provably Efficient CVaR RL in Low-rank MDPs
Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for 3D Molecule Generation
Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators
Improved Probabilistic Image-Text Representations
MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use
MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning
Spatio-Temporal Approximation: A Training-Free SNN Conversion for Transformers
Modulated Phase Diffusor: Content-Oriented Feature Synthesis for Detecting Unknown Objects
LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment
MogaNet: Multi-order Gated Aggregation Network
Deep Generative Clustering with Multimodal Diffusion Variational Autoencoders
Towards Category Unification of 3D Single Object Tracking on Point Clouds
SemiReward: A General Reward Model for Semi-supervised Learning
Learning Hierarchical Polynomials with Three-Layer Neural Networks
Graph Neural Networks for Learning Equivariant Representations of Neural Networks
LEAD: Min-Max Optimization from a Physical Perspective
Kalman Filter for Online Classification of Non-Stationary Data
AlpaGasus: Training a Better Alpaca with Fewer Data
RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design
What does automatic differentiation compute for neural networks?
Unbiased Watermark for Large Language Models
Self-Consuming Generative Models Go MAD
Out-of-Distribution Detection with Negative Prompts
STARC: A General Framework For Quantifying Differences Between Reward Functions
Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions
Theoretical Understanding of Learning from Adversarial Perturbations
Time Fairness in Online Knapsack Problems
Attacking Perceptual Similarity Metrics
A ROBUST DIFFERENTIAL NEURAL ODE OPTIMIZER
GPAvatar: Generalizable and Precise Head Avatar from Image(s)
Chain-of-Experts: When LLMs Meet Complex Operations Research Problems
StructComp: Substituting propagation with Structural Compression in Training Graph Contrastive Learning
TopoMLP: A Simple yet Strong Pipeline for Driving Topology Reasoning
Behaviour Distillation
$\infty$-Diff: Infinite Resolution Diffusion with Subsampled Mollified States
Enhancing Human Experience in Human-Agent Collaboration: A Human-Centered Modeling Approach Based on Positive Human Gain
Synapse: Trajectory-as-Exemplar Prompting with Memory for Computer Control
Debiasing Algorithm through Model Adaptation
On gauge freedom, conservativity and intrinsic dimensionality estimation in diffusion models
Clifford Group Equivariant Simplicial Message Passing Networks
A Flexible Generative Model for Heterogeneous Tabular EHR with Missing Modality
Don't Play Favorites: Minority Guidance for Diffusion Models
Mitigating Emergent Robustness Degradation while Scaling Graph Learning
Frequency-Aware Transformer for Learned Image Compression
Causally Aligned Curriculum Learning
From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction
ED-NeRF: Efficient Text-Guided Editing of 3D Scene With Latent Space NeRF
The Wasserstein Believer: Learning Belief Updates for Partially Observable Environments through Reliable Latent Space Models
FreeNoise: Tuning-Free Longer Video Diffusion via Noise Rescheduling
CAMBranch: Contrastive Learning with Augmented MILPs for Branching
Cycle Consistency Driven Object Discovery
YaRN: Efficient Context Window Extension of Large Language Models
CoT3DRef: Chain-of-Thoughts Data-Efficient 3D Visual Grounding
AgentBench: Evaluating LLMs as Agents
Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks
Towards Aligned Layout Generation via Diffusion Model with Aesthetic Constraints
What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning
Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control
Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs
A Multi-Level Framework for Accelerating Training Transformer Models
Supervised Knowledge Makes Large Language Models Better In-context Learners
VQ-TR: Vector Quantized Attention for Time Series Forecasting
Aligning Relational Learning with Lipschitz Fairness
Understanding In-Context Learning from Repetitions
Symmetric Neural-Collapse Representations with Supervised Contrastive Loss: The Impact of ReLU and Batching
Training Unbiased Diffusion Models From Biased Dataset
DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation
Separate and Diffuse: Using a Pretrained Diffusion Model for Better Source Separation
Conversational Drug Editing Using Retrieval and Domain Feedback
Discovering Temporally-Aware Reinforcement Learning Algorithms
A Precise Characterization of SGD Stability Using Loss Surface Geometry
The Expressive Power of Transformers with Chain of Thought
Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel
ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs
Input-gradient space particle inference for neural network ensembles
Towards Reliable and Efficient Backdoor Trigger Inversion via Decoupling Benign Features
Confidence-aware Reward Optimization for Fine-tuning Text-to-Image Models
A Data-Driven Measure of Relative Uncertainty for Misclassification Detection
What Matters to You? Towards Visual Representation Alignment for Robot Learning
MOFI: Learning Image Representations from Noisy Entity Annotated Images
Large Language Models as Automated Aligners for benchmarking Vision-Language Models
Sample-Efficient Linear Representation Learning from Non-IID Non-Isotropic Data
Hypergraph Dynamic System
Elucidating the Exposure Bias in Diffusion Models
SPDER: Semiperiodic Damping-Enabled Object Representation
Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints
MagicDrive: Street View Generation with Diverse 3D Geometry Control
Gaining Wisdom from Setbacks: Aligning Large Language Models via Mistake Analysis
GeoDiffusion: Text-Prompted Geometric Control for Object Detection Data Generation
DATS: Difficulty-Aware Task Sampler for Meta-Learning Physics-Informed Neural Networks
SparseDFF: Sparse-View Feature Distillation for One-Shot Dexterous Manipulation
Leveraging Uncertainty Estimates To Improve Classifier Performance
Learning Planning Abstractions from Language
Experimental Design for Multi-Channel Imaging via Task-Driven Feature Selection
Fast Ensembling with Diffusion Schrödinger Bridge
Benign Overfitting and Grokking in ReLU Networks for XOR Cluster Data
L2P-MIP: Learning to Presolve for Mixed Integer Programming
Neurosymbolic Grounding for Compositional World Models
Momentum Benefits Non-iid Federated Learning Simply and Provably
Making Pre-trained Language Models Great on Tabular Prediction
Multimodal Patient Representation Learning with Missing Modalities and Labels
Feature-aligned N-BEATS with Sinkhorn divergence
Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning
Video Decomposition Prior: Editing Videos Layer by Layer
New Insight of Variance reduce in Zero-Order Hard-Thresholding: Mitigating Gradient Error and Expansivity Contradictions
Subtractive Mixture Models via Squaring: Representation and Learning
AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models
Bridging Vision and Language Spaces with Assignment Prediction
Modulate Your Spectrum in Self-Supervised Learning
LLM-Assisted Code Cleaning For Training Accurate Code Generators
Implicit bias of SGD in $L_2$-regularized linear DNNs: One-way jumps from high to low rank
Simple Minimax Optimal Byzantine Robust Algorithm for Nonconvex Objectives with Uniform Gradient Heterogeneity
On the Variance of Neural Network Training with respect to Test Sets and Distributions
Graph Parsing Networks
Optimal transport based adversarial patch to leverage large scale attack transferability
SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation
Orbit-Equivariant Graph Neural Networks
Perceptual Group Tokenizer: Building Perception with Iterative Grouping
R-EDL: Relaxing Nonessential Settings of Evidential Deep Learning
Early Neuron Alignment in Two-layer ReLU Networks with Small Initialization
Bootstrapping Variational Information Pursuit with Large Language and Vision Models for Interpretable Image Classification
Dropout-Based Rashomon Set Exploration for Efficient Predictive Multiplicity Estimation
More is Better: when Infinite Overparameterization is Optimal and Overfitting is Obligatory
An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression
Attention-Guided Contrastive Role Representations for Multi-agent Reinforcement Learning
Masked Distillation Advances Self-Supervised Transformer Architecture Search
Learning with a Mole: Transferable latent spatial representations for navigation without reconstruction
End-to-End (Instance)-Image Goal Navigation through Correspondence as an Emergent Phenomenon
Don't Judge by the Look: Towards Motion Coherent Video Representation
Submodular Reinforcement Learning
Conserve-Update-Revise to Cure Generalization and Robustness Trade-off in Adversarial Training
Role of Locality and Weight Sharing in Image-Based Tasks: A Sample Complexity Separation between CNNs, LCNs, and FCNs
Noise Map Guidance: Inversion with Spatial Context for Real Image Editing
A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables
Inherently Interpretable Time Series Classification via Multiple Instance Learning
Consistent algorithms for multi-label classification with macro-at-$k$ metrics
KW-Design: Pushing the Limit of Protein Design via Knowledge Refinement
MOTOR: A Time-to-Event Foundation Model For Structured Medical Records
Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Bandit
Mega-TTS 2: Boosting Prompting Mechanisms for Zero-Shot Speech Synthesis
Light Schrödinger Bridge
Learning with Mixture of Prototypes for Out-of-Distribution Detection
Kill Two Birds with One Stone: Rethinking Data Augmentation for Deep Long-tailed Learning
Can We Evaluate Domain Adaptation Models Without Target-Domain Labels?
Efficient Continual Finite-Sum Minimization
Adversarial Training Should Be Cast as a Non-Zero-Sum Game
Advancing the Lower Bounds: an Accelerated, Stochastic, Second-order Method with Optimal Adaptation to Inexactness
MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts
Magnitude Invariant Parametrizations Improve Hypernetwork Learning
Building Cooperative Embodied Agents Modularly with Large Language Models
Towards Robust Multi-Modal Reasoning via Model Selection
The optimality of kernel classifiers in Sobolev space
Proving Test Set Contamination in Black-Box Language Models
SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression
Is ImageNet worth 1 video? Learning strong image encoders from 1 long unlabelled video
CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing
Learning model uncertainty as variance-minimizing instance weights
A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation
Toward effective protection against diffusion-based mimicry through score distillation
Piecewise Linear Parametrization of Policies: Towards Interpretable Deep Reinforcement Learning
Multi-View Representation is What You Need for Point-Cloud Pre-Training
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
Skill Machines: Temporal Logic Skill Composition in Reinforcement Learning
Designing Skill-Compatible AI: Methodologies and Frameworks in Chess
Forward Learning with Top-Down Feedback: Empirical and Analytical Characterization
LDReg: Local Dimensionality Regularized Self-Supervised Learning
Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI
Mixture of LoRA Experts
KoLA: Carefully Benchmarking World Knowledge of Large Language Models
CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide Images
Knowledge Distillation Based on Transformed Teacher Matching
Foundation Model-oriented Robustness: Robust Image Model Evaluation with Pretrained Models
Pseudo-Generalized Dynamic View Synthesis from a Video
Massively Scalable Inverse Reinforcement Learning in Google Maps
Improved sampling via learned diffusions
Learning Polynomial Problems with $SL(2, \mathbb{R})$-Equivariance
On the hardness of learning under symmetries
Talk like a Graph: Encoding Graphs for Large Language Models
Hybrid Distillation: Connecting Masked Autoencoders with Contrastive Learners
Image Inpainting via Iteratively Decoupled Probabilistic Modeling
Maximum Entropy Heterogeneous-Agent Reinforcement Learning
Nougat: Neural Optical Understanding for Academic Documents
Learning Personalized Causally Invariant Representations for Heterogeneous Federated Clients
ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesis
Tree-Planner: Efficient Close-loop Task Planning with Large Language Models
PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks
Deep Temporal Graph Clustering
DV-3DLane: End-to-end Multi-modal 3D Lane Detection with Dual-view Representation
On the Reliability of Watermarks for Large Language Models
Universal Guidance for Diffusion Models
NEFTune: Noisy Embeddings Improve Instruction Finetuning
Entropy Coding of Unordered Data Structures
A Semantic Invariant Robust Watermark for Large Language Models
Plug-and-Play Policy Planner for Large Language Model Powered Dialogue Agents
Long-tailed Diffusion Models with Oriented Calibration
When Do Prompting and Prefix-Tuning Work? A Theory of Capabilities and Limitations
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Hybrid Internal Model: Learning Agile Legged Locomotion with Simulated Robot Response
Retrieval is Accurate Generation
Illusory Attacks: Information-theoretic detectability matters in adversarial attacks
Consistency-guided Prompt Learning for Vision-Language Models
Differentiable Learning of Generalized Structured Matrices for Efficient Deep Neural Networks
ACRF: Compressing Explicit Neural Radiance Fields via Attribute Compression
Navigating Dataset Documentations in AI: A Large-Scale Analysis of Dataset Cards on HuggingFace
An Analytical Solution to Gauss-Newton Loss for Direct Image Alignment
De novo Protein Design Using Geometric Vector Field Networks
Sample-Efficient Quality-Diversity by Cooperative Coevolution
Weakly-supervised Audio Separation via Bi-modal Semantic Similarity
DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization
Adversarial Feature Map Pruning for Backdoor
Toward Student-oriented Teacher Network Training for Knowledge Distillation
Independent-Set Design of Experiments for Estimating Treatment and Spillover Effects under Network Interference
Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula
Plug-and-Play Posterior Sampling under Mismatched Measurement and Prior Models
Backdoor Contrastive Learning via Bi-level Trigger Optimization
Reward-Consistent Dynamics Models are Strongly Generalizable for Offline Reinforcement Learning
Towards Cheaper Inference in Deep Networks with Lower Bit-Width Accumulators
BioBridge: Bridging Biomedical Foundation Models via Knowledge Graphs
Backdoor Secrets Unveiled: Identifying Backdoor Data with Optimized Scaled Prediction Consistency
DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models
DisenBooth: Identity-Preserving Disentangled Tuning for Subject-Driven Text-to-Image Generation
Parsing neural dynamics with infinite recurrent switching linear dynamical systems
Chain of Hindsight aligns Language Models with Feedback
Discovering Failure Modes of Text-guided Diffusion Models via Adversarial Search
Headless Language Models: Learning without Predicting with Contrastive Weight Tying
MAP IT to Visualize Representations
Waxing-and-Waning: a Generic Similarity-based Framework for Efficient Self-Supervised Learning
Revisiting Data Augmentation in Deep Reinforcement Learning
MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback
Doubly Robust Instance-Reweighted Adversarial Training
CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets
Generative Judge for Evaluating Alignment
Demystifying Linear MDPs and Novel Dynamics Aggregation Framework
Language Modeling Is Compression
The All-Seeing Project: Towards Panoptic Visual Recognition and Understanding of the Open World
Progressive3D: Progressively Local Editing for Text-to-3D Content Creation with Complex Semantic Prompts
An Extensible Framework for Open Heterogeneous Collaborative Perception
On the Joint Interaction of Models, Data, and Features
SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks
Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach
Replay across Experiments: A Natural Extension of Off-Policy RL
FeatUp: A Model-Agnostic Framework for Features at Any Resolution
COCO-Periph: Bridging the Gap Between Human and Machine Perception in the Periphery
Training Socially Aligned Language Models on Simulated Social Interactions
Diverse Projection Ensembles for Distributional Reinforcement Learning
CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction
SafeDreamer: Safe Reinforcement Learning with World Models
Optimal Sample Complexity of Contrastive Learning
CLaM-TTS: Improving Neural Codec Language Model for Zero-Shot Text-to-Speech
Where We Have Arrived in Proving the Emergence of Sparse Interaction Primitives in DNNs
Enhanced Face Recognition using Intra-class Incoherence Constraint
Global Optimality for Non-linear Constrained Restoration Problems via Invexity
Towards Energy Efficient Spiking Neural Networks: An Unstructured Pruning Framework
ADDP: Learning General Representations for Image Recognition and Generation with Alternating Denoising Diffusion Process
GNeRP: Gaussian-guided Neural Reconstruction of Reflective Objects with Noisy Polarization Priors
CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity
Mask-Based Modeling for Neural Radiance Fields
MixSup: Mixed-grained Supervision for Label-efficient LiDAR-based 3D Object Detection
Towards Understanding Sycophancy in Language Models
Algorithms for Caching and MTS with reduced number of predictions
Learning to reconstruct signals from binary measurements alone
Multi-Source Diffusion Models for Simultaneous Music Generation and Separation
$\pi$2vec: Policy Representation with Successor Features
SWAP: Sparse Entropic Wasserstein Regression for Robust Network Pruning
Towards Identifiable Unsupervised Domain Translation: A Diversified Distribution Matching Approach
Conditional Variational Diffusion Models
Can LLM-Generated Misinformation Be Detected?
Pre-training LiDAR-based 3D Object Detectors through Colorization
On the Markov Property of Neural Algorithmic Reasoning: Analyses and Methods
Matrix Manifold Neural Networks++
Neural Atoms: Propagating Long-range Interaction in Molecular Graphs through Efficient Communication Channel
Sum-Product-Set Networks: Deep Tractable Models for Tree-Structured Graphs
Optimistic Bayesian Optimization with Unknown Constraints
Less is More: One-shot Subgraph Reasoning on Large-scale Knowledge Graphs
Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks
Personalize Segment Anything Model with One Shot
On the generalization capacity of neural networks during generic multimodal reasoning
Rethinking and Extending the Probabilistic Inference Capacity of GNNs
The Expressive Power of Low-Rank Adaptation
Multi-Scale Representations by Varying Window Attention for Semantic Segmentation
Retrieval meets Long Context Large Language Models
Dynamic Layer Tying for Parameter-Efficient Transformers
Simplifying Transformer Blocks
Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning
A Branching Decoder for Set Generation
Enhancing High-Resolution 3D Generation through Pixel-wise Gradient Clipping
3D Feature Prediction for Masked-AutoEncoder-Based Point Cloud Pretraining
A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling
Path Choice Matters for Clear Attributions in Path Methods
Instructive Decoding: Instruction-Tuned Large Language Models are Self-Refiner from Noisy Instructions
Estimating Shape Distances on Neural Representations with Limited Samples
DIAGNOSIS: Detecting Unauthorized Data Usages in Text-to-image Diffusion Models
Object centric architectures enable efficient causal representation learning
Pre-Training and Fine-Tuning Generative Flow Networks
Topological data analysis on noisy quantum computers
LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object Detection
Variational Inference for SDEs Driven by Fractional Noise
VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections
LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving
Soft Robust MDPs and Risk-Sensitive MDPs: Equivalence, Policy Gradient, and Sample Complexity
Debiased Collaborative Filtering with Kernel-Based Causal Balancing
Towards Non-Asymptotic Convergence for Diffusion-Based Generative Models
Reverse Forward Curriculum Learning for Extreme Sample and Demo Efficiency
Langevin Monte Carlo for strongly log-concave distributions: Randomized midpoint revisited
S$2$AC: Energy-Based Reinforcement Learning with Stein Soft Actor Critic
FedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent
A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors
SEA: Sparse Linear Attention with Estimated Attention Mask
Depthwise Hyperparameter Transfer in Residual Networks: Dynamics and Scaling Limit
Goodhart's Law in Reinforcement Learning
ReFusion: Improving Natural Language Understanding with Computation-Efficient Retrieval Representation Fusion
An Emulator for Fine-tuning Large Language Models using Small Language Models
Neural Optimal Transport with General Cost Functionals
Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature Attacks
COLLIE: Systematic Construction of Constrained Text Generation Tasks
SWE-bench: Can Language Models Resolve Real-world Github Issues?
Cross-Modal Contextualized Diffusion Models for Text-Guided Visual Generation and Editing
At Which Training Stage Does Code Data Help LLMs Reasoning?
VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs
Expressive Losses for Verified Robustness via Convex Combinations
Language Model Cascades: Token-Level Uncertainty And Beyond
A Linear Algebraic Framework for Counterfactual Generation
Learning to Reject Meets Long-tail Learning
Faithful Rule Extraction for Differentiable Rule Learning Models
Quantifying Network Similarity using Graph Cumulants
Grokking as a First Order Phase Transition in Two Layer Networks
Feature emergence via margin maximization: case studies in algebraic tasks
JointNet: Extending Text-to-Image Diffusion for Dense Distribution Modeling
HyperAttention: Long-context Attention in Near-Linear Time
An operator preconditioning perspective on training in physics-informed machine learning
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
Confidential-DPproof: Confidential Proof of Differentially Private Training
Hindsight PRIORs for Reward Learning from Human Preferences
A Hierarchical Bayesian Model for Few-Shot Meta Learning
The Alignment Problem from a Deep Learning Perspective
Neural Fine-Tuning Search for Few-Shot Learning
FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysis
Align With Purpose: Optimize Desired Properties in CTC Models with a General Plug-and-Play Framework
DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic Diversity
Memorization in Self-Supervised Learning Improves Downstream Generalization
Neural Active Learning Beyond Bandits
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch
CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting
Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting
EasyTPP: Towards Open Benchmarking Temporal Point Processes
Explaining Time Series via Contrastive and Locally Sparse Perturbations
Pathologies of Predictive Diversity in Deep Ensembles
Detecting Pretraining Data from Large Language Models
In-Context Pretraining: Language Modeling Beyond Document Boundaries
RECOMP: Improving Retrieval-Augmented LMs with Context Compression and Selective Augmentation
A Fast and Provable Algorithm for Sparse Phase Retrieval
SILO Language Models: Isolating Legal Risk In a Nonparametric Datastore
Multilingual Jailbreak Challenges in Large Language Models
Lemur: Harmonizing Natural Language and Code for Language Agents
One-shot Active Learning Based on Lewis Weight Sampling for Multiple Deep Models
The Blessing of Randomness: SDE Beats ODE in General Diffusion-based Image Editing
Window Attention is Bugged: How not to Interpolate Position Embeddings
Sharpness-Aware Data Poisoning Attack
NetInfoF Framework: Measuring and Exploiting Network Usable Information
Faster Sampling from Log-Concave Densities over Polytopes via Efficient Linear Solvers
Learning to Reject with a Fixed Predictor: Application to Decontextualization
Demystifying Poisoning Backdoor Attacks from a Statistical Perspective
A 2-Dimensional State Space Layer for Spatial Inductive Bias
High-dimensional SGD aligns with emerging outlier eigenspaces
A Unified and General Framework for Continual Learning
Dynamic Neural Response Tuning
Det-CGD: Compressed Gradient Descent with Matrix Stepsizes for Non-Convex Optimization
BatchPrompt: Accomplish more with less
CODE REPRESENTATION LEARNING AT SCALE
Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors
Forward Learning of Graph Neural Networks
Towards Cross Domain Generalization of Hamiltonian Representation via Meta Learning
AutoChunk: Automated Activation Chunk for Memory-Efficient Deep Learning Inference
One Forward is Enough for Neural Network Training via Likelihood Ratio Method
TorchRL: A data-driven decision-making library for PyTorch
SweetDreamer: Aligning Geometric Priors in 2D diffusion for Consistent Text-to-3D
CellPLM: Pre-training of Cell Language Model Beyond Single Cells
MetaPhysiCa: Improving OOD Robustness in Physics-informed Machine Learning
EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Counting Graph Substructures with Graph Neural Networks
Meta-VBO: Utilizing Prior Tasks in Optimizing Risk Measures with Gaussian Processes
Beyond Accuracy: Evaluating Self-Consistency of Code Large Language Models with IdentityChain
Beyond Imitation: Leveraging Fine-grained Quality Signals for Alignment
In-Context Learning through the Bayesian Prism
Improving Domain Generalization with Domain Relations
Analyzing and Mitigating Object Hallucination in Large Vision-Language Models
Zoology: Measuring and Improving Recall in Efficient Language Models
Protein-Ligand Interaction Prior for Binding-aware 3D Molecule Diffusion Models
TRAM: Bridging Trust Regions and Sharpness Aware Minimization
Holistic Evaluation of Language Models
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design
Temporal Generalization Estimation in Evolving Graphs
The Curse of Diversity in Ensemble-Based Exploration
Beam Enumeration: Probabilistic Explainability For Sample Efficient Self-conditioned Molecular Design
DynaVol: Unsupervised Learning for Dynamic Scenes through Object-Centric Voxelization
Self-Alignment with Instruction Backtranslation
Denoising Task Routing for Diffusion Models
Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps
DreamTime: An Improved Optimization Strategy for Diffusion-Guided 3D Generation
"What Data Benefits My Classifier?" Enhancing Model Performance and Interpretability through Influence-Based Data Selection
TapMo: Shape-aware Motion Generation of Skeleton-free Characters
Embarrassingly Simple Dataset Distillation
Pose Modulated Avatars from Video
SaProt: Protein Language Modeling with Structure-aware Vocabulary
DMV3D: Denoising Multi-view Diffusion Using 3D Large Reconstruction Model
MovingParts: Motion-based 3D Part Discovery in Dynamic Radiance Field
CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery
Two-timescale Extragradient for Finding Local Minimax Points
Near-Optimal Quantum Algorithm for Minimizing the Maximal Loss
Contextual Bandits with Online Neural Regression
Predictive auxiliary objectives in deep RL mimic learning in the brain
Mathematical Justification of Hard Negative Mining via Isometric Approximation Theorem
Score Regularized Policy Optimization through Diffusion Behavior
Amortizing intractable inference in large language models
WildChat: 1M ChatGPT Interaction Logs in the Wild
Real-Fake: Effective Training Data Synthesis Through Distribution Matching
Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference
DAM: Towards a Foundation Model for Forecasting
CPPO: Continual Learning for Reinforcement Learning with Human Feedback
Understanding the Robustness of Randomized Feature Defense Against Query-Based Adversarial Attacks
Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation
Tuning LayerNorm in Attention: Towards Efficient Multi-Modal LLM Finetuning
Grounded Object-Centric Learning
Generalization in diffusion models arises from geometry-adaptive harmonic representations
Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis
Robust NAS under adversarial training: benchmark, theory, and beyond
SE(3)-Stochastic Flow Matching for Protein Backbone Generation
Future Language Modeling from Temporal Document History
AutoCast++: Enhancing World Event Prediction with Zero-shot Ranking-based Context Retrieval
SGD Finds then Tunes Features in Two-Layer Neural Networks with near-Optimal Sample Complexity: A Case Study in the XOR problem
Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL
ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference
InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation
One-shot Empirical Privacy Estimation for Federated Learning
Learning to Act from Actionless Videos through Dense Correspondences
The False Promise of Imitating Proprietary Language Models
Offline RL with Observation Histories: Analyzing and Improving Sample Complexity
Learning From Simplicial Data Based on Random Walks and 1D Convolutions
Strategic Preys Make Acute Predators: Enhancing Camouflaged Object Detectors by Generating Camouflaged Objects
Revisiting the Last-Iterate Convergence of Stochastic Gradient Methods
Large Language Models to Enhance Bayesian Optimization
PF-LRM: Pose-Free Large Reconstruction Model for Joint Pose and Shape Prediction
Approximating Nash Equilibria in Normal-Form Games via Stochastic Optimization
Towards Transparent Time Series Forecasting
Seeking Neural Nuggets: Knowledge Transfer in Large Language Models from a Parametric Perspective
REValueD: Regularised Ensemble Value-Decomposition for Factorisable Markov Decision Processes
Traveling Waves Encode The Recent Past and Enhance Sequence Learning
Adaptive Sharpness-Aware Pruning for Robust Sparse Networks
TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series
LLaMA-Adapter: Efficient Fine-tuning of Large Language Models with Zero-initialized Attention
GROOT: Learning to Follow Instructions by Watching Gameplay Videos
Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems
DreamFlow: High-quality text-to-3D generation by Approximating Probability Flow
Unpaired Image-to-Image Translation via Neural Schrödinger Bridge
Exploring Diffusion Time-steps for Unsupervised Representation Learning
DiffAR: Denoising Diffusion Autoregressive Model for Raw Speech Waveform Generation
FROSTER: Frozen CLIP is A Strong Teacher for Open-Vocabulary Action Recognition
Understanding Catastrophic Forgetting in Language Models via Implicit Inference
Training Diffusion Models with Reinforcement Learning
Shadow Cones: A Generalized Framework for Partial Order Embeddings
Curriculum reinforcement learning for quantum architecture search under hardware errors
Continual Learning in the Presence of Spurious Correlations: Analyses and a Simple Baseline
A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging
Data Debugging with Shapley Importance over Machine Learning Pipelines
Probabilistically Rewired Message-Passing Neural Networks
Motif: Intrinsic Motivation from Artificial Intelligence Feedback
Empirical Likelihood for Fair Classification
SAFLEX: Self-Adaptive Augmentation via Feature Label Extrapolation
Deep Reinforcement Learning for Modelling Protein Complexes
Hypothesis Search: Inductive Reasoning with Language Models
LRR: Language-Driven Resamplable Continuous Representation against Adversarial Tracking Attacks
Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation
Learning to Embed Time Series Patches Independently
Horizon-Free Regret for Linear Markov Decision Processes
Soft Contrastive Learning for Time Series
Towards Faithful XAI Evaluation via Generalization-Limited Backdoor Watermark
Mixture of Weak and Strong Experts on Graphs
Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood
Balancing Act: Constraining Disparate Impact in Sparse Models
Machine Unlearning for Image-to-Image Generative Models
Flow Matching on General Geometries
LipVoicer: Generating Speech from Silent Videos Guided by Lip Reading
Emu: Generative Pretraining in Multimodality
Bespoke Solvers for Generative Flow Models
Candidate Label Set Pruning: A Data-centric Perspective for Deep Partial-label Learning
Kernelised Normalising Flows
Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How
Workflow Discovery from Dialogues in the Low Data Regime
Stabilizing Backpropagation Through Time to Learn Complex Physics
DittoGym: Learning to Control Soft Shape-Shifting Robots
Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models
Zero-Shot Robotic Manipulation with Pre-Trained Image-Editing Diffusion Models
Dictionary Contrastive Learning for Efficient Local Supervision without Auxiliary Networks
VersVideo: Leveraging Enhanced Temporal Diffusion Models for Versatile Video Generation
SEPT: Towards Efficient Scene Representation Learning for Motion Prediction
Selective Visual Representations Improve Convergence and Generalization for Embodied AI
Generalization of Scaled Deep ResNets in the Mean-Field Regime
BrainLM: A foundation model for brain activity recordings
MgNO: Efficient Parameterization of Linear Operators via Multigrid
The Generalization Gap in Offline Reinforcement Learning
Principled Federated Domain Adaptation: Gradient Projection and Auto-Weighting
Teaching Language Models to Hallucinate Less with Synthetic Tasks
Fourier Transporter: Bi-Equivariant Robotic Manipulation in 3D
TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series
Neural Polynomial Gabor Fields for Macro Motion Analysis
To Grok or not to Grok: Disentangling Generalization and Memorization on Corrupted Algorithmic Datasets
Faithful Explanations of Black-box NLP Models Using LLM-generated Counterfactuals
Teaching Large Language Models to Self-Debug
SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Prediction
Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold
Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations
What's in a Prior? Learned Proximal Networks for Inverse Problems
Large Language Models as Optimizers
Enhancing Contrastive Learning for Ordinal Regression via Ordinal Content Preserved Data Augmentation
SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling with Backtracking
One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention
Large Language Models Cannot Self-Correct Reasoning Yet
Incentivized Truthful Communication for Federated Bandits
Identifying Policy Gradient Subspaces
H-GAP: Humanoid Control with a Generalist Planner
Biased Temporal Convolution Graph Network for Time Series Forecasting with Missing Values
Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models
Mixture-of-Experts Meets Instruction Tuning: A Winning Combination for Large Language Models
IRAD: Implicit Representation-driven Image Resampling against Adversarial Attacks
Long-Short-Range Message-Passing: A Physics-Informed Framework to Capture Non-Local Interaction for Scalable Molecular Dynamics Simulation
Decoding Natural Images from EEG for Object Recognition
On the Analysis of GAN-based Image-to-Image Translation with Gaussian Noise Injection
Explaining Kernel Clustering via Decision Trees
CAS: A Probability-Based Approach for Universal Condition Alignment Score
$\alpha$TC-VAE: On the relationship between Disentanglement and Diversity
Select to Perfect: Imitating desired behavior from large multi-agent data
Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND
Understanding Expressivity of GNN in Rule Learning
When Semantic Segmentation Meets Frequency Aliasing
Follow-Up Differential Descriptions: Language Models Resolve Ambiguities for Image Classification
Idempotent Generative Network
ReTaSA: A Nonparametric Functional Estimation Approach for Addressing Continuous Target Shift
DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning
Integrating Planning and Deep Reinforcement Learning via Automatic Induction of Task Substructures
On the Learnability of Watermarks for Language Models
Achieving Fairness in Multi-Agent MDP Using Reinforcement Learning
Stochastic Gradient Descent for Gaussian Processes Done Right
Achieving Sample and Computational Efficient Reinforcement Learning by Action Space Reduction via Grouping
Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks
Image Inpainting via Tractable Steering of Diffusion Models
Multimarginal Generative Modeling with Stochastic Interpolants
Generalized Neural Sorting Networks with Error-Free Differentiable Swap Functions
Meaning Representations from Trajectories in Autoregressive Models
GIO: Gradient Information Optimization for Training Dataset Selection
Parameter-Efficient Multi-Task Model Fusion with Partial Linearization
SLiMe: Segment Like Me
Language Models Represent Space and Time
M3C: A Framework towards Convergent, Flexible, and Unsupervised Learning of Mixture Graph Matching and Clustering
MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning
BadEdit: Backdooring Large Language Models by Model Editing
One For All: Towards Training One Graph Model For All Classification Tasks
Neural Monge Map estimation and its applications
Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability
Symmetric Mean-field Langevin Dynamics for Distributional Minimax Problems
$t^3$-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence
Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion
On the Sample Complexity of Lipschitz Constant Estimation
Increasing Model Capacity for Free: A Simple Strategy for Parameter Efficient Fine-tuning
Image Background Serves as Good Proxy for Out-of-distribution Data
FedTrans: Client-Transparent Utility Estimation for Robust Federated Learning
CLEX: Continuous Length Extrapolation for Large Language Models
Combining Axes Preconditioners through Kronecker Approximation for Deep Learning
The Update-Equivalence Framework for Decision-Time Planning
MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning
Inverse Approximation Theory for Nonlinear Recurrent Neural Networks
Less is More: Fewer Interpretable Region via Submodular Subset Selection
LEMON: Lossless model expansion
Sparse Model Soups: A Recipe for Improved Pruning via Model Averaging
Understanding Addition in Transformers
Deceptive Fairness Attacks on Graphs via Meta Learning
Fair and Efficient Contribution Valuation for Vertical Federated Learning
Federated Text-driven Prompt Generation for Vision-Language Models
Improved Active Learning via Dependent Leverage Score Sampling
Optimal Sketching for Residual Error Estimation for Matrix and Vector Norms
Crystalformer: Infinitely Connected Attention for Periodic Structure Encoding
Large Language Model Cascades with Mixture of Thought Representations for Cost-Efficient Reasoning
PAE: Reinforcement Learning from External Knowledge for Efficient Exploration
CodeChain: Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modules
Image Clustering Conditioned on Text Criteria
How to Fine-Tune Vision Models with SGD
Towards 3D Molecule-Text Interpretation in Language Models
CompA: Addressing the Gap in Compositional Reasoning in Audio-Language Models
How Over-Parameterization Slows Down Gradient Descent in Matrix Sensing: The Curses of Symmetry and Initialization
MT-Ranker: Reference-free machine translation evaluation by inter-system ranking
Parametric Augmentation for Time Series Contrastive Learning
UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition
Towards Robust Out-of-Distribution Generalization Bounds via Sharpness
A Unified Sampling Framework for Solver Searching of Diffusion Probabilistic Models
On the Effect of Batch Size in Byzantine-Robust Distributed Learning
Expressivity of ReLU-Networks under Convex Relaxations
DiffusionSat: A Generative Foundation Model for Satellite Imagery
Denoising Diffusion Bridge Models
Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning
ASMR: Activation-Sharing Multi-Resolution Coordinate Networks for Efficient Inference
Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder
From Bricks to Bridges: Product of Invariances to Enhance Latent Space Communication
Batched Low-Rank Adaptation of Foundation Models
GRAPH-CONSTRAINED DIFFUSION FOR END-TO-END PATH PLANNING
Leveraging Hyperbolic Embeddings for Coarse-to-Fine Robot Design
Impact of Computation in Integral Reinforcement Learning for Continuous-Time Control
Weakly Supervised Virus Capsid Detection with Image-Level Annotations in Electron Microscopy Images
Generalized Schrödinger Bridge Matching
Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning
Patches Are All You Need?
Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation
Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages
Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression?
Ferret: Refer and Ground Anything Anywhere at Any Granularity
Demonstration-Regularized RL
Manifold Diffusion Fields
Language Control Diffusion: Efficiently Scaling through Space, Time, and Tasks
PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization
How do Language Models Bind Entities in Context?
Emergent mechanisms for long timescales depend on training curriculum and affect performance in memory tasks
Structural Estimation of Partially Observed Linear Non-Gaussian Acyclic Model: A Practical Approach with Identifiability
Score-based generative models break the curse of dimensionality in learning a family of sub-Gaussian distributions
FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scaling
Learning Performance-Improving Code Edits
Tensor Programs VI: Feature Learning in Infinite Depth Neural Networks
Privately Aligning Language Models with Reinforcement Learning
Dual RL: Unification and New Methods for Reinforcement and Imitation Learning
Let's Verify Step by Step
Complete and Efficient Graph Transformers for Crystal Material Property Prediction
Uncertainty Quantification via Stable Distribution Propagation
Exploring the Promise and Limits of Real-Time Recurrent Learning
Understanding Convergence and Generalization in Federated Learning through Feature Learning Theory
LLMs Meet VLMs: Boost Open Vocabulary Object Detection with Fine-grained Descriptors
HiGen: Hierarchical Graph Generative Networks
BadChain: Backdoor Chain-of-Thought Prompting for Large Language Models
Human Feedback is not Gold Standard
Bellman Optimal Stepsize Straightening of Flow-Matching Models
Embodied Active Defense: Leveraging Recurrent Feedback to Counter Adversarial Patches
Learning Implicit Representation for Reconstructing Articulated Objects
$\texttt{NAISR}$: A 3D Neural Additive Model for Interpretable Shape Representation
Towards Offline Opponent Modeling with In-context Learning
MVSFormer++: Revealing the Devil in Transformer's Details for Multi-View Stereo
SKILL-MIX: a Flexible and Expandable Family of Evaluations for AI Models
LoTa-Bench: Benchmarking Language-oriented Task Planners for Embodied Agents
Hybrid Directional Graph Neural Network for Molecules
DAFA: Distance-Aware Fair Adversarial Training
Fast-ELECTRA for Efficient Pre-training
Course Correcting Koopman Representations
Generating Pragmatic Examples to Train Neural Program Synthesizers
Data Filtering Networks
Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization
Learning with Language-Guided State Abstractions
On Harmonizing Implicit Subpopulations
On Error Propagation of Diffusion Models
R&B: Region and Boundary Aware Zero-shot Grounded Text-to-image Generation
ScaleCrafter: Tuning-free Higher-Resolution Visual Generation with Diffusion Models
Out-Of-Domain Unlabeled Data Improves Generalization
Poly-View Contrastive Learning
Successor Heads: Recurring, Interpretable Attention Heads In The Wild
Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models
On the Expressivity of Objective-Specification Formalisms in Reinforcement Learning
Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck
DreamClean: Restoring Clean Image Using Deep Diffusion Prior
Enhancing Neural Subset Selection: Integrating Background Information into Set Representations
Probabilistic Adaptation of Black-Box Text-to-Video Models
DREAM: Dual Structured Exploration with Mixup for Open-set Graph Domain Adaption
Partitioning Message Passing for Graph Fraud Detection
Meta-Evolve: Continuous Robot Evolution for One-to-many Policy Transfer
Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding
TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models
Learning Interactive Real-World Simulators
Learning from Sparse Offline Datasets via Conservative Density Estimation
From Graphs to Hypergraphs: Hypergraph Projection and its Reconstruction
Concept Bottleneck Generative Models
Safe Collaborative Filtering
Object-Centric Learning with Slot Mixture Module
Spatio-Temporal Few-Shot Learning via Diffusive Neural Network Generation
Skip-Attention: Improving Vision Transformers by Paying Less Attention
Linear Log-Normal Attention with Unbiased Concentration
Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning
Koopman-based generalization bound: New aspect for full-rank weights
Robust Model-Based Optimization for Challenging Fitness Landscapes
Analytically Tractable Hidden-States Inference in Bayesian Neural Networks
An interpretable error correction method for enhancing code-to-code translation
Random Feature Amplification: Feature Learning and Generalization in Neural Networks
Fiber Monte Carlo
NeRM: Learning Neural Representations for High-Framerate Human Motion Synthesis
A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models
A Framework and Benchmark for Deep Batch Active Learning for Regression
Tackling the Data Heterogeneity in Asynchronous Federated Learning with Cached Update Calibration
ViDA: Homeostatic Visual Domain Adapter for Continual Test Time Adaptation
TextField3D: Towards Enhancing Open-Vocabulary 3D Generation with Noisy Text Fields
Automatic Functional Differentiation in JAX
Manipulating dropout reveals an optimal balance of efficiency and robustness in biological and machine visual systems
$\mathcal{B}$-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis
Transformer-VQ: Linear-Time Transformers via Vector Quantization
What does the Knowledge Neuron Thesis Have to do with Knowledge?
Ghost on the Shell: An Expressive Representation of General 3D Shapes
Intriguing Properties of Generative Classifiers
Octavius: Mitigating Task Interference in MLLMs via LoRA-MoE
Learning semilinear neural operators: A unified recursive framework for prediction and data assimilation.
ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving
Sample-efficient Learning of Infinite-horizon Average-reward MDPs with General Function Approximation
Towards Robust Offline Reinforcement Learning under Diverse Data Corruption
Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative Instructions
Towards domain-invariant Self-Supervised Learning with Batch Styles Standardization
Towards Diverse Behaviors: A Benchmark for Imitation Learning with Human Demonstrations
SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training
Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation
Transformer-Modulated Diffusion Models for Probabilistic Multivariate Time Series Forecasting
Smooth ECE: Principled Reliability Diagrams via Kernel Smoothing
LiDAR: Sensing Linear Probing Performance in Joint Embedding SSL Architectures
Vanishing Gradients in Reinforcement Finetuning of Language Models
What Algorithms can Transformers Learn? A Study in Length Generalization
Neural Network-Based Score Estimation in Diffusion Models: Optimization and Generalization
Enhancing Small Medical Learners with Privacy-preserving Contextual Prompting
Optimal criterion for feature learning of two-layer linear neural network in high dimensional interpolation regime
On the Scalability and Memory Efficiency of Semidefinite Programs for Lipschitz Constant Estimation of Neural Networks
Learning to Act without Actions
Intelligent Switching for Reset-Free RL
PINNACLE: PINN Adaptive ColLocation and Experimental points selection
Quantifying the Sensitivity of Inverse Reinforcement Learning to Misspecification
Effective and Efficient Federated Tree Learning on Hybrid Data
Neural Processing of Tri-Plane Hybrid Neural Fields
Boosting the Adversarial Robustness of Graph Neural Networks: An OOD Perspective
Error Norm Truncation: Robust Training in the Presence of Data Noise for Text Generation Models
Byzantine Robust Cooperative Multi-Agent Reinforcement Learning as a Bayesian Game
SetCSE: Set Operations using Contrastive Learning of Sentence Embeddings
#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models
Harnessing Joint Rain-/Detail-aware Representations to Eliminate Intricate Rains
Pre-training with Random Orthogonal Projection Image Modeling
Debiasing Attention Mechanism in Transformer without Demographics
Unsupervised Pretraining for Fact Verification by Language Model Distillation
Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation
Image Translation as Diffusion Visual Programmers
Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy Evaluation
Adversarial Imitation Learning via Boosting
Bayes Conditional Distribution Estimation for Knowledge Distillation Based on Conditional Mutual Information
ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation
Provable Reward-Agnostic Preference-Based Reinforcement Learning
Bidirectional Temporal Diffusion Model for Temporally Consistent Human Animation
Copula Conformal prediction for multi-step time series prediction
Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining
Adapting Large Language Models via Reading Comprehension
Improving Convergence and Generalization Using Parameter Symmetries
COLEP: Certifiably Robust Learning-Reasoning Conformal Prediction via Probabilistic Circuits
PolyGCL: GRAPH CONTRASTIVE LEARNING via Learnable Spectral Polynomial Filters
Manifold Preserving Guided Diffusion
Motion Guidance: Diffusion-Based Image Editing with Differentiable Motion Estimators
Threaten Spiking Neural Networks through Combining Rate and Temporal Information
FLD: Fourier Latent Dynamics for Structured Motion Representation and Learning
Exploring Target Representations for Masked Autoencoders
Federated Recommendation with Additive Personalization
Neural Language of Thought Models
Text2Reward: Reward Shaping with Language Models for Reinforcement Learning
Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion
Statistical Rejection Sampling Improves Preference Optimization
Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs
Privacy Amplification for Matrix Mechanisms
Negative Label Guided OOD Detection with Pretrained Vision-Language Models
PTaRL: Prototype-based Tabular Representation Learning via Space Calibration
Improved baselines for vision-language pre-training
Constrained Bi-Level Optimization: Proximal Lagrangian Value Function Approach and Hessian-free Algorithm
Error Feedback Reloaded: From Quadratic to Arithmetic Mean of Smoothness Constants
Correlated Noise Provably Beats Independent Noise for Differentially Private Learning
ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers
On the Stability of Expressive Positional Encodings for Graphs
PolyVoice: Language Models for Speech to Speech Translation
Benchmarking Algorithms for Federated Domain Generalization
Compositional Generative Inverse Design
Evaluating Representation Learning on the Protein Structure Universe
AutoVP: An Automated Visual Prompting Framework and Benchmark
On the Hardness of Constrained Cooperative Multi-Agent Reinforcement Learning
Linearity of Relation Decoding in Transformer Language Models
Causal Structure Recovery with Latent Variables under Milder Distributional and Graphical Assumptions
Information Retention via Learning Supplemental Features
Geometry-Aware Projective Mapping for Unbounded Neural Radiance Fields
Off-Policy Primal-Dual Safe Reinforcement Learning
Can Large Language Models Infer Causation from Correlation?
When should we prefer Decision Transformers for Offline Reinforcement Learning?
ARM: Refining Multivariate Forecasting with Adaptive Temporal-Contextual Learning
SAS: Structured Activation Sparsification
Learning Multi-Agent Communication with Contrastive Learning
Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages
Xformer: Hybrid X-Shaped Transformer for Image Denoising
Efficient Backdoor Attacks for Deep Neural Networks in Real-world Scenarios
Dynamics-Informed Protein Design with Structure Conditioning
Identifiable Latent Polynomial Causal Models through the Lens of Change
SYMBOL: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning
Graph Lottery Ticket Automated
Threshold-Consistent Margin Loss for Open-World Deep Metric Learning
The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks.
DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing
Encoding Unitig-level Assembly Graphs with Heterophilous Constraints for Metagenomic Contigs Binning
Adaptive Regret for Bandits Made Possible: Two Queries Suffice
AdaMerging: Adaptive Model Merging for Multi-Task Learning
Statistically Optimal $K$-means Clustering via Nonnegative Low-rank Semidefinite Programming
AutomaTikZ: Text-Guided Synthesis of Scientific Vector Graphics with TikZ
Improved statistical and computational complexity of the mean-field Langevin dynamics under structured data
Zero Bubble (Almost) Pipeline Parallelism
Bridging Neural and Symbolic Representations with Transitional Dictionary Learning
Towards Characterizing Domain Counterfactuals for Invertible Latent Causal Models
Thin-Shell Object Manipulations With Differentiable Physics Simulations
Bayesian Coreset Optimization for Personalized Federated Learning
Beyond Spatio-Temporal Representations: Evolving Fourier Transform for Temporal Graphs
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models
Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks
Hierarchical Context Merging: Better Long Context Understanding for Pre-trained LLMs
Towards Best Practices of Activation Patching in Language Models: Metrics and Methods
Scale-Adaptive Diffusion Model for Complex Sketch Synthesis
Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs
On the Over-Memorization During Natural, Robust and Catastrophic Overfitting
Mastering Memory Tasks with World Models
Towards Principled Representation Learning from Videos for Reinforcement Learning
Expected flow networks in stochastic environments and two-player zero-sum games
Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond
DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models
Energy-conserving equivariant GNN for elasticity of lattice architected metamaterials
SALMON: Self-Alignment with Instructable Reward Models
Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs
Augmenting Transformers with Recursively Composed Multi-grained Representations
Adversarial Training on Purification (AToP): Advancing Both Robustness and Generalization
Large Language Models as Generalizable Policies for Embodied Tasks
The Joint Effect of Task Similarity and Overparameterization on Catastrophic Forgetting — An Analytical Model
Fast Equilibrium of SGD in Generic Situations
Connect, Collapse, Corrupt: Learning Cross-Modal Tasks with Uni-Modal Data
Compositional Preference Models for Aligning LMs
Diffusion Posterior Sampling for Linear Inverse Problem Solving: A Filtering Perspective
Demystifying Local & Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition
Provable Offline Preference-Based Reinforcement Learning
Learning Conditional Invariances through Non-Commutativity
Generative Modeling with Phase Stochastic Bridge
Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation
RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies
Tailoring Self-Rationalizers with Multi-Reward Distillation
Controlling Vision-Language Models for Multi-Task Image Restoration
Frozen Transformers in Language Models Are Effective Visual Encoder Layers
VFLAIR: A Research Library and Benchmark for Vertical Federated Learning
Measuring Vision-Language STEM Skills of Neural Models
Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers
Whole-Song Hierarchical Generation of Symbolic Music Using Cascaded Diffusion Models
MCM: Masked Cell Modeling for Anomaly Detection in Tabular Data
GAIA: Zero-shot Talking Avatar Generation
NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers
CLIP-MUSED: CLIP-Guided Multi-Subject Visual Neural Information Semantic Decoding
How connectivity structure shapes rich and lazy learning in neural circuits
ARGS: Alignment as Reward-Guided Search
PromptTTS 2: Describing and Generating Voices with Text Prompt
Let Models Speak Ciphers: Multiagent Debate through Embeddings
NeuroBack: Improving CDCL SAT Solving using Graph Neural Networks
Understanding when Dynamics-Invariant Data Augmentations Benefit Model-free Reinforcement Learning Updates
Selective Mixup Fine-Tuning for Optimizing Non-Decomposable Objectives
Revisiting Deep Audio-Text Retrieval Through the Lens of Transportation
Text-to-3D with Classifier Score Distillation
Transformers can optimally learn regression mixture models
Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning
Branch-GAN: Improving Text Generation with (not so) Large Language Models
SocioDojo: Building Lifelong Analytical Agents with Real-world Text and Time Series
ECoFLaP: Efficient Coarse-to-Fine Layer-Wise Pruning for Vision-Language Models
Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization
A unique M-pattern for micro-expression spotting in long videos
Progressive Fourier Neural Representation for Sequential Video Compilation
Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning
iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations
A Mutual Information Perspective on Federated Contrastive Learning
Local Graph Clustering with Noisy Labels
DistillSpec: Improving Speculative Decoding via Knowledge Distillation
Faithful Vision-Language Interpretation via Concept Bottleneck Models
AuG-KD: Anchor-Based Mixup Generation for Out-of-Domain Knowledge Distillation
Stylized Offline Reinforcement Learning: Extracting Diverse High-Quality Behaviors from Heterogeneous Datasets
Variance-aware Regret Bounds for Stochastic Contextual Dueling Bandits
Demystifying Embedding Spaces using Large Language Models
A Newborn Embodied Turing Test for Comparing Object Segmentation Across Animals and Machines
DeepZero: Scaling Up Zeroth-Order Optimization for Deep Model Training
Closing the Gap between TD Learning and Supervised Learning - A Generalisation Point of View.
Unveiling the Pitfalls of Knowledge Editing for Large Language Models
Learning Thresholds with Latent Values and Censored Feedback
Extending Power of Nature from Binary to Real-Valued Graph Learning in Real World
Robustifying and Boosting Training-Free Neural Architecture Search
Guess & Sketch: Language Model Guided Transpilation
Zero and Few-shot Semantic Parsing with Ambiguous Inputs
Boosting of Thoughts: Trial-and-Error Problem Solving with Large Language Models
Large-Vocabulary 3D Diffusion Model with Transformer
An Investigation of Representation and Allocation Harms in Contrastive Learning
Channel Vision Transformers: An Image Is Worth 1 x 16 x 16 Words
Solving High Frequency and Multi-Scale PDEs with Gaussian Processes
Adversarial Attacks on Fairness of Graph Neural Networks
Task structure and nonlinearity jointly determine learned representational geometry
Locality Sensitive Sparse Encoding for Learning World Models Online
ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models
Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph
Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy
Jailbreak in pieces: Compositional Adversarial Attacks on Multi-Modal Language Models
Graph Transformers on EHRs: Better Representation Improves Downstream Performance
SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning
Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data
Scalable Modular Network: A Framework for Adaptive Learning via Agreement Routing
Improved Regret Bounds for Non-Convex Online-Within-Online Meta Learning
Compose and Conquer: Diffusion-Based 3D Depth Aware Composable Image Synthesis
Recursive Generalization Transformer for Image Super-Resolution
Reconciling Spatial and Temporal Abstractions for Goal Representation
Magnushammer: A Transformer-Based Approach to Premise Selection
Score Models for Offline Goal-Conditioned Reinforcement Learning
Treatment Effects Estimation By Uniform Transformer
Scaling Laws for Associative Memories
Entropy-MCMC: Sampling from Flat Basins with Ease
LMUFormer: Low Complexity Yet Powerful Spiking Model With Legendre Memory Units
Representation Deficiency in Masked Language Modeling
Sampling Multimodal Distributions with the Vanilla Score: Benefits of Data-Based Initialization
MaGIC: Multi-modality Guided Image Completion
CO2: Efficient Distributed Training with Full Communication-Computation Overlap
Learning Robust Generalizable Radiance Field with Visibility and Feature Augmented Point Representation
Alice Benchmarks: Connecting Real World Re-Identification with the Synthetic
HoloNets: Spectral Convolutions do extend to Directed Graphs
Searching for High-Value Molecules Using Reinforcement Learning and Transformers
Interpretable Meta-Learning of Physical Systems
An Image Is Worth 1000 Lies: Transferability of Adversarial Images across Prompts on Vision-Language Models
Fast Value Tracking for Deep Reinforcement Learning
Interpretable Sparse System Identification: Beyond Recent Deep Learning Techniques on Time-Series Prediction
FedInverse: Evaluating Privacy Leakage in Federated Learning
CircuitNet 2.0: An Advanced Dataset for Promoting Machine Learning Innovations in Realistic Chip Design Environment
Are Transformers with One Layer Self-Attention Using Low-Rank Weight Matrices Universal Approximators?
Self-Supervised Contrastive Learning for Long-term Forecasting
Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning
Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalization
OctoPack: Instruction Tuning Code Large Language Models
Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation
Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning
Rethinking CNN’s Generalization to Backdoor Attack from Frequency Domain
Variance Reduced Halpern Iteration for Finite-Sum Monotone Inclusions
Flow to Better: Offline Preference-based Reinforcement Learning via Preferred Trajectory Generation
LLM Blueprint: Enabling Text-to-Image Generation with Complex and Detailed Prompts
VBH-GNN: Variational Bayesian Heterogeneous Graph Neural Networks for Cross-subject Emotion Recognition
Neural Rate Control for Learned Video Compression
PnP Inversion: Boosting Diffusion-based Editing with 3 Lines of Code
Harnessing Density Ratios for Online Reinforcement Learning
Sliced Denoising: A Physics-Informed Molecular Pre-Training Method
Is Self-Repair a Silver Bullet for Code Generation?
Unified Human-Scene Interaction via Prompted Chain-of-Contacts
Improved Efficiency Based on Learned Saccade and Continuous Scene Reconstruction From Foveated Visual Sampling
Grounding Multimodal Large Language Models to the World
Context-Aware Meta-Learning
A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs
Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery Detection
MetaCoCo: A New Few-Shot Classification Benchmark with Spurious Correlation
Negatively Correlated Ensemble Reinforcement Learning for Online Diverse Game Level Generation
Free from Bellman Completeness: Trajectory Stitching via Model-based Return-conditioned Supervised Learning
Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework
Local Composite Saddle Point Optimization
ASID: Active Exploration for System Identification in Robotic Manipulation
Simple Hierarchical Planning with Diffusion
sRGB Real Noise Modeling via Noise-Aware Sampling with Normalizing Flows
BatteryML: An Open-source Platform for Machine Learning on Battery Degradation
Improving Generalization of Alignment with Human Preferences through Group Invariant Learning
Dynamic Sparse Training with Structured Sparsity
DENEVIL: TOWARDS DECIPHERING AND NAVIGATING THE ETHICAL VALUES OF LARGE LANGUAGE MODELS VIA INSTRUCTION LEARNING
Robustifying State-space Models for Long Sequences via Approximate Diagonalization
Generative Adversarial Equilibrium Solvers
Test-Time Adaptation with CLIP Reward for Zero-Shot Generalization in Vision-Language Models
FedImpro: Measuring and Improving Client Update in Federated Learning
What Makes a Good Prune? Maximal Unstructured Pruning for Maximal Cosine Similarity
Improving LoRA in Privacy-preserving Federated Learning
Efficient Inverse Multiagent Learning
Neural Neighborhood Search for Multi-agent Path Finding
Nemesis: Normalizing the Soft-prompt Vectors of Vision-Language Models
FasterViT: Fast Vision Transformers with Hierarchical Attention
C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion
DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior
Plugin estimators for selective classification with out-of-distribution detection
P$^2$OT: Progressive Partial Optimal Transport for Deep Imbalanced Clustering
Unprocessing Seven Years of Algorithmic Fairness
Adaptive Stochastic Gradient Algorithm for Black-box Multi-Objective Learning
Intriguing Properties of Data Attribution on Diffusion Models
SpaCE: The Spatial Confounding Environment
How Does Unlabeled Data Provably Help Out-of-Distribution Detection?
AdjointDPM: Adjoint Sensitivity Method for Gradient Backpropagation of Diffusion Probabilistic Models
GlucoBench: Curated List of Continuous Glucose Monitoring Datasets with Prediction Benchmarks
Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning
Look, Remember and Reason: Grounded Reasoning in Videos with Language Models
It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech Recognition
MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning
Pushing Boundaries: Mixup's Influence on Neural Collapse
Analyzing and Improving Optimal-Transport-based Adversarial Networks
Two-stage LLM Fine-tuning with Less Specialization and More Generalization
LLCP: Learning Latent Causal Processes for Reasoning-based Video Question Answer
Implicit regularization of deep residual networks towards neural ODEs
Provably Efficient UCB-type Algorithms For Learning Predictive State Representations
Inner Classifier-Free Guidance and Its Taylor Expansion for Diffusion Models
Protein Multimer Structure Prediction via Prompt Learning
Symbol as Points: Panoptic Symbol Spotting via Point-based Representation
Compressing Latent Space via Least Volume
CoLiDE: Concomitant Linear DAG Estimation
EBMDock: Neural Probabilistic Protein-Protein Docking via a Differentiable Energy Model
Going Beyond Neural Network Feature Similarity: The Network Feature Complexity and Its Interpretation Using Category Theory
Habitat 3.0: A Co-Habitat for Humans, Avatars, and Robots
A Unified Framework for Bayesian Optimization under Contextual Uncertainty
Learning Large DAGs is Harder than you Think: Many Losses are Minimal for the Wrong DAG
Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization
Active Retrosynthetic Planning Aware of Route Quality
Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency
Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model
Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy
Non-Exchangeable Conformal Risk Control
Spoken Question Answering and Speech Continuation Using Spectrogram-Powered LLM
USB-NeRF: Unrolling Shutter Bundle Adjusted Neural Radiance Fields
Are Bert Family Good Instruction Followers? A Study on Their Potential And Limitations
Synergistic Patch Pruning for Vision Transformer: Unifying Intra- & Inter-Layer Patch Importance
Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AI
Epitopological learning and Cannistraci-Hebb network shape intelligence brain-inspired theory for ultra-sparse advantage in deep learning
Early Stopping Against Label Noise Without Validation Data
Contrastive Preference Learning: Learning from Human Feedback without Reinforcement Learning
Unknown Domain Inconsistency Minimization for Domain Generalization
Enhancing Transfer Learning with Flexible Nonparametric Posterior Sampling
Finite Scalar Quantization: VQ-VAE Made Simple
Fixed-Budget Differentially Private Best Arm Identification
Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective
Neural Contractive Dynamical Systems
Energy-based Automated Model Evaluation
Unraveling the Enigma of Double Descent: An In-depth Analysis through the Lens of Learned Feature Space
FreeDyG: Frequency Enhanced Continuous-Time Dynamic Graph Model for Link Prediction
Structural Inference with Dynamics Encoding and Partial Correlation Coefficients
SEAL: A Framework for Systematic Evaluation of Real-World Super-Resolution
Toward Optimal Policy Population Growth in Two-Player Zero-Sum Games
Towards Robust and Efficient Cloud-Edge Elastic Model Adaptation via Selective Entropy Distillation
Polynormer: Polynomial-Expressive Graph Transformer in Linear Time
Beyond task performance: evaluating and reducing the flaws of large multimodal models with in-context-learning
A Differentially Private Clustering Algorithm for Well-Clustered Graphs
The Trickle-down Impact of Reward Inconsistency on RLHF
Contrastive Learning is Spectral Clustering on Similarity Graph
Better Neural PDE Solvers Through Data-Free Mesh Movers
Weatherproofing Retrieval for Localization with Generative AI and Geometric Consistency
Memorization Capacity of Multi-Head Attention in Transformers
LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models
Domain-Inspired Sharpness-Aware Minimization Under Domain Shifts
Adaptive Retrieval and Scalable Indexing for k-NN Search with Cross-Encoders
Enhancing Group Fairness in Online Settings Using Oblique Decision Forests
True Knowledge Comes from Practice: Aligning Large Language Models with Embodied Environments via Reinforcement Learning
Maximum Likelihood Estimation is All You Need for Well-Specified Covariate Shift
A Sublinear Adversarial Training Algorithm
PhyloGFN: Phylogenetic inference with generative flow networks
Understanding Certified Training with Interval Bound Propagation
Inducing High Energy-Latency of Large Vision-Language Models with Verbose Images
Performance Gaps in Multi-view Clustering under the Nested Matrix-Tensor Model
Language Model Beats Diffusion - Tokenizer is key to visual generation
ZeRO++: Extremely Efficient Collective Communication for Large Model Training
Multimodal Learning Without Labeled Multimodal Data: Guarantees and Applications
Dual Associated Encoder for Face Restoration
CausalLM is not optimal for in-context learning
An Unforgeable Publicly Verifiable Watermark for Large Language Models
Deep Orthogonal Hypersphere Compression for Anomaly Detection
IMPUS: Image Morphing with Perceptually-Uniform Sampling Using Diffusion Models
Evaluating the Zero-shot Robustness of Instruction-tuned Language Models
Does Writing with Language Models Reduce Content Diversity?
Class Probability Matching with Calibrated Networks for Label Shift Adaption
Few-shot Hybrid Domain Adaptation of Image Generator
Entity-Centric Reinforcement Learning for Object Manipulation from Pixels
Compositional Conservatism: A Transductive Approach in Offline Reinforcement Learning
Adaptive Rational Activations to Boost Deep Reinforcement Learning
InterpGNN: Understand and Improve Generalization Ability of Transdutive GNNs through the Lens of Interplay between Train and Test Nodes
A Progressive Training Framework for Spiking Neural Networks with Learnable Multi-hierarchical Model
Memory-Assisted Sub-Prototype Mining for Universal Domain Adaptation
Bounding Box Stability against Feature Dropout Reflects Detector Generalization across Environments
CIFAR-10-Warehouse: Broad and More Realistic Testbeds in Model Generalization Analysis
Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting
OWL: A Large Language Model for IT Operations
Towards Meta-Pruning via Optimal Transport
Würstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models
REFACTOR: Learning to Extract Theorems from Proofs
From Posterior Sampling to Meaningful Diversity in Image Restoration
Federated Q-Learning: Linear Regret Speedup with Low Communication Cost
Transformer Fusion with Optimal Transport
A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models
Dynamic Neighborhood Construction for Structured Large Discrete Action Spaces
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
A Recipe for Improved Certifiable Robustness
Sparse MoE with Language Guided Routing for Multilingual Machine Translation
Neural Architecture Retrieval
Neural SDF Flow for 3D Reconstruction of Dynamic Scenes
Uni-RLHF: Universal Platform and Benchmark Suite for Reinforcement Learning with Diverse Human Feedback
Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model
Teaching Arithmetic to Small Transformers
ADOPD: A Large-Scale Document Page Decomposition Dataset
Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity
Compressing LLMs: The Truth is Rarely Pure and Never Simple
To the Cutoff... and Beyond? A Longitudinal Perspective on LLM Data Contamination
ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Search
Large Language Models as Tool Makers
LayoutNUWA: Revealing the Hidden Layout Expertise of Large Language Models
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding
Learning Mean Field Games on Sparse Graphs: A Hybrid Graphex Approach
P2Seg: Pointly-supervised Segmentation via Mutual Distillation
SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores
Efficient Subgraph GNNs by Learning Effective Selection Policies
Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning
Label-free Node Classification on Graphs with Large Language Models (LLMs)
LLM-grounded Video Diffusion Models
Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions
Multimodal Web Navigation with Instruction-Finetuned Foundation Models
Function-space Parameterization of Neural Networks for Sequential Learning
One-hot Generalized Linear Model for Switching Brain State Discovery
Large Language Models as Analogical Reasoners
Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs
Annealing Self-Distillation Rectification Improves Adversarial Training
H2O-SDF: Two-phase Learning for 3D Indoor Reconstruction using Object Surface Fields
Boundary Denoising for Video Activity Localization
Out-of-Distribution Detection by Leveraging Between-Layer Transformation Smoothness
RLIF: Interactive Imitation Learning as Reinforcement Learning
Diffusion in Diffusion: Cyclic One-Way Diffusion for Text-Vision-Conditioned Generation
On Trajectory Augmentations for Off-Policy Evaluation
How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions
Alt-Text with Context: Improving Accessibility for Images on Twitter
Combinatorial Bandits for Maximum Value Reward Function under Value-Index Feedback
Reward-Free Curricula for Training Robust World Models
Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical ML
PORF: POSE RESIDUAL FIELD FOR ACCURATE NEURAL SURFACE RECONSTRUCTION
PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis
AGILE3D: Attention Guided Interactive Multi-object 3D Segmentation
Convergence of Bayesian Bilevel Optimization
Functional Interpolation for Relative Positions improves Long Context Transformers
Consistency Training with Learnable Data Augmentation for Graph Anomaly Detection with Limited Supervision
Rotation Has Two Sides: Evaluating Data Augmentation for Deep One-class Classification
Small-scale proxies for large-scale Transformer training instabilities
Lion Secretly Solves a Constrained Optimization: As Lyapunov Predicts
Improving equilibrium propagation without weight symmetry through Jacobian homeostasis
Symmetric Single Index Learning
Tight Rates in Supervised Outlier Transfer Learning
Node2ket: Efficient High-Dimensional Network Embedding in Quantum Hilbert Space
Towards LLM4QPE: Unsupervised Pretraining of Quantum Property Estimation and A Benchmark
Object-Aware Inversion and Reassembly for Image Editing
Analysis of Learning a Flow-based Generative Model from Limited Sample Complexity
What's In My Big Data?
Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks
Minimum width for universal approximation using ReLU networks on compact domain
Provable Memory Efficient Self-Play Algorithm for Model-free Reinforcement Learning
Retro-fallback: retrosynthetic planning in an uncertain world
Generative Human Motion Stylization in Latent Space
TUVF: Learning Generalizable Texture UV Radiance Fields
Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi-Armed Bandits
Monte Carlo guided Denoising Diffusion models for Bayesian linear inverse problems.
Robustness of AI-Image Detectors: Fundamental Limits and Practical Attacks
Fast Imitation via Behavior Foundation Models
Peering Through Preferences: Unraveling Feedback Acquisition for Aligning Large Language Models
MEND: Meta Demonstration Distillation for Efficient and Effective In-Context Learning
Ins-DetCLIP: Aligning Detection Model to Follow Human-Language Instruction
ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
Only Pay for What Is Uncertain: Variance-Adaptive Thompson Sampling
The Consensus Game: Language Model Generation via Equilibrium Search
Tool-Augmented Reward Modeling
Procedural Fairness Through Decoupling Objectionable Data Generating Components
Generalized Policy Iteration using Tensor Approximation for Hybrid Control
Scaling Supervised Local Learning with Augmented Auxiliary Networks
Decongestion by Representation: Learning to Improve Economic Welfare in Marketplaces
Diffusion Models for Multi-Task Generative Modeling
OpenChat: Advancing Open-source Language Models with Mixed-Quality Data
Causal Modelling Agents: Causal Graph Discovery through Synergising Metadata- and Data-driven Reasoning
FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices Using a Computing Power-Aware Scheduler
Bandits with Replenishable Knapsacks: the Best of both Worlds
Bridging State and History Representations: Understanding Self-Predictive RL
Latent 3D Graph Diffusion
State Representation Learning Using an Unbalanced Atlas
Scalable Monotonic Neural Networks
Towards a statistical theory of data selection under weak supervision
Multilinear Operator Networks
BayesPrompt: Prompting Large-Scale Pre-Trained Language Models on Few-shot Inference via Debiased Domain Abstraction
A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks
Bongard-OpenWorld: Few-Shot Reasoning for Free-form Visual Concepts in the Real World
NuwaDynamics: Discovering and Updating in Causal Spatio-Temporal Modeling
Rethinking Information-theoretic Generalization: Loss Entropy Induced PAC Bounds
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training Tasks
Democratizing Fine-grained Visual Recognition with Large Language Models
Analyzing Feed-Forward Blocks in Transformers through the Lens of Attention Maps
GAFormer: Enhancing Timeseries Transformers Through Group-Aware Embeddings
DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text
SpeechTokenizer: Unified Speech Tokenizer for Speech Language Models
Prompt Learning with Quaternion Networks
Prioritized Soft Q-Decomposition for Lexicographic Reinforcement Learning
A Simple Interpretable Transformer for Fine-Grained Image Classification and Analysis
MBR and QE Finetuning: Training-time Distillation of the Best and Most Expensive Decoding Methods
Generalizability of Adversarial Robustness Under Distribution Shifts
UC-NERF: Neural Radiance Field for Under-Calibrated Multi-View Cameras in Autonomous Driving
LUM-ViT: Learnable Under-sampling Mask Vision Transformer for Bandwidth Limited Optical Signal Acquisition
Facing the Elephant in the Room: Visual Prompt Tuning or Full finetuning?
Accelerated Sampling with Stacked Restricted Boltzmann Machines
BECLR: Batch Enhanced Contrastive Few-Shot Learning
DyST: Towards Dynamic Neural Scene Representations on Real-World Videos
Logical Languages Accepted by Transformer Encoders with Hard Attention
Meta-Learning Priors Using Unrolled Proximal Networks
NfgTransformer: Equivariant Representation Learning for Normal-form Games
A Topological Perspective on Demystifying GNN-Based Link Prediction Performance
Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean Speech
Circumventing Concept Erasure Methods For Text-To-Image Generative Models
Continual Learning on a Diet: Learning from Sparsely Labeled Streams Under Constrained Computation
Energy-guided Entropic Neural Optimal Transport
On the Posterior Distribution in Denoising: Application to Uncertainty Quantification
Towards Eliminating Hard Label Constraints in Gradient Inversion Attacks
Soft Mixture Denoising: Beyond the Expressive Bottleneck of Diffusion Models
Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs
Ensemble Distillation for Unsupervised Constituency Parsing
MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding
Approximately Piecewise E(3) Equivariant Point Networks
EventRPG: Event Data Augmentation with Relevance Propagation Guidance
LLMCarbon: Modeling the End-to-End Carbon Footprint of Large Language Models
Implicit Maximum a Posteriori Filtering via Adaptive Optimization
Multi-modal Gaussian Process Variational Autoencoders for Neural and Behavioral Data
ContextRef: Evaluating Referenceless Metrics for Image Description Generation
Diffusion Model for Dense Matching
BENO: Boundary-embedded Neural Operators for Elliptic PDEs
Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL Policies
On Double Descent in Reinforcement Learning with LSTD and Random Features
Bayesian Bi-clustering of Neural Spiking Activity with Latent Structures
Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data
Doubly Robust Proximal Causal Learning for Continuous Treatments
SaNN: Simple Yet Powerful Simplicial-aware Neural Networks
DMBP: Diffusion model-based predictor for robust offline reinforcement learning against state observation perturbations
Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation
Making Retrieval-Augmented Language Models Robust to Irrelevant Context
GAIA: a benchmark for General AI Assistants
Polynomial Width is Sufficient for Set Representation with High-dimensional Features
AUC-CL: A Batchsize-Robust Framework for Self-Supervised Contrastive Representation Learning
Adversarial Causal Bayesian Optimization
A Neural Framework for Generalized Causal Sensitivity Analysis
Fast, Expressive $\mathrm{SE}(n)$ Equivariant Networks through Weight-Sharing in Position-Orientation Space
Most discriminative stimuli for functional cell type clustering
Efficient Backpropagation with Variance Controlled Adaptive Sampling
Improving Intrinsic Exploration by Creating Stationary Objectives
A Dynamical View of the Question of Why
SPTNet: An Efficient Alternative Framework for Generalized Category Discovery with Spatial Prompt Tuning
CoBIT: A Contrastive Bi-directional Image-Text Generation Model
Retrieval-based Disentangled Representation Learning with Natural Language Supervision
Large Language Models are Efficient Learners of Noise-Robust Speech Recognition
Composed Image Retrieval with Text Feedback via Multi-grained Uncertainty Regularization
Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning
Leveraging augmented-Lagrangian techniques for differentiating over infeasible quadratic programs in machine learning
FedDA: Faster Adaptive Gradient Methods for Federated Constrained Optimization
Conformal Language Modeling
GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction
Space Group Constrained Crystal Generation
SliceGPT: Compress Large Language Models by Deleting Rows and Columns
Generating Images with 3D Annotations Using Diffusion Models
The importance of feature preprocessing for differentially private linear optimization
Conditional Instrumental Variable Regression with Representation Learning for Causal Inference
Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data
Improving the Convergence of Dynamic NeRFs via Optimal Transport
Mean Field Theory in Deep Metric Learning
Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models
Abstractors and relational cross-attention: An inductive bias for explicit relational reasoning in Transformers
Ground-A-Video: Zero-shot Grounded Video Editing using Text-to-image Diffusion Models
Get What You Want, Not What You Don't: Image Content Suppression for Text-to-Image Diffusion Models
Confronting Reward Model Overoptimization with Constrained RLHF
Un-Mixing Test-Time Normalization Statistics: Combatting Label Temporal Correlation
InstructPix2NeRF: Instructed 3D Portrait Editing from a Single Image
Interpretable Diffusion via Information Decomposition
LQ-LoRA: Low-rank plus Quantized Matrix Decomposition for Efficient Language Model Finetuning
Adaptive Self-training Framework for Fine-grained Scene Graph Generation
Video Language Planning
Real-time Photorealistic Dynamic Scene Representation and Rendering with 4D Gaussian Splatting
Sparse Weight Averaging with Multiple Particles for Iterative Magnitude Pruning
Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts
Grounding Language Plans in Demonstrations Through Counterfactual Perturbations
Knowledge Card: Filling LLMs' Knowledge Gaps with Plug-in Specialized Language Models
Learning Decentralized Partially Observable Mean Field Control for Artificial Collective Behavior
Less or More From Teacher: Exploiting Trilateral Geometry For Knowledge Distillation
COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL
Neuron-Enhanced AutoEncoder Matrix Completion and Collaborative Filtering: Theory and Practice
Efficient ConvBN Blocks for Transfer Learning and Beyond
Mind Your Augmentation: The Key to Decoupling Dense Self-Supervised Learning
RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
Lipschitz Singularities in Diffusion Models
AUGCAL: Improving Sim2Real Adaptation by Uncertainty Calibration on Augmented Synthetic Images
RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression
Adversarial Adaptive Sampling: Unify PINN and Optimal Transport for the Approximation of PDEs
Vision-Language Foundation Models as Effective Robot Imitators
Learning Nash Equilibria in Rank-1 Games
GTMGC: Using Graph Transformer to Predict Molecule’s Ground-State Conformation
FreeReg: Image-to-Point Cloud Registration Leveraging Pretrained Diffusion Models and Monocular Depth Estimators
Fake It Till Make It: Federated Learning with Consensus-Oriented Generation
Rephrase, Augment, Reason: Visual Grounding of Questions for Vision-Language Models
STREAM: Spatio-TempoRal Evaluation and Analysis Metric for Video Generative Models
METRA: Scalable Unsupervised RL with Metric-Aware Abstraction
Transferring Learning Trajectories of Neural Networks
Scalable and Effective Implicit Graph Neural Networks on Large Graphs
Rethinking Branching on Exact Combinatorial Optimization Solver: The First Deep Symbolic Discovery Framework
GraphPulse: Topological representations for temporal graph property prediction
Lewis's Signaling Game as beta-VAE For Natural Word Lengths and Segments
MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training
Mediator Interpretation and Faster Learning Algorithms for Linear Correlated Equilibria in General Sequential Games
R-MAE: Regions Meet Masked Autoencoders
Label-Focused Inductive Bias over Latent Object Features in Visual Classification
Dissecting learning and forgetting in language model finetuning
Prediction without Preclusion: Recourse Verification with Reachable Sets
Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View
Federated Causal Discovery from Heterogeneous Data
Amortized Network Intervention to Steer the Excitatory Point Processes
Decodable and Sample Invariant Continuous Object Encoder
Tangent Transformers for Composition,Privacy and Removal
Beyond Memorization: Violating Privacy via Inference with Large Language Models
Time Travel in LLMs: Tracing Data Contamination in Large Language Models
How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data
CivRealm: A Learning and Reasoning Odyssey in Civilization for Decision-Making Agents
Masked Audio Generation using a Single Non-Autoregressive Transformer
Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised Learning
lpNTK: Better Generalisation with Less Data via Sample Interaction During Learning
Sudden Drops in the Loss: Syntax Acquisition, Phase Transitions, and Simplicity Bias in MLMs
Don't Trust: Verify -- Grounding LLM Quantitative Reasoning with Autoformalization
Sliced Wasserstein Estimation with Control Variates
VONet: Unsupervised Video Object Learning With Parallel U-Net Attention and Object-wise Sequential VAE
Relay Diffusion: Unifying diffusion process across resolutions for image synthesis
A representation-learning game for classes of prediction tasks
Query-Policy Misalignment in Preference-Based Reinforcement Learning
SparseFormer: Sparse Visual Recognition via Limited Latent Tokens
NECO: NEural Collapse Based Out-of-distribution detection
EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision
Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
Accurate Forgetting for Heterogeneous Federated Continual Learning
EControl: Fast Distributed Optimization with Compression and Error Control
Divide and not forget: Ensemble of selectively trained experts in Continual Learning
UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling
Learning No-Regret Sparse Generalized Linear Models with Varying Observation(s)
Nevis'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research
Protein-ligand binding representation learning from fine-grained interactions
Universal Jailbreak Backdoors from Poisoned Human Feedback
VDT: General-purpose Video Diffusion Transformers via Mask Modeling
EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models
GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge Graphs
Grokking in Linear Estimators -- A Solvable Model that Groks without Understanding
Constrained Decoding for Cross-lingual Label Projection
Efficient Heterogeneous Meta-Learning via Channel Shuffling Modulation
Tag2Text: Guiding Vision-Language Model via Image Tagging
Masked Autoencoders with Multi-Window Local-Global Attention Are Better Audio Learners
Multi-task Learning with 3D-Aware Regularization
Latent Intuitive Physics: Learning to Transfer Hidden Physics from A 3D Video
MixSATGEN: Learning Graph Mixing for SAT Instance Generation
On the Parameterization of Second-Order Optimization Effective towards the Infinite Width
Pareto Deep Long-Tailed Recognition: A Conflict-Averse Solution
Gradual Domain Adaptation via Gradient Flow
Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks
Layer-wise linear mode connectivity
Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogram
Decoupling regularization from the action space
Source-Free and Image-Only Unsupervised Domain Adaptation for Category Level Object Pose Estimation
Learning in reverse causal strategic environments with ramifications on two sided markets
Learning to design protein-protein interactions with enhanced generalization
CLAP: Collaborative Adaptation for Patchwork Learning
Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification
Towards Understanding Factual Knowledge of Large Language Models
Social Reward: Evaluating and Enhancing Generative AI through Million-User Feedback from an Online Creative Community
Invariance-based Learning of Latent Dynamics
Addressing Signal Delay in Deep Reinforcement Learning
GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data
ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate
Differentially Private Synthetic Data via Foundation Model APIs 1: Images
On Differentially Private Federated Linear Contextual Bandits
Federated Wasserstein Distance
Win-Win: Training High-Resolution Vision Transformers from Two Windows
Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement Learning
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
Trajeglish: Traffic Modeling as Next-Token Prediction
Bayesian Low-rank Adaptation for Large Language Models
Detecting, Explaining, and Mitigating Memorization in Diffusion Models
An Intuitive Multi-Frequency Feature Representation for SO(3)-Equivariant Networks
CrossLoco: Human Motion Driven Control of Legged Robots via Guided Unsupervised Reinforcement Learning
Brain decoding: toward real-time reconstruction of visual perception
Cauchy-Schwarz Divergence Information Bottleneck for Regression
Adaptive Window Pruning for Efficient Local Motion Deblurring
Heterogeneous Personalized Federated Learning by Local-Global Updates Mixing via Convergence Rate
ViLMA: A Zero-Shot Benchmark for Linguistic and Temporal Grounding in Video-Language Models
TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting
Continuous-Multiple Image Outpainting in One-Step via Positional Query and A Diffusion-based Approach
Pre-Training Goal-based Models for Sample-Efficient Reinforcement Learning
A Primal-Dual Approach to Solving Variational Inequalities with General Constraints
Assessing Uncertainty in Similarity Scoring: Performance & Fairness in Face Recognition
CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception
Robust Classification via Regression for Learning with Noisy Labels
Does CLIP’s generalization performance mainly stem from high train-test similarity?
GNNBoundary: Towards Explaining Graph Neural Networks through the Lens of Decision Boundaries
Flag Aggregator: Scalable Distributed Training under Failures and Augmented Losses using Convex Optimization
Understanding prompt engineering may not require rethinking generalization
Backdoor Federated Learning by Poisoning Backdoor-Critical Layers
Reverse Diffusion Monte Carlo
Spurious Feature Diversification Improves Out-of-distribution Generalization
Interpreting Robustness Proofs of Deep Neural Networks
OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text
Incentive-Aware Federated Learning with Training-Time Model Rewards
SWAP-NAS: Sample-Wise Activation Patterns for Ultra-fast NAS
Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning
Communication-Efficient Federated Non-Linear Bandit Optimization
Provable Robust Watermarking for AI-Generated Text
Respect the model: Fine-grained and Robust Explanation with Sharing Ratio Decomposition
Exploring Effective Stimulus Encoding via Vision System Modeling for Visual Prostheses
An LLM can Fool Itself: A Prompt-Based Adversarial Attack
WildFusion: Learning 3D-Aware Latent Diffusion Models in View Space
Prometheus: Inducing Fine-Grained Evaluation Capability in Language Models
Enhancing Human-AI Collaboration Through Logic-Guided Reasoning
Synaptic Weight Distributions Depend on the Geometry of Plasticity
ArchLock: Locking DNN Transferability at the Architecture Level with a Zero-Cost Binary Predictor
3D-Aware Hypothesis & Verification for Generalizable Relative Object Pose Estimation
Outlier-Robust Subsampling Techniques for Persistent Homology
On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation
ODEFormer: Symbolic Regression of Dynamical Systems with Transformers
VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks
Efficient Video Diffusion Models via Content-Frame Motion-Latent Decomposition
Neuro-Inspired Information-Theoretic Hierarchical Perception for Multimodal Learning
Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression
Efficient Sharpness-Aware Minimization for Molecular Graph Transformer Models
When can transformers reason with abstract symbols?
Novel Quadratic Constraints for Extending LipSDP beyond Slope-Restricted Activations
DOS: Diverse Outlier Sampling for Out-of-Distribution Detection
Safe RLHF: Safe Reinforcement Learning from Human Feedback
Mayfly: a Neural Data Structure for Graph Stream Summarization
Steve-Eye: Equipping LLM-based Embodied Agents with Visual Perception in Open Worlds
Towards Faithful Explanations: Boosting Rationalization with Shortcuts Discovery
On Adversarial Training without Perturbing all Examples
ReLoRA: High-Rank Training Through Low-Rank Updates
VDC: Versatile Data Cleanser based on Visual-Linguistic Inconsistency by Multimodal Large Language Models
Provable Compositional Generalization for Object-Centric Learning
Neural Common Neighbor with Completion for Link Prediction
Stack Attention: Improving the Ability of Transformers to Model Hierarchical Patterns
DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
Masked Completion via Structured Diffusion with White-Box Transformers
PubDef: Defending Against Transfer Attacks From Public Models
Is This the Subspace You Are Looking for? An Interpretability Illusion for Subspace Activation Patching
Efficient Streaming Language Models with Attention Sinks
Defining and extracting generalizable interaction primitives from DNNs
LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
Fine-Tuning Language Models for Factuality
Minimax optimality of convolutional neural networks for infinite dimensional input-output problems and separation from kernel methods
Training Bayesian Neural Networks with Sparse Subspace Variational Inference
Spatially-Aware Transformers for Embodied Agents
DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness
Seer: Language Instructed Video Prediction with Latent Diffusion Models
Let's do the time-warp-attend: Learning topological invariants of dynamical systems
Querying Easily Flip-flopped Samples for Deep Active Learning
Quasi-Monte Carlo for 3D Sliced Wasserstein
TAB: Temporal Accumulated Batch Normalization in Spiking Neural Networks
Large Content And Behavior Models To Understand, Simulate, And Optimize Content And Behavior
Improved algorithm and bounds for successive projection
Statistical Perspective of Top-K Sparse Softmax Gating Mixture of Experts
Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models
GeoLLM: Extracting Geospatial Knowledge from Large Language Models
LILO: Learning Interpretable Libraries by Compressing and Documenting Code
NOLA: Compressing LoRA using Linear Combination of Random Basis
Learning Hierarchical World Models with Adaptive Temporal Abstractions from Discrete Latent Dynamics
Beyond Vanilla Variational Autoencoders: Detecting Posterior Collapse in Conditional and Hierarchical Variational Autoencoders
Faithful and Efficient Explanations for Neural Networks via Neural Tangent Kernel Surrogate Models
On Characterizing the Trade-off in Invariant Representation Learning
Diffeomorphic Mesh Deformation via Efficient Optimal Transport for Cortical Surface Reconstruction
Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learning
Llemma: An Open Language Model for Mathematics
Zeroth-Order Optimization Meets Human Feedback: Provable Learning via Ranking Oracles
Distributionally Robust Optimization with Bias and Variance Reduction
Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees
Fast Hyperboloid Decision Tree Algorithms
TiC-CLIP: Continual Training of CLIP Models
Neural Ordinary Differential Equations for Modeling Epidemic Spreading
Tree Cross Attention
Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning
Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation
Multi-resolution HuBERT: Multi-resolution Speech Self-Supervised Learning with Masked Unit Prediction
Neur2RO: Neural Two-Stage Robust Optimization
Variational Bayesian Last Layers
Finetuning Text-to-Image Diffusion Models for Fairness
IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models
Improving Non-Transferable Representation Learning by Harnessing Content and Style
AmortizedPeriod: Attention-based Amortized Inference for Periodicity Identification
Decision ConvFormer: Local Filtering in MetaFormer is Sufficient for Decision Making
CABINET: Content Relevance-based Noise Reduction for Table Question Answering
Efficient local linearity regularization to overcome catastrophic overfitting
TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting
Social-Transmotion: Promptable Human Trajectory Prediction
The Devil is in the Neurons: Interpreting and Mitigating Social Biases in Language Models
Controlled Text Generation via Language Model Arithmetic
Neural Fourier Transform: A General Approach to Equivariant Representation Learning
Hybrid Sharing for Multi-Label Image Classification
Learning invariant representations of time-homogeneous stochastic dynamical systems
Label-Agnostic Forgetting: A Supervision-Free Unlearning in Deep Models
On the Fairness ROAD: Robust Optimization for Adversarial Debiasing
Understanding Transferable Representation Learning and Zero-shot Transfer in CLIP
Complex priors and flexible inference in recurrent circuits with dendritic nonlinearities
A Variational Framework for Estimating Continuous Treatment Effects with Measurement Error
SolidGen: An Autoregressive Model for Direct B-rep Synthesis
DEEP NEURAL NETWORK INITIALIZATION WITH SPARSITY INDUCING ACTIVATIONS
Guiding Instruction-based Image Editing via Multimodal Large Language Models
A Foundation Model for Error Correction Codes
Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space
Learning to Make Adherence-aware Advice
Tree Search-Based Policy Optimization under Stochastic Execution Delay
High Fidelity Neural Audio Compression
Sufficient conditions for offline reactivation in recurrent neural networks
A Generalist Agent
Scalable Real-Time Recurrent Learning Using Columnar-Constructive Networks
Real3D-Portrait: One-shot Realistic 3D Talking Portrait Synthesis
Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees
InsertNeRF: Instilling Generalizability into NeRF with HyperNet Modules
Towards Optimal Regret in Adversarial Linear MDPs with Bandit Feedback
Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting
Graph Generation with $K^2$-trees
Memory-Consistent Neural Networks for Imitation Learning
PRIME: Prioritizing Interpretability in Failure Mode Extraction
Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation
OpenTab: Advancing Large Language Models as Open-domain Table Reasoners
Lemur: Integrating Large Language Models in Automated Program Verification
A Simple and Effective Pruning Approach for Large Language Models
Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions
Quadratic models for understanding catapult dynamics of neural networks
Geographic Location Encoding with Spherical Harmonics and Sinusoidal Representation Networks
Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion
Learning to Solve Bilevel Programs with Binary Tender
PoSE: Efficient Context Window Extension of LLMs via Positional Skip-wise Training
Fusing Models with Complementary Expertise
Making RL with Preference-based Feedback Efficient via Randomization
Local Search GFlowNets
Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors
Data-independent Module-aware Pruning for Hierarchical Vision Transformers
Matryoshka Diffusion Models
Pre-training with Synthetic Data Helps Offline Reinforcement Learning
SOHES: Self-supervised Open-world Hierarchical Entity Segmentation
Understanding the Effects of RLHF on LLM Generalisation and Diversity
Overcoming the Pitfalls of Vision-Language Model Finetuning for OOD Generalization
Addressing Loss of Plasticity and Catastrophic Forgetting in Continual Learning
Reinforcement Symbolic Regression Machine
RTFS-Net: Recurrent Time-Frequency Modelling for Efficient Audio-Visual Speech Separation
BRUSLEATTACK: A QUERY-EFFICIENT SCORE- BASED BLACK-BOX SPARSE ADVERSARIAL ATTACK
Learning Energy Decompositions for Partial Inference in GFlowNets
Unlocking the Power of Representations in Long-term Novelty-based Exploration
Augmented Bayesian Policy Search
ImagenHub: Standardizing the evaluation of conditional image generation models
Universal Humanoid Motion Representations for Physics-Based Control
PILOT: An $\mathcal{O}(1/K)$-Convergent Approach for Policy Evaluation with Nonlinear Function Approximation
Estimating Conditional Mutual Information for Dynamic Feature Selection
ALAM: Averaged Low-Precision Activation for Memory-Efficient Training of Transformer Models
Spectrally Transformed Kernel Regression
Adversarial AutoMixup
First-order ANIL provably learns representations despite overparametrisation
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization
GenSim: Generating Robotic Simulation Tasks via Large Language Models
Multi-View Causal Representation Learning with Partial Observability
Long-Term Typhoon Trajectory Prediction: A Physics-Conditioned Approach Without Reanalysis Data
DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models
Leveraging Optimization for Adaptive Attacks on Image Watermarks
CNN Kernels Can Be the Best Shapelets
A Lie Group Approach to Riemannian Batch Normalization
Modelling complex vector drawings with stroke-clouds
WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions
Exposing Text-Image Inconsistency Using Diffusion Models
Task Planning for Visual Room Rearrangement under Partial Observability
FOSI: Hybrid First and Second Order Optimization
InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision Generalists
3D Reconstruction with Generalizable Neural Fields using Scene Priors
Lagrangian Flow Networks for Conservation Laws
fairret: a Framework for Differentiable Fairness Regularization Terms
LitCab: Lightweight Language Model Calibration over Short- and Long-form Responses
Massive Editing for Large Language Models via Meta Learning
Fantastic Generalization Measures are Nowhere to be Found
VeRA: Vector-based Random Matrix Adaptation
Towards Imitation Learning to Branch for MIP: A Hybrid Reinforcement Learning based Sample Augmentation Approach
Implicit Gaussian process representation of vector fields over arbitrary latent manifolds
Reclaiming the Source of Programmatic Policies: Programmatic versus Latent Spaces
V-DETR: DETR with Vertex Relative Position Encoding for 3D Object Detection
TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Experts
Learning Multi-Faceted Prototypical User Interests
PeFLL: Personalized Federated Learning by Learning to Learn
Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks
Delta-AI: Local objectives for amortized inference in sparse graphical models
DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization
Effective Structural Encodings via Local Curvature Profiles
Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering
SyncDreamer: Generating Multiview-consistent Images from a Single-view Image
InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation
Improving Offline RL by Blending Heuristics
Learning dynamic representations of the functional connectome in neurobiological networks
RAPPER: Reinforced Rationale-Prompted Paradigm for Natural Language Explanation in Visual Question Answering
Learning interpretable control inputs and dynamics underlying animal locomotion
The Effectiveness of Random Forgetting for Robust Generalization
AMAGO: Scalable In-Context Reinforcement Learning for Adaptive Agents
Unraveling the Key Components of OOD Generalization via Diversification
Bilevel Optimization under Unbounded Smoothness: A New Algorithm and Convergence Analysis
JoMA: Demystifying Multilayer Transformers via Joint Dynamics of MLP and Attention
ODICE: Revealing the Mystery of Distribution Correction Estimation via Orthogonal-gradient Update
Online GNN Evaluation Under Test-time Graph Distribution Shifts
Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models
Cascading Reinforcement Learning
CCIL: Continuity-Based Data Augmentation for Corrective Imitation Learning
A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning
Disentangling Time Series Representations via Contrastive Independence-of-Support on l-Variational Inference
LoftQ: LoRA-Fine-Tuning-aware Quantization for Large Language Models
ReMasker: Imputing Tabular Data with Masked Autoencoding
A Cognitive Model for Learning Abstract Relational Structures from Memory-based Decision-Making Tasks
Diving Segmentation Model into Pixels
Robust Similarity Learning with Difference Alignment Regularization
Exploring Weight Balancing on Long-Tailed Recognition Problem
Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining Approach
GENOME: Generative Neuro-Symbolic Visual Reasoning by Growing and Reusing Modules
Plug-and-Play: An Efficient Post-training Pruning Method for Large Language Models
Rethinking Channel Dimensions to Isolate Outliers for Low-bit Weight Quantization of Large Language Models
LabelDP-Pro: Learning with Label Differential Privacy via Projections
FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing
Navigating Text-To-Image Customization: From LyCORIS Fine-Tuning to Model Evaluation
Learning to Jointly Understand Visual and Tactile Signals
AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction
A path-norm toolkit for modern networks: consequences, promises and challenges
Deep Confident Steps to New Pockets: Strategies for Docking Generalization
Training-free Multi-objective Diffusion Model for 3D Molecule Generation
Towards Codable Watermarking for Injecting Multi-Bits Information to LLMs
Scaling Laws of RoPE-based Extrapolation
UniTabE: A Universal Pretraining Protocol for Tabular Foundation Model in Data Science
Rethinking Label Poisoning for GNNs: Pitfalls and Attacks
Efficient Score Matching with Deep Equilibrium Layers
Jointly-Learned Exit and Inference for a Dynamic Neural Network
Private Zeroth-Order Nonsmooth Nonconvex Optimization
Can we get the best of both Binary Neural Networks and Spiking Neural Networks for Efficient Computer Vision?
Deep Neural Networks Tend To Extrapolate Predictably
On the Vulnerability of Adversarially Trained Models Against Two-faced Attacks
Scalable Language Model with Generalized Continual Learning
FedCDA: Federated Learning with Cross-rounds Divergence-aware Aggregation
WebArena: A Realistic Web Environment for Building Autonomous Agents
Fantastic Gains and Where to Find Them: On the Existence and Prospect of General Knowledge Transfer between Any Pretrained Model
Language Model Inversion
A Good Learner can Teach Better: Teacher-Student Collaborative Knowledge Distillation
Consistent Video-to-Video Transfer Using Synthetic Dataset
GraphChef: Decision-Tree Recipes to Explain Graph Neural Networks
Benign Oscillation of Stochastic Gradient Descent with Large Learning Rate
Towards Generative Abstract Reasoning: Completing Raven’s Progressive Matrix via Rule Abstraction and Selection
Adaptive deep spiking neural network with global-local learning via balanced excitatory and inhibitory mechanism
Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation
Learning Hierarchical Image Segmentation For Recognition and By Recognition
Uncertainty-aware Constraint Inference in Inverse Constrained Reinforcement Learning
DIFFTACTILE: A Physics-based Differentiable Tactile Simulator for Contact-rich Robotic Manipulation
On the Role of Discrete Tokenization in Visual Representation Learning
In-Context Learning Dynamics with Random Binary Sequences
Influencer Backdoor Attack on Semantic Segmentation
NeurRev: Train Better Sparse Neural Network Practically via Neuron Revitalization
Low Rank Matrix Completion via Robust Alternating Minimization in Nearly Linear Time
Whittle Index with Multiple Actions and State Constraint for Inventory Management
ControlVideo: Training-free Controllable Text-to-video Generation
Jointly Training Large Autoregressive Multimodal Models
Time-Efficient Reinforcement Learning with Stochastic Stateful Policies
MiniLLM: Knowledge Distillation of Large Language Models
Learning Adaptive Multiresolution Transforms via Meta-Framelet-based Graph Convolutional Network
Multiscale Positive-Unlabeled Detection of AI-Generated Texts
Implicit Neural Representation Inference for Low-Dimensional Bayesian Deep Learning
When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method
The Effective Horizon Explains Deep RL Performance in Stochastic Environments
Interpreting CLIP's Image Representation via Text-Based Decomposition
Demystifying CLIP Data
Optimal Sample Complexity for Average Reward Markov Decision Processes
Space and time continuous physics simulation from partial observations
Learning 3D Particle-based Simulators from RGB-D Videos
Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning
The Hedgehog & the Porcupine: Expressive Linear Attentions with Softmax Mimicry
EMO: EARTH MOVER DISTANCE OPTIMIZATION FOR AUTO-REGRESSIVE LANGUAGE MODELING
Adaptive Instrument Design for Indirect Experiments
Towards Foundation Models for Knowledge Graph Reasoning
Locality-Aware Graph Rewiring in GNNs
Curiosity-driven Red-teaming for Large Language Models
The Devil is in the Object Boundary: Towards Annotation-free Instance Segmentation using Foundation Models
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning
LCOT: Linear Circular Optimal Transport
OPTIMAL ROBUST MEMORIZATION WITH RELU NEURAL NETWORKS
Stochastic Controlled Averaging for Federated Learning with Communication Compression
DORSal: Diffusion for Object-centric Representations of Scenes $\textit{et al.}$
REBAR: Retrieval-Based Reconstruction for Time-series Contrastive Learning
T-Rep: Representation Learning for Time Series using Time-Embeddings
Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training
Sharpness-Aware Minimization Enhances Feature Quality via Balanced Learning
Boosting Vanilla Lightweight Vision Transformers via Re-parameterization
Rethinking the Uniformity Metric in Self-Supervised Learning
Detecting Machine-Generated Texts by Multi-Population Aware Optimization for Maximum Mean Discrepancy
SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases
Identifying Representations for Intervention Extrapolation
Fusion Is Not Enough: Single Modal Attacks on Fusion Models for 3D Object Detection
Generalization error of spectral algorithms
CrIBo: Self-Supervised Learning via Cross-Image Object-Level Bootstrapping
The Reasonableness Behind Unreasonable Translation Capability of Large Language Model
On Representation Complexity of Model-based and Model-free Reinforcement Learning
EX-Graph: A Pioneering Dataset Bridging Ethereum and X
Self-Supervised Heterogeneous Graph Learning: a Homophily and Heterogeneity View
Chain of Thought Empowers Transformers to Solve Inherently Serial Problems
Beating Price of Anarchy and Gradient Descent without Regret in Potential Games
Conformal Risk Control
Fair Classifiers that Abstain without Harm
Repelling Random Walks
Separating common from salient patterns with Contrastive Representation Learning
Instant3D: Fast Text-to-3D with Sparse-view Generation and Large Reconstruction Model
Learning the greatest common divisor: explaining transformer predictions
BroGNet: Momentum-Conserving Graph Neural Stochastic Differential Equation for Learning Brownian Dynamics
Diagnosing Transformers: Illuminating Feature Spaces for Clinical Decision-Making
Vision Transformers Need Registers
Efficient and Scalable Graph Generation through Iterative Local Expansion
PlaSma: Procedural Knowledge Models for Language-based Planning and Re-Planning
Lipsum-FT: Robust Fine-Tuning of Zero-Shot Models Using Random Text Guidance
Neural Spectral Methods: Self-supervised learning in the spectral domain
Zero-Shot Continuous Prompt Transfer: Generalizing Task Semantics Across Language Models
RETSim: Resilient and Efficient Text Similarity
MUFFIN: Curating Multi-Faceted Instructions for Improving Instruction Following
ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection
Convolutional Deep Kernel Machines
Theoretical Analysis of Robust Overfitting for Wide DNNs: An NTK Approach
ReSimAD: Zero-Shot 3D Domain Transfer for Autonomous Driving with Source Reconstruction and Target Simulation
I-PHYRE: Interactive Physical Reasoning
Defining Expertise: Applications to Treatment Effect Estimation
A Variational Perspective on Solving Inverse Problems with Diffusion Models
Beyond IID weights: sparse and low-rank deep Neural Networks are also Gaussian Processes
Be Careful What You Smooth For: Label Smoothing Can Be a Privacy Shield but Also a Catalyst for Model Inversion Attacks
Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips
Network Memory Footprint Compression Through Jointly Learnable Codebooks and Mappings
MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning
Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality
LipSim: A Provably Robust Perceptual Similarity Metric
SALMONN: Towards Generic Hearing Abilities for Large Language Models
Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF
Project and Probe: Sample-Efficient Adaptation by Interpolating Orthogonal Features
UNR-Explainer: Counterfactual Explanations for Unsupervised Node Representation Learning Models
On Accelerating Diffusion-Based Sampling Processes via Improved Integration Approximation
Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision
From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module
AutoLoRa: An Automated Robust Fine-Tuning Framework
Dropout Enhanced Bilevel Training
Predicting Emergent Abilities with Infinite Resolution Evaluation
LightHGNN: Distilling Hypergraph Neural Networks into MLPs for 100x Faster Inference
On Diffusion Modeling for Anomaly Detection
Efficient Modulation for Vision Networks
Finite-State Autoregressive Entropy Coding for Efficient Learned Lossless Compression
G$^2$N$^2$ : Weisfeiler and Lehman go grammatical
Time-Varying Propensity Score to Bridge the Gap between the Past and Present
Discovering modular solutions that generalize compositionally
Be More Active! Understanding the Differences Between Mean and Sampled Representations of Variational Autoencoders
Alleviating Exposure Bias in Diffusion Models through Sampling with Shifted Time Steps
Meta Inverse Constrained Reinforcement Learning: Convergence Guarantee and Generalization Analysis
Masked Structural Growth for 2x Faster Language Model Pre-training
On the Generalization and Approximation Capacities of Neural Controlled Differential Equations
Robust Angular Synchronization via Directed Graph Neural Networks
Maximum Entropy Model Correction in Reinforcement Learning
Improving protein optimization with smoothed fitness landscapes
Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight
Quantifying the Plausibility of Context Reliance in Neural Machine Translation
BrainSCUBA: Fine-Grained Natural Language Captions of Visual Cortex Selectivity
Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products
Views Can Be Deceiving: Improved SSL Through Feature Space Augmentation
Flat Minima in Linear Estimation and an Extended Gauss Markov Theorem
Sample-Efficient Multi-Agent RL: An Optimization Perspective
Multimodal Molecular Pretraining via Modality Blending
Online Information Acquisition: Hiring Multiple Agents
RAIN: Your Language Models Can Align Themselves without Finetuning
Lie Group Decompositions for Equivariant Neural Networks
Masks, Signs, And Learning Rate Rewinding
Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation
Random Sparse Lifts: Construction, Analysis and Convergence of finite sparse networks
TabR: Tabular Deep Learning Meets Nearest Neighbors
Conformal Prediction via Regression-as-Classification
The Human-AI Substitution game: active learning from a strategic labeler
Probabilistic Self-supervised Representation Learning via Scoring Rules Minimization
Classification with Conceptual Safeguards
$\mathbb{D}^2$ Pruning: Message Passing for Balancing Diversity & Difficulty in Data Pruning
Scaling physics-informed hard constraints with mixture-of-experts
On Stationary Point Convergence of PPO-Clip
How to Capture Higher-order Correlations? Generalizing Matrix Softmax Attention to Kronecker Computation
General Graph Random Features
Are Models Biased on Text without Gender-related Language?
Uni3D: Exploring Unified 3D Representation at Scale
Privacy-Preserving In-Context Learning for Large Language Models
A Discretization Framework for Robust Contextual Stochastic Optimization
Chain of Log-Concave Markov Chains
A Quadratic Synchronization Rule for Distributed Deep Learning
Perceptual Scales Predicted by Fisher Information Metrics
Protein Discovery with Discrete Walk-Jump Sampling
Fast and unified path gradient estimators for normalizing flows
Out-of-Variable Generalisation for Discriminative Models
A Simple and Scalable Representation for Graph Generation
Listen, Think, and Understand
FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
TokenFlow: Consistent Diffusion Features for Consistent Video Editing
Language-Informed Visual Concept Learning
Turning large language models into cognitive models
Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries
Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts
Unveiling Options with Neural Network Decomposition
Towards the Fundamental Limits of Knowledge Transfer over Finite Domains
Active Test-Time Adaptation: Theoretical Analyses and An Algorithm
Quantifying and Enhancing Multi-modal Robustness with Modality Preference
RingAttention with Blockwise Transformers for Near-Infinite Context
A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data
Improved Techniques for Training Consistency Models
Modeling Boundedly Rational Agents with Latent Inference Budgets
HYPO: Hyperspherical Out-Of-Distribution Generalization
On the Foundations of Shortcut Learning
Emergent Communication with Conversational Repair
SmartPlay : A Benchmark for LLMs as Intelligent Agents
A General Framework for User-Guided Bayesian Optimization
InstructScene: Instruction-Driven 3D Indoor Scene Synthesis with Semantic Graph Prior
HIFA: High-fidelity Text-to-3D Generation with Advanced Diffusion Guidance
Understanding Domain Generalization: A Noise Robustness Perspective
Can Transformers Capture Spatial Relations between Objects?
The LLM Surgeon
Causal-StoNet: Causal Inference for High-Dimensional Complex Data
Equivariant Scalar Fields for Molecular Docking with Fast Fourier Transforms
Belief-Enriched Pessimistic Q-Learning against Adversarial State Perturbations
Diffusion-TS: Interpretable Diffusion for General Time Series Generation
Why is SAM Robust to Label Noise?
An Efficient Tester-Learner for Halfspaces
Batch normalization is sufficient for universal function approximation in CNNs
Neural structure learning with stochastic differential equations
Predictive, scalable and interpretable knowledge tracing on structured domains
Imitation Learning from Observation with Automatic Discount Scheduling
Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition
ImageNet-OOD: Deciphering Modern Out-of-Distribution Detection Algorithms
Gen-Z: Generative Zero-Shot Text Classification with Contextualized Label Descriptions
GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking
A Benchmark Study on Calibration
BEND: Benchmarking DNA Language Models on Biologically Meaningful Tasks
Guaranteed Approximation Bounds for Mixed-Precision Neural Operators
Lifting Architectural Constraints of Injective Flows
Aux-NAS: Exploiting Auxiliary Labels with Negligibly Extra Inference Cost
Language Model Self-improvement by Reinforcement Learning Contemplation
Fast Updating Truncated SVD for Representation Learning with Sparse Matrices
SOInter: A Novel Deep Energy-Based Interpretation Method for Explaining Structured Output Models
Sparse Spiking Neural Network: Exploiting Heterogeneity in Timescales for Pruning Recurrent SNN
Rethinking Model Ensemble in Transfer-based Adversarial Attacks
Policy Rehearsing: Training Generalizable Policies for Reinforcement Learning
ModernTCN: A Modern Pure Convolution Structure for General Time Series Analysis
Prototypical Information Bottlenecking and Disentangling for Multimodal Cancer Survival Prediction
Attention-based Iterative Decomposition for Tensor Product Representation
On the Role of General Function Approximation in Offline Reinforcement Learning
Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings
The Generative AI Paradox: “What It Can Create, It May Not Understand”
The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning
Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement
Evaluating Large Language Models at Evaluating Instruction Following
Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning
The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction
Learning Grounded Action Abstractions from Language
SpikePoint: An Efficient Point-based Spiking Neural Network for Event Cameras Action Recognition
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
Scaling Laws for Sparsely-Connected Foundation Models
From Sparse to Soft Mixtures of Experts
iGraphMix: Input Graph Mixup Method for Node Classification
Retrieval-Enhanced Contrastive Vision-Text Models
Raidar: geneRative AI Detection viA Rewriting
Function Vectors in Large Language Models
Ring-A-Bell! How Reliable are Concept Removal Methods For Diffusion Models?
Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking
Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization
A Policy Gradient Method for Confounded POMDPs
MUSTARD: Mastering Uniform Synthesis of Theorem and Proof Data
LEGO-Prover: Neural Theorem Proving with Growing Libraries
THOUGHT PROPAGATION: AN ANALOGICAL APPROACH TO COMPLEX REASONING WITH LARGE LANGUAGE MODELS
GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher
On the Humanity of Conversational AI: Evaluating the Psychological Portrayal of LLMs
Accurate Retraining-free Pruning for Pretrained Encoder-based Language Models
INViTE: INterpret and Control Vision-Language Models with Text Explanations
Effective pruning of web-scale datasets based on complexity of concept clusters
Sin3DM: Learning a Diffusion Model from a Single 3D Textured Shape
Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignment
DiffEnc: Variational Diffusion with a Learned Encoder
GIM: Learning Generalizable Image Matcher From Internet Videos
DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks
The Unreasonable Effectiveness of Linear Prediction as a Perceptual Metric
FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods
SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer
Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages
Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances
Self-supervised Pocket Pretraining via Protein Fragment-Surroundings Alignment
Solving Diffusion ODEs with Optimal Boundary Conditions for Better Image Super-Resolution
Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning
Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly Detection
DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning
Large Language Models Are Not Robust Multiple Choice Selectors
MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework
In-context Autoencoder for Context Compression in a Large Language Model
GenCorres: Consistent Shape Matching via Coupled Implicit-Explicit Shape Generative Models
Hard-Constrained Deep Learning for Climate Downscaling
PB-LLM: Partially Binarized Large Language Models
Information Bottleneck Analysis of Deep Neural Networks via Lossy Compression
ZipIt! Merging Models from Different Tasks without Training
Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs
MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations
RLCD: Reinforcement Learning from Contrastive Distillation for LM Alignment
Localizing and Editing Knowledge In Text-to-Image Generative Models
Efficient Dynamics Modeling in Interactive Environments with Koopman Theory
Generative Learning for Financial Time Series with Irregular and Scale-Invariant Patterns
Linear attention is (maybe) all you need (to understand Transformer optimization)
Scalable Diffusion for Materials Generation
MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process
RT-Trajectory: Robotic Task Generalization via Hindsight Trajectory Sketches
Differentiable Euler Characteristic Transforms for Shape Classification
Simplicial Representation Learning with Neural $k$-Forms
HAZARD Challenge: Embodied Decision Making in Dynamically Changing Environments
Efficient Multi-agent Reinforcement Learning by Planning
DSPy: Compiling Declarative Language Model Calls into State-of-the-Art Pipelines
On the Stability of Iterative Retraining of Generative Models on their own Data
A Study of Bayesian Neural Network Surrogates for Bayesian Optimization
Fine-Tuned Language Models Generate Stable Inorganic Materials as Text
BooookScore: A systematic exploration of book-length summarization in the era of LLMs
Prediction Error-based Classification for Class-Incremental Learning
Deep Geodesic Canonical Correlation Analysis for Covariance-Based Neuroimaging Data
Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness
The Need for Speed: Pruning Transformers with One Recipe
Adapting to Distribution Shift by Visual Domain Prompt Generation
ImplicitSLIM and How it Improves Embedding-based Collaborative Filtering
We use cookies to store which papers have been visited.
I agree
Successful Page Load
ICLR uses cookies to remember that you are logged in. By using our websites, you agree to the placement of cookies.
Our Privacy Policy »
Accept Cookies
We use cookies to store which papers have been visited.
I agree
