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Theater A
Theater B
Theater C
Virtual
SUN 27 NOV
10 a.m.
Registration Desk:
Registration Desk
(ends 4:00 PM)
MON 28 NOV
5 a.m.
Registration Desk:
Registration Desk
(ends 5:00 PM)
5:30 a.m.
Affinity Workshop:
Women in Machine Learning
(ends 11:00 AM)
6 a.m.
Affinity Workshop:
LatinX in AI
(ends 4:00 PM)
7 a.m.
Workshop:
Queer in AI
(ends 4:00 PM)
NewInML:
New In ML
(ends 3:30 PM)
7:30 a.m.
Expo Talk Panel:
TIPDAT: ML based Optimization Explainer
(ends 8:30 AM)
Expo Talk Panel:
End-to-end cloud-based Document Intelligence Architecture using the open-source Feathr Feature Store, the SynapseML Spark library, and Hugging Face Extractive Question Answering
(ends 8:30 AM)
Expo Workshop:
PyTorch: New advances for large-scale training and performance optimizations
(ends 10:30 AM)
Expo Workshop:
Graph Neural Networks in Tensorflow: A Practical Guide
(ends 10:25 AM)
Expo Workshop:
Fine-tuning stable diffusion models: massive creativity without massive bills
(ends 10:30 AM)
Expo Workshop:
Get ready, your Jupyter Notebook goes to production!
(ends 10:30 AM)
7:40 a.m.
Affinity Workshop:
Black in AI
(ends 4:00 PM)
8 a.m.
Expo Demonstration:
Practical Deployment of Secure Federated Learning: Challenges, Opportunities and Solutions
(duration 2.0 hr)
Expo Demonstration:
Real-time Navigation of Chemical Space with Cloud-Based Inference from MoLFormer
(duration 2.0 hr)
Expo Demonstration:
Full-Stack 3D Scene Understanding on an Extended Reality Headset
(duration 2.0 hr)
Expo Demonstration:
Conditional Compute for On-device Video Understanding
(duration 2.0 hr)
Expo Demonstration:
Efficient super-resolution using 4-bit integer quantization for real-time mobile applications
(duration 2.0 hr)
Expo Demonstration:
Human Modeling and Strategic Reasoning in the Game of Diplomacy
(duration 2.0 hr)
Expo Demonstration:
Software-Delivered AI: Using Sparse-Quantization for Fastest Inference on Deep Neural Networks
(duration 2.0 hr)
Expo Demonstration:
Build Better Models Faster with W&B
(duration 2.0 hr)
Mentorship:
Education Outreach
(ends 1:00 PM)
8:30 a.m.
Expo Talk Panel:
Machine Learning and Optimization for Automated Trading at HRT
(ends 9:30 AM)
Expo Talk Panel:
Challenges & Opportunities for Ethical AI in Practice
(ends 9:30 AM)
9 a.m.
Coffee Break
9:30 a.m.
Affinity Workshop:
Indigenous in AI
(ends 4:00 PM)
Expo Talk Panel:
Using AI for Reduced-Order Modeling
(ends 10:30 AM)
Expo Talk Panel:
Uncertainty quantification for fair and transparent AI-assisted decision-making
(ends 10:30 AM)
11 a.m.
Expo Talk Panel:
Understanding the Landscape of the latest Large Models - Virtual
(ends 12:00 PM)
noon
Expo Demonstration:
Practical Deployment of Secure Federated Learning: Challenges, Opportunities and Solutions
(duration 2.0 hr)
Expo Demonstration:
Real-time Navigation of Chemical Space with Cloud-Based Inference from MoLFormer
(duration 2.0 hr)
Expo Demonstration:
Full-Stack 3D Scene Understanding on an Extended Reality Headset
(duration 2.0 hr)
Expo Demonstration:
Conditional Compute for On-device Video Understanding
(duration 2.0 hr)
Expo Demonstration:
Efficient super-resolution using 4-bit integer quantization for real-time mobile applications
(duration 2.0 hr)
Expo Demonstration:
Human Modeling and Strategic Reasoning in the Game of Diplomacy
(duration 2.0 hr)
Expo Demonstration:
Software-Delivered AI: Using Sparse-Quantization for Fastest Inference on Deep Neural Networks
(duration 2.0 hr)
Expo Demonstration:
Build Better Models Faster with W&B
(duration 2.0 hr)
Expo Talk Panel:
Towards learning agents for solving complex real-world tasks
(ends 1:00 PM)
Expo Talk Panel:
Sparse annotation strategies at scale
(ends 1:00 PM)
Expo Workshop:
Intro to TensorFlow and JAX
(ends 3:00 PM)
Expo Workshop:
AutoGluon: Empowering (MultiModal) AutoML for the next 10 Million users
(ends 3:00 PM)
Expo Workshop:
Impactful graph neural networks via DGL: A Tale of Research and Productization
(ends 3:00 PM)
12:30 p.m.
Affinity Workshop:
Global South in AI
(ends 4:00 PM)
1 p.m.
Expo Talk Panel:
Generative Understanding of 3D Scenes
(ends 2:00 PM)
Expo Talk Panel:
Adapt and Optimize ML Models for Hardware-Aware AI
(ends 2:00 PM)
1:30 p.m.
Coffee Break
2 p.m.
Expo Talk Panel:
Integrating modern machine learning and single cell technologies into drug target discovery - lessons from the frontline.
(ends 3:00 PM)
Expo Talk Panel:
Human-in-the-Loop Is Here to Stay
(ends 3:00 PM)
2:30 p.m.
Affinity Workshop:
North Africans in ML
(ends 4:00 PM)
Affinity Poster Session:
Affinity Poster Session
(ends 4:00 PM)
3 p.m.
Remarks:
Opening Remarks
(ends 3:15 PM)
3:15 p.m.
Invited Talk:
Could a Large Language Model be Conscious?
David Chalmers
(ends 4:00 PM)
4 p.m.
Reception:
Reception
(ends 6:00 PM)
TUE 29 NOV
5 a.m.
Registration Desk:
Registration Desk
(ends 4:00 PM)
7:30 a.m.
Invited Talk:
Algorithms On the Bench: Examining Validity of ML Systems in the Public Sphere
Rediet Abebe
(ends 8:30 AM)
8:30 a.m.
Coffee Break
9 a.m.
Poster Session 1
[9:00-11:00]
Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits
A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking
FedPop: A Bayesian Approach for Personalised Federated Learning
Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach
Functional Ensemble Distillation
Improving Zero-Shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions
Differentially Private Model Compression
Self-explaining deep models with logic rule reasoning
AutoLink: Self-supervised Learning of Human Skeletons and Object Outlines by Linking Keypoints
Behavior Transformers: Cloning $k$ modes with one stone
Equivariant Networks for Zero-Shot Coordination
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization
What You See is What You Classify: Black Box Attributions
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation
Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces
Sparse Fourier Backpropagation in Cryo-EM Reconstruction
Predictive Querying for Autoregressive Neural Sequence Models
Extracting computational mechanisms from neural data using low-rank RNNs
Modeling the Machine Learning Multiverse
AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning
Retrospective Adversarial Replay for Continual Learning
Learning Neural Set Functions Under the Optimal Subset Oracle
Efficient Architecture Search for Diverse Tasks
When Do Flat Minima Optimizers Work?
The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes
LTMD: Learning Improvement of Spiking Neural Networks with Learnable Thresholding Neurons and Moderate Dropout
Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation
UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup
Scalable Multi-agent Covering Option Discovery based on Kronecker Graphs
PaCo: Parameter-Compositional Multi-task Reinforcement Learning
ConfounderGAN: Protecting Image Data Privacy with Causal Confounder
Mask-based Latent Reconstruction for Reinforcement Learning
Improved Fine-Tuning by Better Leveraging Pre-Training Data
Redundancy-Free Message Passing for Graph Neural Networks
Towards Improving Faithfulness in Abstractive Summarization
Delving into Out-of-Distribution Detection with Vision-Language Representations
CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning
Layer Freezing & Data Sieving: Missing Pieces of a Generic Framework for Sparse Training
EfficientFormer: Vision Transformers at MobileNet Speed
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
Large-batch Optimization for Dense Visual Predictions: Training Faster R-CNN in 4.2 Minutes
Semi-Supervised Video Salient Object Detection Based on Uncertainty-Guided Pseudo Labels
UniCLIP: Unified Framework for Contrastive Language-Image Pre-training
Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields
Learning Consistency-Aware Unsigned Distance Functions Progressively from Raw Point Clouds
Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty
Okapi: Generalising Better by Making Statistical Matches Match
Revisiting Sparse Convolutional Model for Visual Recognition
Random Normalization Aggregation for Adversarial Defense
Batch Multi-Fidelity Active Learning with Budget Constraints
Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy
Factuality Enhanced Language Models for Open-Ended Text Generation
Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering
A Closer Look at Weakly-Supervised Audio-Visual Source Localization
GlanceNets: Interpretable, Leak-proof Concept-based Models
AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators
No Free Lunch from Deep Learning in Neuroscience: A Case Study through Models of the Entorhinal-Hippocampal Circuit
Inherently Explainable Reinforcement Learning in Natural Language
EZNAS: Evolving Zero-Cost Proxies For Neural Architecture Scoring
Uncertainty Estimation Using Riemannian Model Dynamics for Offline Reinforcement Learning
Structured Energy Network As a Loss
The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm
Sharpness-Aware Training for Free
Interpreting Operation Selection in Differentiable Architecture Search: A Perspective from Influence-Directed Explanations
Scalable design of Error-Correcting Output Codes using Discrete Optimization with Graph Coloring
Amortized Mixing Coupling Processes for Clustering
GraphQNTK: Quantum Neural Tangent Kernel for Graph Data
ShuffleMixer: An Efficient ConvNet for Image Super-Resolution
Quo Vadis: Is Trajectory Forecasting the Key Towards Long-Term Multi-Object Tracking?
Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking Consistency
Learning Latent Seasonal-Trend Representations for Time Series Forecasting
Pluralistic Image Completion with Gaussian Mixture Models
CoupAlign: Coupling Word-Pixel with Sentence-Mask Alignments for Referring Image Segmentation
Make an Omelette with Breaking Eggs: Zero-Shot Learning for Novel Attribute Synthesis
Graph Few-shot Learning with Task-specific Structures
Co-Modality Graph Contrastive Learning for Imbalanced Node Classification
Recommender Forest for Efficient Retrieval
Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfitting
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks
SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training
Task-Agnostic Graph Explanations
CageNeRF: Cage-based Neural Radiance Field for Generalized 3D Deformation and Animation
LECO: Learnable Episodic Count for Task-Specific Intrinsic Reward
Resource-Adaptive Federated Learning with All-In-One Neural Composition
Block-Recurrent Transformers
Distinguishing Learning Rules with Brain Machine Interfaces
Imitating Past Successes can be Very Suboptimal
Bounded-Regret MPC via Perturbation Analysis: Prediction Error, Constraints, and Nonlinearity
Neural Conservation Laws: A Divergence-Free Perspective
Relaxing Equivariance Constraints with Non-stationary Continuous Filters
Continual Learning In Environments With Polynomial Mixing Times
Non-Linear Coordination Graphs
Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning
Mirror Descent Maximizes Generalized Margin and Can Be Implemented Efficiently
Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design
On the Complexity of Adversarial Decision Making
Convergence beyond the over-parameterized regime using Rayleigh quotients
An Algorithm for Learning Switched Linear Dynamics from Data
Differentially Private Learning Needs Hidden State (Or Much Faster Convergence)
Cluster Randomized Designs for One-Sided Bipartite Experiments
Near-Optimal Randomized Exploration for Tabular Markov Decision Processes
Order-Invariant Cardinality Estimators Are Differentially Private
Mirror Descent with Relative Smoothness in Measure Spaces, with application to Sinkhorn and EM
Generalization Error Bounds on Deep Learning with Markov Datasets
Autoinverse: Uncertainty Aware Inversion of Neural Networks
Optimal Transport of Classifiers to Fairness
Path Independent Equilibrium Models Can Better Exploit Test-Time Computation
Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting
On Sample Optimality in Personalized Collaborative and Federated Learning
Active Ranking without Strong Stochastic Transitivity
How Sampling Impacts the Robustness of Stochastic Neural Networks
Convexity Certificates from Hessians
Optimal Binary Classification Beyond Accuracy
Sample Constrained Treatment Effect Estimation
Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss
SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning
Continual learning: a feature extraction formalization, an efficient algorithm, and fundamental obstructions
Evolution of Neural Tangent Kernels under Benign and Adversarial Training
Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference
Active Exploration for Inverse Reinforcement Learning
A Unified Framework for Deep Symbolic Regression
Learning Generalized Policy Automata for Relational Stochastic Shortest Path Problems
CARD: Classification and Regression Diffusion Models
GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks
So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems
Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders
Learning Representations via a Robust Behavioral Metric for Deep Reinforcement Learning
Transferring Fairness under Distribution Shifts via Fair Consistency Regularization
Bridging the Gap from Asymmetry Tricks to Decorrelation Principles in Non-contrastive Self-supervised Learning
Template based Graph Neural Network with Optimal Transport Distances
A composable machine-learning approach for steady-state simulations on high-resolution grids
SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression
A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent
Stability and Generalization for Markov Chain Stochastic Gradient Methods
Learning Energy Networks with Generalized Fenchel-Young Losses
AZ-whiteness test: a test for signal uncorrelation on spatio-temporal graphs
Equivariant Networks for Crystal Structures
Text Classification with Born's Rule
A Probabilistic Graph Coupling View of Dimension Reduction
Laplacian Autoencoders for Learning Stochastic Representations
UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs
Variational Model Perturbation for Source-Free Domain Adaptation
On the Effect of Pre-training for Transformer in Different Modality on Offline Reinforcement Learning
Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps
EcoFormer: Energy-Saving Attention with Linear Complexity
Dataset Distillation using Neural Feature Regression
Iterative Scene Graph Generation
ALIFE: Adaptive Logit Regularizer and Feature Replay for Incremental Semantic Segmentation
Hyperbolic Feature Augmentation via Distribution Estimation and Infinite Sampling on Manifolds
Provably Adversarially Robust Detection of Out-of-Distribution Data (Almost) for Free
Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees
Global Convergence and Stability of Stochastic Gradient Descent
Regret Bounds for Information-Directed Reinforcement Learning
Truncated Matrix Power Iteration for Differentiable DAG Learning
Error Analysis of Tensor-Train Cross Approximation
Efficient Risk-Averse Reinforcement Learning
Learning Robust Dynamics through Variational Sparse Gating
DiSC: Differential Spectral Clustering of Features
WeightedSHAP: analyzing and improving Shapley based feature attributions
Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures
On the Global Convergence Rates of Decentralized Softmax Gradient Play in Markov Potential Games
Safety Guarantees for Neural Network Dynamic Systems via Stochastic Barrier Functions
A Unified Framework for Alternating Offline Model Training and Policy Learning
Decentralized Local Stochastic Extra-Gradient for Variational Inequalities
Gradient Estimation with Discrete Stein Operators
On the SDEs and Scaling Rules for Adaptive Gradient Algorithms
Root Cause Analysis of Failures in Microservices through Causal Discovery
Low-rank Optimal Transport: Approximation, Statistics and Debiasing
Learning single-index models with shallow neural networks
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning
Efficient identification of informative features in simulation-based inference
Exact learning dynamics of deep linear networks with prior knowledge
Adversarial training for high-stakes reliability
NaturalProver: Grounded Mathematical Proof Generation with Language Models
Open-Ended Reinforcement Learning with Neural Reward Functions
Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs
Memory safe computations with XLA compiler
C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic Forecasting
Continuous MDP Homomorphisms and Homomorphic Policy Gradient
Data augmentation for efficient learning from parametric experts
Generating Training Data with Language Models: Towards Zero-Shot Language Understanding
Deep Surrogate Assisted Generation of Environments
SGAM: Building a Virtual 3D World through Simultaneous Generation and Mapping
Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts
Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution
Fine-tuning language models to find agreement among humans with diverse preferences
A Unified Sequence Interface for Vision Tasks
Learning Probabilistic Models from Generator Latent Spaces with Hat EBM
Deep Generalized Schrödinger Bridge
NSNet: A General Neural Probabilistic Framework for Satisfiability Problems
Distributionally Robust Optimization with Data Geometry
Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inference
On Efficient Online Imitation Learning via Classification
Group Meritocratic Fairness in Linear Contextual Bandits
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise
Knowledge Distillation: Bad Models Can Be Good Role Models
Active Labeling: Streaming Stochastic Gradients
Renyi Differential Privacy of Propose-Test-Release and Applications to Private and Robust Machine Learning
Stability and Generalization of Kernel Clustering: from Single Kernel to Multiple Kernel
Near-Optimal No-Regret Learning Dynamics for General Convex Games
Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data
A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits
Global Convergence of Federated Learning for Mixed Regression
Kernel similarity matching with Hebbian networks
Improved techniques for deterministic l2 robustness
Matryoshka Representation Learning
Ask4Help: Learning to Leverage an Expert for Embodied Tasks
Wavelet Feature Maps Compression for Image-to-Image CNNs
Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF
Learning Dense Object Descriptors from Multiple Views for Low-shot Category Generalization
Semantic Diffusion Network for Semantic Segmentation
Non-Linguistic Supervision for Contrastive Learning of Sentence Embeddings
CUP: Critic-Guided Policy Reuse
XTC: Extreme Compression for Pre-trained Transformers Made Simple and Efficient
ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning
Distilling Representations from GAN Generator via Squeeze and Span
SHINE: SubHypergraph Inductive Neural nEtwork
Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer
Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation
LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model
LION: Latent Point Diffusion Models for 3D Shape Generation
Learning low-dimensional generalizable natural features from retina using a U-net
Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples
The Pitfalls of Regularization in Off-Policy TD Learning
Fine-tuning Language Models over Slow Networks using Activation Quantization with Guarantees
Online Training Through Time for Spiking Neural Networks
General Cutting Planes for Bound-Propagation-Based Neural Network Verification
Universality of Group Convolutional Neural Networks Based on Ridgelet Analysis on Groups
Single Loop Gaussian Homotopy Method for Non-convex Optimization
Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization
Multi-agent Performative Prediction with Greedy Deployment and Consensus Seeking Agents
A Multilabel Classification Framework for Approximate Nearest Neighbor Search
Spectral Bias in Practice: The Role of Function Frequency in Generalization
Influencing Long-Term Behavior in Multiagent Reinforcement Learning
Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP
Giving Feedback on Interactive Student Programs with Meta-Exploration
Unsupervised Object Representation Learning using Translation and Rotation Group Equivariant VAE
Few-Shot Continual Active Learning by a Robot
Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data
VoiceBlock: Privacy through Real-Time Adversarial Attacks with Audio-to-Audio Models
Retrieval-Augmented Diffusion Models
On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models
Toward Robust Spiking Neural Network Against Adversarial Perturbation
Debiased Causal Tree: Heterogeneous Treatment Effects Estimation with Unmeasured Confounding
On the Convergence of Stochastic Multi-Objective Gradient Manipulation and Beyond
Video-based Human-Object Interaction Detection from Tubelet Tokens
OTKGE: Multi-modal Knowledge Graph Embeddings via Optimal Transport
Hierarchical Lattice Layer for Partially Monotone Neural Networks
On the symmetries of the synchronization problem in Cryo-EM: Multi-Frequency Vector Diffusion Maps on the Projective Plane
What Makes a "Good" Data Augmentation in Knowledge Distillation - A Statistical Perspective
HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions
Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks
Revisiting Non-Parametric Matching Cost Volumes for Robust and Generalizable Stereo Matching
Learning Contrastive Embedding in Low-Dimensional Space
Few-Shot Fast-Adaptive Anomaly Detection
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees
Exploration via Planning for Information about the Optimal Trajectory
Theoretical analysis of deep neural networks for temporally dependent observations
Provably sample-efficient RL with side information about latent dynamics
In What Ways Are Deep Neural Networks Invariant and How Should We Measure This?
Truncated proposals for scalable and hassle-free simulation-based inference
Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees
Policy Optimization with Linear Temporal Logic Constraints
Fast Instrument Learning with Faster Rates
Outlier-Robust Sparse Estimation via Non-Convex Optimization
Gradient Methods Provably Converge to Non-Robust Networks
Operator Splitting Value Iteration
Bayesian Optimistic Optimization: Optimistic Exploration for Model-based Reinforcement Learning
Learning from Stochastically Revealed Preference
Augmented RBMLE-UCB Approach for Adaptive Control of Linear Quadratic Systems
An $\alpha$-No-Regret Algorithm For Graphical Bilinear Bandits
Outlier-Robust Sparse Mean Estimation for Heavy-Tailed Distributions
Distributed Inverse Constrained Reinforcement Learning for Multi-agent Systems
Uplifting Bandits
Poisson Flow Generative Models
Multi-agent Dynamic Algorithm Configuration
Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging
A Theory of PAC Learnability under Transformation Invariances
Lost in Latent Space: Examining failures of disentangled models at combinatorial generalisation
Biologically plausible solutions for spiking networks with efficient coding
Phase transitions in when feedback is useful
Simple and Optimal Greedy Online Contention Resolution Schemes
Private Estimation with Public Data
Robustness to Label Noise Depends on the Shape of the Noise Distribution
A general approximation lower bound in $L^p$ norm, with applications to feed-forward neural networks
Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification
Minimax Optimal Online Imitation Learning via Replay Estimation
Approximate Euclidean lengths and distances beyond Johnson-Lindenstrauss
List-Decodable Sparse Mean Estimation
Finite-Time Last-Iterate Convergence for Learning in Multi-Player Games
SAGDA: Achieving $\mathcal{O}(\epsilon^{-2})$ Communication Complexity in Federated Min-Max Learning
Depth is More Powerful than Width with Prediction Concatenation in Deep Forest
Neural Abstractions
Random Rank: The One and Only Strategyproof and Proportionally Fair Randomized Facility Location Mechanism
Smooth Fictitious Play in Stochastic Games with Perturbed Payoffs and Unknown Transitions
The Query Complexity of Cake Cutting
Learning (Very) Simple Generative Models Is Hard
Cryptographic Hardness of Learning Halfspaces with Massart Noise
Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Functions
Accelerated Primal-Dual Gradient Method for Smooth and Convex-Concave Saddle-Point Problems with Bilinear Coupling
Sample-Efficient Learning of Correlated Equilibria in Extensive-Form Games
SAPD+: An Accelerated Stochastic Method for Nonconvex-Concave Minimax Problems
On Optimal Learning Under Targeted Data Poisoning
Taming Fat-Tailed (“Heavier-Tailed” with Potentially Infinite Variance) Noise in Federated Learning
Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs
Better Best of Both Worlds Bounds for Bandits with Switching Costs
Coreset for Line-Sets Clustering
On the Efficient Implementation of High Accuracy Optimality of Profile Maximum Likelihood
Society of Agents: Regret Bounds of Concurrent Thompson Sampling
Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and its Application to Optimization
Kernel Multimodal Continuous Attention
Empirical Gateaux Derivatives for Causal Inference
Bayesian Persuasion for Algorithmic Recourse
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks
The Hessian Screening Rule
Distributed Optimization for Overparameterized Problems: Achieving Optimal Dimension Independent Communication Complexity
Will Bilevel Optimizers Benefit from Loops
Expected Improvement for Contextual Bandits
Fast Neural Kernel Embeddings for General Activations
Maximum Common Subgraph Guided Graph Retrieval: Late and Early Interaction Networks
Hierarchical Agglomerative Graph Clustering in Poly-Logarithmic Depth
Understanding Deep Contrastive Learning via Coordinate-wise Optimization
Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning
Sparse Hypergraph Community Detection Thresholds in Stochastic Block Model
Don't Pour Cereal into Coffee: Differentiable Temporal Logic for Temporal Action Segmentation
Towards Versatile Embodied Navigation
CASA: Category-agnostic Skeletal Animal Reconstruction
Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Media
Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling
Brain Network Transformer
Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems
SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks
The Phenomenon of Policy Churn
Learning to Find Proofs and Theorems by Learning to Refine Search Strategies: The Case of Loop Invariant Synthesis
Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis
Certifying Robust Graph Classification under Orthogonal Gromov-Wasserstein Threats
To update or not to update? Neurons at equilibrium in deep models
Improving Intrinsic Exploration with Language Abstractions
Masked Generative Adversarial Networks are Data-Efficient Generation Learners
Dance of SNN and ANN: Solving binding problem by combining spike timing and reconstructive attention
Theseus: A Library for Differentiable Nonlinear Optimization
Distributed Influence-Augmented Local Simulators for Parallel MARL in Large Networked Systems
Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning
S4ND: Modeling Images and Videos as Multidimensional Signals with State Spaces
Decoupling Features in Hierarchical Propagation for Video Object Segmentation
Is Out-of-Distribution Detection Learnable?
QUARK: Controllable Text Generation with Reinforced Unlearning
Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution
Homomorphic Matrix Completion
Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models
Characterizing the Ventral Visual Stream with Response-Optimized Neural Encoding Models
GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images
What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods
CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders
VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives
Accelerated Training of Physics-Informed Neural Networks (PINNs) using Meshless Discretizations
On the Importance of Gradient Norm in PAC-Bayesian Bounds
Temporally Disentangled Representation Learning
Natural gradient enables fast sampling in spiking neural networks
Triangulation candidates for Bayesian optimization
Near-Isometric Properties of Kronecker-Structured Random Tensor Embeddings
A permutation-free kernel two-sample test
Exponential Family Model-Based Reinforcement Learning via Score Matching
Learning with little mixing
Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Bounds
Communication Efficient Federated Learning for Generalized Linear Bandits
On Scrambling Phenomena for Randomly Initialized Recurrent Networks
Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks
Last-Iterate Convergence of Optimistic Gradient Method for Monotone Variational Inequalities
3DB: A Framework for Debugging Computer Vision Models
Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search
Measuring Data Reconstruction Defenses in Collaborative Inference Systems
Function Classes for Identifiable Nonlinear Independent Component Analysis
Towards Disentangling Information Paths with Coded ResNeXt
Why do We Need Large Batchsizes in Contrastive Learning? A Gradient-Bias Perspective
On Robust Multiclass Learnability
Generalised Mutual Information for Discriminative Clustering
A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning
Anytime-Valid Inference For Multinomial Count Data
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection
FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning
NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks
ETAB: A Benchmark Suite for Visual Representation Learning in Echocardiography
Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation
How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning?
ActionSense: A Multimodal Dataset and Recording Framework for Human Activities Using Wearable Sensors in a Kitchen Environment
Breaking Bad: A Dataset for Geometric Fracture and Reassembly
FlyView: a bio-informed optical flow truth dataset for visual navigation using panoramic stereo vision
Chartalist: Labeled Graph Datasets for UTXO and Account-based Blockchains
SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments
WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models
MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction
xView3-SAR: Detecting Dark Fishing Activity Using Synthetic Aperture Radar Imagery
CEDe: A collection of expert-curated datasets with atom-level entity annotations for Optical Chemical Structure Recognition
FETA: Towards Specializing Foundational Models for Expert Task Applications
CAESAR: An Embodied Simulator for Generating Multimodal Referring Expression Datasets
Evaluating Out-of-Distribution Performance on Document Image Classifiers
Learning Long-Term Crop Management Strategies with CyclesGym
pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning
Myriad: a real-world testbed to bridge trajectory optimization and deep learning
Deep Limits and a Cut-Off Phenomenon for Neural Networks
Non-asymptotic and Accurate Learning of Nonlinear Dynamical Systems
Robust and scalable manifold learning via landmark diffusion for long-term medical signal processing
The Importance of Being Correlated: Implications of Dependence in Joint Spectral Inference across Multiple Networks
Foolish Crowds Support Benign Overfitting
[Re] Exacerbating Algorithmic Bias through Fairness Attacks
[Re] Replication Study of "Fairness and Bias in Online Selection"
[Re] Reproduction Study of Variational Fair Clustering
(ends 11:00 AM)
12:30 p.m.
Invited Talk:
Conformal Prediction in 2022
Emmanuel Candes
(ends 1:30 PM)
1:30 p.m.
Coffee Break
2 p.m.
Poster Session 2
[2:00-4:00]
A Nonconvex Framework for Structured Dynamic Covariance Recovery
Alignment-guided Temporal Attention for Video Action Recognition
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels
On Feature Learning in the Presence of Spurious Correlations
Spatial Mixture-of-Experts
Exploring Length Generalization in Large Language Models
Neural Topological Ordering for Computation Graphs
Unsupervised Multi-View Object Segmentation Using Radiance Field Propagation
Offline Multi-Agent Reinforcement Learning with Knowledge Distillation
Enhanced Meta Reinforcement Learning via Demonstrations in Sparse Reward Environments
Maximum Class Separation as Inductive Bias in One Matrix
Training Uncertainty-Aware Classifiers with Conformalized Deep Learning
Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent
FlowHMM: Flow-based continuous hidden Markov models
Merging Models with Fisher-Weighted Averaging
Holomorphic Equilibrium Propagation Computes Exact Gradients Through Finite Size Oscillations
Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members
Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology
Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels
Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse
Ordered Subgraph Aggregation Networks
Infinite Recommendation Networks: A Data-Centric Approach
Diversity vs. Recognizability: Human-like generalization in one-shot generative models
Geo-SIC: Learning Deformable Geometric Shapes in Deep Image Classifiers
Improving Transformer with an Admixture of Attention Heads
Preservation of the Global Knowledge by Not-True Distillation in Federated Learning
Information-Theoretic GAN Compression with Variational Energy-based Model
VRL3: A Data-Driven Framework for Visual Deep Reinforcement Learning
CLEAR: Generative Counterfactual Explanations on Graphs
Out-of-Distribution Detection with An Adaptive Likelihood Ratio on Informative Hierarchical VAE
Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation
A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning
ClimbQ: Class Imbalanced Quantization Enabling Robustness on Efficient Inferences
Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social Text Classification
SIREN: Shaping Representations for Detecting Out-of-Distribution Objects
Efficient and Effective Multi-task Grouping via Meta Learning on Task Combinations
DENSE: Data-Free One-Shot Federated Learning
SparCL: Sparse Continual Learning on the Edge
Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great Again
Multi-modal Grouping Network for Weakly-Supervised Audio-Visual Video Parsing
Searching for Better Spatio-temporal Alignment in Few-Shot Action Recognition
Learning Generalizable Part-based Feature Representation for 3D Point Clouds
Coordinates Are NOT Lonely - Codebook Prior Helps Implicit Neural 3D representations
Set-based Meta-Interpolation for Few-Task Meta-Learning
Zero-Shot Video Question Answering via Frozen Bidirectional Language Models
Masked Autoencoders As Spatiotemporal Learners
Uncovering the Structural Fairness in Graph Contrastive Learning
AutoMS: Automatic Model Selection for Novelty Detection with Error Rate Control
Learning Best Combination for Efficient N:M Sparsity
C-Mixup: Improving Generalization in Regression
GAPX: Generalized Autoregressive Paraphrase-Identification X
Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models
GAMA: Generative Adversarial Multi-Object Scene Attacks
Composite Feature Selection Using Deep Ensembles
Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods
Discovered Policy Optimisation
Learning Structure from the Ground up---Hierarchical Representation Learning by Chunking
Amortized Inference for Heterogeneous Reconstruction in Cryo-EM
Neural Approximation of Graph Topological Features
Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment
Improving Variational Autoencoders with Density Gap-based Regularization
End-to-end Stochastic Optimization with Energy-based Model
Physics-Embedded Neural Networks: Graph Neural PDE Solvers with Mixed Boundary Conditions
Advancing Model Pruning via Bi-level Optimization
Earthformer: Exploring Space-Time Transformers for Earth System Forecasting
Is Integer Arithmetic Enough for Deep Learning Training?
Dense Interspecies Face Embedding
Learning State-Aware Visual Representations from Audible Interactions
VITA: Video Instance Segmentation via Object Token Association
Graph Convolution Network based Recommender Systems: Learning Guarantee and Item Mixture Powered Strategy
Pyramid Attention For Source Code Summarization
An Empirical Study on Disentanglement of Negative-free Contrastive Learning
Cache-Augmented Inbatch Importance Resampling for Training Recommender Retriever
Draft-and-Revise: Effective Image Generation with Contextual RQ-Transformer
Get More at Once: Alternating Sparse Training with Gradient Correction
Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture
Synergy-of-Experts: Collaborate to Improve Adversarial Robustness
Accelerating Certified Robustness Training via Knowledge Transfer
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency
Self-supervised Amodal Video Object Segmentation
Rethinking Image Restoration for Object Detection
Unsupervised Learning of Shape Programs with Repeatable Implicit Parts
ELASTIC: Numerical Reasoning with Adaptive Symbolic Compiler
Learning dynamics of deep linear networks with multiple pathways
FP8 Quantization: The Power of the Exponent
Pushing the limits of fairness impossibility: Who's the fairest of them all?
Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces
Pseudo-Riemannian Graph Convolutional Networks
PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization
Learning Mixed Multinomial Logits with Provable Guarantees
Truly Deterministic Policy Optimization
A Consistent and Differentiable Lp Canonical Calibration Error Estimator
Frank-Wolfe-based Algorithms for Approximating Tyler's M-estimator
Reinforcement Learning with Logarithmic Regret and Policy Switches
Finite Sample Analysis Of Dynamic Regression Parameter Learning
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets
Network change point localisation under local differential privacy
When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Bandits
Diversified Recommendations for Agents with Adaptive Preferences
Confident Approximate Policy Iteration for Efficient Local Planning in $q^\pi$-realizable MDPs
Parameter-free Regret in High Probability with Heavy Tails
Shape And Structure Preserving Differential Privacy
Multi-layer State Evolution Under Random Convolutional Design
Graph Neural Networks with Adaptive Readouts
On the detrimental effect of invariances in the likelihood for variational inference
Causally motivated multi-shortcut identification and removal
Learning Predictions for Algorithms with Predictions
Near-Optimal Collaborative Learning in Bandits
Incentivizing Combinatorial Bandit Exploration
BR-SNIS: Bias Reduced Self-Normalized Importance Sampling
Differentially Private Learning with Margin Guarantees
Multiclass Learnability Beyond the PAC Framework: Universal Rates and Partial Concept Classes
Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction
Pay attention to your loss : understanding misconceptions about Lipschitz neural networks
What You See is What You Get: Principled Deep Learning via Distributional Generalization
Data-Driven Offline Decision-Making via Invariant Representation Learning
Learning Manifold Dimensions with Conditional Variational Autoencoders
Asymptotics of $\ell_2$ Regularized Network Embeddings
TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations
CS-Shapley: Class-wise Shapley Values for Data Valuation in Classification
Teacher Forcing Recovers Reward Functions for Text Generation
Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure
MGNNI: Multiscale Graph Neural Networks with Implicit Layers
Gaussian Copula Embeddings
On Learning Fairness and Accuracy on Multiple Subgroups
Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning
Sequencer: Deep LSTM for Image Classification
Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts
Sound and Complete Verification of Polynomial Networks
DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing
Generalized Laplacian Eigenmaps
Statistical, Robustness, and Computational Guarantees for Sliced Wasserstein Distances
Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning
RKHS-SHAP: Shapley Values for Kernel Methods
Temporally-Consistent Survival Analysis
ULNeF: Untangled Layered Neural Fields for Mix-and-Match Virtual Try-On
Diffusion-based Molecule Generation with Informative Prior Bridges
Learning with convolution and pooling operations in kernel methods
Optimizing Relevance Maps of Vision Transformers Improves Robustness
Unsupervised Causal Generative Understanding of Images
SelecMix: Debiased Learning by Contradicting-pair Sampling
Controllable Text Generation with Neurally-Decomposed Oracle
Explain My Surprise: Learning Efficient Long-Term Memory by predicting uncertain outcomes
Rethinking Resolution in the Context of Efficient Video Recognition
Unsupervised Visual Representation Learning via Mutual Information Regularized Assignment
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning
Infinite-Fidelity Coregionalization for Physical Simulation
Transformers meet Stochastic Block Models: Attention with Data-Adaptive Sparsity and Cost
Private Set Generation with Discriminative Information
Private Synthetic Data for Multitask Learning and Marginal Queries
Deep Equilibrium Approaches to Diffusion Models
A Reparametrization-Invariant Sharpness Measure Based on Information Geometry
A Statistical Online Inference Approach in Averaged Stochastic Approximation
Understanding Square Loss in Training Overparametrized Neural Network Classifiers
A Deep Reinforcement Learning Framework for Column Generation
Monte Carlo Augmented Actor-Critic for Sparse Reward Deep Reinforcement Learning from Suboptimal Demonstrations
Models Out of Line: A Fourier Lens on Distribution Shift Robustness
Pre-Trained Language Models for Interactive Decision-Making
The Neural Testbed: Evaluating Joint Predictions
Is a Modular Architecture Enough?
A theory of weight distribution-constrained learning
Fast Bayesian Estimation of Point Process Intensity as Function of Covariates
PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits
Parallel Tempering With a Variational Reference
Embrace the Gap: VAEs Perform Independent Mechanism Analysis
Learning Deep Input-Output Stable Dynamics
Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation
Hyperbolic Embedding Inference for Structured Multi-Label Prediction
Surprising Instabilities in Training Deep Networks and a Theoretical Analysis
Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation
Boosting the Performance of Generic Deep Neural Network Frameworks with Log-supermodular CRFs
SnAKe: Bayesian Optimization with Pathwise Exploration
On Measuring Excess Capacity in Neural Networks
Robust Reinforcement Learning using Offline Data
Thinned random measures for sparse graphs with overlapping communities
Value Function Decomposition for Iterative Design of Reinforcement Learning Agents
The Privacy Onion Effect: Memorization is Relative
Evaluating Graph Generative Models with Contrastively Learned Features
Weakly supervised causal representation learning
Kernel Memory Networks: A Unifying Framework for Memory Modeling
Wavelet Score-Based Generative Modeling
Monte Carlo Tree Descent for Black-Box Optimization
Adaptive Sampling for Discovery
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data
Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis
Deep Hierarchical Planning from Pixels
Private Multiparty Perception for Navigation
Mesoscopic modeling of hidden spiking neurons
Toward a realistic model of speech processing in the brain with self-supervised learning
Harmonizing the object recognition strategies of deep neural networks with humans
Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning
Efficient Training of Low-Curvature Neural Networks
Consistent Sufficient Explanations and Minimal Local Rules for explaining the decision of any classifier or regressor
Social-Inverse: Inverse Decision-making of Social Contagion Management with Task Migrations
Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network
Data-Driven Conditional Robust Optimization
Subgame Solving in Adversarial Team Games
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions
Universal Rates for Interactive Learning
Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction
Learning from Label Proportions by Learning with Label Noise
Efficient Methods for Non-stationary Online Learning
Black-Box Generalization: Stability of Zeroth-Order Learning
Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus
Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks
VaiPhy: a Variational Inference Based Algorithm for Phylogeny
Local-Global MCMC kernels: the best of both worlds
Posterior Matching for Arbitrary Conditioning
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting
LiteTransformerSearch: Training-free Neural Architecture Search for Efficient Language Models
Rare Gems: Finding Lottery Tickets at Initialization
Rethinking the compositionality of point clouds through regularization in the hyperbolic space
QC-StyleGAN - Quality Controllable Image Generation and Manipulation
Look Around and Refer: 2D Synthetic Semantics Knowledge Distillation for 3D Visual Grounding
Fine-Grained Semantically Aligned Vision-Language Pre-Training
Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition
Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning
SageMix: Saliency-Guided Mixup for Point Clouds
Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis
Decomposing NeRF for Editing via Feature Field Distillation
Dual-discriminative Graph Neural Network for Imbalanced Graph-level Anomaly Detection
Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving
Hierarchical Normalization for Robust Monocular Depth Estimation
GLIPv2: Unifying Localization and Vision-Language Understanding
AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments
Single-phase deep learning in cortico-cortical networks
Understanding Robust Learning through the Lens of Representation Similarities
EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization
LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning
Scalable Neural Video Representations with Learnable Positional Features
Are All Losses Created Equal: A Neural Collapse Perspective
Lazy and Fast Greedy MAP Inference for Determinantal Point Process
Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor
Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation
Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement
Biological Learning of Irreducible Representations of Commuting Transformations
When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning
PALMER: Perception - Action Loop with Memory for Long-Horizon Planning
Training Subset Selection for Weak Supervision
Contrastive Learning as Goal-Conditioned Reinforcement Learning
CyCLIP: Cyclic Contrastive Language-Image Pretraining
Tempo: Accelerating Transformer-Based Model Training through Memory Footprint Reduction
Trading off Utility, Informativeness, and Complexity in Emergent Communication
Reduced Representation of Deformation Fields for Effective Non-rigid Shape Matching
Revisiting Neural Scaling Laws in Language and Vision
FourierFormer: Transformer Meets Generalized Fourier Integral Theorem
Real-Valued Backpropagation is Unsuitable for Complex-Valued Neural Networks
Enhanced Bilevel Optimization via Bregman Distance
Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model
Learning Robust Rule Representations for Abstract Reasoning via Internal Inferences
Saliency-Aware Neural Architecture Search
Training with More Confidence: Mitigating Injected and Natural Backdoors During Training
First is Better Than Last for Language Data Influence
When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment
Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation
A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal
Maximum Likelihood Training of Implicit Nonlinear Diffusion Model
On Divergence Measures for Bayesian Pseudocoresets
Towards Practical Few-shot Query Sets: Transductive Minimum Description Length Inference
MABSplit: Faster Forest Training Using Multi-Armed Bandits
Reconstructing Training Data From Trained Neural Networks
Information-Theoretic Safe Exploration with Gaussian Processes
Pruning’s Effect on Generalization Through the Lens of Training and Regularization
End-to-end Algorithm Synthesis with Recurrent Networks: Extrapolation without Overthinking
Conformal Prediction with Temporal Quantile Adjustments
Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination
Online PAC-Bayes Learning
On the inability of Gaussian process regression to optimally learn compositional functions
Online Agnostic Multiclass Boosting
Beyond the Best: Distribution Functional Estimation in Infinite-Armed Bandits
Efficient Submodular Optimization under Noise: Local Search is Robust
Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization
A Regret-Variance Trade-Off in Online Learning
On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias
Off-Policy Evaluation for Episodic Partially Observable Markov Decision Processes under Non-Parametric Models
Probabilistic Missing Value Imputation for Mixed Categorical and Ordered Data
Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity
Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization
Coresets for Vertical Federated Learning: Regularized Linear Regression and $K$-Means Clustering
Active Bayesian Causal Inference
All Politics is Local: Redistricting via Local Fairness
Sequence Model Imitation Learning with Unobserved Contexts
Anticipating Performativity by Predicting from Predictions
Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks
The computational and learning benefits of Daleian neural networks
Optimal Algorithms for Decentralized Stochastic Variational Inequalities
Theory and Approximate Solvers for Branched Optimal Transport with Multiple Sources
Algorithms with Prediction Portfolios
Logical Activation Functions: Logit-space equivalents of Probabilistic Boolean Operators
Understanding the Eluder Dimension
A Projection-free Algorithm for Constrained Stochastic Multi-level Composition Optimization
Robust Model Selection and Nearly-Proper Learning for GMMs
On Gap-dependent Bounds for Offline Reinforcement Learning
Perfect Sampling from Pairwise Comparisons
Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization
Finding Correlated Equilibrium of Constrained Markov Game: A Primal-Dual Approach
Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization
Provably expressive temporal graph networks
What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment
Dynamic Fair Division with Partial Information
On Computing Probabilistic Explanations for Decision Trees
Anonymized Histograms in Intermediate Privacy Models
List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering
The First Optimal Acceleration of High-Order Methods in Smooth Convex Optimization
Near-Optimal Sample Complexity Bounds for Constrained MDPs
Verification and search algorithms for causal DAGs
Learning and Covering Sums of Independent Random Variables with Unbounded Support
Fast Mixing of Stochastic Gradient Descent with Normalization and Weight Decay
Robust Testing in High-Dimensional Sparse Models
Dynamic Tensor Product Regression
Semi-supervised Active Linear Regression
Quantum Speedups of Optimizing Approximately Convex Functions with Applications to Logarithmic Regret Stochastic Convex Bandits
Independence Testing for Bounded Degree Bayesian Networks
Re-Analyze Gauss: Bounds for Private Matrix Approximation via Dyson Brownian Motion
Context-Based Dynamic Pricing with Partially Linear Demand Model
Extra-Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods
Neural Lyapunov Control of Unknown Nonlinear Systems with Stability Guarantees
Causal Inference with Non-IID Data using Linear Graphical Models
Submodular Maximization in Clean Linear Time
Continuously Tempered PDMP samplers
Regret Bounds for Multilabel Classification in Sparse Label Regimes
Provable Benefit of Multitask Representation Learning in Reinforcement Learning
BEER: Fast $O(1/T)$ Rate for Decentralized Nonconvex Optimization with Communication Compression
Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards
DataMUX: Data Multiplexing for Neural Networks
Adaptively Exploiting d-Separators with Causal Bandits
Spending Thinking Time Wisely: Accelerating MCTS with Virtual Expansions
Toward Efficient Robust Training against Union of $\ell_p$ Threat Models
Inverse Game Theory for Stackelberg Games: the Blessing of Bounded Rationality
Global Normalization for Streaming Speech Recognition in a Modular Framework
Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline
DeepInteraction: 3D Object Detection via Modality Interaction
Category-Level 6D Object Pose Estimation in the Wild: A Semi-Supervised Learning Approach and A New Dataset
Planning for Sample Efficient Imitation Learning
On Enforcing Better Conditioned Meta-Learning for Rapid Few-Shot Adaptation
Polynomial Neural Fields for Subband Decomposition and Manipulation
Improving Self-Supervised Learning by Characterizing Idealized Representations
Deep Ensembles Work, But Are They Necessary?
Learning to Mitigate AI Collusion on Economic Platforms
Hamiltonian Latent Operators for content and motion disentanglement in image sequences
Posterior Collapse of a Linear Latent Variable Model
TREC: Transient Redundancy Elimination-based Convolution
Unsupervised Reinforcement Learning with Contrastive Intrinsic Control
Learnable Polyphase Sampling for Shift Invariant and Equivariant Convolutional Networks
Unsupervised Cross-Task Generalization via Retrieval Augmentation
Self-Supervised Learning Through Efference Copies
Aligning individual brains with fused unbalanced Gromov Wasserstein
Formalizing Consistency and Coherence of Representation Learning
Non-deep Networks
Mining Unseen Classes via Regional Objectness: A Simple Baseline for Incremental Segmentation
Class-Dependent Label-Noise Learning with Cycle-Consistency Regularization
Solving Quantitative Reasoning Problems with Language Models
GENIE: Higher-Order Denoising Diffusion Solvers
Estimating the Arc Length of the Optimal ROC Curve and Lower Bounding the Maximal AUC
Marksman Backdoor: Backdoor Attacks with Arbitrary Target Class
Fuzzy Learning Machine
Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits
Grounded Video Situation Recognition
Robust Neural Posterior Estimation and Statistical Model Criticism
RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning
Instance-based Learning for Knowledge Base Completion
Sub-exponential time Sum-of-Squares lower bounds for Principal Components Analysis
Tight Mutual Information Estimation With Contrastive Fenchel-Legendre Optimization
A Rotated Hyperbolic Wrapped Normal Distribution for Hierarchical Representation Learning
Finding and Listing Front-door Adjustment Sets
SALSA: Attacking Lattice Cryptography with Transformers
Automatic differentiation of nonsmooth iterative algorithms
Learning from a Sample in Online Algorithms
Trajectory of Mini-Batch Momentum: Batch Size Saturation and Convergence in High Dimensions
Improved Algorithms for Neural Active Learning
Matching in Multi-arm Bandit with Collision
A Spectral Approach to Item Response Theory
JAWS: Auditing Predictive Uncertainty Under Covariate Shift
Bessel Equivariant Networks for Inversion of Transmission Effects in Multi-Mode Optical Fibres
MetricFormer: A Unified Perspective of Correlation Exploring in Similarity Learning
ZSON: Zero-Shot Object-Goal Navigation using Multimodal Goal Embeddings
Learning Recourse on Instance Environment to Enhance Prediction Accuracy
Spherization Layer: Representation Using Only Angles
Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel
Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift
Efficient and Stable Fully Dynamic Facility Location
Coresets for Wasserstein Distributionally Robust Optimization Problems
Differentially Private Online-to-batch for Smooth Losses
How Transferable are Video Representations Based on Synthetic Data?
SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles
GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization
DABS 2.0: Improved Datasets and Algorithms for Universal Self-Supervision
GOOD: A Graph Out-of-Distribution Benchmark
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection
MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge
EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine
DART: Articulated Hand Model with Diverse Accessories and Rich Textures
HandMeThat: Human-Robot Communication in Physical and Social Environments
The Dollar Street Dataset: Images Representing the Geographic and Socioeconomic Diversity of the World
PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation
Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems
Enabling Detailed Action Recognition Evaluation Through Video Dataset Augmentation
Multi-LexSum: Real-world Summaries of Civil Rights Lawsuits at Multiple Granularities
SurDis: A Surface Discontinuity Dataset for Wearable Technology to Assist Blind Navigation in Urban Environments
Ontologue: Declarative Benchmark Construction for Ontological Multi-Label Classification
StrokeRehab: A Benchmark Dataset for Sub-second Action Identification
A new dataset for multilingual keyphrase generation
Communicating Natural Programs to Humans and Machines
ComMU: Dataset for Combinatorial Music Generation
The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
This is the way: designing and compiling LEPISZCZE, a comprehensive NLP benchmark for Polish
Flare7K: A Phenomenological Nighttime Flare Removal Dataset
Online Nonnegative CP-dictionary Learning for Markovian Data
Supervised Dimensionality Reduction and Visualization using Centroid-Encoder
Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization
Joint Estimation and Inference for Data Integration Problems based on Multiple Multi-layered Gaussian Graphical Models
[Re] Explaining in Style: Training a GAN to explain a classifier in StyleSpace
[Re] Replication Study of DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks
[Re] Value Alignment Verification
(ends 4:00 PM)
4 p.m.
Social:
ML Safety NeurIPS Social
(ends 6:00 PM)
Social:
NeurIPS 2022 Space & ML Social Event
(ends 6:00 PM)
Social:
RL Social
(ends 6:00 PM)
Social:
Ethics Review - Open Discussion
(ends 6:00 PM)
Competition:
NeurIPS 2022 Competition Track: Overview & Results
(ends 6:00 PM)
WED 30 NOV
6 a.m.
Registration Desk:
Registration Desk
(ends 4:00 PM)
7:30 a.m.
Invited Talk:
Interaction-Centric AI
Juho Kim
(ends 8:30 AM)
8:30 a.m.
Coffee Break
9 a.m.
Poster Session 3
[9:00-11:00]
Machine Learning on Graphs: A Model and Comprehensive Taxonomy
Cost-Sensitive Self-Training for Optimizing Non-Decomposable Metrics
Discovering and Overcoming Limitations of Noise-engineered Data-free Knowledge Distillation
Exploration via Elliptical Episodic Bonuses
Uni[MASK]: Unified Inference in Sequential Decision Problems
Learning to Navigate Wikipedia by Taking Random Walks
Reinforcement Learning with Neural Radiance Fields
Few-Shot Audio-Visual Learning of Environment Acoustics
You Can’t Count on Luck: Why Decision Transformers and RvS Fail in Stochastic Environments
Decision-Focused Learning without Decision-Making: Learning Locally Optimized Decision Losses
Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation
Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo
Local Latent Space Bayesian Optimization over Structured Inputs
Bayesian Clustering of Neural Spiking Activity Using a Mixture of Dynamic Poisson Factor Analyzers
Spherical Channels for Modeling Atomic Interactions
Understanding the Evolution of Linear Regions in Deep Reinforcement Learning
A General Framework for Auditing Differentially Private Machine Learning
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesis
On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning
Efficient Non-Parametric Optimizer Search for Diverse Tasks
Transform Once: Efficient Operator Learning in Frequency Domain
Sparse Interaction Additive Networks via Feature Interaction Detection and Sparse Selection
Meta-Learning Dynamics Forecasting Using Task Inference
Implicit Neural Representations with Levels-of-Experts
Towards Theoretically Inspired Neural Initialization Optimization
Improving GANs with A Dynamic Discriminator
Towards Learning Universal Hyperparameter Optimizers with Transformers
Graph Self-supervised Learning with Accurate Discrepancy Learning
A2: Efficient Automated Attacker for Boosting Adversarial Training
Boosting Out-of-distribution Detection with Typical Features
Unified Optimal Transport Framework for Universal Domain Adaptation
Geometric Order Learning for Rank Estimation
MACK: Multimodal Aligned Conceptual Knowledge for Unpaired Image-text Matching
Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection
Meta-Learning with Self-Improving Momentum Target
GBA: A Tuning-free Approach to Switch between Synchronous and Asynchronous Training for Recommendation Models
CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference
Semi-supervised Vision Transformers at Scale
AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition
Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively
Self-Supervised Visual Representation Learning with Semantic Grouping
Geometry-aware Two-scale PIFu Representation for Human Reconstruction
360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter Tuning
FeLMi : Few shot Learning with hard Mixup
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification
Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks
One-shot Neural Backdoor Erasing via Adversarial Weight Masking
BILCO: An Efficient Algorithm for Joint Alignment of Time Series
LOT: Layer-wise Orthogonal Training on Improving l2 Certified Robustness
Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation
AutoMTL: A Programming Framework for Automating Efficient Multi-Task Learning
Learning to Discover and Detect Objects
Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation
Unsupervised Learning of Group Invariant and Equivariant Representations
Towards Understanding Grokking: An Effective Theory of Representation Learning
DMAP: a Distributed Morphological Attention Policy for learning to locomote with a changing body
Decentralized Training of Foundation Models in Heterogeneous Environments
Model-based Lifelong Reinforcement Learning with Bayesian Exploration
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation
FedSR: A Simple and Effective Domain Generalization Method for Federated Learning
Sample-Then-Optimize Batch Neural Thompson Sampling
Improving Diffusion Models for Inverse Problems using Manifold Constraints
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning
Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition
Orthogonal Transformer: An Efficient Vision Transformer Backbone with Token Orthogonalization
Jump Self-attention: Capturing High-order Statistics in Transformers
Flamingo: a Visual Language Model for Few-Shot Learning
Visual Prompting via Image Inpainting
MAtt: A Manifold Attention Network for EEG Decoding
Distilled Gradient Aggregation: Purify Features for Input Attribution in the Deep Neural Network
Visual Concepts Tokenization
Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification
HierSpeech: Bridging the Gap between Text and Speech by Hierarchical Variational Inference using Self-supervised Representations for Speech Synthesis
Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning
Weighted Mutual Learning with Diversity-Driven Model Compression
Understanding Benign Overfitting in Gradient-Based Meta Learning
Friendly Noise against Adversarial Noise: A Powerful Defense against Data Poisoning Attack
TransTab: Learning Transferable Tabular Transformers Across Tables
Federated Learning from Pre-Trained Models: A Contrastive Learning Approach
Multi-objective Deep Data Generation with Correlated Property Control
Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphs
The Policy-gradient Placement and Generative Routing Neural Networks for Chip Design
Staircase Attention for Recurrent Processing of Sequences
On the Stability and Scalability of Node Perturbation Learning
Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity
Doubly Robust Counterfactual Classification
PlasticityNet: Learning to Simulate Metal, Sand, and Snow for Optimization Time Integration
Neural Estimation of Submodular Functions with Applications to Differentiable Subset Selection
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare
Learning to Branch with Tree MDPs
Generalization Bounds for Stochastic Gradient Descent via Localized $\varepsilon$-Covers
Global Linear and Local Superlinear Convergence of IRLS for Non-Smooth Robust Regression
A Unifying Framework for Online Optimization with Long-Term Constraints
Beyond IID: data-driven decision-making in heterogeneous environments
Consistent Interpolating Ensembles via the Manifold-Hilbert Kernel
Optimal Efficiency-Envy Trade-Off via Optimal Transport
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL
On the Epistemic Limits of Personalized Prediction
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees
Multi-Class $H$-Consistency Bounds
Debiased Machine Learning without Sample-Splitting for Stable Estimators
The Sample Complexity of One-Hidden-Layer Neural Networks
coVariance Neural Networks
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach
Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales
Adaptive Data Debiasing through Bounded Exploration
Enhancing Safe Exploration Using Safety State Augmentation
Leveraging the Hints: Adaptive Bidding in Repeated First-Price Auctions
Off-Policy Evaluation for Action-Dependent Non-stationary Environments
Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression
Bounding and Approximating Intersectional Fairness through Marginal Fairness
Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints
Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays
A Reduction to Binary Approach for Debiasing Multiclass Datasets
Post-hoc estimators for learning to defer to an expert
A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift
Policy Gradient With Serial Markov Chain Reasoning
Beyond neural scaling laws: beating power law scaling via data pruning
Left Heavy Tails and the Effectiveness of the Policy and Value Networks in DNN-based best-first search for Sokoban Planning
Uncertainty-Aware Reinforcement Learning for Risk-Sensitive Player Evaluation in Sports Game
Structure-Aware Image Segmentation with Homotopy Warping
PhysGNN: A Physics--Driven Graph Neural Network Based Model for Predicting Soft Tissue Deformation in Image--Guided Neurosurgery
Generating Long Videos of Dynamic Scenes
Rethinking Individual Global Max in Cooperative Multi-Agent Reinforcement Learning
Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias
SInGE: Sparsity via Integrated Gradients Estimation of Neuron Relevance
Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs
Torsional Diffusion for Molecular Conformer Generation
Personalized Online Federated Learning with Multiple Kernels
Normalizing Flows for Knockoff-free Controlled Feature Selection
Efficiency Ordering of Stochastic Gradient Descent
Learning Bipartite Graphs: Heavy Tails and Multiple Components
Batch Bayesian optimisation via density-ratio estimation with guarantees
Discovery of Single Independent Latent Variable
Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction
Better Uncertainty Calibration via Proper Scores for Classification and Beyond
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations
Rotation-Equivariant Conditional Spherical Neural Fields for Learning a Natural Illumination Prior
Understanding Programmatic Weak Supervision via Source-aware Influence Function
CoPur: Certifiably Robust Collaborative Inference via Feature Purification
TA-GATES: An Encoding Scheme for Neural Network Architectures
MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learning
BiMLP: Compact Binary Architectures for Vision Multi-Layer Perceptrons
ORIENT: Submodular Mutual Information Measures for Data Subset Selection under Distribution Shift
Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations
Decomposed Knowledge Distillation for Class-Incremental Semantic Segmentation
Rethinking Value Function Learning for Generalization in Reinforcement Learning
Weighted Distillation with Unlabeled Examples
A Theoretical View on Sparsely Activated Networks
Deep Learning Methods for Proximal Inference via Maximum Moment Restriction
Stability and Generalization Analysis of Gradient Methods for Shallow Neural Networks
A Quantitative Geometric Approach to Neural-Network Smoothness
Large-Scale Differentiable Causal Discovery of Factor Graphs
Off-Team Learning
Neural Basis Models for Interpretability
Semantic Exploration from Language Abstractions and Pretrained Representations
Deconfounded Representation Similarity for Comparison of Neural Networks
If Influence Functions are the Answer, Then What is the Question?
Early Stage Convergence and Global Convergence of Training Mildly Parameterized Neural Networks
Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments
Chaotic Regularization and Heavy-Tailed Limits for Deterministic Gradient Descent
Challenging Common Assumptions in Convex Reinforcement Learning
Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification
Contrastive Neural Ratio Estimation
Symmetry Teleportation for Accelerated Optimization
VectorAdam for Rotation Equivariant Geometry Optimization
Neural Stochastic PDEs: Resolution-Invariant Learning of Continuous Spatiotemporal Dynamics
Constrained Predictive Coding as a Biologically Plausible Model of the Cortical Hierarchy
Fault-Aware Neural Code Rankers
End-to-end Symbolic Regression with Transformers
Masked Prediction: A Parameter Identifiability View
Moment Distributionally Robust Tree Structured Prediction
Inference and Sampling for Archimax Copulas
Operative dimensions in unconstrained connectivity of recurrent neural networks
Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Flexible Diffusion Modeling of Long Videos
Free Probability for predicting the performance of feed-forward fully connected neural networks
Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need?
Manifold Interpolating Optimal-Transport Flows for Trajectory Inference
Reinforcement Learning with a Terminator
DigGAN: Discriminator gradIent Gap Regularization for GAN Training with Limited Data
Multi-Agent Reinforcement Learning is a Sequence Modeling Problem
Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation
VoxGRAF: Fast 3D-Aware Image Synthesis with Sparse Voxel Grids
Symmetry-induced Disentanglement on Graphs
An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries
STaR: Bootstrapping Reasoning With Reasoning
Learning Physics Constrained Dynamics Using Autoencoders
LAMP: Extracting Text from Gradients with Language Model Priors
Imbalance Trouble: Revisiting Neural-Collapse Geometry
ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs
Improved Imaging by Invex Regularizers with Global Optima Guarantees
Trade-off between Payoff and Model Rewards in Shapley-Fair Collaborative Machine Learning
Follow-the-Perturbed-Leader for Adversarial Markov Decision Processes with Bandit Feedback
Learning from Distributed Users in Contextual Linear Bandits Without Sharing the Context
Consistency of Constrained Spectral Clustering under Graph Induced Fair Planted Partitions
Adversarial Reprogramming Revisited
AutoML Two-Sample Test
A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning
Randomized Sketches for Clustering: Fast and Optimal Kernel $k$-Means
On-Demand Sampling: Learning Optimally from Multiple Distributions
Constrained Langevin Algorithms with L-mixing External Random Variables
Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization
Fair Wrapping for Black-box Predictions
Do Residual Neural Networks discretize Neural Ordinary Differential Equations?
Recipe for a General, Powerful, Scalable Graph Transformer
Confident Adaptive Language Modeling
Are AlphaZero-like Agents Robust to Adversarial Perturbations?
Non-rigid Point Cloud Registration with Neural Deformation Pyramid
Peripheral Vision Transformer
Mind Reader: Reconstructing complex images from brain activities
RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer
Few-shot Relational Reasoning via Connection Subgraph Pretraining
ASPiRe: Adaptive Skill Priors for Reinforcement Learning
Point Transformer V2: Grouped Vector Attention and Partition-based Pooling
M³ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design
Deep Compression of Pre-trained Transformer Models
A Variational Edge Partition Model for Supervised Graph Representation Learning
MCMAE: Masked Convolution Meets Masked Autoencoders
Neural-Symbolic Entangled Framework for Complex Query Answering
SwinTrack: A Simple and Strong Baseline for Transformer Tracking
OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Models
Learning Neural Acoustic Fields
Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation
Interpolation and Regularization for Causal Learning
Blackbox Attacks via Surrogate Ensemble Search
Green Hierarchical Vision Transformer for Masked Image Modeling
A Practical, Progressively-Expressive GNN
Improving Neural Ordinary Differential Equations with Nesterov's Accelerated Gradient Method
Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime
Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness
DeepTOP: Deep Threshold-Optimal Policy for MDPs and RMABs
Learning Symmetric Rules with SATNet
Evaluation beyond Task Performance: Analyzing Concepts in AlphaZero in Hex
Distributionally Adaptive Meta Reinforcement Learning
HyperTree Proof Search for Neural Theorem Proving
You Only Live Once: Single-Life Reinforcement Learning
CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion
Are all Frames Equal? Active Sparse Labeling for Video Action Detection
The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning
Diffusion-LM Improves Controllable Text Generation
Variable-rate hierarchical CPC leads to acoustic unit discovery in speech
Low-rank lottery tickets: finding efficient low-rank neural networks via matrix differential equations
Stochastic Adaptive Activation Function
Benefits of Permutation-Equivariance in Auction Mechanisms
Random Sharpness-Aware Minimization
On-Device Training Under 256KB Memory
Factorized-FL: Personalized Federated Learning with Parameter Factorization & Similarity Matching
A Closer Look at Prototype Classifier for Few-shot Image Classification
On the Strong Correlation Between Model Invariance and Generalization
“Why Not Other Classes?”: Towards Class-Contrastive Back-Propagation Explanations
Parameter-Efficient Masking Networks
Make Some Noise: Reliable and Efficient Single-Step Adversarial Training
ZooD: Exploiting Model Zoo for Out-of-Distribution Generalization
One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement
Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters
Identifiability of deep generative models without auxiliary information
Beyond spectral gap: the role of the topology in decentralized learning
Change-point Detection for Sparse and Dense Functional Data in General Dimensions
Invariant and Transportable Representations for Anti-Causal Domain Shifts
Robust Feature-Level Adversaries are Interpretability Tools
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks
Anonymous Bandits for Multi-User Systems
Branch & Learn for Recursively and Iteratively Solvable Problems in Predict+Optimize
Model-based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity
Reproducibility in Optimization: Theoretical Framework and Limits
Adversarial Robustness is at Odds with Lazy Training
The Curse of Unrolling: Rate of Differentiating Through Optimization
On the Theoretical Properties of Noise Correlation in Stochastic Optimization
Support Recovery in Sparse PCA with Incomplete Data
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits
A gradient estimator via L1-randomization for online zero-order optimization with two point feedback
Hypothesis Testing for Differentially Private Linear Regression
Sound and Complete Causal Identification with Latent Variables Given Local Background Knowledge
One for All: Simultaneous Metric and Preference Learning over Multiple Users
AdaFocal: Calibration-aware Adaptive Focal Loss
Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Game
Fair Ranking with Noisy Protected Attributes
Off-Policy Evaluation with Deficient Support Using Side Information
How to talk so AI will learn: Instructions, descriptions, and autonomy
Capturing Failures of Large Language Models via Human Cognitive Biases
A time-resolved theory of information encoding in recurrent neural networks
Learning Chaotic Dynamics in Dissipative Systems
On Elimination Strategies for Bandit Fixed-Confidence Identification
(Optimal) Online Bipartite Matching with Degree Information
Scalable and Efficient Non-adaptive Deterministic Group Testing
Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap
A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation
Sample Complexity of Learning Heuristic Functions for Greedy-Best-First and A* Search
Distributionally Robust Optimization via Ball Oracle Acceleration
Online Bipartite Matching with Advice: Tight Robustness-Consistency Tradeoffs for the Two-Stage Model
GraB: Finding Provably Better Data Permutations than Random Reshuffling
House of Cans: Covert Transmission of Internal Datasets via Capacity-Aware Neuron Steganography
A Causal Analysis of Harm
Simple Mechanisms for Welfare Maximization in Rich Advertising Auctions
On Image Segmentation With Noisy Labels: Characterization and Volume Properties of the Optimal Solutions to Accuracy and Dice
Online Algorithms for the Santa Claus Problem
Learning on the Edge: Online Learning with Stochastic Feedback Graphs
Optimal Dynamic Regret in LQR Control
Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs
Optimistic Mirror Descent Either Converges to Nash or to Strong Coarse Correlated Equilibria in Bimatrix Games
Online Convex Optimization with Hard Constraints: Towards the Best of Two Worlds and Beyond
Pessimism for Offline Linear Contextual Bandits using $\ell_p$ Confidence Sets
Mean Estimation in High-Dimensional Binary Markov Gaussian Mixture Models
Queue Up Your Regrets: Achieving the Dynamic Capacity Region of Multiplayer Bandits
The First Optimal Algorithm for Smooth and Strongly-Convex-Strongly-Concave Minimax Optimization
Beyond black box densities: Parameter learning for the deviated components
A Best-of-Both-Worlds Algorithm for Bandits with Delayed Feedback
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma
Policy Optimization for Markov Games: Unified Framework and Faster Convergence
A Damped Newton Method Achieves Global $\mathcal O \left(\frac{1}{k^2}\right)$ and Local Quadratic Convergence Rate
Local Identifiability of Deep ReLU Neural Networks: the Theory
Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discovery
Online Allocation and Learning in the Presence of Strategic Agents
Signal Recovery with Non-Expansive Generative Network Priors
Practical Adversarial Multivalid Conformal Prediction
Sublinear Algorithms for Hierarchical Clustering
Large-scale Optimization of Partial AUC in a Range of False Positive Rates
Stability Analysis and Generalization Bounds of Adversarial Training
Globally Gated Deep Linear Networks
Generalization Analysis on Learning with a Concurrent Verifier
Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation
Test Time Adaptation via Conjugate Pseudo-labels
Best of Both Worlds Model Selection
Logical Credal Networks
Low-Rank Modular Reinforcement Learning via Muscle Synergy
LogiGAN: Learning Logical Reasoning via Adversarial Pre-training
Self-supervised surround-view depth estimation with volumetric feature fusion
Promising or Elusive? Unsupervised Object Segmentation from Real-world Single Images
Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework
Sparse Probabilistic Circuits via Pruning and Growing
Explaining Preferences with Shapley Values
Implications of Model Indeterminacy for Explanations of Automated Decisions
Self-Explaining Deviations for Coordination
A Direct Approximation of AIXI Using Logical State Abstractions
Adjoint-aided inference of Gaussian process driven differential equations
Beyond L1: Faster and Better Sparse Models with skglm
Robust Imitation of a Few Demonstrations with a Backwards Model
Improving Policy Learning via Language Dynamics Distillation
Monocular Dynamic View Synthesis: A Reality Check
VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming
Test-Time Training with Masked Autoencoders
Learning Modular Simulations for Homogeneous Systems
Flowification: Everything is a normalizing flow
Focal Modulation Networks
OpenAUC: Towards AUC-Oriented Open-Set Recognition
Beyond Separability: Analyzing the Linear Transferability of Contrastive Representations to Related Subpopulations
Contrastive Adapters for Foundation Model Group Robustness
Two-layer neural network on infinite dimensional data: global optimization guarantee in the mean-field regime
Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective
Riemannian Neural SDE: Learning Stochastic Representations on Manifolds
Discovering Design Concepts for CAD Sketches
🏘️ ProcTHOR: Large-Scale Embodied AI Using Procedural Generation
Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?
Risk-Driven Design of Perception Systems
Finding Differences Between Transformers and ConvNets Using Counterfactual Simulation Testing
On the Generalization Power of the Overfitted Three-Layer Neural Tangent Kernel Model
Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuits
On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds
Constants of motion network
Graph Scattering beyond Wavelet Shackles
On Margins and Generalisation for Voting Classifiers
Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality
Learning Infinite-Horizon Average-Reward Restless Multi-Action Bandits via Index Awareness
Conservative Dual Policy Optimization for Efficient Model-Based Reinforcement Learning
Provable Defense against Backdoor Policies in Reinforcement Learning
Cost-efficient Gaussian tensor network embeddings for tensor-structured inputs
Online Learning and Pricing for Network Revenue Management with Reusable Resources
MultiScan: Scalable RGBD scanning for 3D environments with articulated objects
FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning
DeepFoids: Adaptive Bio-Inspired Fish Simulation with Deep Reinforcement Learning
Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty
Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective
Graph Reordering for Cache-Efficient Near Neighbor Search
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness
The Gyro-Structure of Some Matrix Manifolds
Revisit last-iterate convergence of mSGD under milder requirement on step size
Robustness Analysis of Video-Language Models Against Visual and Language Perturbations
Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs
AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation
JAHS-Bench-201: A Foundation For Research On Joint Architecture And Hyperparameter Search
Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time
Towards Better Evaluation for Dynamic Link Prediction
Avalon: A Benchmark for RL Generalization Using Procedurally Generated Worlds
GriddlyJS: A Web IDE for Reinforcement Learning
AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies
VLMbench: A Compositional Benchmark for Vision-and-Language Manipulation
OpenFilter: A Framework to Democratize Research Access to Social Media AR Filters
Forecasting Future World Events With Neural Networks
Active-Passive SimStereo - Benchmarking the Cross-Generalization Capabilities of Deep Learning-based Stereo Methods
TAP-Vid: A Benchmark for Tracking Any Point in a Video
EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records
IKEA-Manual: Seeing Shape Assembly Step by Step
Model Zoos: A Dataset of Diverse Populations of Neural Network Models
mRI: Multi-modal 3D Human Pose Estimation Dataset using mmWave, RGB-D, and Inertial Sensors
A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement Prediction
Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Models
A Large Scale Search Dataset for Unbiased Learning to Rank
Open High-Resolution Satellite Imagery: The WorldStrat Dataset – With Application to Super-Resolution
Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization
MSDS: A Large-Scale Chinese Signature and Token Digit String Dataset for Handwriting Verification
Truncated Emphatic Temporal Difference Methods for Prediction and Control
IALE: Imitating Active Learner Ensembles
Fast and Robust Rank Aggregation against Model Misspecification
All You Need is a Good Functional Prior for Bayesian Deep Learning
[Re] Solving Phase Retrieval With a Learned Reference
[Re] Strategic classification made practical: reproduction
[Re] Graph Edit Networks
(ends 11:00 AM)
12:30 p.m.
Invited Talk:
Blueprint for an AI Bill of Rights Making Automated Systems Work for the American People
Alondra Nelson
(ends 1:30 PM)
1:30 p.m.
Coffee Break
2 p.m.
Poster Session 4
[2:00-4:00]
Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data
Active Learning Through a Covering Lens
Sparse Winning Tickets are Data-Efficient Image Recognizers
Active Learning Helps Pretrained Models Learn the Intended Task
Star Temporal Classification: Sequence Modeling with Partially Labeled Data
S-PIFu: Integrating Parametric Human Models with PIFu for Single-view Clothed Human Reconstruction
Interaction-Grounded Learning with Action-Inclusive Feedback
Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithm
NCP: Neural Correspondence Prior for Effective Unsupervised Shape Matching
Environment Diversification with Multi-head Neural Network for Invariant Learning
Denoising Diffusion Restoration Models
Predictive Coding beyond Gaussian Distributions
Trajectory balance: Improved credit assignment in GFlowNets
The least-control principle for local learning at equilibrium
Universally Expressive Communication in Multi-Agent Reinforcement Learning
Introspective Learning : A Two-Stage approach for Inference in Neural Networks
Exploit Reward Shifting in Value-Based Deep-RL: Optimistic Curiosity-Based Exploration and Conservative Exploitation via Linear Reward Shaping
Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis
ToDD: Topological Compound Fingerprinting in Computer-Aided Drug Discovery
Spartan: Differentiable Sparsity via Regularized Transportation
S3GC: Scalable Self-Supervised Graph Clustering
HUMUS-Net: Hybrid Unrolled Multi-scale Network Architecture for Accelerated MRI Reconstruction
DreamShard: Generalizable Embedding Table Placement for Recommender Systems
Navigating Memory Construction by Global Pseudo-Task Simulation for Continual Learning
Graph Learning Assisted Multi-Objective Integer Programming
CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations
Roadblocks for Temporarily Disabling Shortcuts and Learning New Knowledge
Contrastive Graph Structure Learning via Information Bottleneck for Recommendation
Mining Multi-Label Samples from Single Positive Labels
Reinforced Genetic Algorithm for Structure-based Drug Design
Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP
Class-Aware Adversarial Transformers for Medical Image Segmentation
Debiased Self-Training for Semi-Supervised Learning
Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning
Single-Stage Visual Relationship Learning using Conditional Queries
Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging
DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection
OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds
Physically-Based Face Rendering for NIR-VIS Face Recognition
HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces
Scaling Multimodal Pre-Training via Cross-Modality Gradient Harmonization
Deep Bidirectional Language-Knowledge Graph Pretraining
Vision Transformers provably learn spatial structure
Rethinking and Improving Robustness of Convolutional Neural Networks: a Shapley Value-based Approach in Frequency Domain
Self-Supervised Fair Representation Learning without Demographics
Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing
Sequence-to-Set Generative Models
Does GNN Pretraining Help Molecular Representation?
Visual correspondence-based explanations improve AI robustness and human-AI team accuracy
Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens
Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design
Transformers from an Optimization Perspective
Human-Robotic Prosthesis as Collaborating Agents for Symmetrical Walking
Locating and Editing Factual Associations in GPT
Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantification
Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics
DOMINO: Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement Learning
UniGAN: Reducing Mode Collapse in GANs using a Uniform Generator
On the Convergence Theory for Hessian-Free Bilevel Algorithms
Meta-Auto-Decoder for Solving Parametric Partial Differential Equations
Deep Active Learning by Leveraging Training Dynamics
Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence
Invertible Monotone Operators for Normalizing Flows
S$^3$-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint
Cross-Linked Unified Embedding for cross-modality representation learning
u-HuBERT: Unified Mixed-Modal Speech Pretraining And Zero-Shot Transfer to Unlabeled Modality
A Closer Look at the Adversarial Robustness of Deep Equilibrium Models
A Transformer-Based Object Detector with Coarse-Fine Crossing Representations
Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks
Semi-Supervised Semantic Segmentation via Gentle Teaching Assistant
BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis
Deep Attentive Belief Propagation: Integrating Reasoning and Learning for Solving Constraint Optimization Problems
Robust Calibration with Multi-domain Temperature Scaling
CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks
Data-Efficient Augmentation for Training Neural Networks
Learning Distinct and Representative Modes for Image Captioning
GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs
Bootstrapped Transformer for Offline Reinforcement Learning
INRAS: Implicit Neural Representation for Audio Scenes
AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness
On the Symmetries of Deep Learning Models and their Internal Representations
Gradient Descent: The Ultimate Optimizer
Maximizing Revenue under Market Shrinkage and Market Uncertainty
Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions
In Defense of the Unitary Scalarization for Deep Multi-Task Learning
A Unified Analysis of Federated Learning with Arbitrary Client Participation
DNA: Proximal Policy Optimization with a Dual Network Architecture
Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent
Sampling without Replacement Leads to Faster Rates in Finite-Sum Minimax Optimization
Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs
Gradient Descent Is Optimal Under Lower Restricted Secant Inequality And Upper Error Bound
Posterior and Computational Uncertainty in Gaussian Processes
Luckiness in Multiscale Online Learning
Learning-Augmented Algorithms for Online Linear and Semidefinite Programming
Dynamic Pricing with Monotonicity Constraint under Unknown Parametric Demand Model
An Asymptotically Optimal Batched Algorithm for the Dueling Bandit Problem
Precise Regret Bounds for Log-loss via a Truncated Bayesian Algorithm
The Neural Covariance SDE: Shaped Infinite Depth-and-Width Networks at Initialization
Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks
Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes
A Data-Augmentation Is Worth A Thousand Samples: Analytical Moments And Sampling-Free Training
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis
Maximizing and Satisficing in Multi-armed Bandits with Graph Information
Thompson Sampling Efficiently Learns to Control Diffusion Processes
Counterfactual Fairness with Partially Known Causal Graph
Non-Convex Bilevel Games with Critical Point Selection Maps
Provably tuning the ElasticNet across instances
On Learning and Refutation in Noninteractive Local Differential Privacy
Learning in Observable POMDPs, without Computationally Intractable Oracles
On the Robustness of Graph Neural Diffusion to Topology Perturbations
Robust Learning against Relational Adversaries
Toward Understanding Privileged Features Distillation in Learning-to-Rank
Lifting Weak Supervision To Structured Prediction
Meta Reinforcement Learning with Finite Training Tasks - a Density Estimation Approach
Symbolic Distillation for Learned TCP Congestion Control
On the Effectiveness of Persistent Homology
Fairness without Demographics through Knowledge Distillation
Pre-Trained Model Reusability Evaluation for Small-Data Transfer Learning
HSDF: Hybrid Sign and Distance Field for Modeling Surfaces with Arbitrary Topologies
Where to Pay Attention in Sparse Training for Feature Selection?
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects
RényiCL: Contrastive Representation Learning with Skew Rényi Divergence
Look where you look! Saliency-guided Q-networks for generalization in visual Reinforcement Learning
On Embeddings for Numerical Features in Tabular Deep Learning
SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery
(De-)Randomized Smoothing for Decision Stump Ensembles
Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning
Wasserstein $K$-means for clustering probability distributions
Coordinate Linear Variance Reduction for Generalized Linear Programming
Proximal Point Imitation Learning
Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation
Why neural networks find simple solutions: The many regularizers of geometric complexity
Learning Articulated Rigid Body Dynamics with Lagrangian Graph Neural Network
Learning interacting dynamical systems with latent Gaussian process ODEs
Capturing Graphs with Hypo-Elliptic Diffusions
MOVE: Unsupervised Movable Object Segmentation and Detection
3DILG: Irregular Latent Grids for 3D Generative Modeling
Unsupervised Multi-Object Segmentation by Predicting Probable Motion Patterns
Learning Concept Credible Models for Mitigating Shortcuts
Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning
BayesPCN: A Continually Learnable Predictive Coding Associative Memory
DeVRF: Fast Deformable Voxel Radiance Fields for Dynamic Scenes
ATD: Augmenting CP Tensor Decomposition by Self Supervision
Robust Models are less Over-Confident
HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details
ELIAS: End-to-End Learning to Index and Search in Large Output Spaces
Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold
DISCO: Adversarial Defense with Local Implicit Functions
Invariance-Aware Randomized Smoothing Certificates
Bayesian inference via sparse Hamiltonian flows
Score-based Generative Modeling Secretly Minimizes the Wasserstein Distance
Relational Reasoning via Set Transformers: Provable Efficiency and Applications to MARL
Robustness to Unbounded Smoothness of Generalized SignSGD
Doubly-Asynchronous Value Iteration: Making Value Iteration Asynchronous in Actions
Associating Objects and Their Effects in Video through Coordination Games
Masked Autoencoding for Scalable and Generalizable Decision Making
In the Eye of the Beholder: Robust Prediction with Causal User Modeling
Autoregressive Perturbations for Data Poisoning
Generalization Analysis of Message Passing Neural Networks on Large Random Graphs
Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions
Graph Neural Network Bandits
Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization
Grounding Aleatoric Uncertainty for Unsupervised Environment Design
BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach
A Theoretical Framework for Inference Learning
High-dimensional Additive Gaussian Processes under Monotonicity Constraints
On the generalization of learning algorithms that do not converge
Proppo: a Message Passing Framework for Customizable and Composable Learning Algorithms
FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation
Reinforcement Learning with Non-Exponential Discounting
A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs
A Lagrangian Duality Approach to Active Learning
Non-identifiability and the Blessings of Misspecification in Models of Molecular Fitness
Uncalibrated Models Can Improve Human-AI Collaboration
Increasing Confidence in Adversarial Robustness Evaluations
Procedural Image Programs for Representation Learning
Structuring Representations Using Group Invariants
Most Activation Functions Can Win the Lottery Without Excessive Depth
Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport
Faster Deep Reinforcement Learning with Slower Online Network
Online Reinforcement Learning for Mixed Policy Scopes
What are the best Systems? New Perspectives on NLP Benchmarking
ALMA: Hierarchical Learning for Composite Multi-Agent Tasks
Intra-agent speech permits zero-shot task acquisition
Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies
Hybrid Neural Autoencoders for Stimulus Encoding in Visual and Other Sensory Neuroprostheses
Data Distributional Properties Drive Emergent In-Context Learning in Transformers
Drawing out of Distribution with Neuro-Symbolic Generative Models
FIRE: Semantic Field of Words Represented as Non-Linear Functions
Do Current Multi-Task Optimization Methods in Deep Learning Even Help?
Diffusion Models as Plug-and-Play Priors
ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicine
Robust Bayesian Regression via Hard Thresholding
Optimal-er Auctions through Attention
Multi-Fidelity Best-Arm Identification
Adaptive Oracle-Efficient Online Learning
Towards Consistency in Adversarial Classification
Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks
Beyond Adult and COMPAS: Fair Multi-Class Prediction via Information Projection
Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization
A Single-timescale Analysis for Stochastic Approximation with Multiple Coupled Sequences
A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits
Evaluated CMI Bounds for Meta Learning: Tightness and Expressiveness
Discrete-Convex-Analysis-Based Framework for Warm-Starting Algorithms with Predictions
projUNN: efficient method for training deep networks with unitary matrices
$\alpha$-ReQ : Assessing Representation Quality in Self-Supervised Learning by measuring eigenspectrum decay
Structural Knowledge Distillation for Object Detection
Scalable Interpretability via Polynomials
An empirical analysis of compute-optimal large language model training
Object Scene Representation Transformer
Generative Neural Articulated Radiance Fields
Hand-Object Interaction Image Generation
Paraphrasing Is All You Need for Novel Object Captioning
A Theoretical Study on Solving Continual Learning
RecursiveMix: Mixed Learning with History
Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs
GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models
Multiview Human Body Reconstruction from Uncalibrated Cameras
Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks
RainNet: A Large-Scale Imagery Dataset and Benchmark for Spatial Precipitation Downscaling
Alleviating "Posterior Collapse'' in Deep Topic Models via Policy Gradient
Refining Low-Resource Unsupervised Translation by Language Disentanglement of Multilingual Translation Model
ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Time
Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules
What Can the Neural Tangent Kernel Tell Us About Adversarial Robustness?
Understanding Hyperdimensional Computing for Parallel Single-Pass Learning
Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties
Accelerating Sparse Convolution with Column Vector-Wise Sparsity
Exact Shape Correspondence via 2D graph convolution
Global Optimal K-Medoids Clustering of One Million Samples
Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise
Sample-Efficient Reinforcement Learning of Partially Observable Markov Games
FasterRisk: Fast and Accurate Interpretable Risk Scores
LieGG: Studying Learned Lie Group Generators
Shield Decentralization for Safe Multi-Agent Reinforcement Learning
ProtoX: Explaining a Reinforcement Learning Agent via Prototyping
On the Generalizability and Predictability of Recommender Systems
Adversarial Unlearning: Reducing Confidence Along Adversarial Directions
Neurosymbolic Deep Generative Models for Sequence Data with Relational Constraints
Memory Efficient Continual Learning with Transformers
Inverse Design for Fluid-Structure Interactions using Graph Network Simulators
Cross-modal Learning for Image-Guided Point Cloud Shape Completion
Beyond Mahalanobis Distance for Textual OOD Detection
Panchromatic and Multispectral Image Fusion via Alternating Reverse Filtering Network
Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study
Tight Analysis of Extra-gradient and Optimistic Gradient Methods For Nonconvex Minimax Problems
Look More but Care Less in Video Recognition
Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation
APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction
Rethinking the Reverse-engineering of Trojan Triggers
The Mechanism of Prediction Head in Non-contrastive Self-supervised Learning
Exploring evolution-aware & -free protein language models as protein function predictors
M$^4$I: Multi-modal Models Membership Inference
Deep Model Reassembly
Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering
Stochastic Multiple Target Sampling Gradient Descent
Probable Domain Generalization via Quantile Risk Minimization
Fair and Optimal Decision Trees: A Dynamic Programming Approach
An Information-Theoretic Framework for Deep Learning
Learning Tractable Probabilistic Models from Inconsistent Local Estimates
Domain Adaptation meets Individual Fairness. And they get along.
Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms
Interventions, Where and How? Experimental Design for Causal Models at Scale
On A Mallows-type Model For (Ranked) Choices
Forward-Backward Latent State Inference for Hidden Continuous-Time semi-Markov Chains
Regret Bounds for Risk-Sensitive Reinforcement Learning
Rate-Optimal Online Convex Optimization in Adaptive Linear Control
Zonotope Domains for Lagrangian Neural Network Verification
Generalizing Bayesian Optimization with Decision-theoretic Entropies
Regularized Gradient Descent Ascent for Two-Player Zero-Sum Markov Games
Wasserstein Logistic Regression with Mixed Features
The Role of Baselines in Policy Gradient Optimization
Active Learning Polynomial Threshold Functions
Quantile Constrained Reinforcement Learning: A Reinforcement Learning Framework Constraining Outage Probability
Generalised Implicit Neural Representations
Score-Based Generative Models Detect Manifolds
Mixture-of-Experts with Expert Choice Routing
Coresets for Relational Data and The Applications
Factored Adaptation for Non-Stationary Reinforcement Learning
Counterfactual harm
Addressing Leakage in Concept Bottleneck Models
Compositional generalization through abstract representations in human and artificial neural networks
Efficient coding, channel capacity, and the emergence of retinal mosaics
Learning in Congestion Games with Bandit Feedback
Optimal Weak to Strong Learning
Log-Linear-Time Gaussian Processes Using Binary Tree Kernels
Sharp Analysis of Stochastic Optimization under Global Kurdyka-Lojasiewicz Inequality
Learning Distributions Generated by Single-Layer ReLU Networks in the Presence of Arbitrary Outliers
Instance-optimal PAC Algorithms for Contextual Bandits
Learning the Structure of Large Networked Systems Obeying Conservation Laws
Local Linear Convergence of Gradient Methods for Subspace Optimization via Strict Complementarity
A Near-Optimal Primal-Dual Method for Off-Policy Learning in CMDP
The alignment property of SGD noise and how it helps select flat minima: A stability analysis
Weisfeiler and Leman Go Walking: Random Walk Kernels Revisited
Polynomial-Time Optimal Equilibria with a Mediator in Extensive-Form Games
An $\alpha$-regret analysis of Adversarial Bilateral Trade
Algorithms and Hardness for Learning Linear Thresholds from Label Proportions
Decomposable Non-Smooth Convex Optimization with Nearly-Linear Gradient Oracle Complexity
Optimal Query Complexities for Dynamic Trace Estimation
On Scalable Testing of Samplers
Private Isotonic Regression
Neural Network Architecture Beyond Width and Depth
Faster and Scalable Algorithms for Densest Subgraph and Decomposition
Differentially Private Covariance Revisited
A Fourier Approach to Mixture Learning
On Non-Linear operators for Geometric Deep Learning
Linear Label Ranking with Bounded Noise
Estimation of Entropy in Constant Space with Improved Sample Complexity
Efficient Sampling on Riemannian Manifolds via Langevin MCMC
Near-Optimal Regret for Adversarial MDP with Delayed Bandit Feedback
Understanding Deep Neural Function Approximation in Reinforcement Learning via $\epsilon$-Greedy Exploration
Communication-efficient distributed eigenspace estimation with arbitrary node failures
Implicit Bias of Gradient Descent on Reparametrized Models: On Equivalence to Mirror Descent
Causal Discovery in Linear Latent Variable Models Subject to Measurement Error
Log-Concave and Multivariate Canonical Noise Distributions for Differential Privacy
A Quadrature Rule combining Control Variates and Adaptive Importance Sampling
Decision Trees with Short Explainable Rules
Task-level Differentially Private Meta Learning
Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity
Robust Imitation via Mirror Descent Inverse Reinforcement Learning
Learning Partial Equivariances From Data
Exploitability Minimization in Games and Beyond
Communication-Efficient Topologies for Decentralized Learning with $O(1)$ Consensus Rate
Hardness in Markov Decision Processes: Theory and Practice
Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget
Decoupled Context Processing for Context Augmented Language Modeling
PerfectDou: Dominating DouDizhu with Perfect Information Distillation
Neural Transmitted Radiance Fields
Self-Supervised Pretraining for Large-Scale Point Clouds
GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis
Streaming Radiance Fields for 3D Video Synthesis
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
Analyzing Data-Centric Properties for Graph Contrastive Learning
On the Limitations of Stochastic Pre-processing Defenses
Hedging as Reward Augmentation in Probabilistic Graphical Models
Riemannian Score-Based Generative Modelling
On the Spectral Bias of Convolutional Neural Tangent and Gaussian Process Kernels
Causality Preserving Chaotic Transformation and Classification using Neurochaos Learning
Exploring through Random Curiosity with General Value Functions
Masked Autoencoders that Listen
Sharing Knowledge for Meta-learning with Feature Descriptions
ReFactor GNNs: Revisiting Factorisation-based Models from a Message-Passing Perspective
New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound
Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
Conditional Diffusion Process for Inverse Halftoning
Prompt Certified Machine Unlearning with Randomized Gradient Smoothing and Quantization
Training language models to follow instructions with human feedback
Learning to Accelerate Partial Differential Equations via Latent Global Evolution
Robust $\phi$-Divergence MDPs
Offline Goal-Conditioned Reinforcement Learning via $f$-Advantage Regression
Translation-equivariant Representation in Recurrent Networks with a Continuous Manifold of Attractors
Visual Clues: Bridging Vision and Language Foundations for Image Paragraph Captioning
Learning Audio-Visual Dynamics Using Scene Graphs for Audio Source Separation
Towards a Standardised Performance Evaluation Protocol for Cooperative MARL
Mismatched No More: Joint Model-Policy Optimization for Model-Based RL
Continual Learning with Evolving Class Ontologies
The Franz-Parisi Criterion and Computational Trade-offs in High Dimensional Statistics
Conformalized Fairness via Quantile Regression
Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules
An Analysis of Ensemble Sampling
Resolving the data ambiguity for periodic crystals
Split-kl and PAC-Bayes-split-kl Inequalities for Ternary Random Variables
On the Sample Complexity of Stabilizing LTI Systems on a Single Trajectory
On the convergence of policy gradient methods to Nash equilibria in general stochastic games
Certifying Some Distributional Fairness with Subpopulation Decomposition
Biologically Inspired Dynamic Thresholds for Spiking Neural Networks
Precise Learning Curves and Higher-Order Scalings for Dot-product Kernel Regression
Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees
Sketching based Representations for Robust Image Classification with Provable Guarantees
Causality-driven Hierarchical Structure Discovery for Reinforcement Learning
Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners
Foundation Posteriors for Approximate Probabilistic Inference
Unsupervised Image-to-Image Translation with Density Changing Regularization
Pruning Neural Networks via Coresets and Convex Geometry: Towards No Assumptions
Overparameterization from Computational Constraints
Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets
Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation
Giga-scale Kernel Matrix-Vector Multiplication on GPU
USB: A Unified Semi-supervised Learning Benchmark for Classification
DC-BENCH: Dataset Condensation Benchmark
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies
PROSPECT: Labeled Tandem Mass Spectrometry Dataset for Machine Learning in Proteomics
FLAIR: Federated Learning Annotated Image Repository
The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games
ConfLab: A Data Collection Concept, Dataset, and Benchmark for Machine Analysis of Free-Standing Social Interactions in the Wild
MOMA-LRG: Language-Refined Graphs for Multi-Object Multi-Actor Activity Parsing
Touch and Go: Learning from Human-Collected Vision and Touch
A Survey and Datasheet Repository of Publicly Available US Criminal Justice Datasets
MATE: Benchmarking Multi-Agent Reinforcement Learning in Distributed Target Coverage Control
How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios
MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control
OpenSRH: optimizing brain tumor surgery using intraoperative stimulated Raman histology
DDXPlus: A New Dataset For Automatic Medical Diagnosis
ViSioNS: Visual Search in Natural Scenes Benchmark
NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning
Finding Naturally Occurring Physical Backdoors in Image Datasets
AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier–Stokes Solutions
OpenXAI: Towards a Transparent Evaluation of Model Explanations
LAION-5B: An open large-scale dataset for training next generation image-text models
OccGen: Selection of Real-world Multilingual Parallel Data Balanced in Gender within Occupations
Fairness-Aware PAC Learning from Corrupted Data
LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data
Multi-Agent Multi-Armed Bandits with Limited Communication
When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint
[Re] Nondeterminism and Instability in Neural Network Optimization
[Re] Replication Study of "Fairness and Bias in Online Selection"
[Re] Reproduction and Extension of "Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation"
[Re] Learning to count everything
(ends 4:00 PM)
4 p.m.
Town Hall:
NeurIPS Town Hall
(ends 5:00 PM)
Social:
Industry, Academia, and the In-Betweens
(ends 6:00 PM)
Social:
From Old Biases to New Opportunities: Annotator Empowerment and Data Excellence
(ends 6:00 PM)
Social:
Early-Career Researchers Interested in ML & Climate Change
(ends 6:00 PM)
Social:
Gulf Coast AI
(ends 6:00 PM)
Social:
Women in AI Ignite
(ends 5:30 PM)
THU 1 DEC
6 a.m.
Registration Desk:
Registration Desk
(ends 4:00 PM)
7:30 a.m.
Invited Talk:
The Data-Centric Era: How ML is Becoming an Experimental Science
Isabelle Guyon
(ends 8:30 AM)
8:30 a.m.
Coffee Break
9 a.m.
Poster Session 5
[9:00-11:00]
Learning Operators with Coupled Attention
Single Model Uncertainty Estimation via Stochastic Data Centering
A Neural Pre-Conditioning Active Learning Algorithm to Reduce Label Complexity
Retaining Knowledge for Learning with Dynamic Definition
Amortized Inference for Causal Structure Learning
Multi-Game Decision Transformers
NS3: Neuro-symbolic Semantic Code Search
NeMF: Neural Motion Fields for Kinematic Animation
COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics
Towards Safe Reinforcement Learning with a Safety Editor Policy
A Solver-free Framework for Scalable Learning in Neural ILP Architectures
Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers
Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation
Concrete Score Matching: Generalized Score Matching for Discrete Data
Structuring Uncertainty for Fine-Grained Sampling in Stochastic Segmentation Networks
NOMAD: Nonlinear Manifold Decoders for Operator Learning
Modeling Human Exploration Through Resource-Rational Reinforcement Learning
Hierarchical classification at multiple operating points
Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees
Sparsity in Continuous-Depth Neural Networks
Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning
Diagonal State Spaces are as Effective as Structured State Spaces
UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes
Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videos
Cluster and Aggregate: Face Recognition with Large Probe Set
Reinforcement Learning with Automated Auxiliary Loss Search
Distribution-Informed Neural Networks for Domain Adaptation Regression
Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?
Association Graph Learning for Multi-Task Classification with Category Shifts
Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition
OST: Improving Generalization of DeepFake Detection via One-Shot Test-Time Training
Grow and Merge: A Unified Framework for Continuous Categories Discovery
Revisiting Sliced Wasserstein on Images: From Vectorization to Convolution
Meta-Complementing the Semantics of Short Texts in Neural Topic Models
Learning Equivariant Segmentation with Instance-Unique Querying
DivBO: Diversity-aware CASH for Ensemble Learning
Towards Efficient Post-training Quantization of Pre-trained Language Models
FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction
How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders
Transferring Pre-trained Multimodal Representations with Cross-modal Similarity Matching
A Neural Corpus Indexer for Document Retrieval
I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification
ElasticMVS: Learning elastic part representation for self-supervised multi-view stereopsis
Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts
DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotations
MaskTune: Mitigating Spurious Correlations by Forcing to Explore
Neuron with Steady Response Leads to Better Generalization
Invariance Learning based on Label Hierarchy
Bidirectional Learning for Offline Infinite-width Model-based Optimization
Energy-Based Contrastive Learning of Visual Representations
FR: Folded Rationalization with a Unified Encoder
Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models
Unsupervised Adaptation from Repeated Traversals for Autonomous Driving
Generating multivariate time series with COmmon Source CoordInated GAN (COSCI-GAN)
Unsupervised Learning of Equivariant Structure from Sequences
An Analytical Theory of Curriculum Learning in Teacher-Student Networks
Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting
MAgNet: Mesh Agnostic Neural PDE Solver
Iterative Structural Inference of Directed Graphs
Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning
A Unifying Framework of Off-Policy General Value Function Evaluation
Langevin Autoencoders for Learning Deep Latent Variable Models
SketchBoost: Fast Gradient Boosted Decision Tree for Multioutput Problems
Your Transformer May Not be as Powerful as You Expect
GRASP: Navigating Retrosynthetic Planning with Goal-driven Policy
High-Order Pooling for Graph Neural Networks with Tensor Decomposition
TreeMoCo: Contrastive Neuron Morphology Representation Learning
SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos
TVLT: Textless Vision-Language Transformer
Molecule Generation by Principal Subgraph Mining and Assembling
AttCAT: Explaining Transformers via Attentive Class Activation Tokens
Joint Learning of 2D-3D Weakly Supervised Semantic Segmentation
Transcormer: Transformer for Sentence Scoring with Sliding Language Modeling
Expansion and Shrinkage of Localization for Weakly-Supervised Semantic Segmentation
Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination
Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems
Phase Transition from Clean Training to Adversarial Training
On the Robustness of Deep Clustering Models: Adversarial Attacks and Defenses
LIFT: Language-Interfaced Fine-Tuning for Non-language Machine Learning Tasks
Improving 3D-aware Image Synthesis with A Geometry-aware Discriminator
Watermarking for Out-of-distribution Detection
Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs
InsNet: An Efficient, Flexible, and Performant Insertion-based Text Generation Model
Prune and distill: similar reformatting of image information along rat visual cortex and deep neural networks
Is this the Right Neighborhood? Accurate and Query Efficient Model Agnostic Explanations
Recruitment Strategies That Take a Chance
Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spaces
Physics-Informed Implicit Representations of Equilibrium Network Flows
Modeling Transitivity and Cyclicity in Directed Graphs via Binary Code Box Embeddings
Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints
A Boosting Approach to Reinforcement Learning
Active Learning with Neural Networks: Insights from Nonparametric Statistics
Optimal Scaling for Locally Balanced Proposals in Discrete Spaces
Non-Stationary Bandits under Recharging Payoffs: Improved Planning with Sublinear Regret
Effective Dimension in Bandit Problems under Censorship
Partial Identification of Treatment Effects with Implicit Generative Models
A Universal Error Measure for Input Predictions Applied to Online Graph Problems
Minimax Regret for Cascading Bandits
Defining and Characterizing Reward Gaming
Beyond Time-Average Convergence: Near-Optimal Uncoupled Online Learning via Clairvoyant Multiplicative Weights Update
Non-convex online learning via algorithmic equivalence
Annihilation of Spurious Minima in Two-Layer ReLU Networks
On global convergence of ResNets: From finite to infinite width using linear parameterization
GULP: a prediction-based metric between representations
A Combinatorial Perspective on the Optimization of Shallow ReLU Networks
Learning sparse features can lead to overfitting in neural networks
Conformal Off-Policy Prediction in Contextual Bandits
Movement Penalized Bayesian Optimization with Application to Wind Energy Systems
Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning
The Implicit Delta Method
Statistically Meaningful Approximation: a Case Study on Approximating Turing Machines with Transformers
Archimedes Meets Privacy: On Privately Estimating Quantiles in High Dimensions Under Minimal Assumptions
Markovian Interference in Experiments
Whitening Convergence Rate of Coupling-based Normalizing Flows
When Does Differentially Private Learning Not Suffer in High Dimensions?
DASCO: Dual-Generator Adversarial Support Constrained Offline Reinforcement Learning
Are GANs overkill for NLP?
Positive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimization
Direct Advantage Estimation
Simplified Graph Convolution with Heterophily
GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games
Exploration-Guided Reward Shaping for Reinforcement Learning under Sparse Rewards
Relational Proxies: Emergent Relationships as Fine-Grained Discriminators
Time-Conditioned Dances with Simplicial Complexes: Zigzag Filtration Curve based Supra-Hodge Convolution Networks for Time-series Forecasting
MCL-GAN: Generative Adversarial Networks with Multiple Specialized Discriminators
Learning Individualized Treatment Rules with Many Treatments: A Supervised Clustering Approach Using Adaptive Fusion
Exploiting the Relationship Between Kendall's Rank Correlation and Cosine Similarity for Attribution Protection
Back Razor: Memory-Efficient Transfer Learning by Self-Sparsified Backpropagation
Not All Bits have Equal Value: Heterogeneous Precisions via Trainable Noise
Pure Transformers are Powerful Graph Learners
Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms
Bayesian Active Learning with Fully Bayesian Gaussian Processes
$k$-Sliced Mutual Information: A Quantitative Study of Scalability with Dimension
Accelerated Projected Gradient Algorithms for Sparsity Constrained Optimization Problems
Sparse Gaussian Process Hyperparameters: Optimize or Integrate?
Detecting Abrupt Changes in Sequential Pairwise Comparison Data
Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers
Learning Fractional White Noises in Neural Stochastic Differential Equations
On the Frequency-bias of Coordinate-MLPs
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors
GAUDI: A Neural Architect for Immersive 3D Scene Generation
Learning Debiased Classifier with Biased Committee
PAC: Assisted Value Factorization with Counterfactual Predictions in Multi-Agent Reinforcement Learning
Recurrent Memory Transformer
Fast Vision Transformers with HiLo Attention
Dynamic Sparse Network for Time Series Classification: Learning What to “See”
Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solution
Extrapolative Continuous-time Bayesian Neural Network for Fast Training-free Test-time Adaptation
Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxation
Bridging the Gap: Unifying the Training and Evaluation of Neural Network Binary Classifiers
Can Hybrid Geometric Scattering Networks Help Solve the Maximum Clique Problem?
Unsupervised Learning From Incomplete Measurements for Inverse Problems
On the Double Descent of Random Features Models Trained with SGD
Generalization Properties of NAS under Activation and Skip Connection Search
Neur2SP: Neural Two-Stage Stochastic Programming
Receding Horizon Inverse Reinforcement Learning
Neural Attentive Circuits
Interaction Modeling with Multiplex Attention
Measures of Information Reflect Memorization Patterns
Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent
Blessing of Depth in Linear Regression: Deeper Models Have Flatter Landscape Around the True Solution
Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions
Proximal Learning With Opponent-Learning Awareness
ResQ: A Residual Q Function-based Approach for Multi-Agent Reinforcement Learning Value Factorization
Joint Entropy Search For Maximally-Informed Bayesian Optimization
Can Push-forward Generative Models Fit Multimodal Distributions?
Learning Interface Conditions in Domain Decomposition Solvers
Chaotic Dynamics are Intrinsic to Neural Network Training with SGD
An In-depth Study of Stochastic Backpropagation
Automatic Differentiation of Programs with Discrete Randomness
Weakly Supervised Representation Learning with Sparse Perturbations
Domain Generalization without Excess Empirical Risk
Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions
Distinguishing discrete and continuous behavioral variability using warped autoregressive HMMs
Handcrafted Backdoors in Deep Neural Networks
Multitasking Models are Robust to Structural Failure: A Neural Model for Bilingual Cognitive Reserve
Representing Spatial Trajectories as Distributions
Exact Solutions of a Deep Linear Network
Systematic improvement of neural network quantum states using Lanczos
Score-Based Diffusion meets Annealed Importance Sampling
Factored DRO: Factored Distributionally Robust Policies for Contextual Bandits
On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting
Learning General World Models in a Handful of Reward-Free Deployments
LISA: Learning Interpretable Skill Abstractions from Language
SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections
Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks
FourierNets enable the design of highly non-local optical encoders for computational imaging
Learning to Scaffold: Optimizing Model Explanations for Teaching
SCONE: Surface Coverage Optimization in Unknown Environments by Volumetric Integration
VisCo Grids: Surface Reconstruction with Viscosity and Coarea Grids
A Conditional Randomization Test for Sparse Logistic Regression in High-Dimension
Reduction Algorithms for Persistence Diagrams of Networks: CoralTDA and PrunIT
A Consolidated Cross-Validation Algorithm for Support Vector Machines via Data Reduction
RISE: Robust Individualized Decision Learning with Sensitive Variables
Learning NP-Hard Multi-Agent Assignment Planning using GNN: Inference on a Random Graph and Provable Auction-Fitted Q-learning
Tractable Optimality in Episodic Latent MABs
Causal Identification under Markov equivalence: Calculus, Algorithm, and Completeness
Non-Gaussian Tensor Programs
Nonparametric Uncertainty Quantification for Single Deterministic Neural Network
Nest Your Adaptive Algorithm for Parameter-Agnostic Nonconvex Minimax Optimization
DP-PCA: Statistically Optimal and Differentially Private PCA
DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning
Acceleration in Distributed Sparse Regression
Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox
Chefs' Random Tables: Non-Trigonometric Random Features
Rapid Model Architecture Adaption for Meta-Learning
UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification
Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers
MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction
Implicit Warping for Animation with Image Sets
Learning Viewpoint-Agnostic Visual Representations by Recovering Tokens in 3D Space
VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts
Few-shot Image Generation via Adaptation-Aware Kernel Modulation
Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models
TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers
SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG
Locally Hierarchical Auto-Regressive Modeling for Image Generation
Deep Generative Model for Periodic Graphs
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
AutoST: Towards the Universal Modeling of Spatio-temporal Sequences
Egocentric Video-Language Pretraining
Learning Active Camera for Multi-Object Navigation
Maximum a posteriori natural scene reconstruction from retinal ganglion cells with deep denoiser priors
Assaying Out-Of-Distribution Generalization in Transfer Learning
Not too little, not too much: a theoretical analysis of graph (over)smoothing
The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models
Squeezeformer: An Efficient Transformer for Automatic Speech Recognition
GREED: A Neural Framework for Learning Graph Distance Functions
Zeroth-Order Negative Curvature Finding: Escaping Saddle Points without Gradients
Does Momentum Change the Implicit Regularization on Separable Data?
Lower Bounds on Randomly Preconditioned Lasso via Robust Sparse Designs
Robust On-Policy Sampling for Data-Efficient Policy Evaluation in Reinforcement Learning
Redundant representations help generalization in wide neural networks
Explainability Via Causal Self-Talk
Learn what matters: cross-domain imitation learning with task-relevant embeddings
Learning to Compare Nodes in Branch and Bound with Graph Neural Networks
MoCoDA: Model-based Counterfactual Data Augmentation
Subsidiary Prototype Alignment for Universal Domain Adaptation
A Fast Post-Training Pruning Framework for Transformers
Dynamic Inverse Reinforcement Learning for Characterizing Animal Behavior
NeuPhysics: Editable Neural Geometry and Physics from Monocular Videos
Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights
DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learning
Power and limitations of single-qubit native quantum neural networks
SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning
Heatmap Distribution Matching for Human Pose Estimation
Tensor Program Optimization with Probabilistic Programs
Skills Regularized Task Decomposition for Multi-task Offline Reinforcement Learning
Benign Overfitting in Two-layer Convolutional Neural Networks
Task Discovery: Finding the Tasks that Neural Networks Generalize on
Retrieve, Reason, and Refine: Generating Accurate and Faithful Patient Instructions
NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis
MORA: Improving Ensemble Robustness Evaluation with Model Reweighing Attack
Unsupervised Representation Learning from Pre-trained Diffusion Probabilistic Models
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models
Domain Adaptation under Open Set Label Shift
Lethal Dose Conjecture on Data Poisoning
Emergent Communication: Generalization and Overfitting in Lewis Games
Asymptotic Properties for Bayesian Neural Network in Besov Space
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification
Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models
Multi-fidelity Monte Carlo: a pseudo-marginal approach
Near-Optimal Correlation Clustering with Privacy
Rethinking Variational Inference for Probabilistic Programs with Stochastic Support
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams
Positively Weighted Kernel Quadrature via Subsampling
On the non-universality of deep learning: quantifying the cost of symmetry
Globally Convergent Policy Search for Output Estimation
Optimistic Tree Searches for Combinatorial Black-Box Optimization
Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints
IMED-RL: Regret optimal learning of ergodic Markov decision processes
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits
KSD Aggregated Goodness-of-fit Test
Latent Hierarchical Causal Structure Discovery with Rank Constraints
In Differential Privacy, There is Truth: on Vote-Histogram Leakage in Ensemble Private Learning
Explicit Tradeoffs between Adversarial and Natural Distributional Robustness
A Mean-Field Game Approach to Cloud Resource Management with Function Approximation
On Privacy and Personalization in Cross-Silo Federated Learning
Byzantine Spectral Ranking
Learning to Follow Instructions in Text-Based Games
Convergent Representations of Computer Programs in Human and Artificial Neural Networks
Emergence of Hierarchical Layers in a Single Sheet of Self-Organizing Spiking Neurons
Matrix Multiplicative Weights Updates in Quantum Zero-Sum Games: Conservation Laws & Recurrence
Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts
Exploring the Whole Rashomon Set of Sparse Decision Trees
Robust Rent Division
A Classification of $G$-invariant Shallow Neural Networks
Polynomial time guarantees for the Burer-Monteiro method
A sharp NMF result with applications in network modeling
Uncoupled Learning Dynamics with $O(\log T)$ Swap Regret in Multiplayer Games
Average Sensitivity of Euclidean k-Clustering
A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions
Dynamic pricing and assortment under a contextual MNL demand
Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix Completion
Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization)
Performative Power
Is Sortition Both Representative and Fair?
Improved Bounds on Neural Complexity for Representing Piecewise Linear Functions
Nearly-Tight Bounds for Testing Histogram Distributions
Quantum Algorithms for Sampling Log-Concave Distributions and Estimating Normalizing Constants
A consistently adaptive trust-region method
Single-pass Streaming Lower Bounds for Multi-armed Bandits Exploration with Instance-sensitive Sample Complexity
On Infinite Separations Between Simple and Optimal Mechanisms
Near-Optimal Private and Scalable $k$-Clustering
Asymptotics of smoothed Wasserstein distances in the small noise regime
Finite-Sample Maximum Likelihood Estimation of Location
High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation
Fast Distance Oracles for Any Symmetric Norm
Momentum Aggregation for Private Non-convex ERM
Differentially Private Generalized Linear Models Revisited
Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization
A Simple and Optimal Policy Design for Online Learning with Safety against Heavy-tailed Risk
Optimal and Adaptive Monteiro-Svaiter Acceleration
Continuous Deep Q-Learning in Optimal Control Problems: Normalized Advantage Functions Analysis
Falsification before Extrapolation in Causal Effect Estimation
Non-monotonic Resource Utilization in the Bandits with Knapsacks Problem
Bezier Gaussian Processes for Tall and Wide Data
A Characterization of Semi-Supervised Adversarially Robust PAC Learnability
Fairness in Federated Learning via Core-Stability
A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power
Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
On the consistent estimation of optimal Receiver Operating Characteristic (ROC) curve
Iron: Private Inference on Transformers
Unsupervised Learning under Latent Label Shift
Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets
Tsetlin Machine for Solving Contextual Bandit Problems
Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding
Mutual Information Divergence: A Unified Metric for Multimodal Generative Models
Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection
AD-DROP: Attribution-Driven Dropout for Robust Language Model Fine-Tuning
Trading off Image Quality for Robustness is not Necessary with Regularized Deterministic Autoencoders
Training stochastic stabilized supralinear networks by dynamics-neutral growth
Using Mixup as a Regularizer Can Surprisingly Improve Accuracy & Out-of-Distribution Robustness
Rashomon Capacity: A Metric for Predictive Multiplicity in Classification
Parametrically Retargetable Decision-Makers Tend To Seek Power
Structured Recognition for Generative Models with Explaining Away
A Continuous Time Framework for Discrete Denoising Models
TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s
Pragmatically Learning from Pedagogical Demonstrations in Multi-Goal Environments
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning
Emergent Graphical Conventions in a Visual Communication Game
A Geometric Perspective on Variational Autoencoders
Semantic Probabilistic Layers for Neuro-Symbolic Learning
DevFly: Bio-Inspired Development of Binary Connections for Locality Preserving Sparse Codes
Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork
NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation
Periodic Graph Transformers for Crystal Material Property Prediction
MCVD - Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation
Approximation with CNNs in Sobolev Space: with Applications to Classification
MultiGuard: Provably Robust Multi-label Classification against Adversarial Examples
Theoretically Provable Spiking Neural Networks
CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers
ReCo: Retrieve and Co-segment for Zero-shot Transfer
Online Decision Mediation
Collaborative Decision Making Using Action Suggestions
When does dough become a bagel? Analyzing the remaining mistakes on ImageNet
Toward Equation of Motion for Deep Neural Networks: Continuous-time Gradient Descent and Discretization Error Analysis
Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization
Differentially Private Linear Sketches: Efficient Implementations and Applications
Pruning has a disparate impact on model accuracy
Differentiable Analog Quantum Computing for Optimization and Control
CCCP is Frank-Wolfe in disguise
Towards Optimal Communication Complexity in Distributed Non-Convex Optimization
Better SGD using Second-order Momentum
Data-Efficient Structured Pruning via Submodular Optimization
Safe Opponent-Exploitation Subgame Refinement
Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning
Efficient Aggregated Kernel Tests using Incomplete $U$-statistics
Feature Learning in $L_2$-regularized DNNs: Attraction/Repulsion and Sparsity
Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning
Structure-Preserving 3D Garment Modeling with Neural Sewing Machines
WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents
Few-shot Learning for Feature Selection with Hilbert-Schmidt Independence Criterion
Training and Inference on Any-Order Autoregressive Models the Right Way
BagFlip: A Certified Defense Against Data Poisoning
Dynamic Learning in Large Matching Markets
How Powerful are K-hop Message Passing Graph Neural Networks
Global Convergence of Direct Policy Search for State-Feedback $\mathcal{H}_\infty$ Robust Control: A Revisit of Nonsmooth Synthesis with Goldstein Subdifferential
CARLANE: A Lane Detection Benchmark for Unsupervised Domain Adaptation from Simulation to multiple Real-World Domains
PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding
OpenFWI: Large-scale Multi-structural Benchmark Datasets for Full Waveform Inversion
PDEBench: An Extensive Benchmark for Scientific Machine Learning
AutoWS-Bench-101: Benchmarking Automated Weak Supervision with 100 Labels
Why do tree-based models still outperform deep learning on typical tabular data?
A Benchmark for Compositional Visual Reasoning
SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learning
PeRFception: Perception using Radiance Fields
Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world
VeriDark: A Large-Scale Benchmark for Authorship Verification on the Dark Web
FACT: Learning Governing Abstractions Behind Integer Sequences
Geoclidean: Few-Shot Generalization in Euclidean Geometry
MBW: Multi-view Bootstrapping in the Wild
HAPI: A Large-scale Longitudinal Dataset of Commercial ML API Predictions
Change Event Dataset for Discovery from Spatio-temporal Remote Sensing Imagery
3DOS: Towards 3D Open Set Learning - Benchmarking and Understanding Semantic Novelty Detection on Point Clouds
K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions
TempEL: Linking Dynamically Evolving and Newly Emerging Entities
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability
Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark
LIPS - Learning Industrial Physical Simulation benchmark suite
Attraction-Repulsion Spectrum in Neighbor Embeddings
Decimated Framelet System on Graphs and Fast G-Framelet Transforms
On the Approximation of Cooperative Heterogeneous Multi-Agent Reinforcement Learning (MARL) using Mean Field Control (MFC)
A Unified Statistical Learning Model for Rankings and Scores with Application to Grant Panel Review
Rethinking Nonlinear Instrumental Variable Models through Prediction Validity
Sufficient reductions in regression with mixed predictors
[Re] An Implementation of Fair Robust Learning
[Re] Exacerbating Algorithmic Bias through Fairness Attacks
[Re] Transparent Object Tracking Benchmark
(ends 11:00 AM)
12:30 p.m.
Invited Talk:
The Forward-Forward Algorithm for Training Deep Neural Networks
Geoffrey Hinton
(ends 1:30 PM)
1:30 p.m.
Coffee Break
2 p.m.
Poster Session 6
[2:00-4:00]
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
Ambiguous Images With Human Judgments for Robust Visual Event Classification
Recovery and Generalization in Over-Realized Dictionary Learning
Self-Supervised Learning with an Information Maximization Criterion
PAC Prediction Sets for Meta-Learning
BiT: Robustly Binarized Multi-distilled Transformer
CEBaB: Estimating the Causal Effects of Real-World Concepts on NLP Model Behavior
VICRegL: Self-Supervised Learning of Local Visual Features
GenSDF: Two-Stage Learning of Generalizable Signed Distance Functions
Disentangling Transfer in Continual Reinforcement Learning
Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees
TUSK: Task-Agnostic Unsupervised Keypoints
Generalization Gap in Amortized Inference
Online Deep Equilibrium Learning for Regularization by Denoising
Deep invariant networks with differentiable augmentation layers
VICE: Variational Interpretable Concept Embeddings
A contrastive rule for meta-learning
Diagnosing failures of fairness transfer across distribution shift in real-world medical settings
A Contrastive Framework for Neural Text Generation
Redeeming intrinsic rewards via constrained optimization
NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification
Model Preserving Compression for Neural Networks
Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints
Robust Binary Models by Pruning Randomly-initialized Networks
Training Spiking Neural Networks with Local Tandem Learning
Constrained GPI for Zero-Shot Transfer in Reinforcement Learning
Improving Task-Specific Generalization in Few-Shot Learning via Adaptive Vicinal Risk Minimization
DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems
HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences
Noise Attention Learning: Enhancing Noise Robustness by Gradient Scaling
Self-Aware Personalized Federated Learning
Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification
Towards Understanding the Mixture-of-Experts Layer in Deep Learning
Improved Feature Distillation via Projector Ensemble
Two-Stream Network for Sign Language Recognition and Translation
Knowledge Distillation from A Stronger Teacher
Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation
Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models
Feature-Proxy Transformer for Few-Shot Segmentation
Mask Matching Transformer for Few-Shot Segmentation
Prototypical VoteNet for Few-Shot 3D Point Cloud Object Detection
DaDA: Distortion-aware Domain Adaptation for Unsupervised Semantic Segmentation
AniFaceGAN: Animatable 3D-Aware Face Image Generation for Video Avatars
Selective compression learning of latent representations for variable-rate image compression
Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification
Few-Shot Non-Parametric Learning with Deep Latent Variable Model
Revisiting Heterophily For Graph Neural Networks
Distributionally robust weighted k-nearest neighbors
ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
SeqPATE: Differentially Private Text Generation via Knowledge Distillation
Inductive Logical Query Answering in Knowledge Graphs
Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks
K-LITE: Learning Transferable Visual Models with External Knowledge
Diffusion Visual Counterfactual Explanations
Graph Neural Networks are Dynamic Programmers
Assistive Teaching of Motor Control Tasks to Humans
Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off
Black-box coreset variational inference
Supervising the Multi-Fidelity Race of Hyperparameter Configurations
Distributional Reward Estimation for Effective Multi-agent Deep Reinforcement Learning
Amortized Projection Optimization for Sliced Wasserstein Generative Models
CoNSoLe: Convex Neural Symbolic Learning
Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity
WaveBound: Dynamic Error Bounds for Stable Time Series Forecasting
Hierarchical Channel-spatial Encoding for Communication-efficient Collaborative Learning
Spatial Pruned Sparse Convolution for Efficient 3D Object Detection
LASSIE: Learning Articulated Shapes from Sparse Image Ensemble via 3D Part Discovery
Self-Supervised Learning of Brain Dynamics from Broad Neuroimaging Data
Motion Transformer with Global Intention Localization and Local Movement Refinement
Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection
SemMAE: Semantic-Guided Masking for Learning Masked Autoencoders
Improving Multi-Task Generalization via Regularizing Spurious Correlation
Label-Aware Global Consistency for Multi-Label Learning with Single Positive Labels
One Model to Edit Them All: Free-Form Text-Driven Image Manipulation with Semantic Modulations
Latency-aware Spatial-wise Dynamic Networks
Why Do Artificially Generated Data Help Adversarial Robustness
Pre-activation Distributions Expose Backdoor Neurons
Recovering Private Text in Federated Learning of Language Models
Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networks
Towards Robust Blind Face Restoration with Codebook Lookup Transformer
S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning
NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation
UDC: Unified DNAS for Compressible TinyML Models for Neural Processing Units
Using Embeddings for Causal Estimation of Peer Influence in Social Networks
Batch size-invariance for policy optimization
Top Two Algorithms Revisited
Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources
When does return-conditioned supervised learning work for offline reinforcement learning?
Communication Efficient Distributed Learning for Kernelized Contextual Bandits
TaSIL: Taylor Series Imitation Learning
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization
Efficient Active Learning with Abstention
No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation
Integral Probability Metrics PAC-Bayes Bounds
A Fast Scale-Invariant Algorithm for Non-negative Least Squares with Non-negative Data
Online Minimax Multiobjective Optimization: Multicalibeating and Other Applications
Nonstationary Dual Averaging and Online Fair Allocation
The price of unfairness in linear bandits with biased feedback
Augmenting Online Algorithms with $\varepsilon$-Accurate Predictions
A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models
Spectral Bias Outside the Training Set for Deep Networks in the Kernel Regime
Symplectic Spectrum Gaussian Processes: Learning Hamiltonians from Noisy and Sparse Data
Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks
On the Parameterization and Initialization of Diagonal State Space Models
On the Discrimination Risk of Mean Aggregation Feature Imputation in Graphs
Staggered Rollout Designs Enable Causal Inference Under Interference Without Network Knowledge
Near-Optimal Multi-Agent Learning for Safe Coverage Control
Deep Counterfactual Estimation with Categorical Background Variables
Using Partial Monotonicity in Submodular Maximization
A New Family of Generalization Bounds Using Samplewise Evaluated CMI
Tight Lower Bounds on Worst-Case Guarantees for Zero-Shot Learning with Attributes
Bellman Residual Orthogonalization for Offline Reinforcement Learning
Riemannian Diffusion Models
Formulating Robustness Against Unforeseen Attacks
Characterizing Datapoints via Second-Split Forgetting
Fairness Transferability Subject to Bounded Distribution Shift
Oracle Inequalities for Model Selection in Offline Reinforcement Learning
Simulation-guided Beam Search for Neural Combinatorial Optimization
Stars: Tera-Scale Graph Building for Clustering and Learning
Estimating and Explaining Model Performance When Both Covariates and Labels Shift
Simultaneous Missing Value Imputation and Structure Learning with Groups
Signal Processing for Implicit Neural Representations
Regularized Molecular Conformation Fields
GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations
When Does Group Invariant Learning Survive Spurious Correlations?
Diverse Weight Averaging for Out-of-Distribution Generalization
KERPLE: Kernelized Relative Positional Embedding for Length Extrapolation
Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome
Improving Certified Robustness via Statistical Learning with Logical Reasoning
Cooperative Distribution Alignment via JSD Upper Bound
Generalization Bounds with Minimal Dependency on Hypothesis Class via Distributionally Robust Optimization
Minimax Optimal Fixed-Budget Best Arm Identification in Linear Bandits
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients
A Kernelised Stein Statistic for Assessing Implicit Generative Models
Effects of Data Geometry in Early Deep Learning
Deep Differentiable Logic Gate Networks
Exploring the Latent Space of Autoencoders with Interventional Assays
Semi-Discrete Normalizing Flows through Differentiable Tessellation
Video Diffusion Models
What is Where by Looking: Weakly-Supervised Open-World Phrase-Grounding without Text Inputs
Generative multitask learning mitigates target-causing confounding
Adversarial Task Up-sampling for Meta-learning
DHRL: A Graph-Based Approach for Long-Horizon and Sparse Hierarchical Reinforcement Learning
Entropy-Driven Mixed-Precision Quantization for Deep Network Design
Efficient and Effective Augmentation Strategy for Adversarial Training
Dataset Inference for Self-Supervised Models
Exposing and Exploiting Fine-Grained Block Structures for Fast and Accurate Sparse Training
Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?
Alleviating Adversarial Attacks on Variational Autoencoders with MCMC
Smoothed Embeddings for Certified Few-Shot Learning
Instability and Local Minima in GAN Training with Kernel Discriminators
Beyond the Return: Off-policy Function Estimation under User-specified Error-measuring Distributions
On Kernelized Multi-Armed Bandits with Constraints
Target alignment in truncated kernel ridge regression
Local Bayesian optimization via maximizing probability of descent
MEMO: Test Time Robustness via Adaptation and Augmentation
Latent Planning via Expansive Tree Search
Efficient and Effective Optimal Transport-Based Biclustering
Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks
On the Adversarial Robustness of Mixture of Experts
Scalable Distributional Robustness in a Class of Non-Convex Optimization with Guarantees
A framework for bilevel optimization that enables stochastic and global variance reduction algorithms
Trust Region Policy Optimization with Optimal Transport Discrepancies: Duality and Algorithm for Continuous Actions
LobsDICE: Offline Learning from Observation via Stationary Distribution Correction Estimation
A Stochastic Linearized Augmented Lagrangian Method for Decentralized Bilevel Optimization
Amortized Proximal Optimization
Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernel
On the difficulty of learning chaotic dynamics with RNNs
Learning to Configure Computer Networks with Neural Algorithmic Reasoning
Autoregressive Search Engines: Generating Substrings as Document Identifiers
Efficient learning of nonlinear prediction models with time-series privileged information
Revisiting Active Sets for Gaussian Process Decoders
Tractable Function-Space Variational Inference in Bayesian Neural Networks
RNNs of RNNs: Recursive Construction of Stable Assemblies of Recurrent Neural Networks
Use-Case-Grounded Simulations for Explanation Evaluation
Autoformalization with Large Language Models
VCT: A Video Compression Transformer
Diffusion Curvature for Estimating Local Curvature in High Dimensional Data
Linear tree shap
Kernel Interpolation with Sparse Grids
Adaptive Interest for Emphatic Reinforcement Learning
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems
Towards Improving Calibration in Object Detection Under Domain Shift
ELIGN: Expectation Alignment as a Multi-Agent Intrinsic Reward
Model-Based Imitation Learning for Urban Driving
Repairing Neural Networks by Leaving the Right Past Behind
Meta-Reinforcement Learning with Self-Modifying Networks
The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning
NeuForm: Adaptive Overfitting for Neural Shape Editing
Semantic uncertainty intervals for disentangled latent spaces
Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces
First Hitting Diffusion Models for Generating Manifold, Graph and Categorical Data
On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond
Multi-Objective Deep Learning with Adaptive Reference Vectors
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning
Envy-free Policy Teaching to Multiple Agents
When are Local Queries Useful for Robust Learning?
Generalization for multiclass classification with overparameterized linear models
Statistical Learning and Inverse Problems: A Stochastic Gradient Approach
Algorithms that Approximate Data Removal: New Results and Limitations
Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms
Efficient Phi-Regret Minimization in Extensive-Form Games via Online Mirror Descent
Reinforcement Learning in a Birth and Death Process: Breaking the Dependence on the State Space
Provably Efficient Model-Free Constrained RL with Linear Function Approximation
Convergence for score-based generative modeling with polynomial complexity
Efficient Dataset Distillation using Random Feature Approximation
The Effects of Regularization and Data Augmentation are Class Dependent
EpiGRAF: Rethinking training of 3D GANs
Insights into Pre-training via Simpler Synthetic Tasks
GPT3.int8(): 8-bit Matrix Multiplication for Transformers at Scale
STNDT: Modeling Neural Population Activity with Spatiotemporal Transformers
Neural Shape Deformation Priors
SPoVT: Semantic-Prototype Variational Transformer for Dense Point Cloud Semantic Completion
Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone
Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer
SKFlow: Learning Optical Flow with Super Kernels
Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks
Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative
3D Concept Grounding on Neural Fields
Bridge the Gap Between Architecture Spaces via A Cross-Domain Predictor
Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation
Uncertainty-Aware Hierarchical Refinement for Incremental Implicitly-Refined Classification
PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds
ShapeCrafter: A Recursive Text-Conditioned 3D Shape Generation Model
On the Learning Mechanisms in Physical Reasoning
Benign, Tempered, or Catastrophic: Toward a Refined Taxonomy of Overfitting
The trade-offs of model size in large recommendation models : 100GB to 10MB Criteo-tb DLRM model
Geodesic Graph Neural Network for Efficient Graph Representation Learning
Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited
SignRFF: Sign Random Fourier Features
Rapidly Mixing Multiple-try Metropolis Algorithms for Model Selection Problems
What is a Good Metric to Study Generalization of Minimax Learners?
Non-stationary Bandits with Knapsacks
Semi-Supervised Learning with Decision Trees: Graph Laplacian Tree Alternating Optimization
Learning on Arbitrary Graph Topologies via Predictive Coding
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning
Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos
Patching open-vocabulary models by interpolating weights
Generative Visual Prompt: Unifying Distributional Control of Pre-Trained Generative Models
Learning Options via Compression
On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress
Rethinking Generalization in Few-Shot Classification
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model
Deep Fourier Up-Sampling
Provable Subspace Identification Under Post-Nonlinear Mixtures
Distributed Distributionally Robust Optimization with Non-Convex Objectives
GhostNetV2: Enhance Cheap Operation with Long-Range Attention
Exploiting Semantic Relations for Glass Surface Detection
A Simple Approach to Automated Spectral Clustering
Hiding Images in Deep Probabilistic Models
Analyzing Lottery Ticket Hypothesis from PAC-Bayesian Theory Perspective
Large Language Models are Zero-Shot Reasoners
Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization
Randomized Channel Shuffling: Minimal-Overhead Backdoor Attack Detection without Clean Datasets
Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning
A Simple Decentralized Cross-Entropy Method
The Missing Invariance Principle found -- the Reciprocal Twin of Invariant Risk Minimization
Active Learning with Safety Constraints
A PAC-Bayesian Generalization Bound for Equivariant Networks
When to Intervene: Learning Optimal Intervention Policies for Critical Events
Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm
Efficient and Modular Implicit Differentiation
Detection and Localization of Changes in Conditional Distributions
Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers
DGD^2: A Linearly Convergent Distributed Algorithm For High-dimensional Statistical Recovery
Robust Streaming PCA
Trimmed Maximum Likelihood Estimation for Robust Generalized Linear Model
Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space
Off-Policy Evaluation with Policy-Dependent Optimization Response
Bayesian Risk Markov Decision Processes
Variational inference via Wasserstein gradient flows
Alternating Mirror Descent for Constrained Min-Max Games
Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions
Inducing Equilibria via Incentives: Simultaneous Design-and-Play Ensures Global Convergence
Mean Estimation with User-level Privacy under Data Heterogeneity
Intrinsic dimensionality estimation using Normalizing Flows
Washing The Unwashable : On The (Im)possibility of Fairwashing Detection
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank
Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models
Explainable Reinforcement Learning via Model Transforms
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability
Neural Circuit Architectural Priors for Embodied Control
Action-modulated midbrain dopamine activity arises from distributed control policies
When are Offline Two-Player Zero-Sum Markov Games Solvable?
Private and Communication-Efficient Algorithms for Entropy Estimation
Approximate Value Equivalence
Size and depth of monotone neural networks: interpolation and approximation
Characterization of Excess Risk for Locally Strongly Convex Population Risk
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs
A Near-Optimal Best-of-Both-Worlds Algorithm for Online Learning with Feedback Graphs
Optimal Rates for Regularized Conditional Mean Embedding Learning
The Burer-Monteiro SDP method can fail even above the Barvinok-Pataki bound
Contextual Dynamic Pricing with Unknown Noise: Explore-then-UCB Strategy and Improved Regrets
Efficient Frameworks for Generalized Low-Rank Matrix Bandit Problems
Generic bounds on the approximation error for physics-informed (and) operator learning
Zero-Sum Stochastic Stackelberg Games
Fair and Efficient Allocations Without Obvious Manipulations
Faster Linear Algebra for Distance Matrices
SQ Lower Bounds for Learning Single Neurons with Massart Noise
A Differentially Private Linear-Time fPTAS for the Minimum Enclosing Ball Problem
Subquadratic Kronecker Regression with Applications to Tensor Decomposition
Optimal Gradient Sliding and its Application to Optimal Distributed Optimization Under Similarity
Adam Can Converge Without Any Modification On Update Rules
Oracle-Efficient Online Learning for Smoothed Adversaries
Active Learning of Classifiers with Label and Seed Queries
Near-Optimal Goal-Oriented Reinforcement Learning in Non-Stationary Environments
Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning
Improved Coresets for Euclidean $k$-Means
Sampling from Log-Concave Distributions with Infinity-Distance Guarantees
Stochastic Halpern Iteration with Variance Reduction for Stochastic Monotone Inclusions
Sequential Information Design: Learning to Persuade in the Dark
Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization
Exponential Separations in Symmetric Neural Networks
Distributional Convergence of the Sliced Wasserstein Process
Bridging Central and Local Differential Privacy in Data Acquisition Mechanisms
Unlabelled Sample Compression Schemes for Intersection-Closed Classes and Extremal Classes
On Margin Maximization in Linear and ReLU Networks
Memorization and Optimization in Deep Neural Networks with Minimum Over-parameterization
A Scalable Deterministic Global Optimization Algorithm for Training Optimal Decision Tree
Is $L^2$ Physics Informed Loss Always Suitable for Training Physics Informed Neural Network?
Recursive Reasoning in Minimax Games: A Level $k$ Gradient Play Method
Concept Activation Regions: A Generalized Framework For Concept-Based Explanations
Fair Rank Aggregation
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning
Recursive Reinforcement Learning
The price of ignorance: how much does it cost to forget noise structure in low-rank matrix estimation?
PDSketch: Integrated Domain Programming, Learning, and Planning
Human-AI Shared Control via Policy Dissection
Embodied Scene-aware Human Pose Estimation
A Character-Level Length-Control Algorithm for Non-Autoregressive Sentence Summarization
E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance
Semi-Supervised Generative Models for Multiagent Trajectories
Scale-invariant Learning by Physics Inversion
Compositional Generalization in Unsupervised Compositional Representation Learning: A Study on Disentanglement and Emergent Language
Are Defenses for Graph Neural Networks Robust?
Human-AI Collaborative Bayesian Optimisation
On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity
Modular Flows: Differential Molecular Generation
Error Correction Code Transformer
BYOL-Explore: Exploration by Bootstrapped Prediction
TransBoost: Improving the Best ImageNet Performance using Deep Transduction
MorphTE: Injecting Morphology in Tensorized Embeddings
Attention-based Neural Cellular Automata
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RL
VER: Scaling On-Policy RL Leads to the Emergence of Navigation in Embodied Rearrangement
Redistribution of Weights and Activations for AdderNet Quantization
Eliciting Thinking Hierarchy without a Prior
First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization
Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials
Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy
Dataset Distillation via Factorization
Oscillatory Tracking of Continuous Attractor Neural Networks Account for Phase Precession and Procession of Hippocampal Place Cells
Palm up: Playing in the Latent Manifold for Unsupervised Pretraining
Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors' Reasoning with Deep Reinforcement Learning
Large-Scale Retrieval for Reinforcement Learning
What Can Transformers Learn In-Context? A Case Study of Simple Function Classes
Chain of Thought Imitation with Procedure Cloning
First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces
On the Identifiability of Nonlinear ICA: Sparsity and Beyond
Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators
Nonlinear MCMC for Bayesian Machine Learning
Transition to Linearity of General Neural Networks with Directed Acyclic Graph Architecture
Fast Algorithms for Packing Proportional Fairness and its Dual
Approaching Quartic Convergence Rates for Quasi-Stochastic Approximation with Application to Gradient-Free Optimization
Robust Generalized Method of Moments: A Finite Sample Viewpoint
Adapting to Online Label Shift with Provable Guarantees
Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries
Reconstruction on Trees and Low-Degree Polynomials
Benign Underfitting of Stochastic Gradient Descent
Using natural language and program abstractions to instill human inductive biases in machines
Revisiting Injective Attacks on Recommender Systems
PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies
SIXO: Smoothing Inference with Twisted Objectives
Robust Graph Structure Learning via Multiple Statistical Tests
Fairness Reprogramming
Private Graph All-Pairwise-Shortest-Path Distance Release with Improved Error Rate
Optimal Comparator Adaptive Online Learning with Switching Cost
Supervised Training of Conditional Monge Maps
Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference
CGLB: Benchmark Tasks for Continual Graph Learning
ADBench: Anomaly Detection Benchmark
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs
Is one annotation enough? - A data-centric image classification benchmark for noisy and ambiguous label estimation
Dungeons and Data: A Large-Scale NetHack Dataset
Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning
EPIC-KITCHENS VISOR Benchmark: VIdeo Segmentations and Object Relations
EgoTaskQA: Understanding Human Tasks in Egocentric Videos
SCAMPS: Synthetics for Camera Measurement of Physiological Signals
Robustness Disparities in Face Detection
Long Range Graph Benchmark
CLEVRER-Humans: Describing Physical and Causal Events the Human Way
CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks
OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics
SkinCon: A skin disease dataset densely annotated by domain experts for fine-grained debugging and analysis
Understanding Aesthetics with Language: A Photo Critique Dataset for Aesthetic Assessment
ENS-10: A Dataset For Post-Processing Ensemble Weather Forecasts
Hard ImageNet: Segmentations for Objects with Strong Spurious Cues
Video compression dataset and benchmark of learning-based video-quality metrics
TweetNERD - End to End Entity Linking Benchmark for Tweets
BigBio: A Framework for Data-Centric Biomedical Natural Language Processing
Pythae: Unifying Generative Autoencoders in Python - A Benchmarking Use Case
A Dataset for Efforts Towards Achieving the Sustainable Development Goal of Safe Working Environments
(f,Gamma)-Divergences: Interpolating between f-Divergences and Integral Probability Metrics
A Bregman Learning Framework for Sparse Neural Networks
D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data
A Primer for Neural Arithmetic Logic Modules
[Re] Explaining in Style: Training a GAN to explain a classifier in StyleSpace
[Re] A Cluster-based Approach for Improving Isotropy in Contextual Embedding Space
[Re] Replication study of 'Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling'
(ends 4:00 PM)
4 p.m.
Social:
Interdisciplinary ML Mixer
(ends 6:00 PM)
Social:
K-Pop in NeurIPS
(ends 6:00 PM)
Social:
Negotiation Workshop: Feel More Confident Negotiating Your Next Offer in AI
(ends 6:00 PM)
Social:
Open Mic Night
(ends 6:00 PM)
Social:
Haben, The Deafblind Woman Who Conquered Harvard Law
(ends 6:00 PM)
FRI 2 DEC
5:15 a.m.
Registration Desk:
Registration Desk
(ends 3:00 PM)
5:30 a.m.
Workshop:
Second Workshop on Efficient Natural Language and Speech Processing (ENLSP-II)
(ends 4:00 PM)
5:50 a.m.
Workshop:
Progress and Challenges in Building Trustworthy Embodied AI
(ends 3:00 PM)
6 a.m.
Workshop:
AI for Accelerated Materials Design (AI4Mat)
(ends 3:00 PM)
Workshop:
Synthetic Data for Empowering ML Research
(ends 3:00 PM)
Workshop:
AI for Science: Progress and Promises
(ends 4:00 PM)
6:15 a.m.
Workshop:
Order up! The Benefits of Higher-Order Optimization in Machine Learning
(ends 3:00 PM)
6:20 a.m.
Workshop:
3rd Offline Reinforcement Learning Workshop: Offline RL as a "Launchpad"
(ends 3:30 PM)
6:30 a.m.
Workshop:
Human in the Loop Learning (HiLL) Workshop at NeurIPS 2022
(ends 3:00 PM)
Workshop:
Memory in Artificial and Real Intelligence (MemARI)
(ends 3:00 PM)
Workshop:
LaReL: Language and Reinforcement Learning
(ends 3:00 PM)
Workshop:
INTERPOLATE — First Workshop on Interpolation Regularizers and Beyond
(ends 4:00 PM)
Workshop:
Causal Machine Learning for Real-World Impact
(ends 3:00 PM)
Workshop:
Table Representation Learning
(ends 3:45 PM)
Workshop:
Federated Learning: Recent Advances and New Challenges
(ends 3:00 PM)
Workshop:
Has it Trained Yet? A Workshop for Algorithmic Efficiency in Practical Neural Network Training
(ends 3:00 PM)
6:40 a.m.
Workshop:
New Frontiers in Graph Learning
(ends 3:00 PM)
6:45 a.m.
Workshop:
Shared Visual Representations in Human and Machine Intelligence (SVRHM)
(ends 4:00 PM)
6:50 a.m.
Workshop:
NeurIPS 2022 Workshop on Score-Based Methods
(ends 3:00 PM)
6:55 a.m.
Workshop:
Medical Imaging meets NeurIPS
(ends 3:00 PM)
7 a.m.
Workshop:
NeurIPS 2022 Workshop on Meta-Learning
(ends 4:00 PM)
Workshop:
Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems
(ends 4:00 PM)
Workshop:
All Things Attention: Bridging Different Perspectives on Attention
(ends 4:00 PM)
Workshop:
Robustness in Sequence Modeling
(ends 3:00 PM)
Workshop:
Learning from Time Series for Health
(ends 3:00 PM)
8 a.m.
Coffee Break
1 p.m.
Coffee Break
SAT 3 DEC
5:15 a.m.
Registration Desk:
Registration Desk
(ends 10:00 AM)
5:30 a.m.
Workshop:
Algorithmic Fairness through the Lens of Causality and Privacy
(ends 2:55 PM)
Workshop:
Gaze meets ML
(ends 3:00 PM)
Workshop:
Temporal Graph Learning Workshop
(ends 3:00 PM)
Workshop:
Reinforcement Learning for Real Life (RL4RealLife) Workshop
(ends 3:00 PM)
5:50 a.m.
Workshop:
Machine Learning and the Physical Sciences
(ends 3:00 PM)
6:15 a.m.
Workshop:
I Can’t Believe It’s Not Better: Understanding Deep Learning Through Empirical Falsification
(ends 3:00 PM)
Workshop:
Symmetry and Geometry in Neural Representations (NeurReps)
(ends 3:00 PM)
Workshop:
Self-Supervised Learning: Theory and Practice
(ends 3:00 PM)
Workshop:
The Fourth Workshop on AI for Humanitarian Assistance and Disaster Response
(ends 2:15 PM)
6:20 a.m.
Workshop:
Machine Learning for Autonomous Driving
(ends 3:00 PM)
6:30 a.m.
Workshop:
Machine Learning for Systems
(ends 2:30 PM)
Workshop:
Information-Theoretic Principles in Cognitive Systems
(ends 3:00 PM)
Workshop:
Machine Learning in Structural Biology Workshop
(ends 3:00 PM)
6:45 a.m.
Workshop:
Broadening Research Collaborations
(ends 3:00 PM)
Workshop:
Decentralization and Trustworthy Machine Learning in Web3: Methodologies, Platforms, and Applications
(ends 2:00 PM)
6:50 a.m.
Workshop:
Foundation Models for Decision Making
(ends 2:30 PM)
Workshop:
Transfer Learning for Natural Language Processing
(ends 3:00 PM)
6:55 a.m.
Workshop:
MATH-AI: Toward Human-Level Mathematical Reasoning
(ends 3:00 PM)
Workshop:
OPT 2022: Optimization for Machine Learning
(ends 2:50 PM)
7 a.m.
Workshop:
A causal view on dynamical systems
(ends 3:00 PM)
Workshop:
InterNLP: Workshop on Interactive Learning for Natural Language Processing
(ends 2:55 PM)
Workshop:
Workshop on Distribution Shifts: Connecting Methods and Applications
(ends 3:00 PM)
7:30 a.m.
Workshop:
Human Evaluation of Generative Models
(ends 2:15 PM)
8 a.m.
Coffee Break
1 p.m.
Coffee Break
MON 5 DEC
2 a.m.
Tutorial:
The Role of Meta-learning for Few-shot Learning
(ends 4:30 AM)
5 a.m.
Tutorial:
Foundational Robustness of Foundation Models
(ends 7:30 AM)
Tutorial:
Lifelong Learning Machines
(ends 7:30 AM)
6:30 a.m.
Affinity Workshop:
Women in Machine Learning - Virtual
(ends 1:00 PM)
8 a.m.
Tutorial:
Neurosymbolic Programming
(ends 10:30 AM)
Tutorial:
Probabilistic Circuits: Representations, Inference, Learning and Applications
(ends 10:30 AM)
Tutorial:
Advances in NLP and their Applications to Healthcare
(ends 10:30 AM)
11 a.m.
Tutorial:
Advances in Bayesian Optimization
(ends 1:30 PM)
Tutorial:
Incentive-Aware Machine Learning: A Tale of Robustness, Fairness, Improvement, and Performativity
(ends 1:30 PM)
Tutorial:
Algorithmic fairness: at the intersections
(ends 1:30 PM)
Affinity Poster Session:
Virtual Affinity Poster Session
(ends 1:00 PM)
2 p.m.
Tutorial:
Data Compression with Machine Learning
(ends 4:30 PM)
Tutorial:
Creative Culture and Machine Learning
(ends 4:30 PM)
5 p.m.
Tutorial:
Fair and Socially Responsible ML for Recommendations: Challenges and Perspectives
(ends 7:30 PM)
Tutorial:
Theory and Practice of Efficient and Accurate Dataset Construction
(ends 7:30 PM)
TUE 6 DEC
3 a.m.
Competition:
The MineRL BASALT Competition on Fine-tuning from Human Feedback
(ends 6:00 AM)
Competition:
Traffic4cast 2022 – Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from simple Road Counters
(ends 6:30 AM)
Competition:
Cross-Domain MetaDL: Any-Way Any-Shot Learning Competition with Novel Datasets from Practical Domains
(ends 5:35 AM)
Competition:
Real Robot Challenge III - Learning Dexterous Manipulation from Offline Data in the Real World
(ends 6:00 AM)
Competition:
Causal Insights for Learning Paths in Education
(ends 6:00 AM)
Competition:
IGLU: Interactive Grounded Language Understanding in a Collaborative Environment
(ends 6:00 AM)
5 a.m.
Competition:
Driving SMARTS
(ends 7:00 AM)