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SUN 10 DEC
7 a.m.
Registration Desk
(ends 4:00 PM)
9 a.m.
Expo Talk Panel:
Open-source Quantization with Advanced Data Formats
(ends 9:50 AM)
Expo Demonstration:
Transforming the Cost of Transformer Inference with Positron AI
(ends 3:00 PM)
Expo Demonstration:
Real-time Portrait Video Relighting with AI-Generated HDRI maps on Mobile
(ends 3:00 PM)
Expo Demonstration:
EXAONE Universe: Toward Mindful Language Modeling for Insights and Inspiration
(ends 3:00 PM)
Expo Demonstration:
Fast and Accurate Inference of LLaMA2-Chat 7B on a Smartphone via Quantization Aware Training and Speculative Decoding with Knowledge Distillation
(ends 3:00 PM)
Expo Demonstration:
Enhanced Segmentation for Video Conferencing with On-device Learning (ODL)
(ends 3:00 PM)
Expo Demonstration:
Fast visual content generation with Stable Diffusion on mobile devices
(ends 3:00 PM)
Expo Demonstration:
Cutting-Edge AI Approaches for IT Diagnosis
(duration 1.5 hr)
Expo Demonstration:
FlowPilot: An LLM-Powered System for Enterprise Data Integration
(duration 1.5 hr)
Expo Demonstration:
The BAM Laboratory - Empowering AI Builders with User-Centric Tooling
(duration 1.5 hr)
Expo Demonstration:
Optimizing LLMs for Code: Fine-tuning Techniques & Architectures
(ends 3:00 PM)
Expo Demonstration:
Deep Dive into LLM Agents: From Structure to Task Automation at Scale
(ends 3:00 PM)
10 a.m.
Expo Talk Panel:
The Kitchen and the Lab: Bringing reproducibility and traceability to Bloomberg’s ML practices
(ends 10:50 AM)
Expo Talk Panel:
Optimizing and Reasoning about LLM Inference: from First Principles to SOTA Techniques
(ends 10:50 AM)
Expo Talk Panel:
On-device Personal Voice for Accessibility
(ends 10:50 AM)
10:30 a.m.
Expo Demonstration:
Log Diagnosis powered via Large Language Model for ITOps
(duration 1.5 hr)
Expo Demonstration:
Industrial demonstration of popular backbones for Time Series Foundation Models
(duration 1.5 hr)
Expo Demonstration:
IBM Intelligent Remediation for ITOps Incidents powered by Generative AI
(duration 1.5 hr)
11 a.m.
Expo Talk Panel:
Artificial Intelligence & Machine Learning Across the Entire Drug Development Pipeline
(ends 11:50 AM)
Expo Talk Panel:
Understanding the Effectiveness of Large Language Models in Code Translation
(ends 11:50 AM)
Expo Talk Panel:
Graph Learning Meets Artificial Intelligence
(ends 11:50 AM)
Expo Talk Panel:
Conversational Recommendations: Present and Future
(ends 11:50 AM)
noon
Expo Demonstration:
Cutting-Edge AI Approaches for IT Diagnosis
(duration 1.5 hr)
Expo Demonstration:
FlowPilot: An LLM-Powered System for Enterprise Data Integration
(duration 1.5 hr)
Expo Demonstration:
The BAM Laboratory - Empowering AI Builders with User-Centric Tooling
(duration 1.5 hr)
Expo Talk Panel:
Resonator: Music Space
(ends 12:50 PM)
Expo Talk Panel:
Tabular Representation Learning for Dataset Discovery over Data Lakes
(ends 12:50 PM)
Expo Talk Panel:
Knowledge Base For Everyone
(ends 12:50 PM)
Expo Workshop:
Building using Llama 2
(ends 2:00 PM)
Expo Workshop:
AutoGluon 1.0: AutoML at Your Fingertips
(ends 2:00 PM)
Expo Workshop:
Efficient AI Experimentation with Ax
(ends 2:00 PM)
Expo Workshop:
Knowledge-enhanced AI for Industry Verticals
(ends 2:00 PM)
1 p.m.
Break:
Break
(ends 1:30 PM)
Expo Talk Panel:
MACHINE LEARNING AND OPTIMIZATION FOR AUTOMATED TRADING AT HRT
(ends 1:50 PM)
Expo Talk Panel:
Large Language Models for Artificial Expert Intelligence
(ends 1:50 PM)
Expo Talk Panel:
Reinforcement Learning: Trends, Applications, and Challenges
(ends 1:50 PM)
1:30 p.m.
Expo Demonstration:
Log Diagnosis powered via Large Language Model for ITOps
(duration 1.5 hr)
Expo Demonstration:
Industrial demonstration of popular backbones for Time Series Foundation Models
(duration 1.5 hr)
Expo Demonstration:
IBM Intelligent Remediation for ITOps Incidents powered by Generative AI
(duration 1.5 hr)
2 p.m.
Expo Talk Panel:
Using AI to Improve Control Design Workflows
(ends 2:50 PM)
Expo Talk Panel:
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
(ends 2:50 PM)
Expo Talk Panel:
The Future is Here: A Deep Dive into Autonomous Agents
(ends 2:50 PM)
Expo Workshop:
MARBLE 2: The Second workshop on Machine Learning and Artificial Intelligence for Biologics Engineering
(ends 4:00 PM)
Expo Workshop:
Communication without language barriers: recent advances in translation foundation models
(ends 4:00 PM)
Expo Workshop:
Production-ready Reinforcement Learning Active Training
(ends 4:00 PM)
Expo Workshop:
Sony’s Media Content Restoration and Editing with Deep Generative Models and Beyond.
(ends 4:00 PM)
2:30 p.m.
Break:
Break
(ends 3:00 PM)
3 p.m.
Expo Talk Panel:
Empirical rigor in ML as a massively parallelizable challenge
(ends 3:50 PM)
Expo Talk Panel:
From Theory to Practice: Incorporating ML Models into Safety-Critical Systems
(ends 3:50 PM)
Expo Talk Panel:
Augmenting PromptOPS with Partner Products: A Comprehensive Framework for Input-Output Guardrails in Large Language Models
(ends 3:50 PM)
Expo Demonstration:
Better Model Development? Make Data the Star of the Show
(ends 4:00 PM)
4 p.m.
Expo Talk Panel:
Smaller models can pack a punch in the era of Large Language Models
(ends 4:50 PM)

MON 11 DEC
5:30 a.m.
Registration Desk
(ends 4:00 PM)
6 a.m.
Workshop:
Women in ML
(ends 1:30 PM)
6:15 a.m.
Workshop:
Black in AI
(ends 1:30 PM)
Workshop:
New in ML
(ends 1:30 PM)
Workshop:
Latinx in AI
(ends 1:30 PM)
6:30 a.m.
Workshop:
North Africans in ML
(ends 1:30 PM)
Workshop:
Muslims in ML
(ends 1:30 PM)
7 a.m.
Workshop:
Indigenous in AI/ML
(ends 1:15 PM)
7:15 a.m.
Break:
Break
(ends 8:05 AM)
7:30 a.m.
Workshop:
Queer in AI
(ends 1:30 PM)
7:45 a.m.
Tutorial:
Governance & Accountability for ML: Existing Tools, Ongoing Efforts, & Future Directions
(ends 10:15 AM)
Tutorial:
How to Work With Real Humans in Human-AI Systems
(ends 10:15 AM)
Tutorial:
Language Models Meet World Models
(ends 10:15 AM)
Tutorial:
Recent and Upcoming Developments in Randomized Numerical Linear Algebra for ML
(ends 10:15 AM)
Tutorial:
Contributing to an Efficient and Democratized Large Model Era
(ends 10:15 AM)
Tutorial:
Latent Diffusion Models: Is the Generative AI Revolution Happening in Latent Space?
(ends 10:15 AM)
Tutorial:
Data Contribution Estimation for Machine Learning
(ends 10:15 AM)
9 a.m.
Workshop:
Global South AI
(ends 12:00 PM)
10 a.m.
Education Outreach:
Education Outreach
(ends 11:40 AM)
10:15 a.m.
Lunch:
Lunch (On Your Own)
(ends 11:45 AM)
10:45 a.m.
Mentorship:
Science Communication for AI Researchers - A Quick Introduction
(ends 2:00 PM)
11:45 a.m.
Tutorial:
Machine Learning for Theorem Proving
(ends 2:15 PM)
Tutorial:
Application Development using Large Language Models
(ends 2:15 PM)
Tutorial:
Data-Centric AI for reliable and responsible AI: from theory to practice
(ends 2:15 PM)
Tutorial:
Do You Prefer Learning with Preferences?
(ends 2:15 PM)
Tutorial:
Modeling and Exploiting Data Heterogeneity under Distribution Shifts
(ends 2:15 PM)
Tutorial:
Reconsidering Overfitting in the Age of Overparameterized Models
(ends 2:15 PM)
Tutorial:
What can we do about NeurIPS Reviewer #2?Challenges, Solutions, Experiments and Open Problemsin Peer Review
(ends 2:15 PM)
1:30 p.m.
Affinity Poster Session:
Joint Poster Session
(ends 2:30 PM)
2 p.m.
Break:
Break
(ends 2:45 PM)
3 p.m.
Opening Remarks:
Remarks
(ends 3:25 PM)
3:25 p.m.
Invited Talk:
NextGenAI: The Delusion of Scaling and the Future of Generative AI
Björn Ommer
(ends 4:15 PM)
4:15 p.m.
Reception:
Welcome Reception
(ends 6:30 PM)
4:30 p.m.
Creative AI Performances 1 [4:30-6:00]
Chairs: Jean Oh · Isabelle Guyon
s 4:30-6:00
[4:30] Voice Scroll
[4:30] The WHOOPS! Gallery: An Intersection of AI, Creativity, and the Unusual
[4:30] Kiss/Crash
[4:30] Emergent Rhythm — Real-time AI Generative DJ Set
[4:30] Fusion: Landscape and Beyond
[4:30] Entanglement
(ends 6:00 PM)

TUE 12 DEC
5:30 a.m.
Registration Desk
(ends 4:00 PM)
6:30 a.m.
Invited Talk:
The Many Faces of Responsible AI
Lora Aroyo
(ends 7:20 AM)
7:15 a.m.
Break:
Break
(ends 8:05 AM)
8 a.m.
Oral 1A RL [8:00-8:45]
Orals 8:00-8:45
[8:00] Ordering-based Conditions for Global Convergence of Policy Gradient Methods
[8:15] When Demonstrations meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning
[8:30] Online RL in Linearly $q^\pi$-Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore
(ends 8:45 AM)
Oral 1B Datasets & Benchmarks [8:00-8:45]
Orals 8:00-8:45
[8:00] LeanDojo: Theorem Proving with Retrieval-Augmented Language Models
[8:15] OpenAssistant Conversations - Democratizing Large Language Model Alignment
[8:30] DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
(ends 8:45 AM)
Oral 1C Tractable models [8:00-8:45]
Orals 8:00-8:45
[8:00] How to Turn Your Knowledge Graph Embeddings into Generative Models
[8:15] Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming Approach
[8:30] Characteristic Circuits
(ends 8:45 AM)
Oral 1D DL Theory [8:00-8:45]
Orals 8:00-8:45
[8:00] Sharpness Minimization Algorithms Do Not Only Minimize Sharpness To Achieve Better Generalization
[8:15] Abide by the law and follow the flow: conservation laws for gradient flows
[8:30] A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning
(ends 8:45 AM)
8:45 a.m.
Poster Session 1 [8:45-10:45]
Posters 8:45-10:45
PIXIU: A Comprehensive Benchmark, Instruction Dataset and Large Language Model for Finance
FETV: A Benchmark for Fine-Grained Evaluation of Open-Domain Text-to-Video Generation
Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking
ChessGPT: Bridging Policy Learning and Language Modeling
EMBERSim: A Large-Scale Databank for Boosting Similarity Search in Malware Analysis
AllSim: Simulating and Benchmarking Resource Allocation Policies in Multi-User Systems
Disentangled Wasserstein Autoencoder for T-Cell Receptor Engineering
Fragment-based Pretraining and Finetuning on Molecular Graphs
Pre-Training Protein Encoder via Siamese Sequence-Structure Diffusion Trajectory Prediction
AbDiffuser: full-atom generation of in-vitro functioning antibodies
Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model
Density of States Prediction of Crystalline Materials via Prompt-guided Multi-Modal Transformer
Smooth, exact rotational symmetrization for deep learning on point clouds
AirDelhi: Fine-Grained Spatio-Temporal Particulate Matter Dataset From Delhi For ML based Modeling
SubseasonalClimateUSA: A Dataset for Subseasonal Forecasting and Benchmarking
OceanBench: The Sea Surface Height Edition
M5HisDoc: A Large-scale Multi-style Chinese Historical Document Analysis Benchmark
ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning
HA-ViD: A Human Assembly Video Dataset for Comprehensive Assembly Knowledge Understanding
DVSOD: RGB-D Video Salient Object Detection
Hierarchical Open-vocabulary Universal Image Segmentation
FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models
Query-based Temporal Fusion with Explicit Motion for 3D Object Detection
Disentangled Counterfactual Learning for Physical Audiovisual Commonsense Reasoning
Zero-shot Visual Relation Detection via Composite Visual Cues from Large Language Models
VisIT-Bench: A Dynamic Benchmark for Evaluating Instruction-Following Vision-and-Language Models
PrObeD: Proactive Object Detection Wrapper
DreamSparse: Escaping from Plato’s Cave with 2D Diffusion Model Given Sparse Views
Generalizable One-shot 3D Neural Head Avatar
Video-Mined Task Graphs for Keystep Recognition in Instructional Videos
SANFlow: Semantic-Aware Normalizing Flow for Anomaly Detection
Deep Optimal Transport: A Practical Algorithm for Photo-realistic Image Restoration
Deep Non-line-of-sight Imaging from Under-scanning Measurements
ProBio: A Protocol-guided Multimodal Dataset for Molecular Biology Lab
Rewrite Caption Semantics: Bridging Semantic Gaps for Language-Supervised Semantic Segmentation
What’s Left? Concept Grounding with Logic-Enhanced Foundation Models
Species196: A One-Million Semi-supervised Dataset for Fine-grained Species Recognition
Fine-Grained Cross-View Geo-Localization Using a Correlation-Aware Homography Estimator
Leave No Stone Unturned: Mine Extra Knowledge for Imbalanced Facial Expression Recognition
Knowledge Diffusion for Distillation
GenS: Generalizable Neural Surface Reconstruction from Multi-View Images
Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object Detection
ConRad: Image Constrained Radiance Fields for 3D Generation from a Single Image
StoryBench: A Multifaceted Benchmark for Continuous Story Visualization
3D Indoor Instance Segmentation in an Open-World
Dense and Aligned Captions (DAC) Promote Compositional Reasoning in VL Models
CityRefer: Geography-aware 3D Visual Grounding Dataset on City-scale Point Cloud Data
Subject-driven Text-to-Image Generation via Apprenticeship Learning
Topological RANSAC for instance verification and retrieval without fine-tuning
Michelangelo: Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representation
LoCoOp: Few-Shot Out-of-Distribution Detection via Prompt Learning
RenderMe-360: A Large Digital Asset Library and Benchmarks Towards High-fidelity Head Avatars
Aligning Gradient and Hessian for Neural Signed Distance Function
CP-SLAM: Collaborative Neural Point-based SLAM System
STXD: Structural and Temporal Cross-Modal Distillation for Multi-View 3D Object Detection
FaceComposer: A Unified Model for Versatile Facial Content Creation
One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization
T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional Text-to-image Generation
A Multi-modal Global Instance Tracking Benchmark (MGIT): Better Locating Target in Complex Spatio-temporal and Causal Relationship
XAGen: 3D Expressive Human Avatars Generation
Mr. HiSum: A Large-scale Dataset for Video Highlight Detection and Summarization
Learning to Parameterize Visual Attributes for Open-set Fine-grained Retrieval
SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation
Online Map Vectorization for Autonomous Driving: A Rasterization Perspective
Are These the Same Apple? Comparing Images Based on Object Intrinsics
StEik: Stabilizing the Optimization of Neural Signed Distance Functions and Finer Shape Representation
LayoutPrompter: Awaken the Design Ability of Large Language Models
JourneyDB: A Benchmark for Generative Image Understanding
Kiki or Bouba? Sound Symbolism in Vision-and-Language Models
Global Structure-Aware Diffusion Process for Low-light Image Enhancement
A High-Resolution Dataset for Instance Detection with Multi-View Object Capture
Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with Text
Dual Mean-Teacher: An Unbiased Semi-Supervised Framework for Audio-Visual Source Localization
OpenIllumination: A Multi-Illumination Dataset for Inverse Rendering Evaluation on Real Objects
Glance and Focus: Memory Prompting for Multi-Event Video Question Answering
AND: Adversarial Neural Degradation for Learning Blind Image Super-Resolution
PackQViT: Faster Sub-8-bit Vision Transformers via Full and Packed Quantization on the Mobile
ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction
NurViD: A Large Expert-Level Video Database for Nursing Procedure Activity Understanding
Emergent Correspondence from Image Diffusion
OpenMask3D: Open-Vocabulary 3D Instance Segmentation
LeanDojo: Theorem Proving with Retrieval-Augmented Language Models
WordScape: a Pipeline to extract multilingual, visually rich Documents with Layout Annotations from Web Crawl Data
UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal Prediction
HubRouter: Learning Global Routing via Hub Generation and Pin-hub Connection
ALGO: Synthesizing Algorithmic Programs with Generated Oracle Verifiers
The Cambridge Law Corpus: A Corpus for Legal AI Research
Metis: Understanding and Enhancing In-Network Regular Expressions
HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution
ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design
StressID: a Multimodal Dataset for Stress Identification
VTaC: A Benchmark Dataset of Ventricular Tachycardia Alarms from ICU Monitors
Differentiable sorting for censored time-to-event data.
Coupled Reconstruction of Cortical Surfaces by Diffeomorphic Mesh Deformation
Reliable Off-Policy Learning for Dosage Combinations
rPPG-Toolbox: Deep Remote PPG Toolbox
EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Images
Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction
RaLEs: a Benchmark for Radiology Language Evaluations
Aligning Synthetic Medical Images with Clinical Knowledge using Human Feedback
Thrust: Adaptively Propels Large Language Models with External Knowledge
How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources
OpenAssistant Conversations - Democratizing Large Language Model Alignment
DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction
Towards Efficient Pre-Trained Language Model via Feature Correlation Distillation
MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment Tasks
PopSign ASL v1.0: An Isolated American Sign Language Dataset Collected via Smartphones
Rethinking the Role of Token Retrieval in Multi-Vector Retrieval
Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
Deductive Verification of Chain-of-Thought Reasoning
CLadder: Assessing Causal Reasoning in Language Models
SpokenWOZ: A Large-Scale Speech-Text Benchmark for Spoken Task-Oriented Dialogue Agents
LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models
Mathematical Capabilities of ChatGPT
SituatedGen: Incorporating Geographical and Temporal Contexts into Generative Commonsense Reasoning
Binarized Neural Machine Translation
Don’t Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learner
Unified Segment-to-Segment Framework for Simultaneous Sequence Generation
Faith and Fate: Limits of Transformers on Compositionality
Diplomat: A Dialogue Dataset for Situated PragMATic Reasoning
ToolQA: A Dataset for LLM Question Answering with External Tools
Bayesian target optimisation for high-precision holographic optogenetics
Human-in-the-Loop Optimization for Deep Stimulus Encoding in Visual Prostheses
Multimodal Deep Learning Model Unveils Behavioral Dynamics of V1 Activity in Freely Moving Mice
Taking the neural sampling code very seriously: A data-driven approach for evaluating generative models of the visual system
Are Vision Transformers More Data Hungry Than Newborn Visual Systems?
Dis-inhibitory neuronal circuits can control the sign of synaptic plasticity
Learning better with Dale’s Law: A Spectral Perspective
Estimating Noise Correlations Across Continuous Conditions With Wishart Processes
Data-Driven Network Neuroscience: On Data Collection and Benchmark
Sequential Memory with Temporal Predictive Coding
Extraction and Recovery of Spatio-Temporal Structure in Latent Dynamics Alignment with Diffusion Models
CORNN: Convex optimization of recurrent neural networks for rapid inference of neural dynamics
Plug-and-Play Stability for Intracortical Brain-Computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication
Bounded rationality in structured density estimation
SwiFT: Swin 4D fMRI Transformer
Uncovering motifs of concurrent signaling across multiple neuronal populations
Long Sequence Hopfield Memory
Slow and Weak Attractor Computation Embedded in Fast and Strong E-I Balanced Neural Dynamics
Learning Topology-Agnostic EEG Representations with Geometry-Aware Modeling
Scalable Transformer for PDE Surrogate Modeling
Online PCA in Converging Self-consistent Field Equations
Geometry-Informed Neural Operator for Large-Scale 3D PDEs
SUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physics
Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior
Equivariant flow matching
Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics
Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data
BubbleML: A Multiphase Multiphysics Dataset and Benchmarks for Machine Learning
SoundCam: A Dataset for Finding Humans Using Room Acoustics
ANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulation
Newton–Cotes Graph Neural Networks: On the Time Evolution of Dynamic Systems
Learning Efficient Surrogate Dynamic Models with Graph Spline Networks
Operator Learning with Neural Fields: Tackling PDEs on General Geometries
Domain Agnostic Fourier Neural Operators
Classical Simulation of Quantum Circuits: Parallel Environments and Benchmark
SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models
Learning Score-based Grasping Primitive for Human-assisting Dexterous Grasping
What Truly Matters in Trajectory Prediction for Autonomous Driving?
Hierarchical Adaptive Value Estimation for Multi-modal Visual Reinforcement Learning
Grounded Decoding: Guiding Text Generation with Grounded Models for Embodied Agents
ScenarioNet: Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling
Model-Based Control with Sparse Neural Dynamics
Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detection
LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting
CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completion
AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weeks
Why Does Sharpness-Aware Minimization Generalize Better Than SGD?
Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training
Augmentation-Free Dense Contrastive Knowledge Distillation for Efficient Semantic Segmentation
Alternating Updates for Efficient Transformers
Training Chain-of-Thought via Latent-Variable Inference
Module-wise Adaptive Distillation for Multimodality Foundation Models
CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models
Reconciling Competing Sampling Strategies of Network Embedding
Triangulation Residual Loss for Data-efficient 3D Pose Estimation
SEENN: Towards Temporal Spiking Early Exit Neural Networks
Towards Unbounded Machine Unlearning
Deep Contract Design via Discontinuous Networks
H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models
On Sparse Modern Hopfield Model
Training-free Diffusion Model Adaptation for Variable-Sized Text-to-Image Synthesis
Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation
Validated Image Caption Rating Dataset
One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning
RD-Suite: A Benchmark for Ranking Distillation
Investigating how ReLU-networks encode symmetries
GNeSF: Generalizable Neural Semantic Fields
GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image
Improving multimodal datasets with image captioning
DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology
Continuous-Time Functional Diffusion Processes
DreamWaltz: Make a Scene with Complex 3D Animatable Avatars
Rethinking Conditional Diffusion Sampling with Progressive Guidance
Add and Thin: Diffusion for Temporal Point Processes
Improving Diffusion-Based Image Synthesis with Context Prediction
Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability
Beta Diffusion
Precision-Recall Divergence Optimization for Generative Modeling with GANs and Normalizing Flows
Concept Algebra for (Score-Based) Text-Controlled Generative Models
On the Generalization Properties of Diffusion Models
Arbitrarily Scalable Environment Generators via Neural Cellular Automata
UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models
Temporal Dynamic Quantization for Diffusion Models
Language Quantized AutoEncoders: Towards Unsupervised Text-Image Alignment
Joint Prompt Optimization of Stacked LLMs using Variational Inference
Expanding Small-Scale Datasets with Guided Imagination
Inserting Anybody in Diffusion Models via Celeb Basis
Customizable Image Synthesis with Multiple Subjects
Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models
GlyphControl: Glyph Conditional Control for Visual Text Generation
Score-based Data Assimilation
Diffusion Probabilistic Models for Structured Node Classification
Censored Sampling of Diffusion Models Using 3 Minutes of Human Feedback
Semi-Implicit Denoising Diffusion Models (SIDDMs)
Reinforcement-Enhanced Autoregressive Feature Transformation: Gradient-steered Search in Continuous Space for Postfix Expressions
Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder
Constructing Non-isotropic Gaussian Diffusion Model Using Isotropic Gaussian Diffusion Model for Image Editing
Synthcity: a benchmark framework for diverse use cases of tabular synthetic data
Euler-Lagrange Analysis of Generative Adversarial Networks
Equivariant Spatio-Temporal Attentive Graph Networks to Simulate Physical Dynamics
Efficient Learning of Linear Graph Neural Networks via Node Subsampling
A Comprehensive Study on Text-attributed Graphs: Benchmarking and Rethinking
Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyond
CAT-Walk: Inductive Hypergraph Learning via Set Walks
TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs
A Theory of Link Prediction via Relational Weisfeiler-Leman on Knowledge Graphs
Temporal Graph Benchmark for Machine Learning on Temporal Graphs
Fine-grained Expressivity of Graph Neural Networks
Approximately Equivariant Graph Networks
TopoSRL: Topology preserving self-supervised Simplicial Representation Learning
Lovász Principle for Unsupervised Graph Representation Learning
Demystifying Oversmoothing in Attention-Based Graph Neural Networks
Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions
WalkLM: A Uniform Language Model Fine-tuning Framework for Attributed Graph Embedding
Are GATs Out of Balance?
PRODIGY: Enabling In-context Learning Over Graphs
GSLB: The Graph Structure Learning Benchmark
ASIF: Coupled Data Turns Unimodal Models to Multimodal without Training
Kissing to Find a Match: Efficient Low-Rank Permutation Representation
Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors
Analyzing Vision Transformers for Image Classification in Class Embedding Space
Topology-Aware Uncertainty for Image Segmentation
Learning Adaptive Tensorial Density Fields for Clean Cryo-ET Reconstruction
Task-aware Distributed Source Coding under Dynamic Bandwidth
An Inverse Scaling Law for CLIP Training
Online learning of long-range dependencies
Generalizable Lightweight Proxy for Robust NAS against Diverse Perturbations
Improving Adversarial Robustness via Information Bottleneck Distillation
Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models
Cola: A Benchmark for Compositional Text-to-image Retrieval
Holistic Transfer: Towards Non-Disruptive Fine-Tuning with Partial Target Data
Towards Last-layer Retraining for Group Robustness with Fewer Annotations
Don’t blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy
Blurred-Dilated Method for Adversarial Attacks
RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions
Asymmetric Certified Robustness via Feature-Convex Neural Networks
AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking
HAP: Structure-Aware Masked Image Modeling for Human-Centric Perception
Implicit Contrastive Representation Learning with Guided Stop-gradient
[Re] Masked Autoencoders Are Small Scale Vision Learners: A Reproduction Under Resource Constraints
Reverse Engineering Self-Supervised Learning
Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning
Towards In-context Scene Understanding
Slimmed Asymmetrical Contrastive Learning and Cross Distillation for Lightweight Model Training
SSL4EO-L: Datasets and Foundation Models for Landsat Imagery
Taming Local Effects in Graph-based Spatiotemporal Forecasting
Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints
Automatic Integration for Spatiotemporal Neural Point Processes
Transformers learn through gradual rank increase
On the Power of SVD in the Stochastic Block Model
Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration
Precise asymptotic generalization for multiclass classification with overparameterized linear models
Polynomially Over-Parameterized Convolutional Neural Networks Contain Structured Strong Winning Lottery Tickets
Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability
Theoretical Analysis of the Inductive Biases in Deep Convolutional Networks
Training shallow ReLU networks on noisy data using hinge loss: when do we overfit and is it benign?
Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks
The Crucial Role of Normalization in Sharpness-Aware Minimization
Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Linear Subspaces
Abide by the law and follow the flow: conservation laws for gradient flows
Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory
On skip connections and normalisation layers in deep optimisation
Provable Guarantees for Neural Networks via Gradient Feature Learning
Small Transformers Compute Universal Metric Embeddings
Learning DAGs from Data with Few Root Causes
Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data
CLIP-OGD: An Experimental Design for Adaptive Neyman Allocation in Sequential Experiments
Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing
Analysis of Variance of Multiple Causal Networks
Intervention Generalization: A View from Factor Graph Models
Conditional independence testing under misspecified inductive biases
DataPerf: Benchmarks for Data-Centric AI Development
PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning.
Markovian Sliced Wasserstein Distances: Beyond Independent Projections
Interaction Measures, Partition Lattices and Kernel Tests for High-Order Interactions
Koopman Kernel Regression
Towards a Unified Analysis of Kernel-based Methods Under Covariate Shift
Sharp Spectral Rates for Koopman Operator Learning
Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization
Improved Best-of-Both-Worlds Guarantees for Multi-Armed Bandits: FTRL with General Regularizers and Multiple Optimal Arms
Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks
Embedding Space Interpolation Beyond Mini-Batch, Beyond Pairs and Beyond Examples
SNEkhorn: Dimension Reduction with Symmetric Entropic Affinities
MAViL: Masked Audio-Video Learners
Differentiable Neuro-Symbolic Reasoning on Large-Scale Knowledge Graphs
Cross-modal Prompts: Adapting Large Pre-trained Models for Audio-Visual Downstream Tasks
Invariant Anomaly Detection under Distribution Shifts: A Causal Perspective
DynaDojo: An Extensible Platform for Benchmarking Scaling in Dynamical System Identification
When Does Confidence-Based Cascade Deferral Suffice?
Learning Curves for Deep Structured Gaussian Feature Models
Scalarization for Multi-Task and Multi-Domain Learning at Scale
What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic Segmentation
Optimal testing using combined test statistics across independent studies
Order Matters in the Presence of Dataset Imbalance for Multilingual Learning
Language Semantic Graph Guided Data-Efficient Learning
To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning
FedL2P: Federated Learning to Personalize
Adaptive Test-Time Personalization for Federated Learning
Mixed Samples as Probes for Unsupervised Model Selection in Domain Adaptation
ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learning
Zero-Shot Anomaly Detection via Batch Normalization
Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents
FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning
Orthogonal Non-negative Tensor Factorization based Multi-view Clustering
SwapPrompt: Test-Time Prompt Adaptation for Vision-Language Models
Large sample spectral analysis of graph-based multi-manifold clustering
Multi-Swap k-Means++
Semi-Supervised Domain Generalization with Known and Unknown Classes
Harnessing Hard Mixed Samples with Decoupled Regularizer
Binary Classification with Confidence Difference
Path following algorithms for $\ell_2$-regularized $M$-estimation with approximation guarantee
Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity
Neural Fields with Hard Constraints of Arbitrary Differential Order
On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms
Projection-Free Methods for Stochastic Simple Bilevel Optimization with Convex Lower-level Problem
Zeroth-Order Methods for Nondifferentiable, Nonconvex, and Hierarchical Federated Optimization
Non-Convex Bilevel Optimization with Time-Varying Objective Functions
Distributionally Robust Linear Quadratic Control
Connected Superlevel Set in (Deep) Reinforcement Learning and its Application to Minimax Theorems
Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities
Boosting with Tempered Exponential Measures
Computational Guarantees for Doubly Entropic Wasserstein Barycenters
A Unified Discretization Framework for Differential Equation Approach with Lyapunov Arguments for Convex Optimization
Optimizing over trained GNNs via symmetry breaking
Advancing Bayesian Optimization via Learning Correlated Latent Space
Differentiable Sampling of Categorical Distributions Using the CatLog-Derivative Trick
DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method
GloptiNets: Scalable Non-Convex Optimization with Certificates
Globally solving the Gromov-Wasserstein problem for point clouds in low dimensional Euclidean spaces
Decentralized Matrix Sensing: Statistical Guarantees and Fast Convergence
Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt
Robust Learning for Smoothed Online Convex Optimization with Feedback Delay
Exponentially Convergent Algorithms for Supervised Matrix Factorization
Rigorous Runtime Analysis of MOEA/D for Solving Multi-Objective Minimum Weight Base Problems
(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability
Stable Nonconvex-Nonconcave Training via Linear Interpolation
Oracle Complexity of Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Functional Constrained Optimization
LogSpecT: Feasible Graph Learning Model from Stationary Signals with Recovery Guarantees
Weitzman's Rule for Pandora's Box with Correlations
A Unified Approach for Maximizing Continuous DR-submodular Functions
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy
Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure
A Guide Through the Zoo of Biased SGD
Failure-Aware Gaussian Process Optimization with Regret Bounds
Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization
Accelerated Zeroth-order Method for Non-Smooth Stochastic Convex Optimization Problem with Infinite Variance
Bayesian Optimization with Cost-varying Variable Subsets
Self-Correcting Bayesian Optimization through Bayesian Active Learning
How to Turn Your Knowledge Graph Embeddings into Generative Models
Moment Matching Denoising Gibbs Sampling
L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inference
Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning
Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming Approach
Hierarchical Semi-Implicit Variational Inference with Application to Diffusion Model Acceleration
Quantification of Uncertainty with Adversarial Models
Collapsed Inference for Bayesian Deep Learning
Function Space Bayesian Pseudocoreset for Bayesian Neural Networks
Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks
Energy-Based Sliced Wasserstein Distance
On the Identifiability and Interpretability of Gaussian Process Models
Probabilistic Exponential Integrators
Approximate inference of marginals using the IBIA framework
Characteristic Circuits
Discriminative Calibration: Check Bayesian Computation from Simulations and Flexible Classifier
Learning Rate Free Sampling in Constrained Domains
Fast Conditional Mixing of MCMC Algorithms for Non-log-concave Distributions
Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond
Structure Learning with Adaptive Random Neighborhood Informed MCMC
Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network
Amortized Reparametrization: Efficient and Scalable Variational Inference for Latent SDEs
KuaiSim: A Comprehensive Simulator for Recommender Systems
Behavior Alignment via Reward Function Optimization
Online Nonstochastic Model-Free Reinforcement Learning
Supervised Pretraining Can Learn In-Context Reinforcement Learning
Learning World Models with Identifiable Factorization
Facing Off World Model Backbones: RNNs, Transformers, and S4
Imitation Learning from Imperfection: Theoretical Justifications and Algorithms
Policy Optimization for Continuous Reinforcement Learning
Finite-Time Analysis of Whittle Index based Q-Learning for Restless Multi-Armed Bandits with Neural Network Function Approximation
Offline Reinforcement Learning with Differential Privacy
SustainGym: Reinforcement Learning Environments for Sustainable Energy Systems
When Demonstrations meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning
Model-Free Active Exploration in Reinforcement Learning
Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals
Probabilistic Inference in Reinforcement Learning Done Right
Learning Generalizable Agents via Saliency-guided Features Decorrelation
Accelerating Exploration with Unlabeled Prior Data
Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates
Provably Efficient Offline Reinforcement Learning in Regular Decision Processes
Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples
Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization
Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning
Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans
Offline Imitation Learning with Variational Counterfactual Reasoning
ODE-based Recurrent Model-free Reinforcement Learning for POMDPs
Reinforcement Learning with Fast and Forgetful Memory
COOM: A Game Benchmark for Continual Reinforcement Learning
LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning
Guide Your Agent with Adaptive Multimodal Rewards
Contrastive Modules with Temporal Attention for Multi-Task Reinforcement Learning
Dynamics Generalisation in Reinforcement Learning via Adaptive Context-Aware Policies
LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios
On the Convergence and Sample Complexity Analysis of Deep Q-Networks with $\epsilon$-Greedy Exploration
Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards
Learning from Visual Observation via Offline Pretrained State-to-Go Transformer
Learning from Active Human Involvement through Proxy Value Propagation
Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control
MIMEx: Intrinsic Rewards from Masked Input Modeling
Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach
Kernelized Reinforcement Learning with Order Optimal Regret Bounds
Ordering-based Conditions for Global Convergence of Policy Gradient Methods
The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning
Learning Shared Safety Constraints from Multi-task Demonstrations
Conservative Offline Policy Adaptation in Multi-Agent Games
Policy Space Diversity for Non-Transitive Games
IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL
Information Design in Multi-Agent Reinforcement Learning
DIFFER:Decomposing Individual Reward for Fair Experience Replay in Multi-Agent Reinforcement Learning
Diverse Conventions for Human-AI Collaboration
How to Fine-tune the Model: Unified Model Shift and Model Bias Policy Optimization
Egocentric Planning for Scalable Embodied Task Achievement
Optimistic Exploration in Reinforcement Learning Using Symbolic Model Estimates
On the Planning Abilities of Large Language Models - A Critical Investigation
Percentile Criterion Optimization in Offline Reinforcement Learning
Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learning
Train Hard, Fight Easy: Robust Meta Reinforcement Learning
A Path to Simpler Models Starts With Noise
State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding
Disentangling Cognitive Diagnosis with Limited Exercise Labels
Interpretability at Scale: Identifying Causal Mechanisms in Alpaca
Renku: a platform for sustainable data science
REASONER: An Explainable Recommendation Dataset with Comprehensive Labeling Ground Truths
OpenDataVal: a Unified Benchmark for Data Valuation
SmoothHess: ReLU Network Feature Interactions via Stein's Lemma
Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance
Towards a fuller understanding of neurons with Clustered Compositional Explanations
GLIME: General, Stable and Local LIME Explanation
StateMask: Explaining Deep Reinforcement Learning through State Mask
Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models
Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models
SynMob: Creating High-Fidelity Synthetic GPS Trajectory Dataset for Urban Mobility Analysis
Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint
Face Reconstruction from Facial Templates by Learning Latent Space of a Generator Network
Participatory Personalization in Classification
Stable Bias: Evaluating Societal Representations in Diffusion Models
Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness
Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing
Gaussian Differential Privacy on Riemannian Manifolds
Better Private Linear Regression Through Better Private Feature Selection
Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information
Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks
The Target-Charging Technique for Privacy Analysis across Interactive Computations
Differentially Private Image Classification by Learning Priors from Random Processes
Optimal privacy guarantees for a relaxed threat model: Addressing sub-optimal adversaries in differentially private machine learning
Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations
Differentially Private Approximate Near Neighbor Counting in High Dimensions
Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization
Penguin: Parallel-Packed Homomorphic Encryption for Fast Graph Convolutional Network Inference
LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference
On Differentially Private Sampling from Gaussian and Product Distributions
Tree-Rings Watermarks: Invisible Fingerprints for Diffusion Images
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
TRIAGE: Characterizing and auditing training data for improved regression
ParaFuzz: An Interpretability-Driven Technique for Detecting Poisoned Samples in NLP
RECESS Vaccine for Federated Learning: Proactive Defense Against Model Poisoning Attacks
ProPILE: Probing Privacy Leakage in Large Language Models
Where Did I Come From? Origin Attribution of AI-Generated Images
Stability Guarantees for Feature Attributions with Multiplicative Smoothing
Neural Polarizer: A Lightweight and Effective Backdoor Defense via Purifying Poisoned Features
Black-box Backdoor Defense via Zero-shot Image Purification
UniT: A Unified Look at Certified Robust Training against Text Adversarial Perturbation
TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery
What Distributions are Robust to Indiscriminate Poisoning Attacks for Linear Learners?
Punctuation-level Attack: Single-shot and Single Punctuation Can Fool Text Models
Selective Sampling and Imitation Learning via Online Regression
Contextual Bandits and Imitation Learning with Preference-Based Active Queries
Learning Domain-Aware Detection Head with Prompt Tuning
SoTTA: Robust Test-Time Adaptation on Noisy Data Streams
Subspace Identification for Multi-Source Domain Adaptation
A Fast and Accurate Estimator for Large Scale Linear Model via Data Averaging
Global Identifiability of $\ell_1$-based Dictionary Learning via Matrix Volume Optimization
Mode Connectivity in Auction Design
Persuading Farsighted Receivers in MDPs: the Power of Honesty
Logarithmic-Regret Quantum Learning Algorithms for Zero-Sum Games
Is Learning in Games Good for the Learners?
Fair Allocation of Indivisible Chores: Beyond Additive Costs
The Equivalence of Dynamic and Strategic Stability under Regularized Learning in Games
Supply-Side Equilibria in Recommender Systems
Efficient Subgame Refinement for Extensive-form Games
Data Market Design through Deep Learning
On the Interplay between Social Welfare and Tractability of Equilibria
List and Certificate Complexities in Replicable Learning
Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise
Learning Mixtures of Gaussians Using the DDPM Objective
Can semi-supervised learning use all the data effectively? A lower bound perspective
Recovering Unbalanced Communities in the Stochastic Block Model with Application to Clustering with a Faulty Oracle
Feature Adaptation for Sparse Linear Regression
Bottleneck Structure in Learned Features: Low-Dimension vs Regularity Tradeoff
Agnostically Learning Single-Index Models using Omnipredictors
A unified framework for information-theoretic generalization bounds
Unified Lower Bounds for Interactive High-dimensional Estimation under Information Constraints
Bayesian Learning of Optimal Policies in Markov Decision Processes with Countably Infinite State-Space
Regularization properties of adversarially-trained linear regression
A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learning
A Theory of Unsupervised Translation Motivated by Understanding Animal Communication
Generalization in the Face of Adaptivity: A Bayesian Perspective
A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning
Smoothed Analysis of Sequential Probability Assignment
A Batch-to-Online Transformation under Random-Order Model
Adversarial Attacks on Online Learning to Rank with Click Feedback
No-Regret Learning with Unbounded Losses: The Case of Logarithmic Pooling
High-dimensional Contextual Bandit Problem without Sparsity
Online Convex Optimization with Unbounded Memory
Fast Asymptotically Optimal Algorithms for Non-Parametric Stochastic Bandits
Practical Contextual Bandits with Feedback Graphs
On the Minimax Regret for Online Learning with Feedback Graphs
Dynamic Pricing and Learning with Bayesian Persuasion
Multi-task Representation Learning for Pure Exploration in Bilinear Bandits
Stochastic Multi-armed Bandits: Optimal Trade-off among Optimality, Consistency, and Tail Risk
Practical Sharpness-Aware Minimization Cannot Converge All the Way to Optima
Tight Risk Bounds for Gradient Descent on Separable Data
The probability flow ODE is provably fast
Matrix Compression via Randomized Low Rank and Low Precision Factorization
Optimistic Active Exploration of Dynamical Systems
When is Agnostic Reinforcement Learning Statistically Tractable?
Online RL in Linearly $q^\pi$-Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore
Optimal Convergence Rate for Exact Policy Mirror Descent in Discounted Markov Decision Processes
Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback
Provably (More) Sample-Efficient Offline RL with Options
Global Optimality and Finite Sample Analysis of Softmax Off-Policy Actor Critic under State Distribution Mismatch
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model
Evaluating Self-Supervised Learning for Molecular Graph Embeddings
End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics
Near Optimal Reconstruction of Spherical Harmonic Expansions
Distribution-Free Statistical Dispersion Control for Societal Applications
AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs
BoardgameQA: A Dataset for Natural Language Reasoning with Contradictory Information
Provably Efficient Algorithm for Nonstationary Low-Rank MDPs
Locality-Aware Generalizable Implicit Neural Representation
DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models
Understanding and Mitigating Copying in Diffusion Models
On the Sublinear Regret of GP-UCB
Maximum State Entropy Exploration using Predecessor and Successor Representations
Sharpness Minimization Algorithms Do Not Only Minimize Sharpness To Achieve Better Generalization
Revisit Weakly-Supervised Audio-Visual Video Parsing from the Language Perspective
ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting
May the Force be with You: Unified Force-Centric Pre-Training for 3D Molecular Conformations
A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning
PLANNER: Generating Diversified Paragraph via Latent Language Diffusion Model
TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph
The Best of Both Worlds in Network Population Games: Reaching Consensus and Convergence to Equilibrium
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks
Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics
Data-Informed Geometric Space Selection
Degraded Polygons Raise Fundamental Questions of Neural Network Perception
Language Model Alignment with Elastic Reset
DISCS: A Benchmark for Discrete Sampling
Should We Learn Most Likely Functions or Parameters?
Certification of Distributional Individual Fairness
Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models
Norm-based Generalization Bounds for Sparse Neural Networks
Exploring Why Object Recognition Performance Degrades Across Income Levels and Geographies with Factor Annotations
Estimating Causal Effects Identifiable from a Combination of Observations and Experiments
Cheaply Estimating Inference Efficiency Metrics for Autoregressive Transformer Models
MCUFormer: Deploying Vision Tranformers on Microcontrollers with Limited Memory
Reproducibility Study of ”CartoonX: Cartoon Explanations of Image Classifiers”
Reproducibility study of 'Proto2Proto: Can you recognise the car, the way I do?'
Mitigating Source Bias for Fairer Weak Supervision
Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research
Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy
[Re] Hierarchical Shrinkage: Improving the Accuracy and Interpretability of Tree-Based Methods
Monte Carlo Tree Search with Boltzmann Exploration
[Re] VAE Approximation Error: ELBO and Exponential Families
NeuroEvoBench: Benchmarking Evolutionary Optimizers for Deep Learning Applications
Holistic Evaluation of Text-to-Image Models
HT-Step: Aligning Instructional Articles with How-To Videos
(ends 10:45 AM)
10:45 a.m.
Lunch:
Lunch (On Your Own)
(ends 12:15 PM)
12:15 p.m.
Invited Talk:
Coherence statistics, self-generated experience and why young humans are much smarter than current AI.
Linda Smith
(ends 1:05 PM)
1:05 p.m.
Creative AI Session 1 [1:05-1:40]
Chairs: Jean Oh · Isabelle Guyon
s 1:05-1:40
[1:05] Kiss/Crash
[1:05] Blabrecs: An AI-Based Game of Nonsense Word Creation
[1:05] Androgynous and Mixed Race Human Face
[1:05] Voice Scroll
[1:05] The WHOOPS! Gallery: An Intersection of AI, Creativity, and the Unusual
[1:05] Visions of Resilience: Augmented Diversity
[1:05] salad bowl
[1:05] AI Applications to Illustrate Native American Arts: Birdsongs: Using Transfer Learning to Augment Image Generation Models
[1:05] Visualising AI
[1:05] Creating playful comics together with AI
(ends 1:40 PM)
Test Of Time:
Test of Time Award Talk:Distributed Representations of Words and Phrases and their Compositionality
(ends 1:25 PM)
1:20 p.m.
Break:
Coffee Only
(ends 1:45 PM)
1:40 p.m.
Oral 2A Efficient Learning [1:40-2:40]
Orals 1:40-2:40
[1:40] Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture
[1:55] QLoRA: Efficient Finetuning of Quantized LLMs
[2:10] Scaling Data-Constrained Language Models
[2:25] Bridging Discrete and Backpropagation: Straight-Through and Beyond
(ends 2:40 PM)
Oral 2B Objects/ Neuroscience/Vision [1:40-2:40]
Orals 1:40-2:40
[1:40] Rotating Features for Object Discovery
[1:55] Linguistic Binding in Diffusion Models: Enhancing Attribute Correspondence through Attention Map Alignment
[2:10] Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation
[2:25] Emergence of Shape Bias in Convolutional Neural Networks through Activation Sparsity
(ends 2:40 PM)
Oral 2C Causality [1:40-2:40]
Orals 1:40-2:40
[1:40] Learning Linear Causal Representations from Interventions under General Nonlinear Mixing
[1:55] A Measure-Theoretic Axiomatisation of Causality
[2:10] Conformal Meta-learners for Predictive Inference of Individual Treatment Effects
[2:25] Causal normalizing flows: from theory to practice
(ends 2:40 PM)
Oral 2D Privacy [1:40-2:40]
Orals 1:40-2:40
[1:40] Nearly Tight Bounds For Differentially Private Multiway Cut
[1:55] Privacy Auditing with One (1) Training Run
[2:10] Private Everlasting Prediction
[2:25] User-Level Differential Privacy With Few Examples Per User
(ends 2:40 PM)
2:45 p.m.
Break:
Break
(ends 3:30 PM)
3:15 p.m.
Poster Session 2 [3:15-5:15]
Posters 3:15-5:15
A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability
Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation
AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning
Accelerating Molecular Graph Neural Networks via Knowledge Distillation
TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials
LinkerNet: Fragment Poses and Linker Co-Design with 3D Equivariant Diffusion
GeoTMI: Predicting Quantum Chemical Property with Easy-to-Obtain Geometry via Positional Denoising
Implicit Transfer Operator Learning: Multiple Time-Resolution Models for Molecular Dynamics
De novo Drug Design using Reinforcement Learning with Multiple GPT Agents
Semantic segmentation of sparse irregular point clouds for leaf/wood discrimination
Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models
StreamNet: Memory-Efficient Streaming Tiny Deep Learning Inference on the Microcontroller
Adaptive Contextual Perception: How To Generalize To New Backgrounds and Ambiguous Objects
3D-Aware Visual Question Answering about Parts, Poses and Occlusions
Mip-Grid: Anti-aliased Grid Representations for Neural Radiance Fields
MG-ViT: A Multi-Granularity Method for Compact and Efficient Vision Transformers
CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image Steganography
Lookup Table meets Local Laplacian Filter: Pyramid Reconstruction Network for Tone Mapping
SPAE: Semantic Pyramid AutoEncoder for Multimodal Generation with Frozen LLMs
PAPR: Proximity Attention Point Rendering
Unsupervised Optical Flow Estimation with Dynamic Timing Representation for Spike Camera
Structure from Duplicates: Neural Inverse Graphics from a Pile of Objects
Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP
Label-efficient Segmentation via Affinity Propagation
DAC-DETR: Divide the Attention Layers and Conquer
Mitigating the Effect of Incidental Correlations on Part-based Learning
Generative Category-level Object Pose Estimation via Diffusion Models
Tuning Multi-mode Token-level Prompt Alignment across Modalities
Discovering Intrinsic Spatial-Temporal Logic Rules to Explain Human Actions
M$^{2}$SODAI: Multi-Modal Maritime Object Detection Dataset With RGB and Hyperspectral Image Sensors
CamoPatch: An Evolutionary Strategy for Generating Camoflauged Adversarial Patches
Decorate3D: Text-Driven High-Quality Texture Generation for Mesh Decoration in the Wild
DDF-HO: Hand-Held Object Reconstruction via Conditional Directed Distance Field
Align Your Prompts: Test-Time Prompting with Distribution Alignment for Zero-Shot Generalization
Compressed Video Prompt Tuning
ReTR: Modeling Rendering Via Transformer for Generalizable Neural Surface Reconstruction
Saving 100x Storage: Prototype Replay for Reconstructing Training Sample Distribution in Class-Incremental Semantic Segmentation
Masked Image Residual Learning for Scaling Deeper Vision Transformers
LuminAIRe: Illumination-Aware Conditional Image Repainting for Lighting-Realistic Generation
Segment Anything in 3D with NeRFs
Real-World Image Super-Resolution as Multi-Task Learning
VPGTrans: Transfer Visual Prompt Generator across LLMs
What Do Deep Saliency Models Learn about Visual Attention?
Mask Propagation for Efficient Video Semantic Segmentation
Stanford-ORB: A Real-World 3D Object Inverse Rendering Benchmark
CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusion
Towards Label-free Scene Understanding by Vision Foundation Models
Real3D-AD: A Dataset of Point Cloud Anomaly Detection
Tame a Wild Camera: In-the-Wild Monocular Camera Calibration
MixFormerV2: Efficient Fully Transformer Tracking
TMT-VIS: Taxonomy-aware Multi-dataset Joint Training for Video Instance Segmentation
Learning Neural Implicit through Volume Rendering with Attentive Depth Fusion Priors
Towards A Richer 2D Understanding of Hands at Scale
HotBEV: Hardware-oriented Transformer-based Multi-View 3D Detector for BEV Perception
QuantSR: Accurate Low-bit Quantization for Efficient Image Super-Resolution
ViSt3D: Video Stylization with 3D CNN
IDEA: An Invariant Perspective for Efficient Domain Adaptive Image Retrieval
Uni3DETR: Unified 3D Detection Transformer
Training on Foveated Images Improves Robustness to Adversarial Attacks
Volume Feature Rendering for Fast Neural Radiance Field Reconstruction
Multi-body SE(3) Equivariance for Unsupervised Rigid Segmentation and Motion Estimation
From Pixels to UI Actions: Learning to Follow Instructions via Graphical User Interfaces
DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning
SALSA VERDE: a machine learning attack on LWE with sparse small secrets
GeoPhy: Differentiable Phylogenetic Inference via Geometric Gradients of Tree Topologies
ProteinNPT: Improving Protein Property Prediction and Design with Non-Parametric Transformers
PoET: A generative model of protein families as sequences-of-sequences
Neural Frailty Machine: Beyond proportional hazard assumption in neural survival regressions
Med-UniC: Unifying Cross-Lingual Medical Vision-Language Pre-Training by Diminishing Bias
AVIS: Autonomous Visual Information Seeking with Large Language Model Agent
The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs
Composing Parameter-Efficient Modules with Arithmetic Operation
HyTrel: Hypergraph-enhanced Tabular Data Representation Learning
Parameterizing Context: Unleashing the Power of Parameter-Efficient Fine-Tuning and In-Context Tuning for Continual Table Semantic Parsing
Augmenting Language Models with Long-Term Memory
CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
Large language models transition from integrating across position-yoked, exponential windows to structure-yoked, power-law windows
Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering
Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation
Birth of a Transformer: A Memory Viewpoint
Fine-Grained Human Feedback Gives Better Rewards for Language Model Training
AR-Diffusion: Auto-Regressive Diffusion Model for Text Generation
Preference-grounded Token-level Guidance for Language Model Fine-tuning
From Cloze to Comprehension: Retrofitting Pre-trained Masked Language Models to Pre-trained Machine Reader
Grammar Prompting for Domain-Specific Language Generation with Large Language Models
Intrinsic Dimension Estimation for Robust Detection of AI-Generated Texts
ComSL: A Composite Speech-Language Model for End-to-End Speech-to-Text Translation
Incomplete Multimodality-Diffused Emotion Recognition
Self-Evaluation Guided Beam Search for Reasoning
ASL Citizen: A Community-Sourced Dataset for Advancing Isolated Sign Language Recognition
DOSE: Diffusion Dropout with Adaptive Prior for Speech Enhancement
Gaussian Partial Information Decomposition: Bias Correction and Application to High-dimensional Data
Exploring Loss Functions for Time-based Training Strategy in Spiking Neural Networks
Scaling laws for language encoding models in fMRI
Computing a human-like reaction time metric from stable recurrent vision models
Explaining V1 Properties with a Biologically Constrained Deep Learning Architecture
Energy Guided Diffusion for Generating Neurally Exciting Images
AMAG: Additive, Multiplicative and Adaptive Graph Neural Network For Forecasting Neuron Activity
Strong and Precise Modulation of Human Percepts via Robustified ANNs
Convolution Monge Mapping Normalization for learning on sleep data
Emergence of Shape Bias in Convolutional Neural Networks through Activation Sparsity
A normative theory of social conflict
Neural Data Transformer 2: Multi-context Pretraining for Neural Spiking Activity
Large Language Models are Visual Reasoning Coordinators
Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb
Hierarchical VAEs provide a normative account of motion processing in the primate brain
Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits with Dynamical Similarity Analysis
Joint processing of linguistic properties in brains and language models
Spike-driven Transformer
Self-Supervised Learning of Representations for Space Generates Multi-Modular Grid Cells
DeWave: Discrete Encoding of EEG Waves for EEG to Text Translation
Training neural operators to preserve invariant measures of chaotic attractors
MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under nonparametrized geometrical variability
Solving Inverse Physics Problems with Score Matching
PETAL: Physics Emulation Through Averaged Linearizations for Solving Inverse Problems
Transformer-based Planning for Symbolic Regression
Unifying Predictions of Deterministic and Stochastic Physics in Mesh-reduced Space with Sequential Flow Generative Model
Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning
Find What You Want: Learning Demand-conditioned Object Attribute Space for Demand-driven Navigation
Online POMDP Planning with Anytime Deterministic Guarantees
Information Maximizing Curriculum: A Curriculum-Based Approach for Learning Versatile Skills
Accelerating Motion Planning via Optimal Transport
Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?
POMDP Planning for Object Search in Partially Unknown Environment
FGPrompt: Fine-grained Goal Prompting for Image-goal Navigation
Adaptive Normalization for Non-stationary Time Series Forecasting: A Temporal Slice Perspective
Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment
Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors
Diversify Your Vision Datasets with Automatic Diffusion-based Augmentation
HyP-NeRF: Learning Improved NeRF Priors using a HyperNetwork
Meet in the Middle: A New Pre-training Paradigm
Lightweight Vision Transformer with Bidirectional Interaction
Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization
Tanh Works Better with Asymmetry
A General Framework for Equivariant Neural Networks on Reductive Lie Groups
Clustering the Sketch: Dynamic Compression for Embedding Tables
Systematic Visual Reasoning through Object-Centric Relational Abstraction
Learning Sample Difficulty from Pre-trained Models for Reliable Prediction
Bridging Discrete and Backpropagation: Straight-Through and Beyond
Module-wise Training of Neural Networks via the Minimizing Movement Scheme
You Only Condense Once: Two Rules for Pruning Condensed Datasets
Sharpness-Aware Minimization Leads to Low-Rank Features
AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback
Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture
Probabilistic Weight Fixing: Large-scale training of neural network weight uncertainties for quantisation.
ScaleLong: Towards More Stable Training of Diffusion Model via Scaling Network Long Skip Connection
A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks
Inconsistency, Instability, and Generalization Gap of Deep Neural Network Training
Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity
Accurate Interpolation for Scattered Data through Hierarchical Residual Refinement
Geometric Transformer with Interatomic Positional Encoding
Random-Access Infinite Context Length for Transformers
Designing Robust Transformers using Robust Kernel Density Estimation
Schema-learning and rebinding as mechanisms of in-context learning and emergence
Polyhedron Attention Module: Learning Adaptive-order Interactions
Coneheads: Hierarchy Aware Attention
The Quantization Model of Neural Scaling
Pruning vs Quantization: Which is Better?
QLoRA: Efficient Finetuning of Quantized LLMs
How a Student becomes a Teacher: learning and forgetting through Spectral methods
DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models
TextDiffuser: Diffusion Models as Text Painters
A Unified Framework for U-Net Design and Analysis
Energy Discrepancies: A Score-Independent Loss for Energy-Based Models
Learning Space-Time Continuous Latent Neural PDEs from Partially Observed States
Addressing Negative Transfer in Diffusion Models
Scenario Diffusion: Controllable Driving Scenario Generation With Diffusion
PTQD: Accurate Post-Training Quantization for Diffusion Models
Generative Modeling through the Semi-dual Formulation of Unbalanced Optimal Transport
Learning Energy-based Model via Dual-MCMC Teaching
In-Context Learning Unlocked for Diffusion Models
Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach
Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing
Diffusion Model for Graph Inverse Problems: Towards Effective Source Localization on Complex Networks
DPM-Solver-v3: Improved Diffusion ODE Solver with Empirical Model Statistics
AmadeusGPT: a natural language interface for interactive animal behavioral analysis
Optimal Transport-Guided Conditional Score-Based Diffusion Model
Direct Diffusion Bridge using Data Consistency for Inverse Problems
DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models
Complexity Matters: Rethinking the Latent Space for Generative Modeling
Im-Promptu: In-Context Composition from Image Prompts
Gaussian Mixture Solvers for Diffusion Models
Score-based Generative Modeling through Stochastic Evolution Equations in Hilbert Spaces
Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models
Score-based Source Separation with Applications to Digital Communication Signals
Recommender Systems with Generative Retrieval
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
Divide, Evaluate, and Refine: Evaluating and Improving Text-to-Image Alignment with Iterative VQA Feedback
Towards Efficient Image Compression Without Autoregressive Models
Idempotent Learned Image Compression with Right-Inverse
Bootstrapping Vision-Language Learning with Decoupled Language Pre-training
TopP&R: Robust Support Estimation Approach for Evaluating Fidelity and Diversity in Generative Models
MultiFusion: Fusing Pre-Trained Models for Multi-Lingual, Multi-Modal Image Generation
HeadSculpt: Crafting 3D Head Avatars with Text
Training Energy-Based Normalizing Flow with Score-Matching Objectives
Linguistic Binding in Diffusion Models: Enhancing Attribute Correspondence through Attention Map Alignment
VideoComposer: Compositional Video Synthesis with Motion Controllability
Three Iterations of (d − 1)-WL Test Distinguish Non Isometric Clouds of d-dimensional Points
Graph Contrastive Learning with Stable and Scalable Spectral Encoding
Reversible and irreversible bracket-based dynamics for deep graph neural networks
No Change, No Gain: Empowering Graph Neural Networks with Expected Model Change Maximization for Active Learning
Implicit Convolutional Kernels for Steerable CNNs
Learning Large Graph Property Prediction via Graph Segment Training
Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding
Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals
Calibrate and Boost Logical Expressiveness of GNN Over Multi-Relational and Temporal Graphs
Recurrent Temporal Revision Graph Networks
Live Graph Lab: Towards Open, Dynamic and Real Transaction Graphs with NFT
V-InFoR: A Robust Graph Neural Networks Explainer for Structurally Corrupted Graphs
Modeling Dynamics over Meshes with Gauge Equivariant Nonlinear Message Passing
LD2: Scalable Heterophilous Graph Neural Network with Decoupled Embeddings
Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition
Universal Prompt Tuning for Graph Neural Networks
MeGraph: Capturing Long-Range Interactions by Alternating Local and Hierarchical Aggregation on Multi-Scaled Graph Hierarchy
MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data
Learning Unseen Modality Interaction
CAPro: Webly Supervised Learning with Cross-modality Aligned Prototypes
Rotating Features for Object Discovery
CoLLAT: On Adding Fine-grained Audio Understanding to Language Models using Token-Level Locked-Language Tuning
Topological Obstructions and How to Avoid Them
Convolutional State Space Models for Long-Range Spatiotemporal Modeling
Evolving Connectivity for Recurrent Spiking Neural Networks
Robust Learning with Progressive Data Expansion Against Spurious Correlation
Video Dynamics Prior: An Internal Learning Approach for Robust Video Enhancements
A Theory of Transfer-Based Black-Box Attacks: Explanation and Implications
Fantastic Robustness Measures: The Secrets of Robust Generalization
Exploiting Connections between Lipschitz Structures for Certifiably Robust Deep Equilibrium Models
The Adversarial Consistency of Surrogate Risks for Binary Classification
On the Connection between Pre-training Data Diversity and Fine-tuning Robustness
(Almost) Provable Error Bounds Under Distribution Shift via Disagreement Discrepancy
Hierarchical Randomized Smoothing
Characterizing the Optimal $0-1$ Loss for Multi-class Classification with a Test-time Attacker
Optimal Transport Model Distributional Robustness
Effective Targeted Attacks for Adversarial Self-Supervised Learning
Learning Invariant Representations with a Nonparametric Nadaraya-Watson Head
Cross-Scale MAE: A Tale of Multiscale Exploitation in Remote Sensing
Identifiable Contrastive Learning with Automatic Feature Importance Discovery
Test-time Training for Matching-based Video Object Segmentation
Self-supervised Object-Centric Learning for Videos
No Representation Rules Them All in Category Discovery
To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis
Understanding Contrastive Learning via Distributionally Robust Optimization
Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression
Scale-teaching: Robust Multi-scale Training for Time Series Classification with Noisy Labels
Change point detection and inference in multivariate non-parametric models under mixing conditions
Pairwise Causality Guided Transformers for Event Sequences
Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation
Globally injective and bijective neural operators
What Makes Data Suitable for a Locally Connected Neural Network? A Necessary and Sufficient Condition Based on Quantum Entanglement.
Provable Advantage of Curriculum Learning on Parity Targets with Mixed Inputs
The Exact Sample Complexity Gain from Invariances for Kernel Regression
Reliable learning in challenging environments
Representational Strengths and Limitations of Transformers
A General Theory of Correct, Incorrect, and Extrinsic Equivariance
Convergence of mean-field Langevin dynamics: time-space discretization, stochastic gradient, and variance reduction
Scaling Data-Constrained Language Models
Phase diagram of early training dynamics in deep neural networks: effect of the learning rate, depth, and width
Learning threshold neurons via edge of stability
On student-teacher deviations in distillation: does it pay to disobey?
Estimating Propensity for Causality-based Recommendation without Exposure Data
Causal discovery from observational and interventional data across multiple environments
Global Optimality in Bivariate Gradient-based DAG Learning
A Cross-Moment Approach for Causal Effect Estimation
Conformal Meta-learners for Predictive Inference of Individual Treatment Effects
Causal normalizing flows: from theory to practice
A Measure-Theoretic Axiomatisation of Causality
Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions
Passive learning of active causal strategies in agents and language models
Reusable Slotwise Mechanisms
Inner Product-based Neural Network Similarity
Loss Decoupling for Task-Agnostic Continual Learning
Beyond probability partitions: Calibrating neural networks with semantic aware grouping
Conformalized matrix completion
Neuro-symbolic Learning Yielding Logical Constraints
Time-uniform confidence bands for the CDF under nonstationarity
Flow: Per-instance Personalized Federated Learning
Neural approximation of Wasserstein distance via a universal architecture for symmetric and factorwise group invariant functions
Diffusion Representation for Asymmetric Kernels via Magnetic Transform