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SUN 23 JUL
1 p.m.
Registration
(ends 8:00 PM)
2:30 p.m.
Lunch (On Your Own)
3:30 p.m.
Expo Talk Panel with Coffee & a Snack:
Artificial Generative Innovation – How AI will change how humans innovate
(ends 4:30 PM)
4:30 p.m.
Expo Talk Panel with Coffee & a Snack:
Leveraging Self-Attention Models for Logistics problems in Amazon
(ends 5:30 PM)
Expo Talk Panel:
Generative AI and Science
(ends 5:30 PM)
5 p.m.
Exhibit Hall Open
5:30 p.m.
Coffee Only Break 310, 320
6 p.m.
Expo Talk Panel:
Graph Neural Networks in TensorFlow: a Practical Guide
(ends 7:00 PM)
Expo Talk Panel:
Learning Iconic Scenes with Differential Privacy
(ends 7:00 PM)
7 p.m.
Coffee Break - Exhibit Hall 3
8 p.m.
Expo Talk Panel:
Vowpal Wabbit: year in review and looking ahead in an LLM world
(ends 9:00 PM)
Expo Talk Panel:
Colossal-AI: Breakthroughs in Efficient AI
(ends 9:00 PM)
MON 24 JUL
11 a.m.
Registration
(ends 10:00 PM)
11:30 a.m.
Panel:
ICML Education Outreach Panel
(ends 12:30 PM)
11:45 a.m.
Affinity Workshop:
LatinX in AI (LXAI) Workshop
(ends 8:00 PM)
12:30 p.m.
Tutorial:
Tutorial on Multimodal Machine Learning: Principles, Challenges, and Open Questions
(ends 3:00 PM)
Tutorial:
Reinforcement Learning from Human Feedback: A Tutorial *
(ends 3:00 PM)
Tutorial:
Optimal Transport in Learning, Control, and Dynamical Systems
(ends 3:00 PM)
1 p.m.
Exhibit Hall Open
1:30 p.m.
Coffee Break
3 p.m.
Lunch -(On Your Own)
4:30 p.m.
Tutorial:
Self-Supervised Learning in Vision: from Research Advances to Best Practices
(ends 6:30 PM)
Tutorial:
Disinformation, Fake News and Computational Propaganda: Challenges and Opportunities for Machine Learning Research
(ends 6:30 PM)
Tutorial:
How to DP-fy ML: A Practical Tutorial to Machine Learning with Differential Privacy
(ends 6:30 PM)
6:30 p.m.
Coffee Break
7 p.m.
Tutorial:
Responsible AI for Generative AI in Practice: Lessons Learned and Open Challenges
(ends 9:00 PM)
Tutorial:
Recent Advances in the Generalization Theory of Neural Networks *
(ends 9:00 PM)
Tutorial:
Discovering Agent-Centric Latent States in Theory and in Practice
(ends 9:00 PM)
9:15 p.m.
Welcome Reception:
Welcome Reception
(ends 11:00 PM)
9:30 p.m.
EXPO Attendee Raffle Prize Give Away:
EXPO Attendee Raffle Prize Give Away
(ends 9:45 PM)
TUE 25 JUL
11 a.m.
Registration
(ends 9:00 PM)
noon
Opening Remarks:
Opening Remarks
(ends 12:15 PM)
12:15 p.m.
Invited Talk:
Taking the Pulse Of Ethical ML in Health
Marzyeh Ghassemi
(ends 1:30 PM)
1 p.m.
Exhibit Hall Open
1:30 p.m.
Coffee Break
2 p.m.
Poster Session 1 [2:00-3:30]
POUF: Prompt-Oriented Unsupervised Fine-tuning for Large Pre-trained Models
Faith-Shap: The Faithful Shapley Interaction Index
Learning to Learn from APIs: Black-Box Data-Free Meta-Learning
Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization
Learning Compiler Pass Orders using Coreset and Normalized Value Prediction
TabLeak: Tabular Data Leakage in Federated Learning
Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language
Do You Remember? Overcoming Catastrophic Forgetting for Fake Audio Detection
Improving Fair Training under Correlation Shifts
Phase-aware Adversarial Defense for Improving Adversarial Robustness
Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting
Unlocking Slot Attention by Changing Optimal Transport Costs
Optimizing Hyperparameters with Conformal Quantile Regression
Sample and Predict Your Latent: Modality-free Sequential Disentanglement via Contrastive Estimation
Test-time Adaptation with Slot-Centric Models
Long-Tailed Recognition by Mutual Information Maximization between Latent Features and Ground-Truth Labels
Cooperative Open-ended Learning Framework for Zero-Shot Coordination
Model-Bellman Inconsistency for Model-based Offline Reinforcement Learning
Eventual Discounting Temporal Logic Counterfactual Experience Replay
PAC Generalization via Invariant Representations
Nonparametric Generative Modeling with Conditional Sliced-Wasserstein Flows
Few-Sample Feature Selection via Feature Manifold Learning
Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation
Why does Throwing Away Data Improve Worst-Group Error?
TabDDPM: Modelling Tabular Data with Diffusion Models
A Closer Look at the Intervention Procedure of Concept Bottleneck Models
On Investigating the Conservative Property of Score-Based Generative Models
Estimating Causal Effects using a Multi-task Deep Ensemble
Differentiable and Transportable Structure Learning
Monotonic Location Attention for Length Generalization
Cramming: Training a Language Model on a single GPU in one day.
Federated Conformal Predictors for Distributed Uncertainty Quantification
From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning
Complexity of Block Coordinate Descent with Proximal Regularization and Applications to Wasserstein CP-dictionary Learning
A Model-Based Method for Minimizing CVaR and Beyond
Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients
Rotation and Translation Invariant Representation Learning with Implicit Neural Representations
Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding
Adaptive Coordination in Social Embodied Rearrangement
Active Policy Improvement from Multiple Black-box Oracles
Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior Inference
Robust Collaborative Learning with Linear Gradient Overhead
Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data
On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation
Reprogramming Pretrained Language Models for Antibody Sequence Infilling
FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization
The Wisdom of Hindsight Makes Language Models Better Instruction Followers
Deep Regression Unlearning
Bit Allocation using Optimization
QuantumDARTS: Differentiable Quantum Architecture Search for Variational Quantum Algorithms
Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows
DRew: Dynamically Rewired Message Passing with Delay
Few-bit Backward: Quantized Gradients of Activation Functions for Memory Footprint Reduction
Understanding Int4 Quantization for Language Models: Latency Speedup, Composability, and Failure Cases
SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient
Efficient Online Reinforcement Learning with Offline Data
Infusing Lattice Symmetry Priors in Attention Mechanisms for Sample-Efficient Abstract Geometric Reasoning
Locally Regularized Neural Differential Equations: Some Black Boxes were meant to remain closed!
The Edge of Orthogonality: A Simple View of What Makes BYOL Tick
Second-order regression models exhibit progressive sharpening to the edge of stability
Graph Neural Networks with Learnable and Optimal Polynomial Bases
SE(3) diffusion model with application to protein backbone generation
Predicting Ordinary Differential Equations with Transformers
Universal Morphology Control via Contextual Modulation
Interactive Object Placement with Reinforcement Learning
GNOT: A General Neural Operator Transformer for Operator Learning
Local Vertex Colouring Graph Neural Networks
Transformers Meet Directed Graphs
NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-aware Diffusion
Under-Counted Tensor Completion with Neural Incorporation of Attributes
Context-Aware Bayesian Network Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning
Learning for Edge-Weighted Online Bipartite Matching with Robustness Guarantees
Smart Initial Basis Selection for Linear Programs
Conditional Graph Information Bottleneck for Molecular Relational Learning
Discover and Cure: Concept-aware Mitigation of Spurious Correlation
Reconstructive Neuron Pruning for Backdoor Defense
ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation
Why do Nearest Neighbor Language Models Work?
Retrieval-Augmented Multimodal Language Modeling
Optimizing Mode Connectivity for Class Incremental Learning
Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models
Variational Autoencoding Neural Operators
TAN Without a Burn: Scaling Laws of DP-SGD
A Coupled Flow Approach to Imitation Learning
On Kinetic Optimal Probability Paths for Generative Models
Bayesian Design Principles for Frequentist Sequential Learning
Speeding Up Bellman Ford via Minimum Violation Permutations
On User-Level Private Convex Optimization
Delay-Adapted Policy Optimization and Improved Regret for Adversarial MDP with Delayed Bandit Feedback
FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks
Contrastive Learning Meets Homophily: Two Birds with One Stone
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video
ODS: Test-Time Adaptation in the Presence of Open-World Data Shift
Fractional Denoising for 3D Molecular Pre-training
Self-Interpretable Time Series Prediction with Counterfactual Explanations
Prompting Large Language Model for Machine Translation: A Case Study
Uncertain Evidence in Probabilistic Models and Stochastic Simulators
Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting
Pricing Experimental Design: Causal Effect, Expected Revenue and Tail Risk
Tight and fast generalization error bound of graph embedding in metric space
Extrapolated Random Tree for Regression
Horizon-Free and Variance-Dependent Reinforcement Learning for Latent Markov Decision Processes
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes
Active causal structure learning with advice
Active Ranking of Experts Based on their Performances in Many Tasks
Sequential Changepoint Detection via Backward Confidence Sequences
Quantum Lower Bounds for Finding Stationary Points of Nonconvex Functions
The Monge Gap: A Regularizer to Learn All Transport Maps
Learning Distributions over Quantum Measurement Outcomes
Approximation Algorithms for Fair Range Clustering
Short-lived High-volume Bandits
Optimal randomized multilevel Monte Carlo for repeatedly nested expectations
Minimax estimation of discontinuous optimal transport maps: The semi-discrete case
Single Point-Based Distributed Zeroth-Order Optimization with a Non-Convex Stochastic Objective Function
Global Selection of Contrastive Batches via Optimization on Sample Permutations
Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability
Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series
Topologically Faithful Image Segmentation via Induced Matching of Persistence Barcodes
MetricGAN-OKD: Multi-Metric Optimization of MetricGAN via Online Knowledge Distillation for Speech Enhancement
FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models
A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening
Dataset Distillation with Convexified Implicit Gradients
Fast Federated Machine Unlearning with Nonlinear Functional Theory
Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models
Boosting Offline Reinforcement Learning with Action Preference Query
SemSup-XC: Semantic Supervision for Zero and Few-shot Extreme Classification
Uncertainty Estimation for Molecules: Desiderata and Methods
Reliable Measures of Spread in High Dimensional Latent Spaces
Causal Strategic Classification: A Tale of Two Shifts
Learning Preconditioners for Conjugate Gradient PDE Solvers
Flexible Phase Dynamics for Bio-Plausible Contrastive Learning
Learning Belief Representations for Partially Observable Deep RL
A Neural PDE Solver with Temporal Stencil Modeling
Towards Understanding Ensemble Distillation in Federated Learning
Improved Active Multi-Task Representation Learning via Lasso
Bag of Tricks for Training Data Extraction from Language Models
GFlowNet-EM for Learning Compositional Latent Variable Models
Optimizing DDPM Sampling with Shortcut Fine-Tuning
The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms
A Reinforcement Learning Framework for Dynamic Mediation Analysis
Additive Causal Bandits with Unknown Graph
VA-learning as a more efficient alternative to Q-learning
MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation
Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling
Emergence of Sparse Representations from Noise
The Benefits of Model-Based Generalization in Reinforcement Learning
K-SHAP: Policy Clustering Algorithm for Anonymous Multi-Agent State-Action Pairs
Inflow, Outflow, and Reciprocity in Machine Learning
Infinite Action Contextual Bandits with Reusable Data Exhaust
Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games
Improved Online Learning Algorithms for CTR Prediction in Ad Auctions
Bandits with Knapsacks: Advice on Time-Varying Demands
A Kernel-Based View of Language Model Fine-Tuning
Linear CNNs Discover the Statistical Structure of the Dataset Using Only the Most Dominant Frequencies
Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations
Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels
Slot-VAE: Object-Centric Scene Generation with Slot Attention
CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification
Efficient Training of Language Models using Few-Shot Learning
Identifying Useful Learnwares for Heterogeneous Label Spaces
Adaptive Compositional Continual Meta-Learning
D2Match: Leveraging Deep Learning and Degeneracy for Subgraph Matching
What Makes Entities Similar? A Similarity Flooding Perspective for Multi-sourced Knowledge Graph Embeddings
FedHPO-Bench: A Benchmark Suite for Federated Hyperparameter Optimization
A theory of continuous generative flow networks
Neural signature kernels as infinite-width-depth-limits of controlled ResNets
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference
A Robust Test for the Stationarity Assumption in Sequential Decision Making
FedCR: Personalized Federated Learning Based on Across-Client Common Representation with Conditional Mutual Information Regularization
Auxiliary Learning as an Asymmetric Bargaining Game
Retrosynthetic Planning with Dual Value Networks
Accelerated Cyclic Coordinate Dual Averaging with Extrapolation for Composite Convex Optimization
Generating Private Synthetic Data with Genetic Algorithms
AdaBoost is not an Optimal Weak to Strong Learner
Confidence and Dispersity Speak: Characterizing Prediction Matrix for Unsupervised Accuracy Estimation
Unsupervised Skill Discovery for Learning Shared Structures across Changing Environments
Learning Neural Constitutive Laws from Motion Observations for Generalizable PDE Dynamics
Learning Neural PDE Solvers with Parameter-Guided Channel Attention
SLAMB: Accelerated Large Batch Training with Sparse Communication
GuardHFL: Privacy Guardian for Heterogeneous Federated Learning
Continuation Path Learning for Homotopy Optimization
NeuralSlice: Neural 3D Triangle Mesh Reconstruction via Slicing 4D Tetrahedral Meshes
Margin-based Neural Network Watermarking
Sequential Underspecified Instrument Selection for Cause-Effect Estimation
JAWS-X: Addressing Efficiency Bottlenecks of Conformal Prediction Under Standard and Feedback Covariate Shift
Constrained Phi-Equilibria
Diffusion Models are Minimax Optimal Distribution Estimators
Statistical Indistinguishability of Learning Algorithms
Cluster Explanation via Polyhedral Descriptions
Geometric Clifford Algebra Networks
Multisample Flow Matching: Straightening Flows with Minibatch Couplings
Memory-Based Dual Gaussian Processes for Sequential Learning
Training-Free Neural Active Learning with Initialization-Robustness Guarantees
Regret Bounds for Markov Decision Processes with Recursive Optimized Certainty Equivalents
Correcting discount-factor mismatch in on-policy policy gradient methods
A Fast Optimistic Method for Monotone Variational Inequalities
Doubly Adversarial Federated Bandits
Demystifying Disagreement-on-the-Line in High Dimensions
ReDi: Efficient Learning-Free Diffusion Inference via Trajectory Retrieval
Unifying Molecular and Textual Representations via Multi-task Language Modelling
Evidential Interactive Learning for Medical Image Captioning
Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation
Hindsight Learning for MDPs with Exogenous Inputs
Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples
Adversarially Robust PAC Learnability of Real-Valued Functions
Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path
MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations
Truncating Trajectories in Monte Carlo Reinforcement Learning
Approximately Optimal Core Shapes for Tensor Decompositions
Data Structures for Density Estimation
Internet Explorer: Targeted Representation Learning on the Open Web
Learning Unforeseen Robustness from Out-of-distribution Data Using Equivariant Domain Translator
DRCFS: Doubly Robust Causal Feature Selection
Polarity Is All You Need to Learn and Transfer Faster
Homomorphism AutoEncoder --- Learning Group Structured Representations from Observed Transitions
RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents
Multiple Thinking Achieving Meta-Ability Decoupling for Object Navigation
Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling
A Robust Optimisation Perspective on Counterexample-Guided Repair of Neural Networks
Instant Soup: Cheap Pruning Ensembles in A Single Pass Can Draw Lottery Tickets from Large Models
Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels
Do Machine Learning Models Learn Statistical Rules Inferred from Data?
Set-membership Belief State-based Reinforcement Learning for POMDPs
Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference
Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards
Online Learning with Feedback Graphs: The True Shape of Regret
High Probability Convergence of Stochastic Gradient Methods
On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits
Sequence Modeling with Multiresolution Convolutional Memory
Dropout Reduces Underfitting
Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis
Uncovering Adversarial Risks of Test-Time Adaptation
Trapdoor Normalization with Irreversible Ownership Verification
Detecting Out-of-distribution Data through In-distribution Class Prior
Automatic Intrinsic Reward Shaping for Exploration in Deep Reinforcement Learning
MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation
Unsupervised Out-of-Distribution Detection with Diffusion Inpainting
Markovian Gaussian Process Variational Autoencoders
On Heterogeneous Treatment Effects in Heterogeneous Causal Graphs
Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity
Large Language Models Struggle to Learn Long-Tail Knowledge
Invariance in Policy Optimisation and Partial Identifiability in Reward Learning
Global optimality for Euclidean CCCP under Riemannian convexity
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice
On the Occupancy Measure of Non-Markovian Policies in Continuous MDPs
Achieving High Accuracy with PINNs via Energy Natural Gradient Descent
A Distribution Optimization Framework for Confidence Bounds of Risk Measures
A Kernel Stein Test of Goodness of Fit for Sequential Models
PCA-based Multi-Task Learning: a Random Matrix Approach
Neural Status Registers
Kernel Logistic Regression Approximation of an Understandable ReLU Neural Network
Free-Form Variational Inference for Gaussian Process State-Space Models
Sparse Learning of Dynamical Systems in RKHS: An Operator-Theoretic Approach
Rethinking Backdoor Attacks
Do Perceptually Aligned Gradients Imply Robustness?
Understanding Oversquashing in GNNs through the Lens of Effective Resistance
One-Shot Compression of Large Edge-Exchangeable Graphs using Bits-Back Coding
Solving High-Dimensional PDEs with Latent Spectral Models
Two-Scale Gradient Descent Ascent Dynamics Finds Mixed Nash Equilibria of Continuous Games: A Mean-Field Perspective
An Information-Theoretic Analysis of Nonstationary Bandit Learning
Learning Representations without Compositional Assumptions
Meta-learning Parameterized Skills
On the Convergence Rate of Gaussianization with Random Rotations
Intrinsic Sliced Wasserstein Distances for Comparing Collections of Probability Distributions on Manifolds and Graphs
Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets
TGRL: An Algorithm for Teacher Guided Reinforcement Learning
The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing
FeDXL: Provable Federated Learning for Deep X-Risk Optimization
Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization
Efficient List-Decodable Regression using Batches
Approximate Stein Classes for Truncated Density Estimation
Rethinking Weak Supervision in Helping Contrastive Learning
Spatial Implicit Neural Representations for Global-Scale Species Mapping
Interventional Causal Representation Learning
Denoising MCMC for Accelerating Diffusion-Based Generative Models
Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies
Poisoning Generative Replay in Continual Learning to Promote Forgetting
Continual Task Allocation in Meta-Policy Network via Sparse Prompting
Text-To-Concept (and Back) via Cross-Model Alignment
Crafting Training Degradation Distribution for the Accuracy-Generalization Trade-off in Real-World Super-Resolution
Maximum Optimality Margin: A Unified Approach for Contextual Linear Programming and Inverse Linear Programming
Bidirectional Looking with A Novel Double Exponential Moving Average to Adaptive and Non-adaptive Momentum Optimizers
State and parameter learning with PARIS particle Gibbs
On Computing Optimal Tree Ensembles
Opponent-Limited Online Search for Imperfect Information Games
Robust Subtask Learning for Compositional Generalization
Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits
Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning
Fully Dynamic Submodular Maximization over Matroids
One-sided Matrix Completion from Two Observations Per Row
The Saddle-Point Method in Differential Privacy
Geometric Autoencoders - What You See is What You Decode
Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape
DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation
Continual Learning in Linear Classification on Separable Data
The Implicit Regularization of Dynamical Stability in Stochastic Gradient Descent
Multi-channel Autobidding with Budget and ROI Constraints
Raising the Cost of Malicious AI-Powered Image Editing
(ends 3:30 PM)
3:30 p.m.
Lunch -(On Your Own)
5 p.m.
Poster Session 2 [5:00-6:30]
Policy Evaluation and Temporal-Difference Learning in Continuous Time and Space: A Martingale Approach
Distributed Stochastic Gradient Descent: Nonconvexity, Nonsmoothness, and Convergence to Local Minima
Weakly Supervised Disentangled Generative Causal Representation Learning
Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism
Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks
Non-asymptotic Properties of Individualized Treatment Rules from Sequentially Rule-Adaptive Trials
Data-Derived Weak Universal Consistency
On the Convergence Rates of Policy Gradient Methods
Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence
Existence, Stability and Scalability of Orthogonal Convolutional Neural Networks
Constraint Reasoning Embedded Structured Prediction
Project and Forget: Solving Large-Scale Metric Constrained Problems
CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms
Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables
MALTS: Matching After Learning to Stretch
Exploiting locality in high-dimensional Factorial hidden Markov models
abess: A Fast Best-Subset Selection Library in Python and R
Adversarial Classification: Necessary Conditions and Geometric Flows
On Generalizations of Some Distance Based Classifiers for HDLSS Data
Selective Machine Learning of the Average Treatment Effect with an Invalid Instrumental Variable
XAI Beyond Classification: Interpretable Neural Clustering
Conditions and Assumptions for Constraint-based Causal Structure Learning
Flexible Model Aggregation for Quantile Regression
A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates
Deep linear networks can benignly overfit when shallow ones do
Global Convergence of Sub-gradient Method for Robust Matrix Recovery: Small Initialization, Noisy Measurements, and Over-parameterization
Knowledge Hypergraph Embedding Meets Relational Algebra
A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models
Minimal Width for Universal Property of Deep RNN
Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments
Towards Learning to Imitate from a Single Video Demonstration
Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities
The multimarginal optimal transport formulation of adversarial multiclass classification
Sampling random graph homomorphisms and applications to network data analysis
Cluster-Specific Predictions with Multi-Task Gaussian Processes
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations
Lifelong Language Pretraining with Distribution-Specialized Experts
PreNAS: Preferred One-Shot Learning Towards Efficient Neural Architecture Search
Random Shuffle Transformer for Image Restoration
Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection
Investigating the Role of Model-Based Learning in Exploration and Transfer
Tractable Control for Autoregressive Language Generation
Scaling of Class-wise Training Losses for Post-hoc Calibration
The Benefits of Mixup for Feature Learning
SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning
Probabilistic Attention-to-Influence Neural Models for Event Sequences
Conditional Tree Matching for Inference-Time Adaptation of Tree Prediction Models
Loss-Guided Diffusion Models for Plug-and-Play Controllable Generation
Disentangled Multi-Fidelity Deep Bayesian Active Learning
Self-supervised Neural Factor Analysis for Disentangling Utterance-level Speech Representations
Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the Least
Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning
Transformers as Algorithms: Generalization and Stability in In-context Learning
Unveiling The Mask of Position-Information Pattern Through the Mist of Image Features
Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach
Learning the Right Layers a Data-Driven Layer-Aggregation Strategy for Semi-Supervised Learning on Multilayer Graphs
Revisiting the Linear-Programming Framework for Offline RL with General Function Approximation
Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup
SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series
Towards Trustworthy Explanation: On Causal Rationalization
Wasserstein Barycenter Matching for Graph Size Generalization of Message Passing Neural Networks
Neural Algorithmic Reasoning with Causal Regularisation
Beam Tree Recursive Cells
Bigger, Better, Faster: Human-level Atari with human-level efficiency
How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control
Sharp Variance-Dependent Bounds in Reinforcement Learning: Best of Both Worlds in Stochastic and Deterministic Environments
Constrained Efficient Global Optimization of Expensive Black-box Functions
Learning-Rate-Free Learning by D-Adaptation
Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems
DUET: 2D Structured and Approximately Equivariant Representations
Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection
PaLM-E: An Embodied Multimodal Language Model
Language Instructed Reinforcement Learning for Human-AI Coordination
ContraBAR: Contrastive Bayes-Adaptive Deep RL
Flash: Concept Drift Adaptation in Federated Learning
Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees
Perturbation Analysis of Neural Collapse
Towards Robust Graph Incremental Learning on Evolving Graphs
Learning Lightweight Object Detectors via Multi-Teacher Progressive Distillation
Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication
Interpretable Neural-Symbolic Concept Reasoning
Adversarial Collaborative Learning on Non-IID Features
Efficient Latency-Aware CNN Depth Compression via Two-Stage Dynamic Programming
Gradient-based Wang--Landau Algorithm: A Novel Sampler for Output Distribution of Neural Networks over the Input Space
Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC
Dual Propagation: Accelerating Contrastive Hebbian Learning with Dyadic Neurons
SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge
Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time
Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning
A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs
Ewald-based Long-Range Message Passing for Molecular Graphs
A Theoretical Analysis of the Learning Dynamics under Class Imbalance
How much does Initialization Affect Generalization?
Gradient Descent Monotonically Decreases the Sharpness of Gradient Flow Solutions in Scalar Networks and Beyond
Towards Reliable Neural Specifications
FAENet: Frame Averaging Equivariant GNN for Materials Modeling
Feature Programming for Multivariate Time Series Prediction
Neural Latent Aligner: Cross-trial Alignment for Learning Representations of Complex, Naturalistic Neural Data
Rethinking Visual Reconstruction: Experience-Based Content Completion Guided by Visual Cues
LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation
Graph Positional Encoding via Random Feature Propagation
Structure-informed Language Models Are Protein Designers
Robust Camera Pose Refinement for Multi-Resolution Hash Encoding
Causal Structure Learning for Latent Intervened Non-stationary Data
Robust Situational Reinforcement Learning in Face of Context Disturbances
Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing
Non-stationary Reinforcement Learning under General Function Approximation
Iterative Approximate Cross-Validation
A Critical View of Vision-Based Long-Term Dynamics Prediction Under Environment Misalignment
Data Poisoning Attacks Against Multimodal Encoders
Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples
Multi-View Masked World Models for Visual Robotic Manipulation
Exploring the Benefits of Training Expert Language Models over Instruction Tuning
AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
Robust Weak Supervision with Variational Auto-Encoders
Bidirectional Learning for Offline Model-based Biological Sequence Design
Fast Inference from Transformers via Speculative Decoding
Action Matching: Learning Stochastic Dynamics from Samples
Private Federated Learning with Autotuned Compression
Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption
A theory of representation learning gives a deep generalisation of kernel methods
Optimistic Planning by Regularized Dynamic Programming
Fast Combinatorial Algorithms for Min Max Correlation Clustering
Semi Bandit dynamics in Congestion Games: Convergence to Nash Equilibrium and No-Regret Guarantees.
Global Optimization with Parametric Function Approximation
Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks
Surrogate Module Learning: Reduce the Gradient Error Accumulation in Training Spiking Neural Networks
One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale
Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise?
Active Learning based Structural Inference
Superhuman Fairness
PWSHAP: A Path-Wise Explanation Model for Targeted Variables
Covariate balancing using the integral probability metric for causal inference
B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding
Collaborative Causal Inference with Fair Incentives
WL meet VC
Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization
Refined Regret for Adversarial MDPs with Linear Function Approximation
User-level Private Stochastic Convex Optimization with Optimal Rates
Delayed Bandits: When Do Intermediate Observations Help?
Bandit Online Linear Optimization with Hints and Queries
Dimensionality Reduction for General KDE Mode Finding
The Power of Uniform Sampling for k-Median
Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps
Fast $(1+\varepsilon)$-Approximation Algorithms for Binary Matrix Factorization
Sketching Meets Differential Privacy: Fast Algorithm for Dynamic Kronecker Projection Maintenance
Reward-Mixing MDPs with Few Latent Contexts are Learnable
Near-Optimal Quantum Coreset Construction Algorithms for Clustering
The Fast Johnson-Lindenstrauss Transform Is Even Faster
Minimum Width of Leaky-ReLU Neural Networks for Uniform Universal Approximation
Towards Theoretical Understanding of Inverse Reinforcement Learning
Nearly Optimal Competitive Ratio for Online Allocation Problems with Two-sided Resource Constraints and Finite Requests
Lowering the Pre-training Tax for Gradient-based Subset Training: A Lightweight Distributed Pre-Training Toolkit
Learning Temporally AbstractWorld Models without Online Experimentation
Learning to Decouple Complex Systems
Regularization-free Diffeomorphic Temporal Alignment Nets
Deep Perturbation Learning: Enhancing the Network Performance via Image Perturbations
Optimizing the Collaboration Structure in Cross-Silo Federated Learning
InfoOT: Information Maximizing Optimal Transport
Stabilizing Transformer Training by Preventing Attention Entropy Collapse
Diffusion Models for Black-Box Optimization
Learning Mixtures of Markov Chains and MDPs
Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction
Importance Weighted Expectation-Maximization for Protein Sequence Design
Offline Meta Reinforcement Learning with In-Distribution Online Adaptation
X-Paste: Revisiting Scalable Copy-Paste for Instance Segmentation using CLIP and StableDiffusion
Open-VCLIP: Transforming CLIP to an Open-vocabulary Video Model via Interpolated Weight Optimization
Data-Copying in Generative Models: A Formal Framework
Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagement
The Persistent Laplacian for Data Science: Evaluating Higher-Order Persistent Spectral Representations of Data
MG-GNN: Multigrid Graph Neural Networks for Learning Multilevel Domain Decomposition Methods
Harmonic Neural Networks
Representation-Driven Reinforcement Learning
Directed Chain Generative Adversarial Networks
Weakly Supervised Regression with Interval Targets
Weighted Sampling without Replacement for Deep Top-$k$ Classification
Are Diffusion Models Vulnerable to Membership Inference Attacks?
Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces
DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm
Quantile Credit Assignment
Statistical Inference on Multi-armed Bandits with Delayed Feedback
Multi-User Reinforcement Learning with Low Rank Rewards
Model-based Offline Reinforcement Learning with Count-based Conservatism
TIDE: Time Derivative Diffusion for Deep Learning on Graphs
A Picture of the Space of Typical Learnable Tasks
Analyzing Diffusion as Serial Reproduction
Formalizing Preferences Over Runtime Distributions
Principled Offline RL in the Presence of Rich Exogenous Information
Differential Privacy, Linguistic Fairness, and Training Data Influence: Impossibility and Possibility Theorems for Multilingual Language Models
Inverse Reinforcement Learning without Reinforcement Learning
Team Belief DAG: Generalizing the Sequence Form to Team Games for Fast Computation of Correlated Team Max-Min Equilibria via Regret Minimization
Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Model
How Bad is Top-$K$ Recommendation under Competing Content Creators?
Task-Specific Skill Localization in Fine-tuned Language Models
What do CNNs Learn in the First Layer and Why? A Linear Systems Perspective
A Three-regime Model of Network Pruning
Stochastic Gradient Descent-Induced Drift of Representation in a Two-Layer Neural Network
SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning
Uncertainty Estimation by Fisher Information-based Evidential Deep Learning
Linkless Link Prediction via Relational Distillation
Controlled Text Generation with Natural Language Instructions
Efficient Personalized Federated Learning via Sparse Model-Adaptation
From Relational Pooling to Subgraph GNNs: A Universal Framework for More Expressive Graph Neural Networks
On the Generalization of Multi-modal Contrastive Learning
On Pitfalls of Test-Time Adaptation
Counterfactual Identifiability of Bijective Causal Models
Multi-Layer Neural Networks as Trainable Ladders of Hilbert Spaces
Taming graph kernels with random features
SGD with AdaGrad Stepsizes: Full Adaptivity with High Probability to Unknown Parameters, Unbounded Gradients and Affine Variance
An Instrumental Variable Approach to Confounded Off-Policy Evaluation
Fair yet Asymptotically Equal Collaborative Learning
Fair and Accurate Decision Making through Group-Aware Learning
Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexity
Matrix Estimation for Individual Fairness
Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA
On the Impact of Knowledge Distillation for Model Interpretability
Neuro-Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal
ClimaX: A foundation model for weather and climate
Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory
No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation
Symmetry-Aware Robot Design with Structured Subgroups
Traversing Between Modes in Function Space for Fast Ensembling
Understanding and Defending Patched-based Adversarial Attacks for Vision Transformer
Sequential Kernelized Independence Testing
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning
Achieving Hierarchy-Free Approximation for Bilevel Programs with Equilibrium Constraints
On Excess Mass Behavior in Gaussian Mixture Models with Orlicz-Wasserstein Distances
Label differential privacy and private training data release
Moccasin: Efficient Tensor Rematerialization for Neural Networks
Rigid Body Flows for Sampling Molecular Crystal Structures
The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning
Training Normalizing Flows from Dependent Data
Bayesian online change point detection with Hilbert space approximate Student-t process
Input uncertainty propagation through trained neural networks
Principled Acceleration of Iterative Numerical Methods Using Machine Learning
Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits
A Two-Stage Active Learning Algorithm for k-Nearest Neighbors
Semi-Dual Unbalanced Quadratic Optimal Transport: fast statistical rates and convergent algorithm.
Contextual Conservative Interleaving Bandits
Are Neurons Actually Collapsed? On the Fine-Grained Structure in Neural Representations
Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian
Graph Switching Dynamical Systems
Probabilistic Concept Bottleneck Models
Function-Space Regularization in Neural Networks: A Probabilistic Perspective
Parallel Online Clustering of Bandits via Hedonic Game
Towards Constituting Mathematical Structures for Learning to Optimize
Tighter Information-Theoretic Generalization Bounds from Supersamples
Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs
CLUTR: Curriculum Learning via Unsupervised Task Representation Learning
ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs
Causal Bounds in Quasi-Markovian Graphs
Generalized Reductions: Making any Hierarchical Clustering Fair and Balanced with Low Cost
SpotEM: Efficient Video Search for Episodic Memory
Provably Invariant Learning without Domain Information
Evaluating Unsupervised Denoising Requires Unsupervised Metrics
Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learning
Identifying Interpretable Subspaces in Image Representations
Causal Proxy Models for Concept-based Model Explanations
Neural Stochastic Differential Games for Time-series Analysis
Implicit Neural Spatial Representations for Time-dependent PDEs
FARE: Provably Fair Representation Learning with Practical Certificates
Improving Visual Prompt Tuning for Self-supervised Vision Transformers
On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline
A Closer Look at Few-shot Classification Again
Bilevel Optimization with Coupled Decision-Dependent Distributions
Approximation and Estimation Ability of Transformers for Sequence-to-Sequence Functions with Infinite Dimensional Input
Federated Linear Contextual Bandits with User-level Differential Privacy
Unconstrained Online Learning with Unbounded Losses
Doubly Optimal No-Regret Learning in Monotone Games
Brainformers: Trading Simplicity for Efficiency
A Modern Look at the Relationship between Sharpness and Generalization
SRATTA: Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning.
Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks
On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation
Progressive Purification for Instance-Dependent Partial Label Learning
LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework
Multi-Objective GFlowNets
Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling
Long-Term Rhythmic Video Soundtracker
Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift
Neural Diffusion Processes
Estimating Joint Treatment Effects by Combining Multiple Experiments
Generalized Implicit Follow-The-Regularized-Leader
PAL: Program-aided Language Models
Looped Transformers as Programmable Computers
Is Learning Summary Statistics Necessary for Likelihood-free Inference?
The Computational Complexity of Concise Hypersphere Classification
Partial Optimality in Cubic Correlation Clustering
Accelerated Stochastic Optimization Methods under Quasar-convexity
Off-Policy Average Reward Actor-Critic with Deterministic Policy Search
Stochastic Gradient Descent under Markovian Sampling Schemes
Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions
Estimation Beyond Data Reweighting: Kernel Method of Moments
spred: Solving L1 Penalty with SGD
Graph Neural Tangent Kernel: Convergence on Large Graphs
Functional Neural Networks: Shift invariant models for functional data with applications to EEG classification
Compositional Score Modeling for Simulation-Based Inference
Unearthing InSights into Mars: Unsupervised Source Separation with Limited Data
Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems
Performative Recommendation: Diversifying Content via Strategic Incentives
"Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shifts
Block Subsampled Randomized Hadamard Transform for Nyström Approximation on Distributed Architectures
Latent Traversals in Generative Models as Potential Flows
Competitive Gradient Optimization
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation
Learning Dynamic Query Combinations for Transformer-based Object Detection and Segmentation
Human-Timescale Adaptation in an Open-Ended Task Space
Expected Gradients of Maxout Networks and Consequences to Parameter Initialization
A Category-theoretical Meta-analysis of Definitions of Disentanglement
On Preemption and Learning in Stochastic Scheduling
Adversarial Cheap Talk
Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks
Improving the Model Consistency of Decentralized Federated Learning
One-Step Estimator for Permuted Sparse Recovery
Improving l1-Certified Robustness via Randomized Smoothing by Leveraging Box Constraints
Neural FIM for learning Fisher information metrics from point cloud data
Learn to Accumulate Evidence from All Training Samples: Theory and Practice
IncDSI: Incrementally Updatable Document Retrieval
Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure
The case for 4-bit precision: k-bit Inference Scaling Laws
A Toy Model of Universality: Reverse Engineering how Networks Learn Group Operations
Generated Graph Detection
LIV: Language-Image Representations and Rewards for Robotic Control
PromptBoosting: Black-Box Text Classification with Ten Forward Passes
Learning Controllable Degradation for Real-World Super-Resolution via Constrained Flows
PASTA: Pessimistic Assortment Optimization
Which Tricks are Important for Learning to Rank?
Neural Wasserstein Gradient Flows for Discrepancies with Riesz Kernels
Multicalibration as Boosting for Regression
Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation
On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters
Posterior Sampling for Deep Reinforcement Learning
Multi-Agent Best Arm Identification with Private Communications
A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel
Multi-agent Online Scheduling: MMS Allocations for Indivisible Items
Double-Weighting for Covariate Shift Adaptation
Online Local Differential Private Quantile Inference via Self-normalization
Dink-Net: Neural Clustering on Large Graphs
MABe22: A Multi-Species Multi-Task Benchmark for Learned Representations of Behavior
Fast Excess Risk Rates via Offset Rademacher Complexity
Settling the Reward Hypothesis
Transcendental Idealism of Planner: Evaluating Perception from Planning Perspective for Autonomous Driving
Algorithms for bounding contribution for histogram estimation under user-level privacy
(ends 6:30 PM)
6:30 p.m.
Coffee Only Break
7 p.m.
Invited Talk:
Machine Learning with Social Purpose
Shakir Mohamed
(ends 8:00 PM)
8 p.m.
Coffee Break
8:30 p.m.
Oral A1 Causal Learning, RL, Personalization [8:30-10:00]
Oral s 8:30-9:50
[8:30] Bayesian Design Principles for Frequentist Sequential Learning
[8:38] Towards Theoretical Understanding of Inverse Reinforcement Learning
[8:46] On the Power of Pre-training for Generalization in RL: Provable Benefits and Hardness
[8:54] Delayed Feedback in Kernel Bandits
[9:02] Provably Learning Object-Centric Representations
[9:10] Task-specific experimental design for treatment effect estimation
[9:18] Are labels informative in semi-supervised learning? Estimating and leveraging the missing-data mechanism.
[9:26] Interventional Causal Representation Learning
[9:34] Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge
[9:42] Sequential Underspecified Instrument Selection for Cause-Effect Estimation
(ends 10:00 PM)
Oral A2 Computer Vision and Efficient ML [8:30-10:00]
Oral s 8:30-9:50
[8:30] Raising the Cost of Malicious AI-Powered Image Editing
[8:38] Dynamics-inspired Neuromorphic Visual Representation Learning
[8:46] Scaling Vision Transformers to 22 Billion Parameters
[8:54] Facial Expression Recognition with Adaptive Frame Rate based on Multiple Testing Correction
[9:02] Fourmer: An Efficient Global Modeling Paradigm for Image Restoration
[9:10] Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mapping
[9:18] Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
[9:26] Rockmate: an Efficient, Fast, Automatic and Generic Tool for Re-materialization in PyTorch
[9:34] SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge
[9:42] Fast Inference from Transformers via Speculative Decoding
(ends 10:00 PM)
Oral A3 ML Theory [8:30-10:00]
Oral s 8:30-9:58
[8:30] Self-Repellent Random Walks on General Graphs - Achieving Minimal Sampling Variance via Nonlinear Markov Chains
[8:38] Tighter Lower Bounds for Shuffling SGD: Random Permutations and Beyond
[8:46] Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression
[8:54] Tighter Information-Theoretic Generalization Bounds from Supersamples
[9:02] Beyond the Universal Law of Robustness: Sharper Laws for Random Features and Neural Tangent Kernels
[9:10] Bayes-optimal Learning of Deep Random Networks of Extensive-width
[9:18] Why does Throwing Away Data Improve Worst-Group Error?
[9:26] Marginalization is not Marginal: No Bad VAE Local Minima when Learning Optimal Sparse Representations
[9:34] Sharper Bounds for $\ell_p$ Sensitivity Sampling
[9:42] AdaBoost is not an Optimal Weak to Strong Learner
[9:50] Generalization on the Unseen, Logic Reasoning and Degree Curriculum
(ends 10:00 PM)
Oral A4 Diffusion [8:30-10:00]
Oral s 8:30-9:50
[8:30] AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners
[8:38] Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples
[8:46] Graphically Structured Diffusion Models
[8:54] Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines?
[9:02] Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models
[9:10] Diffusion Models are Minimax Optimal Distribution Estimators
[9:18] GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration
[9:26] OCD: Learning to Overfit with Conditional Diffusion Models
[9:34] Denoising MCMC for Accelerating Diffusion-Based Generative Models
[9:42] Cones: Concept Neurons in Diffusion Models for Customized Generation
(ends 10:00 PM)
Oral A5 Reinforcement Learning 1 [8:30-10:00]
Oral s 8:30-9:50
[8:30] Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the Machiavelli Benchmark
[8:38] Information-Theoretic State Space Model for Multi-View Reinforcement Learning
[8:46] Reparameterized Policy Learning for Multimodal Trajectory Optimization
[8:54] Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL
[9:02] Subequivariant Graph Reinforcement Learning in 3D Environments
[9:10] A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs
[9:18] Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap
[9:26] Efficient RL via Disentangled Environment and Agent Representations
[9:34] Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning
[9:42] On the Statistical Benefits of Temporal Difference Learning
(ends 10:00 PM)
Oral A6 Reinforcement Learning 2 [8:30-10:00]
Oral s 8:30-9:50
[8:30] Learning GFlowNets From Partial Episodes For Improved Convergence And Stability
[8:38] The Dormant Neuron Phenomenon in Deep Reinforcement Learning
[8:46] Reinforcement Learning from Passive Data via Latent Intentions
[8:54] Best of Both Worlds Policy Optimization
[9:02] Exponential Smoothing for Off-Policy Learning
[9:10] Quantile Credit Assignment
[9:18] Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels
[9:26] Hierarchies of Reward Machines
[9:34] Human-Timescale Adaptation in an Open-Ended Task Space
[9:42] Settling the Reward Hypothesis
(ends 10:00 PM)
WED 26 JUL
11 a.m.
Registration
(ends 9:00 PM)
12:30 p.m.
Invited Talk:
The Future of ML in Biology: CRISPR for Health and Climate
Jennifer Doudna
(ends 1:30 PM)
1 p.m.
Exhibit Hall Open
1:30 p.m.
Coffee Break
2 p.m.
Poster Session 3 [2:00-3:30]
On Uni-Modal Feature Learning in Supervised Multi-Modal Learning
Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning
ChiPFormer: Transferable Chip Placement via Offline Decision Transformer
Machine Learning Force Fields with Data Cost Aware Training
Out-of-Domain Robustness via Targeted Augmentations
Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation
Bidirectional Adaptation for Robust Semi-Supervised Learning with Inconsistent Data Distributions
FAIRER: Fairness as Decision Rationale Alignment
ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction
Entropy-driven Unsupervised Keypoint Representation Learning in Videos
CocktailSGD: Fine-tuning Foundation Models over 500Mbps Networks
Optimization for Amortized Inverse Problems
Deep Latent State Space Models for Time-Series Generation
HyperTuning: Toward Adapting Large Language Models without Back-propagation
When does Privileged information Explain Away Label Noise?
Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning
Differentiable Multi-Target Causal Bayesian Experimental Design
From Temporal to Contemporaneous Iterative Causal Discovery in the Presence of Latent Confounders
Reinforcement Learning in Low-rank MDPs with Density Features
When and How Does Known Class Help Discover Unknown Ones? Provable Understanding Through Spectral Analysis
Temporal Label Smoothing for Early Event Prediction
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift
Extrapolative Controlled Sequence Generation via Iterative Refinement
Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction
Hyperbolic Representation Learning: Revisiting and Advancing
COLA: Orchestrating Error Coding and Learning for Robust Neural Network Inference Against Hardware Defects
Learning Prescriptive ReLU Networks
Representer Point Selection for Explaining Regularized High-dimensional Models
Parallel Neurosymbolic Integration with Concordia
SparseGPT: Massive Language Models Can be Accurately Pruned in One-Shot
The Flan Collection: Designing Data and Methods for Effective Instruction Tuning
Multi-Fidelity Covariance Estimation in the Log-Euclidean Geometry
Pareto Regret Analyses in Multi-objective Multi-armed Bandit
Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching
Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals
Orthogonality-Enforced Latent Space in Autoencoders: An Approach to Learning Disentangled Representations
SinDDM: A Single Image Denoising Diffusion Model
Distilling Internet-Scale Vision-Language Models into Embodied Agents
Emergent Agentic Transformer from Chain of Hindsight Experience
Bootstrapped Representations in Reinforcement Learning
PPG Reloaded: An Empirical Study on What Matters in Phasic Policy Gradient
An SDE for Modeling SAM: Theory and Insights
Beyond the Universal Law of Robustness: Sharper Laws for Random Features and Neural Tangent Kernels
MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer Tasks
FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning
Are Large Kernels Better Teachers than Transformers for ConvNets?
Neural Prediction Errors enable Analogical Visual Reasoning in Human Standard Intelligence Tests
Test-Time Style Shifting: Handling Arbitrary Styles in Domain Generalization
CrossSplit: Mitigating Label Noise Memorization through Data Splitting
Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning
Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network
On Sampling with Approximate Transport Maps
Differentiable Simulations for Enhanced Sampling of Rare Events
Scaling Spherical CNNs
Hardware-Aware Compression with Random Operation Access Specific Tile (ROAST) Hashing
Can Large Language Models Reason about Program Invariants?
A Unifying Framework to the Analysis of Interaction Methods using Synergy Functions
Scaling Vision Transformers to 22 Billion Parameters
On Regularization and Inference with Label Constraints
On Bridging the Gap between Mean Field and Finite Width Deep Random Multilayer Perceptron with Batch Normalization
SAM operates far from home: eigenvalue regularization as a dynamical phenomenon
Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction
End-to-end Differentiable Clustering with Associative Memories
Fourmer: An Efficient Global Modeling Paradigm for Image Restoration
HOPE: High-order Graph ODE For Modeling Interacting Dynamics
SlotGAT: Slot-based Message Passing for Heterogeneous Graphs
Graph Inductive Biases in Transformers without Message Passing
Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generation
Autoregressive Diffusion Model for Graph Generation
Optimal Arms Identification with Knapsacks
Adaptive IMLE for Few-shot Pretraining-free Generative Modelling
Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching
Demystifying Uneven Vulnerability of Link Stealing Attacks against Graph Neural Networks
Bayesian Reparameterization of Reward-Conditioned Reinforcement Learning with Energy-based Models
MonoNeRF: Learning Generalizable NeRFs from Monocular Videos without Camera Poses
Muse: Text-To-Image Generation via Masked Generative Transformers
A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models
VectorMapNet: End-to-end Vectorized HD Map Learning
A Statistical Perspective on Retrieval-Based Models
Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL
Actor-Critic Alignment for Offline-to-Online Reinforcement Learning
Pre-computed memory or on-the-fly encoding? A hybrid approach to retrieval augmentation makes the most of your compute
Universal Physics-Informed Neural Networks: Symbolic Differential Operator Discovery with Sparse Data
PAC-Bayesian Offline Contextual Bandits With Guarantees
Exponential Smoothing for Off-Policy Learning
A Kernelized Stein Discrepancy for Biological Sequences
Sequential Counterfactual Risk Minimization
Optimal LP Rounding and Linear-Time Approximation Algorithms for Clustering Edge-Colored Hypergraphs
Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models
Improved Online Conformal Prediction via Strongly Adaptive Online Learning
A Study on Transformer Configuration and Training Objective
Learning to Boost Training by Periodic Nowcasting Near Future Weights
Evolving Semantic Prototype Improves Generative Zero-Shot Learning
A Universal Unbiased Method for Classification from Aggregate Observations
LipsNet: A Smooth and Robust Neural Network with Adaptive Lipschitz Constant for High Accuracy Optimal Control
BiRT: Bio-inspired Replay in Vision Transformers for Continual Learning
On the Effectiveness of Offline RL for Dialogue Response Generation
Generalized Disparate Impact for Configurable Fairness Solutions in ML
Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits
Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten
On the Connection Between MPNN and Graph Transformer
A Fully First-Order Method for Stochastic Bilevel Optimization
Horizon-free Learning for Markov Decision Processes and Games: Stochastically Bounded Rewards and Improved Bounds
Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits
Concurrent Shuffle Differential Privacy Under Continual Observation
The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation
Constant Matters: Fine-grained Error Bound on Differentially Private Continual Observation
Projected Tensor Power Method for Hypergraph Community Recovery
LegendreTron: Uprising Proper Multiclass Loss Learning
A Nearly-Optimal Bound for Fast Regression with $\ell_\infty$ Guarantee
Adapting to game trees in zero-sum imperfect information games
Efficient Rate Optimal Regret for Adversarial Contextual MDPs Using Online Function Approximation
Tight Regret Bounds for Single-pass Streaming Multi-armed Bandits
Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories
Fast Rates for Maximum Entropy Exploration
Achieving Linear Speedup in Non-IID Federated Bilevel Learning
Measuring the Impact of Programming Language Distribution
Hierarchical Imitation Learning with Vector Quantized Models
CataBEEM: Integrating Latent Interaction Categories in Node-wise Community Detection Models for Network Data
Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics
SAAL: Sharpness-Aware Active Learning
Structural Re-weighting Improves Graph Domain Adaptation
Total Variation Graph Neural Networks
STEP: Learning N:M Structured Sparsity Masks from Scratch with Precondition
Distributional Offline Policy Evaluation with Predictive Error Guarantees
Decentralized SGD and Average-direction SAM are Asymptotically Equivalent
Go Beyond Imagination: Maximizing Episodic Reachability with World Models
Multi-task Hierarchical Adversarial Inverse Reinforcement Learning
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation
On Provable Copyright Protection for Generative Models
Attributing Image Generative Models using Latent Fingerprints
On the Robustness of Text Vectorizers
Neural Inverse Operators for Solving PDE Inverse Problems
Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally Coupled Oscillatory Recurrent Neural Networks
Graph Reinforcement Learning for Network Control via Bi-Level Optimization
Image Restoration with Mean-Reverting Stochastic Differential Equations
Variance Control for Distributional Reinforcement Learning
Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value
Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEs
Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs
Policy Gradient in Robust MDPs with Global Convergence Guarantee
Stable Estimation of Heterogeneous Treatment Effects
Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling
Fair and Optimal Classification via Post-Processing
Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP
End-to-end Training of Deep Boltzmann Machines by Unbiased Contrastive Divergence with Local Mode Initialization
A Generalization of ViT/MLP-Mixer to Graphs
Model-agnostic Measure of Generalization Difficulty
The Acquisition of Physical Knowledge in Generative Neural Networks
PAC Prediction Sets for Large Language Models of Code
Deep Laplacian-based Options for Temporally-Extended Exploration
Learning-augmented private algorithms for multiple quantile release
Efficient Exploration via Epistemic-Risk-Seeking Policy Optimization
Best Arm Identification in Multi-Agent Multi-Armed Bandits
Probably Anytime-Safe Stochastic Combinatorial Semi-Bandits
Who Needs to Know? Minimal Knowledge for Optimal Coordination
Mimetic Initialization of Self-Attention Layers
Data Representations' Study of Latent Image Manifolds
On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology
General Sequential Episodic Memory Model
Shape-Guided Dual-Memory Learning for 3D Anomaly Detection
Calibrating Multimodal Learning
DualHSIC: HSIC-Bottleneck and Alignment for Continual Learning
ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts
Anchor Sampling for Federated Learning with Partial Client Participation
A Complete Expressiveness Hierarchy for Subgraph GNNs via Subgraph Weisfeiler-Lehman Tests
Towards Deep Attention in Graph Neural Networks: Problems and Remedies
On the Impact of Algorithmic Recourse on Social Segregation
Memory-Based Meta-Learning on Non-Stationary Distributions
Polynomial Preconditioning for Gradient Methods
Delay-agnostic Asynchronous Coordinate Update Algorithm
Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time
Parameter-Level Soft-Masking for Continual Learning
Low Complexity Homeomorphic Projection to Ensure Neural-Network Solution Feasibility for Optimization over (Non-)Convex Set
On the Within-Group Fairness of Screening Classifiers
Provable Benefit of Mixup for Finding Optimal Decision Boundaries
Large Language Models Can Be Easily Distracted by Irrelevant Context
Resurrecting Recurrent Neural Networks for Long Sequences
NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data
Differentially Private Optimization on Large Model at Small Cost
SpeedDETR: Speed-aware Transformers for End-to-end Object Detection
NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition
Less is More: Task-aware Layer-wise Distillation for Language Model Compression
Understanding Backdoor Attacks through the Adaptability Hypothesis
Proximal Causal Learning of Conditional Average Treatment Effects
Fast Private Kernel Density Estimation via Locality Sensitive Quantization
Approximate Causal Effect Identification under Weak Confounding
Regression with Label Permutation in Generalized Linear Model
Multi-Task Differential Privacy Under Distribution Skew
Over-parametrization via Lifting for Low-rank Matrix Sensing: Conversion of Spurious Solutions to Strict Saddle Points
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion
Towards Understanding and Improving GFlowNet Training
Certifying Ensembles: A General Certification Theory with S-Lipschitzness
Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy
Adaptive Barrier Smoothing for First-Order Policy Gradient with Contact Dynamics
Variational Sparse Inverse Cholesky Approximation for Latent Gaussian Processes via Double Kullback-Leibler Minimization
BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic Programming
EF21-P and Friends: Improved Theoretical Communication Complexity for Distributed Optimization with Bidirectional Compression
Existence and Estimation of Critical Batch Size for Training Generative Adversarial Networks with Two Time-Scale Update Rule
Bayesian Neural Networks Avoid Encoding Complex and Perturbation-Sensitive Concepts
StriderNet: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes
On the Functional Similarity of Robust and Non-Robust Neural Representations
Aligning Language Models with Preferences through $f$-divergence Minimization
On the Convergence of Federated Averaging with Cyclic Client Participation
Understanding Gradient Regularization in Deep Learning: Efficient Finite-Difference Computation and Implicit Bias
Robustly Learning a Single Neuron via Sharpness
Online Restless Bandits with Unobserved States
Beyond Reward: Offline Preference-guided Policy Optimization
Streaming Submodular Maximization with Differential Privacy
Atari-5: Distilling the Arcade Learning Environment down to Five Games
DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature
An Effective Meaningful Way to Evaluate Survival Models
Efficient RL via Disentangled Environment and Agent Representations
Learning Deductive Reasoning from Synthetic Corpus based on Formal Logic
Towards Understanding Generalization of Graph Neural Networks
Learning Physical Models that Can Respect Conservation Laws
Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?
Architecture-Agnostic Masked Image Modeling -- From ViT back to CNN
Curious Replay for Model-based Adaptation
Temporally Consistent Transformers for Video Generation
Adaptive Estimation of Graphical Models under Total Positivity
Nearly-tight Bounds for Deep Kernel Learning
Best of Both Worlds Policy Optimization
Optimal Stochastic Non-smooth Non-convex Optimization through Online-to-Non-convex Conversion
High-dimensional Location Estimation via Norm Concentration for Subgamma Vectors
CRISP: Curriculum based Sequential neural decoders for Polar code family
Banker Online Mirror Descent: A Universal Approach for Delayed Online Bandit Learning
Scalable Adaptive Computation for Iterative Generation
On the Training Instability of Shuffling SGD with Batch Normalization
Reasons for the Superiority of Stochastic Estimators over Deterministic Ones: Robustness, Consistency and Perceptual Quality
On the Robustness of Randomized Ensembles to Adversarial Perturbations
Exploring Model Dynamics for Accumulative Poisoning Discovery
Adversarial Learning of Distributional Reinforcement Learning
Understanding the Complexity Gains of Single-Task RL with a Curriculum
Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models
Understanding and Generalizing Contrastive Learning from the Inverse Optimal Transport Perspective
Constrained Causal Bayesian Optimization
Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects
LinSATNet: The Positive Linear Satisfiability Neural Networks
Specializing Smaller Language Models towards Multi-Step Reasoning
Not all Strongly Rayleigh Distributions Have Small Probabilistic Generating Circuits
Tighter Bounds on the Expressivity of Transformer Encoders
Equivariant Architectures for Learning in Deep Weight Spaces
H-Likelihood Approach to Deep Neural Networks with Temporal-Spatial Random Effects for High-Cardinality Categorical Features
Fairness in Streaming Submodular Maximization over a Matroid Constraint
Difference of submodular minimization via DC programming
Scalable Safe Policy Improvement via Monte Carlo Tree Search
Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-type Samplers
Enabling First-Order Gradient-Based Learning for Equilibrium Computation in Markets
Semiparametrically Efficient Off-Policy Evaluation in Linear Markov Decision Processes
Sequential Predictive Conformal Inference for Time Series
Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances
Generative Adversarial Symmetry Discovery
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification
OCD: Learning to Overfit with Conditional Diffusion Models
Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models
Conformalization of Sparse Generalized Linear Models
Protecting Language Generation Models via Invisible Watermarking
The Dormant Neuron Phenomenon in Deep Reinforcement Learning
Model Transferability with Responsive Decision Subjects
Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs
FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation
Tighter Lower Bounds for Shuffling SGD: Random Permutations and Beyond
Facial Expression Recognition with Adaptive Frame Rate based on Multiple Testing Correction
Lookahead When It Matters: Adaptive Non-causal Transformers for Streaming Neural Transducers
PAC-Bayesian Generalization Bounds for Adversarial Generative Models
How to address monotonicity for model risk management?
Causal Modeling of Policy Interventions From Treatment–Outcome Sequences
Towards Robust and Safe Reinforcement Learning with Benign Off-policy Data
Low-Switching Policy Gradient with Exploration via Online Sensitivity Sampling
Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization
On Enhancing Expressive Power via Compositions of Single Fixed-Size ReLU Network
Convex Geometry of ReLU-layers, Injectivity on the Ball and Local Reconstruction
Image generation with shortest path diffusion
On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement Learning
LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning
InGram: Inductive Knowledge Graph Embedding via Relation Graphs
UPSCALE: Unconstrained Channel Pruning
Path Neural Networks: Expressive and Accurate Graph Neural Networks
Rethink DARTS Search Space and Renovate a New Benchmark
Variational Open-Domain Question Answering
Gradient-Free Structured Pruning with Unlabeled Data
GREAD: Graph Neural Reaction-Diffusion Networks
Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources
Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration
Rockmate: an Efficient, Fast, Automatic and Generic Tool for Re-materialization in PyTorch
The Statistical Scope of Multicalibration
Cross-Entropy Loss Functions: Theoretical Analysis and Applications
On Many-Actions Policy Gradient
The Test of Tests: A Framework for Differentially Private Hypothesis Testing
Automated Search for Conjectures on Mathematical Constants using Analysis of Integer Sequences
Bandit Multi-linear DR-Submodular Maximization and Its Applications on Adversarial Submodular Bandits
Dimension-independent Certified Neural Network Watermarks via Mollifier Smoothing
Nonparametric Extensions of Randomized Response for Private Confidence Sets
Revisiting Weighted Aggregation in Federated Learning with Neural Networks
SegCLIP: Patch Aggregation with Learnable Centers for Open-Vocabulary Semantic Segmentation
Cooperation in the Latent Space: The Benefits of Adding Mixture Components in Variational Autoencoders
From Perception to Programs: Regularize, Overparameterize, and Amortize
Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice
A Hybrid Quantum-Classical Approach based on the Hadamard Transform for the Convolutional Layer
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings
(ends 3:30 PM)
3:30 p.m.
Lunch -(On Your Own)
5 p.m.
Poster Session 4 [5:00-6:30]
Continual Learners are Incremental Model Generalizers
CLIPood: Generalizing CLIP to Out-of-Distributions
Effectively Using Public Data in Privacy Preserving Machine Learning
Graph Generative Model for Benchmarking Graph Neural Networks
Learning Intuitive Policies Using Action Features
Shiftable Context: Addressing Training-Inference Context Mismatch in Simultaneous Speech Translation
Improving Adversarial Robustness Through the Contrastive-Guided Diffusion Process
Deep Graph Representation Learning and Optimization for Influence Maximization
Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning
InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models
Scalable Set Encoding with Universal Mini-Batch Consistency and Unbiased Full Set Gradient Approximation
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning
Consistency Models
XTab: Cross-table Pretraining for Tabular Transformers
Simple and Fast Group Robustness by Automatic Feature Reweighting
Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs
Hypervolume Knowledge Gradient: A Lookahead Approach for Multi-Objective Bayesian Optimization with Partial Information
Nonlinear Causal Discovery with Latent Confounders
Marginalization is not Marginal: No Bad VAE Local Minima when Learning Optimal Sparse Representations
MonoFlow: Rethinking Divergence GANs via the Perspective of Wasserstein Gradient Flows
Reachability-Aware Laplacian Representation in Reinforcement Learning
A Large-Scale Study of Probabilistic Calibration in Neural Network Regression
Hypothesis Transfer Learning with Surrogate Classification Losses: Generalization Bounds through Algorithmic Stability
Synthetic data for model selection
Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions
Feature Expansion for Graph Neural Networks
GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets
Predicting Rare Events by Shrinking Towards Proportional Odds
Decoding Layer Saliency in Language Transformers
Hyena Hierarchy: Towards Larger Convolutional Language Models
Towards Controlled Data Augmentations for Active Learning
Mirror Sinkhorn: Fast Online Optimization on Transport Polytopes
Online Nonstochastic Control with Adversarial and Static Constraints
Accelerated Primal-Dual Methods for Convex-Strongly-Concave Saddle Point Problems
Simplifying Momentum-based Positive-definite Submanifold Optimization with Applications to Deep Learning
Restoration based Generative Models
Self-supervised learning of Split Invariant Equivariant representations
Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames
Simple Embodied Language Learning as a Byproduct of Meta-Reinforcement Learning
Simplified Temporal Consistency Reinforcement Learning
Reinforcement Learning from Passive Data via Latent Intentions
Differentially Private Sharpness-Aware Training
Provably and Practically Efficient Neural Contextual Bandits
Deterministic equivalent and error universality of deep random features learning
How Does Information Bottleneck Help Deep Learning?
Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning
End-to-End Full-Atom Antibody Design
A Closer Look at Self-Supervised Lightweight Vision Transformers
Trompt: Towards a Better Deep Neural Network for Tabular Data
Pre-training for Speech Translation: CTC Meets Optimal Transport
LSDS++ : Dual Sampling for Accelerated k-means++
Efficient Parametric Approximations of Neural Network Function Space Distance
Why Target Networks Stabilise Temporal Difference Methods
Can Forward Gradient Match Backpropagation?
Toward Large Kernel Models
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
Nugget: Neural Agglomerative Embeddings of Text
Mechanistic Mode Connectivity
Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
Evaluating Self-Supervised Learning via Risk Decomposition
Width and Depth Limits Commute in Residual Networks
On the Convergence of SARSA with Linear Function Approximation
Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue Clouds
Differentiable Tree Operations Promote Compositional Generalization
Bayesian Progressive Deep Topic Model with Knowledge Informed Textual Data Coarsening Process
Learning Subpocket Prototypes for Generalizable Structure-based Drug Design
LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation
Fisher Information Embedding for Node and Graph Learning
Composer: Creative and Controllable Image Synthesis with Composable Conditions
Vector Quantized Wasserstein Auto-Encoder
Tensor Gaussian Process with Contraction for Multi-Channel Imaging Analysis
Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments
Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning
Nested Elimination: A Simple Algorithm for Best-Item Identification From Choice-Based Feedback
Regularizing Towards Soft Equivariance Under Mixed Symmetries
Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability
Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting
Future-conditioned Unsupervised Pretraining for Decision Transformer
Controllability-Aware Unsupervised Skill Discovery
Text-To-4D Dynamic Scene Generation
CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets
A Unified Audio-Visual Learning Framework for Localization, Separation, and Recognition
Policy Contrastive Imitation Learning
Supported Trust Region Optimization for Offline Reinforcement Learning
Synthetic Data, Real Errors: How (Not) to Publish and Use Synthetic Data
Controlled Differential Equations on Long Sequences via Non-standard Wavelets
Reinforcement Learning with History Dependent Dynamic Contexts
When do Minimax-fair Learning and Empirical Risk Minimization Coincide?
Sampling-Based Accuracy Testing of Posterior Estimators for General Inference
Learning to Maximize Mutual Information for Dynamic Feature Selection
Submodular Order Functions and Assortment Optimization
Federated Heavy Hitter Recovery under Linear Sketching
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning
Revisiting Discriminative vs. Generative Classifiers: Theory and Implications
Continual Vision-Language Representation Learning with Off-Diagonal Information
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation
Loss Balancing for Fair Supervised Learning
PixelAsParam: A Gradient View on Diffusion Sampling with Guidance
Demonstration-free Autonomous Reinforcement Learning via Implicit and Bidirectional Curriculum
Tuning Computer Vision Models With Task Rewards
Fairness in Matching under Uncertainty
Multiply Robust Off-policy Evaluation and Learning under Truncation by Death
The Ideal Continual Learner: An Agent That Never Forgets
Oscillation-free Quantization for Low-bit Vision Transformers
Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies
Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost
Dynamic Constrained Submodular Optimization with Polylogarithmic Update Time
$H$-Consistency Bounds for Pairwise Misranking Loss Surrogates
Differentially Private Hierarchical Clustering with Provable Approximation Guarantees
Accelerated Infeasibility Detection of Constrained Optimization and Fixed-Point Iterations
Near-Optimal Cryptographic Hardness of Agnostically Learning Halfspaces and ReLU Regression under Gaussian Marginals
On the Optimality of Misspecified Kernel Ridge Regression
Communication-Constrained Bandits under Additive Gaussian Noise
Quantum Speedups for Zero-Sum Games via Improved Dynamic Gibbs Sampling
Nearly Optimal Algorithms with Sublinear Computational Complexity for Online Kernel Regression
Maximal Initial Learning Rates in Deep ReLU Networks
Dynamical Linear Bandits
Fast Algorithms for Distributed k-Clustering with Outliers
Adaptive Computation with Elastic Input Sequence
Stein Variational Goal Generation for adaptive Exploration in Multi-Goal Reinforcement Learning
Weighted Flow Diffusion for Local Graph Clustering with Node Attributes: an Algorithm and Statistical Guarantees
Wrapped Cauchy Distributed Angular Softmax for Long-Tailed Visual Recognition
FedDisco: Federated Learning with Discrepancy-Aware Collaboration
Personalized Federated Learning with Inferred Collaboration Graphs
ModelDiff: A Framework for Comparing Learning Algorithms
Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks
TR0N: Translator Networks for 0-Shot Plug-and-Play Conditional Generation
SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process
Averaged Method of Multipliers for Bi-Level Optimization without Lower-Level Strong Convexity
SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models
One-shot Imitation in a Non-Stationary Environment via Multi-Modal Skill
GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarks
Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions
Theoretical Behavior of XAI Methods in the Presence of Suppressor Variables
A Watermark for Large Language Models
Towards Explaining Distribution Shifts
Topological Point Cloud Clustering
Learning Affinity with Hyperbolic Representation for Spatial Propagation
Continuous Spatiotemporal Transformer
Trainability, Expressivity and Interpretability in Gated Neural ODEs
DevFormer: A Symmetric Transformer for Context-Aware Device Placement
Subequivariant Graph Reinforcement Learning in 3D Environments
Tilted Sparse Additive Models
Dual Focal Loss for Calibration
A Flexible Diffusion Model
Learning Control by Iterative Inversion
On the Global Convergence of Risk-Averse Policy Gradient Methods with Expected Conditional Risk Measures
Difference-in-Differences Meets Tree-based Methods: Heterogeneous Treatment Effects Estimation with Unmeasured Confounding
Fundamental Tradeoffs in Learning with Prior Information
Reinforcement Learning Can Be More Efficient with Multiple Rewards
Minimizing Trajectory Curvature of ODE-based Generative Models
All in a Row: Compressed Convolution Networks for Graphs
Generalization on the Unseen, Logic Reasoning and Degree Curriculum
Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines?
AutoCoreset: An Automatic Practical Coreset Construction Framework
Automatic Data Augmentation via Invariance-Constrained Learning
On the Privacy-Robustness-Utility Trilemma in Distributed Learning
Target-based Surrogates for Stochastic Optimization
Regret Minimization and Convergence to Equilibria in General-sum Markov Games
Online Mechanism Design for Information Acquisition
Sequential Strategic Screening
Nonlinear Advantage: Trained Networks Might Not Be As Complex as You Think
Lottery Tickets in Evolutionary Optimization: On Sparse Backpropagation-Free Trainability
Dynamics-inspired Neuromorphic Visual Representation Learning
Prototype-oriented unsupervised anomaly detection for multivariate time series
Provable Dynamic Fusion for Low-Quality Multimodal Data
Master-ASR: Achieving Multilingual Scalability and Low-Resource Adaptation in ASR with Modular Learning
MAGANet: Achieving Combinatorial Generalization by Modeling a Group Action
Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction
Towards credible visual model interpretation with path attribution
GOAT: A Global Transformer on Large-scale Graphs
CodeIPPrompt: Intellectual Property Infringement Assessment of Code Language Models
On the Stepwise Nature of Self-Supervised Learning
Gradient Descent Finds the Global Optima of Two-Layer Physics-Informed Neural Networks
Optimal Shrinkage for Distributed Second-Order Optimization
Personalized Federated Learning under Mixture of Distributions
Does Continual Learning Equally Forget All Parameters?
Alternately Optimized Graph Neural Networks
Optimizing NOTEARS Objectives via Topological Swaps
Subset Selection Based On Multiple Rankings in the Presence of Bias: Effectiveness of Fairness Constraints for Multiwinner Voting Score Functions
Hiding Data Helps: On the Benefits of Masking for Sparse Coding
Learnability and Algorithm for Continual Learning
Revisiting Structured Variational Autoencoders
Training Deep Surrogate Models with Large Scale Online Learning
KDEformer: Accelerating Transformers via Kernel Density Estimation
SpENCNN: Orchestrating Encoding and Sparsity for Fast Homomorphically Encrypted Neural Network Inference
Efficient Learning of Mesh-Based Physical Simulation with Bi-Stride Multi-Scale Graph Neural Network
Online Prototype Alignment for Few-shot Policy Transfer
Adversarial Parameter Attack on Deep Neural Networks
Mixture Proportion Estimation Beyond Irreducibility
Sketch-Flip-Merge: Mergeable Sketches for Private Distinct Counting
Structure Learning of Latent Factors via Clique Search on Correlation Thresholded Graphs
Conformal Prediction with Missing Values
Multi-Task Off-Policy Learning from Bandit Feedback
Spurious Valleys and Clustering Behavior of Neural Networks
User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems
Learning GFlowNets From Partial Episodes For Improved Convergence And Stability
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification
Instrumental Variable Estimation of Average Partial Causal Effects
Stabilizing GANs' Training with Brownian Motion Controller
Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems
Minimalistic Predictions to Schedule Jobs with Online Precedence Constraints
One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training
Does a Neural Network Really Encode Symbolic Concepts?
FLEX: an Adaptive Exploration Algorithm for Nonlinear Systems
Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs
Concept-based Explanations for Out-of-Distribution Detectors
Men Also Do Laundry: Multi-Attribute Bias Amplification
Scaling Laws for Reward Model Overoptimization
HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic Encryption
Benign Overfitting in Deep Neural Networks under Lazy Training
Gradient Descent Converges Linearly for Logistic Regression on Separable Data
Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy
Multi-Agent Learning from Learners
Neural Network Accelerated Implicit Filtering: Integrating Neural Network Surrogates With Provably Convergent Derivative Free Optimization Methods
Improved Algorithms for Multi-period Multi-class Packing Problems with Bandit Feedback
Automatically Auditing Large Language Models via Discrete Optimization
In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation
Online Platt Scaling with Calibeating
Stratified Adversarial Robustness with Rejection
Properties of the Mallows Model Depending on the Number of Alternatives: A Warning for an Experimentalist
Motion Question Answering via Modular Motion Programs
Pretraining Language Models with Human Preferences
Improving Bi-level Optimization Based Methods with Inspiration from Humans' Classroom Study Techniques
Quantifying the Variability Collapse of Neural Networks
On the Estimation of Gaussian Mixture Copula Models
Understanding the Distillation Process from Deep Generative Models to Tractable Probabilistic Circuits
Learning Antidote Data to Individual Unfairness
Structured Cooperative Learning with Graphical Model Priors
Coordinate Descent Methods for Fractional Minimization
Optimal Convergence Rates for Agnostic Nyström Kernel Learning
Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation
Convergence of First-Order Methods for Constrained Nonconvex Optimization with Dependent Data
Learning Mixtures of Gaussians with Censored Data
Contextual Combinatorial Bandits with Probabilistically Triggered Arms
Chemically Transferable Generative Backmapping of Coarse-Grained Proteins
Transformers Learn In-Context by Gradient Descent
Robust Explanation for Free or At the Cost of Faithfulness
Graph Contrastive Backdoor Attacks
Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score
Identification of the Adversary from a Single Adversarial Example
HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagation
Multi-task Representation Learning for Pure Exploration in Linear Bandits
Open-Vocabulary Universal Image Segmentation with MaskCLIP
Integrating Prior Knowledge in Contrastive Learning with Kernel
Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge
Proper Scoring Rules for Survival Analysis
QAS-Bench: Rethinking Quantum Architecture Search and A Benchmark
On the Power of Foundation Models
Polyhedral Complex Extraction from ReLU Networks using Edge Subdivision
Causal Discovery with Latent Confounders Based on Higher-Order Cumulants
Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning
Optimal Sets and Solution Paths of ReLU Networks
Provable Reset-free Reinforcement Learning by No-Regret Reduction
Gibbsian Polar Slice Sampling
Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes
Subsample Ridge Ensembles: Equivalences and Generalized Cross-Validation
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes
I$^2$SB: Image-to-Image Schrödinger Bridge
Learning Hidden Markov Models When the Locations of Missing Observations are Unknown
GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration
A Deep Conjugate Direction Method for Iteratively Solving Linear Systems
Von Mises Mixture Distributions for Molecular Conformation Generation
Forget Unlearning: Towards True Data-Deletion in Machine Learning
Understanding Plasticity in Neural Networks
Individually Fair Learning with One-Sided Feedback
Multi-class Graph Clustering via Approximated Effective $p$-Resistance
STEERING : Stein Information Directed Exploration for Model-Based Reinforcement Learning
Optimal No-Regret Learning for One-Sided Lipschitz Functions
Towards a Persistence Diagram that is Robust to Noise and Varied Densities
Robust Speech Recognition via Large-Scale Weak Supervision
LookupFFN: Making Transformers Compute-lite for CPU inference
Differentially Private Distributed Bayesian Linear Regression with MCMC
Are Equivariant Equilibrium Approximators Beneficial?
The Impact of Exploration on Convergence and Performance of Multi-Agent Q-Learning Dynamics
Tight Certification of Adversarially Trained Neural Networks via Nonconvex Low-Rank Semidefinite Relaxations
Fast Online Node Labeling for Very Large Graphs
From Adaptive Query Release to Machine Unlearning
Brauer's Group Equivariant Neural Networks
Cold Analysis of Rao-Blackwellized Straight-Through Gumbel-Softmax Gradient Estimator
Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection
Transformed Distribution Matching for Missing Value Imputation
Git-Theta: A Git Extension for Collaborative Development of Machine Learning Models
Moderately Distributional Exploration for Domain Generalization
Improving Expert Predictions with Conformal Prediction
On the Expressive Power of Geometric Graph Neural Networks
CLUSTSEG: Clustering for Universal Segmentation
Mu$^2$SLAM: Multitask, Multilingual Speech and Language Models
Input Perturbation Reduces Exposure Bias in Diffusion Models
RGE: A Repulsive Graph Rectification for Node Classification via Influence
What can online reinforcement learning with function approximation benefit from general coverage conditions?
Quantum Policy Gradient Algorithm with Optimized Action Decoding
A new near-linear time algorithm for k-nearest neighbor search using a compressed cover tree
Lower Bounds for Learning in Revealing POMDPs
Cut your Losses with Squentropy
Statistical Inference and A/B Testing for First-Price Pacing Equilibria
Blossom: an Anytime Algorithm for Computing Optimal Decision Trees
Quantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks with Quantum Computation
Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime
Generalized-Smooth Nonconvex Optimization is As Efficient As Smooth Nonconvex Optimization
Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues
Propensity Matters: Measuring and Enhancing Balancing for Recommendation
RLEG: Vision-Language Representation Learning with Diffusion-based Embedding Generation
Disentangled Generative Models for Robust Prediction of System Dynamics
Social learning spontaneously emerges by searching optimal heuristics with deep reinforcement learning
Computational Asymmetries in Robust Classification
CircuitNet: A Generic Neural Network to Realize Universal Circuit Motif Modeling
Better Training of GFlowNets with Local Credit and Incomplete Trajectories
(ends 6:30 PM)
6:30 p.m.
Coffee Break
7 p.m.
Oral B1 Language Models: Human Impact [7:00-8:30]
Oral s 7:00-8:12
[7:00] When Personalization Harms Performance: Reconsidering the Use of Group Attributes in Prediction
[7:08] Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling
[7:16] Whose Opinions Do Language Models Reflect?
[7:24] A Watermark for Large Language Models
[7:32] DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature
[7:40] Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies
[7:48] Inflow, Outflow, and Reciprocity in Machine Learning
[7:56] Structure-informed Language Models Are Protein Designers
[8:04] Transformers Learn In-Context by Gradient Descent
(ends 8:30 PM)
Oral B2 Language Models: Algorithms and Architecture [7:00-8:30]
Oral s 7:00-8:12
[7:00] Pretraining Language Models with Human Preferences
[7:08] Instant Soup: Cheap Pruning Ensembles in A Single Pass Can Draw Lottery Tickets from Large Models
[7:16] Specializing Smaller Language Models towards Multi-Step Reasoning
[7:24] SparseGPT: Massive Language Models Can be Accurately Pruned in One-Shot
[7:32] Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models
[7:40] FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU
[7:48] BPipe: Memory-Balanced Pipeline Parallelism for Training Large Language Models
[7:56] Tractable Control for Autoregressive Language Generation
[8:04] Equivariant Architectures for Learning in Deep Weight Spaces
(ends 8:30 PM)
Oral B3 Privacy [7:00-8:30]
Oral s 7:00-8:20
[7:00] Nonparametric Extensions of Randomized Response for Private Confidence Sets
[7:08] Differentially Private Hierarchical Clustering with Provable Approximation Guarantees
[7:16] Tight Data Access Bounds for Private Top-$k$ Selection
[7:24] JAWS-X: Addressing Efficiency Bottlenecks of Conformal Prediction Under Standard and Feedback Covariate Shift
[7:32] Active Ranking of Experts Based on their Performances in Many Tasks
[7:40] The Price of Differential Privacy under Continual Observation
[7:48] HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic Encryption
[7:56] Sketch-Flip-Merge: Mergeable Sketches for Private Distinct Counting
[8:04] Fast Private Kernel Density Estimation via Locality Sensitive Quantization
[8:12] Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning
(ends 8:30 PM)
Oral B4 Robustness / Adversarial / Rl Bandits [7:00-8:30]
Oral s 7:00-8:20
[7:00] Adversarial Policies Beat Superhuman Go AIs
[7:08] Adapting to game trees in zero-sum imperfect information games
[7:16] Semi Bandit dynamics in Congestion Games: Convergence to Nash Equilibrium and No-Regret Guarantees.
[7:24] Delving into Noisy Label Detection with Clean Data
[7:32] Robustly Learning a Single Neuron via Sharpness
[7:40] Data Feedback Loops: Model-driven Amplification of Dataset Biases
[7:48] Towards Reliable Neural Specifications
[7:56] Do Perceptually Aligned Gradients Imply Robustness?
[8:04] ODS: Test-Time Adaptation in the Presence of Open-World Data Shift
[8:12] Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression: Fast Convergence and Partial Participation
(ends 8:30 PM)
Oral B5 Self/Semi-Supervised Learning and Interpretability / Observing Aspects of NN [7:00-8:30]
Oral s 7:00-8:20
[7:00] RankMe: Assessing the Downstream Performance of Pretrained Self-Supervised Representations by Their Rank
[7:08] Evaluating Self-Supervised Learning via Risk Decomposition
[7:16] BEATs: Audio Pre-Training with Acoustic Tokenizers
[7:24] Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language
[7:32] Bidirectional Adaptation for Robust Semi-Supervised Learning with Inconsistent Data Distributions
[7:40] TRAK: Attributing Model Behavior at Scale
[7:48] Understanding Plasticity in Neural Networks
[7:56] Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods
[8:04] Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice
[8:12] Brauer's Group Equivariant Neural Networks
(ends 8:30 PM)
Panel:
AI and Marginalized Languages
(ends 8:30 PM)
8:45 p.m.
Town Hall:
ICML Business Meeting - all attendees
(ends 9:15 PM)
10 p.m.
THU 27 JUL
11 a.m.
Registration
(ends 8:00 PM)
11:30 a.m.
Test Of Time:
Learning Fair Representations
(ends 12:00 PM)
12:30 p.m.
Invited Talk:
Proxy objectives in reinforcement learning from human feedback
John Schulman
(ends 1:30 PM)
1 p.m.
Coffee Break
1:30 p.m.
Poster Session 5 [1:30-3:00]
Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining
Underspecification Presents Challenges for Credibility in Modern Machine Learning
BEATs: Audio Pre-Training with Acoustic Tokenizers
OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models
Run-off Election: Improved Provable Defense against Data Poisoning Attacks
Target-Aware Generative Augmentations for Single-Shot Adaptation
AbODE: Ab initio antibody design using conjoined ODEs
Mitigating Spurious Correlations in Multi-modal Models during Fine-tuning
Great Models Think Alike: Improving Model Reliability via Inter-Model Latent Agreement
Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships
Robust Perception through Equivariance
Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design
Offline Reinforcement Learning with Closed-Form Policy Improvement Operators
StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis
Tied-Augment: Controlling Representation Similarity Improves Data Augmentation
Contextual Reliability: When Different Features Matter in Different Contexts
Cell-Free Latent Go-Explore
RACE: Improve Multi-Agent Reinforcement Learning with Representation Asymmetry and Collaborative Evolution
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design
A Model-free Closeness-of-influence Test for Features in Supervised Learning
Towards Understanding and Reducing Graph Structural Noise for GNNs
A New PHO-rmula for Improved Performance of Semi-Structured Networks
Naive imputation implicitly regularizes high-dimensional linear models
In Search for a Generalizable Method for Source Free Domain Adaptation
Applied Online Algorithms with Heterogeneous Predictors
MultiAdam: Parameter-wise Scale-invariant Optimizer for Multiscale Training of Physics-informed Neural Networks
From Hypergraph Energy Functions to Hypergraph Neural Networks
Explainability as statistical inference
Distribution Free Prediction Sets for Node Classification
Simple Hardware-Efficient Long Convolutions for Sequence Modeling
Taxonomy-Structured Domain Adaptation
Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms
Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization
Generalized Polyak Step Size for First Order Optimization with Momentum
Learning Globally Smooth Functions on Manifolds
simple diffusion: End-to-end diffusion for high resolution images
Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mapping
Hyperbolic Image-text Representations
MyoDex: A Generalizable Prior for Dexterous Manipulation
Predictable MDP Abstraction for Unsupervised Model-Based RL
Guiding Pretraining in Reinforcement Learning with Large Language Models
Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning
Why Is Public Pretraining Necessary for Private Model Training?
Discrete Key-Value Bottleneck
Is Overfitting Necessary for Implicit Video Representation?
Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization
AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation
End-to-End Multi-Object Detection with a Regularized Mixture Model
Linear optimal partial transport embedding
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels
A Mathematical Model for Curriculum Learning for Parities
Gaussian processes at the Helm(holtz): A more fluid model for ocean currents
Quantized Distributed Training of Large Models with Convergence Guarantees
FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU
Efficiently predicting high resolution mass spectra with graph neural networks
Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression
Model-based Reinforcement Learning with Scalable Composite Policy Gradient Estimators
The Role of Entropy and Reconstruction in Multi-View Self-Supervised Learning
Bayes-optimal Learning of Deep Random Networks of Extensive-width
Special Properties of Gradient Descent with Large Learning Rates
Group Equivariant Fourier Neural Operators for Partial Differential Equations
Hierarchical Neural Coding for Controllable CAD Model Generation
PLay: Parametrically Conditioned Layout Generation using Latent Diffusion
Effective Neural Topic Modeling with Embedding Clustering Regularization
A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining
Relevant Walk Search for Explaining Graph Neural Networks
Exphormer: Sparse Transformers for Graphs
Quantum 3D Graph Learning with Applications to Molecule Embedding
Generative Graph Dictionary Learning
Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills
Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints
Multiplier Bootstrap-based Exploration
DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian Inference
Graph Mixup with Soft Alignments
Fighting Fire with Fire: Contrastive Debiasing without Bias-free Data via Generative Bias-transformation
UMD: Unsupervised Model Detection for X2X Backdoor Attacks
Repository-Level Prompt Generation for Large Language Models of Code
Domain Adaptation for Time Series Under Feature and Label Shifts
Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization
Fair Densities via Boosting the Sufficient Statistics of Exponential Families
PFNs4BO: In-Context Learning for Bayesian Optimization
Generalized Teacher Forcing for Learning Chaotic Dynamics
Towards a better understanding of representation dynamics under TD-learning
Leveraging Demonstrations to Improve Online Learning: Quality Matters
Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning
New metrics and search algorithms for weighted causal DAGs
One-Shot Federated Conformal Prediction
The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond
Finding Generalization Measures by Contrasting Signal and Noise
Magneto: A Foundation Transformer
Revisiting Pseudo-Label for Single-Positive Multi-Label Learning
Nonparametric Iterative Machine Teaching
dugMatting: Decomposed-Uncertainty-Guided Matting
For Pre-Trained Vision Models in Motor Control, Not All Policy Learning Methods are Created Equal
Explainable Data-Driven Optimization: From Context to Decision and Back Again
Improved Policy Evaluation for Randomized Trials of Algorithmic Resource Allocation
Shortest Edit Path Crossover: A Theory-driven Solution to the Permutation Problem in Evolutionary Neural Architecture Search
ELSA: Efficient Label Shift Adaptation through the Lens of Semiparametric Models
Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space
Smooth Non-stationary Bandits
Revisiting Simple Regret: Fast Rates for Returning a Good Arm
Tight Data Access Bounds for Private Top-$k$ Selection
Omnipredictors for Constrained Optimization
Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano
Escaping saddle points in zeroth-order optimization: the power of two-point estimators
Attribute-Efficient PAC Learning of Low-Degree Polynomial Threshold Functions with Nasty Noise
Statistical Learning under Heterogenous Distribution Shift
Combinatorial Neural Bandits
Does Sparsity Help in Learning Misspecified Linear Bandits?
DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning
Understanding Self-Distillation in the Presence of Label Noise
Self-Repellent Random Walks on General Graphs - Achieving Minimal Sampling Variance via Nonlinear Markov Chains
DSGD-CECA: Decentralized SGD with Communication-Optimal Exact Consensus Algorithm
Exploring Chemical Space with Score-based Out-of-distribution Generation
Thompson Sampling with Diffusion Generative Prior
Improving Graph Generation by Restricting Graph Bandwidth
Coupled Variational Autoencoder
SDDM: Score-Decomposed Diffusion Models on Manifolds for Unpaired Image-to-Image Translation
Half-Hop: A graph upsampling approach for slowing down message passing
Implicit Jacobian regularization weighted with impurity of probability output
Generative Pretraining for Black-Box Optimization
Learning Unnormalized Statistical Models via Compositional Optimization
Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression: Fast Convergence and Partial Participation
MetaDiffuser: Diffusion Model as Conditional Planner for Offline Meta-RL
Grounding Language Models to Images for Multimodal Inputs and Outputs
Change is Hard: A Closer Look at Subpopulation Shift
Task-specific experimental design for treatment effect estimation
2D-Shapley: A Framework for Fragmented Data Valuation
SurProGenes: Survival Risk-Ordered Representation of Cancer Patients and Genes for the Identification of Prognostic Genes
On the Forward Invariance of Neural ODEs
Inferring Relational Potentials in Interacting Systems
Towards Omni-generalizable Neural Methods for Vehicle Routing Problems
Lazy Agents: A New Perspective on Solving Sparse Reward Problem in Multi-agent Reinforcement Learning
Towards Understanding Generalization of Macro-AUC in Multi-label Learning
Supervised Metric Learning to Rank for Retrieval via Contextual Similarity Optimization
Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks
Reflected Diffusion Models
End-to-End Learning for Stochastic Optimization: A Bayesian Perspective
Normalizing Flows for Interventional Density Estimation
Identifiability and Generalizability in Constrained Inverse Reinforcement Learning
Strategic Classification with Unknown User Manipulations
Fast Sampling of Diffusion Models via Operator Learning
Variational Mixture of HyperGenerators for Learning Distributions over Functions
How Do Transformers Learn Topic Structure: Towards a Mechanistic Understanding
On the Relationship Between Explanation and Prediction: A Causal View
Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm
How Many Perturbations Break This Model? Evaluating Robustness Beyond Adversarial Accuracy
Offline Learning in Markov Games with General Function Approximation
When is Realizability Sufficient for Off-Policy Reinforcement Learning?
Optimal Rates and Efficient Algorithms for Online Bayesian Persuasion
Federated Online and Bandit Convex Optimization
Learning useful representations for shifting tasks and distributions
Can Neural Network Memorization Be Localized?
Meta-Learning the Inductive Bias of Simple Neural Circuits
Feature learning in deep classifiers through Intermediate Neural Collapse
SGD with Large Step Sizes Learns Sparse Features
Generative Decoding of Visual Stimuli
Context Consistency Regularization for Label Sparsity in Time Series
Explore and Exploit the Diverse Knowledge in Model Zoo for Domain Generalization
Towards Unbiased Training in Federated Open-world Semi-supervised Learning
Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering
TIPS: Topologically Important Path Sampling for Anytime Neural Networks
Emergent Asymmetry of Precision and Recall for Measuring Fidelity and Diversity of Generative Models in High Dimensions
IRNeXt: Rethinking Convolutional Network Design for Image Restoration
Whose Opinions Do Language Models Reflect?
Global optimality of Elman-type RNNs in the mean-field regime
Sample Complexity of Probability Divergences under Group Symmetry
Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
Pruning via Sparsity-indexed ODE: a Continuous Sparsity Viewpoint
DIVISION: Memory Efficient Training via Dual Activation Precision
Transformer-based Stagewise Decomposition for Large-Scale Multistage Stochastic Optimization
Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation
Linearly Constrained Bilevel Optimization: A Smoothed Implicit Gradient Approach
Paging with Succinct Predictions
Identifiability of Label Noise Transition Matrix
NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation
Random Grid Neural Processes for Parametric Partial Differential Equations
MODeL: Memory Optimizations for Deep Learning
Bi-directional Masks for Efficient N:M Sparse Training
RSC: Accelerate Graph Neural Networks Training via Randomized Sparse Computations
Ske2Grid: Skeleton-to-Grid Representation Learning for Action Recognition
DDGR: Continual Learning with Deep Diffusion-based Generative Replay
Regression with Sensor Data Containing Incomplete Observations
Causal Isotonic Calibration for Heterogeneous Treatment Effects
Fully-Adaptive Composition in Differential Privacy
Unveiling the Latent Space Geometry of Push-Forward Generative Models
Compressing Tabular Data via Latent Variable Estimation
Discrete Continuous Optimization Framework for Simultaneous Clustering and Training in Mixture Models
Theoretical Bounds on the Network Community Profile from Low-rank Semi-definite Programming
Automatically marginalized MCMC in probabilistic programming
Long Horizon Temperature Scaling
Extending Conformal Prediction to Hidden Markov Models with Exact Validity via de Finetti's Theorem for Markov Chains
Efficient preconditioned stochastic gradient descent for estimation in latent variable models
Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons
Adaptive Annealed Importance Sampling with Constant Rate Progress
Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective
Provable Data Subset Selection For Efficient Neural Networks Training
Speed-Oblivious Online Scheduling: Knowing (Precise) Speeds is not Necessary
Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models
CoDi: Co-evolving Contrastive Diffusion Models for Mixed-type Tabular Synthesis
Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search
High-dimensional Clustering onto Hamiltonian Cycle
RLSbench: Domain Adaptation Under Relaxed Label Shift
Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments
Vertical Federated Graph Neural Network for Recommender System
Benign Overfitting in Two-layer ReLU Convolutional Neural Networks
On the Global Convergence of Fitted Q-Iteration with Two-layer Neural Network Parametrization
Policy Regularization with Dataset Constraint for Offline Reinforcement Learning
Internally Rewarded Reinforcement Learning
Second-Order Optimization with Lazy Hessians
Semi-Parametric Contextual Pricing Algorithm using Cox Proportional Hazards Model
Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling
ACAT: Adversarial Counterfactual Attention for Classification and Detection in Medical Imaging
Conformal Inference is (almost) Free for Neural Networks Trained with Early Stopping
System Identification of Neural Systems: If We Got It Right, Would We Know?
Modeling Dynamic Environments with Scene Graph Memory
Diagnosis, Feedback, Adaptation: A Human-in-the-Loop Framework for Test-Time Policy Adaptation
MEWL: Few-shot multimodal word learning with referential uncertainty
Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvature
GLOBE-CE: A Translation Based Approach for Global Counterfactual Explanations
Quantifying the Knowledge in GNNs for Reliable Distillation into MLPs
Model-Aware Contrastive Learning: Towards Escaping the Dilemmas
Adversarial Policies Beat Superhuman Go AIs
Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the Machiavelli Benchmark
AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners
Unifying Nesterov's Accelerated Gradient Methods for Convex and Strongly Convex Objective Functions
Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron
Trading-Off Payments and Accuracy in Online Classification with Paid Stochastic Experts
Improved Analysis of Score-based Generative Modeling: User-Friendly Bounds under Minimal Smoothness Assumptions
Delayed Feedback in Kernel Bandits
A Scalable Frank-Wolfe-Based Algorithm for the Max-Cut SDP
R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents
Random Teachers are Good Teachers
Weak Proxies are Sufficient and Preferable for Fairness with Missing Sensitive Attributes
A Critical Revisit of Adversarial Robustness in 3D Point Cloud Recognition with Diffusion-Driven Purification
Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models
Semi-Offline Reinforcement Learning for Optimized Text Generation
Compressed Decentralized Proximal Stochastic Gradient Method for Nonconvex Composite Problems with Heterogeneous Data
Defects of Convolutional Decoder Networks in Frequency Representation
Cones: Concept Neurons in Diffusion Models for Customized Generation
On the Role of Attention in Prompt-tuning
Diffusion Based Representation Learning
On Data Manifolds Entailed by Structural Causal Models
Distribution-dependent McDiarmid-type Inequalities for Functions of Unbounded Interaction
Towards Learning Geometric Eigen-Lengths Crucial for Fitting Tasks
Topological Singularity Detection at Multiple Scales
Estimating Possible Causal Effects with Latent Variables via Adjustment
Exact Inference in High-order Structured Prediction
Learning Rate Schedules in the Presence of Distribution Shift
Leveraging Offline Data in Online Reinforcement Learning
MixFlows: principled variational inference via mixed flows
Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds
Sketched Ridgeless Linear Regression: The Role of Downsampling
Beyond the Edge of Stability via Two-step Gradient Updates
E$(n)$ Equivariant Message Passing Simplicial Networks
Private Statistical Estimation of Many Quantiles
Learning the Dynamics of Sparsely Observed Interacting Systems
The Power of Learned Locally Linear Models for Nonlinear Policy Optimization
Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees
Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning
Controllable Neural Symbolic Regression
Robust and private stochastic linear bandits
Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs
Forward-Backward Gaussian Variational Inference via JKO in the Bures-Wasserstein Space
Buying Information for Stochastic Optimization
Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning
Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models?
Robustness in Multimodal Learning under Train-Test Modality Mismatch
Revisiting Sampling for Combinatorial Optimization
Nonparametric Density Estimation under Distribution Drift
Network Effects in Performative Prediction Games
A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems
Momentum Ensures Convergence of SIGNSGD under Weaker Assumptions
DADAO: Decoupled Accelerated Decentralized Asynchronous Optimization
Layered State Discovery for Incremental Autonomous Exploration
How Jellyfish Characterise Alternating Group Equivariant Neural Networks
Optimally-weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference
Are labels informative in semi-supervised learning? Estimating and leveraging the missing-data mechanism.
Probabilistic Imputation for Time-series Classification with Missing Data
Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat
Delving into Noisy Label Detection with Clean Data
Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models
The Hessian perspective into the Nature of Convolutional Neural Networks
N$\text{A}^\text{2}$Q: Neural Attention Additive Model for Interpretable Multi-Agent Q-Learning
Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language
Data Efficient Neural Scaling Law via Model Reusing
Beyond Lipschitz Smoothness: A Tighter Analysis for Nonconvex Optimization
Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability
Robust and Scalable Bayesian Online Changepoint Detection
On Distribution Dependent Sub-Logarithmic Query Time of Learned Indexing
Near-Optimal $\Phi$-Regret Learning in Extensive-Form Games
Multi-Objective Population Based Training
Divide and Conquer Dynamic Programming: An Almost Linear Time Change Point Detection Methodology in High Dimensions
The Regret of Exploration and the Control of Bad Episodes in Reinforcement Learning
Efficient Quantum Algorithms for Quantum Optimal Control
Understanding the Role of Feedback in Online Learning with Switching Costs
Prometheus: Taming Sample and Communication Complexities in Constrained Decentralized Stochastic Bilevel Learning
The Catalog Problem: Clustering and Ordering Variable-Sized Sets
A General Representation Learning Framework with Generalization Performance Guarantees
On the Initialization of Graph Neural Networks
EM-Network: Oracle Guided Self-distillation for Sequence Learning
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes
Multi-Task Structural Learning using Local Task Similarity induced Neuron Creation and Removal
Generalization Bounds using Data-Dependent Fractal Dimensions
Improving Graph Neural Networks with Learnable Propagation Operators
CAB: Comprehensive Attention Benchmarking on Long Sequence Modeling
What Can Be Learnt With Wide Convolutional Neural Networks?
(ends 3:00 PM)
3 p.m.
Lunch -(On Your Own)
4:30 p.m.
Poster Session 6 [4:30-6:00]
Effective Structured Prompting by Meta-Learning and Representative Verbalizer
Text Generation with Diffusion Language Models: A Pre-training Approach with Continuous Paragraph Denoise
Parallel $Q$-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation
ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines
MultiRobustBench: Benchmarking Robustness Against Multiple Attacks
Multi-Environment Pretraining Enables Transfer to Action Limited Datasets
Discovering Object-Centric Generalized Value Functions From Pixels
FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction
When Sparsity Meets Contrastive Models: Less Graph Data Can Bring Better Class-Balanced Representations
Boosting Graph Contrastive Learning via Graph Contrastive Saliency
Simple Disentanglement of Style and Content in Visual Representations
Meta Optimal Transport
Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation
Reparameterized Policy Learning for Multimodal Trajectory Optimization
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks
Controlling Type Confounding in Ad Hoc Teamwork with Instance-wise Teammate Feedback Rectification
Revisiting Domain Randomization via Relaxed State-Adversarial Policy Optimization
Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL
On the Identifiability and Estimation of Causal Location-Scale Noise Models
Improving Adversarial Robustness of Deep Equilibrium Models with Explicit Regulations Along the Neural Dynamics
Estimating Heterogeneous Treatment Effects: Mutual Information Bounds and Learning Algorithms
Margin-based sampling in high dimensions: When being active is less efficient than staying passive
Non-autoregressive Conditional Diffusion Models for Time Series Prediction
DiscoBAX - Discovery of optimal intervention sets in genomic experiment design
Implicit Graph Neural Networks: A Monotone Operator Viewpoint
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries
Equivariance with Learned Canonicalization Functions
Graphically Structured Diffusion Models
TRAK: Attributing Model Behavior at Scale
Fast Online Value-Maximizing Prediction Sets with Conformal Cost Control
Recovery Bounds on Class-Based Optimal Transport: A Sum-of-Norms Regularization Framework
Cyclic Block Coordinate Descent With Variance Reduction for Composite Nonconvex Optimization
Toward Efficient Gradient-Based Value Estimation
Conditionally Strongly Log-Concave Generative Models
SinFusion: Training Diffusion Models on a Single Image or Video
Stable and Consistent Prediction of 3D Characteristic Orientation via Invariant Residual Learning
Learning Dense Correspondences between Photos and Sketches
VIMA: Robot Manipulation with Multimodal Prompts
Jump-Start Reinforcement Learning
ILLUME: Rationalizing Vision-Language Models through Human Interactions
LeadFL: Client Self-Defense against Model Poisoning in Federated Learning
Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods
Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions
Learning Instance-Specific Augmentations by Capturing Local Invariances
NTK-approximating MLP Fusion for Efficient Language Model Fine-tuning
Compositional Exemplars for In-context Learning
Provable Multi-instance Deep AUC Maximization with Stochastic Pooling
Towards Quantum Machine Learning for Constrained Combinatorial Optimization: a Quantum QAP Solver
Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models
Constrained Monotonic Neural Networks
General Covariance Data Augmentation for Neural PDE Solvers
Fast as CHITA: Neural Network Pruning with Combinatorial Optimization
GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models
Learning to Design Analog Circuits to Meet Threshold Specifications
Overcoming Simplicity Bias in Deep Networks using a Feature Sieve
Low-Variance Gradient Estimation in Unrolled Computation Graphs with ES-Single
Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks
Gradient Descent in Neural Networks as Sequential Learning in Reproducing Kernel Banach Space
How Powerful are Shallow Neural Networks with Bandlimited Random Weights?
Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere
A Conditional Normalizing Flow for Accelerated Multi-Coil MR Imaging
LongCoder: A Long-Range Pre-trained Language Model for Code Completion
Deep Temporal Sets with Evidential Reinforced Attentions for Unique Behavioral Pattern Discovery
Distortion and Uncertainty Aware Loss for Panoramic Depth Completion
UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers
Equivariant Polynomials for Graph Neural Networks
COMCAT: Towards Efficient Compression and Customization of Attention-Based Vision Models
Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering
Geometric Latent Diffusion Models for 3D Molecule Generation
Constrained Decision Transformer for Offline Safe Reinforcement Learning
Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap
Robust Satisficing MDPs
Featured Graph Coarsening with Similarity Guarantees
DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution Models
Revisiting Data-Free Knowledge Distillation with Poisoned Teachers
Abstract-to-Executable Trajectory Translation for One-Shot Task Generalization
Efficient Sequence Transduction by Jointly Predicting Tokens and Durations
LEVER: Learning to Verify Language-to-Code Generation with Execution
Interval Bound Interpolation for Few-shot Learning with Few Tasks
Towards Sustainable Learning: Coresets for Data-efficient Deep Learning
Conformal Prediction Sets for Graph Neural Networks
Continuously Parameterized Mixture Models
Why Random Pruning Is All We Need to Start Sparse
Understanding Self-Predictive Learning for Reinforcement Learning
On the Statistical Benefits of Temporal Difference Learning
A Connection between One-Step RL and Critic Regularization in Reinforcement Learning
Revisiting Bellman Errors for Offline Model Selection
Predictive Flows for Faster Ford-Fulkerson
Differential Privacy has Bounded Impact on Fairness in Classification
Improved Learning-Augmented Algorithms for the Multi-Option Ski Rental Problem via Best-Possible Competitive Analysis
Robust Non-Linear Feedback Coding via Power-Constrained Deep Learning
Feature Directions Matter: Long-Tailed Learning via Rotated Balanced Representation
$\pi$-Tuning: Transferring Multimodal Foundation Models with Optimal Multi-task Interpolation
FREDIS: A Fusion Framework of Refinement and Disambiguation for Unreliable Partial Label Learning
Which Invariance Should We Transfer? A Causal Minimax Learning Approach
Auxiliary Modality Learning with Generalized Curriculum Distillation
Adaptive Smoothing Gradient Learning for Spiking Neural Networks
An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning
Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making
Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts
A/B Testing in Network Data with Covariate-Adaptive Randomization
Alternating Local Enumeration (TnALE): Solving Tensor Network Structure Search with Fewer Evaluations
Consistency of Multiple Kernel Clustering
Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR
A Framework for Adapting Offline Algorithms to Solve Combinatorial Multi-Armed Bandit Problems with Bandit Feedback
Weighted Tallying Bandits: Overcoming Intractability via Repeated Exposure Optimality
The Price of Differential Privacy under Continual Observation
Subset-Based Instance Optimality in Private Estimation
A Law of Robustness beyond Isoperimetry
Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective
Sample Complexity Bounds for Learning High-dimensional Simplices in Noisy Regimes
Phase Transitions in the Detection of Correlated Databases
A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints
Sharper Bounds for $\ell_p$ Sensitivity Sampling
Tighter Analysis for ProxSkip
Learning Functional Distributions with Private Labels
Stochastic Gradient Succeeds for Bandits
A Unified Optimization Framework of ANN-SNN Conversion: Towards Optimal Mapping from Activation Values to Firing Rates
Masked Trajectory Models for Prediction, Representation, and Control
Information-Theoretic State Space Model for Multi-View Reinforcement Learning
Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks
Personalized Subgraph Federated Learning
Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networks
Anti-Exploration by Random Network Distillation
Distance Weighted Supervised Learning for Offline Interaction Data
Likelihood Adjusted Semidefinite Programs for Clustering Heterogeneous Data
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks
Complementary Attention for Multi-Agent Reinforcement Learning
Multi-Modal Classifiers for Open-Vocabulary Object Detection
Hyperparameters in Reinforcement Learning and How To Tune Them
Never mind the metrics---what about the uncertainty? Visualising binary confusion matrix metric distributions to put performance in perspective
Data Feedback Loops: Model-driven Amplification of Dataset Biases
When Personalization Harms Performance: Reconsidering the Use of Group Attributes in Prediction
Fair Neighbor Embedding
Kernel Sufficient Dimension Reduction and Variable Selection for Compositional Data via Amalgamation
Adaptive Whitening in Neural Populations with Gain-modulating Interneurons
Hierarchies of Reward Machines
Learning Deep Time-index Models for Time Series Forecasting
What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL?
Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity
OpenFE: Automated Feature Generation with Expert-level Performance
Linear Time GPs for Inferring Latent Trajectories from Neural Spike Trains
Unscented Autoencoder
Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning
Learning in POMDPs is Sample-Efficient with Hindsight Observability
On the Power of Pre-training for Generalization in RL: Provable Benefits and Hardness
Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling
GFlowOut: Dropout with Generative Flow Networks
Quantifying Human Priors over Social and Navigation Networks
Biases in Evaluation of Molecular Optimization Methods and Bias Reduction Strategies
Recovering Top-Two Answers and Confusion Probability in Multi-Choice Crowdsourcing
Learning Perturbations to Explain Time Series Predictions
Incentivizing Exploration with Linear Contexts and Combinatorial Actions
From Robustness to Privacy and Back
Learning to Bid in Repeated First-Price Auctions with Budgets
Distributed Linear Bandits under Communication Constraints
Statistical Foundations of Prior-Data Fitted Networks
Neural networks trained with SGD learn distributions of increasing complexity
Scaling Laws for Multilingual Neural Machine Translation
Explaining the effects of non-convergent MCMC in the training of Energy-Based Models
Metagenomic Binning using Connectivity-constrained Variational Autoencoders
Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation
Beyond In-Domain Scenarios: Robust Density-Aware Calibration
Prefer to Classify: Improving Text Classifiers via Auxiliary Preference Learning
Disentangled Multiplex Graph Representation Learning
Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks
Provably Learning Object-Centric Representations
BiBench: Benchmarking and Analyzing Network Binarization
Scaling Laws for Generative Mixed-Modal Language Models
Sampling-based Nyström Approximation and Kernel Quadrature
Adversarial robustness of amortized Bayesian inference
Discover-Then-Rank Unlabeled Support Vectors in the Dual Space for Multi-Class Active Learning
Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization
Algorithmic Stability of Heavy-Tailed SGD with General Loss Functions
Mixing Predictions for Online Metric Algorithms
Understanding the Impact of Adversarial Robustness on Accuracy Disparity
SeedGNN: Graph Neural Network for Supervised Seeded Graph Matching
BPipe: Memory-Balanced Pipeline Parallelism for Training Large Language Models
DoCoFL: Downlink Compression for Cross-Device Federated Learning
GNN&GBDT-Guided Fast Optimizing Framework for Large-scale Integer Programming
Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D
NNSplitter: An Active Defense Solution for DNN Model via Automated Weight Obfuscation
Data-Driven Subgroup Identification for Linear Regression
Simplex Random Features
Abstracting Imperfect Information Away from Two-Player Zero-Sum Games
Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data
Proper Losses for Discrete Generative Models
ClusterFuG: Clustering Fully connected Graphs by Multicut
Efficient Graph Field Integrators Meet Point Clouds
Dirichlet Diffusion Score Model for Biological Sequence Generation
Sequential Monte Carlo Learning for Time Series Structure Discovery
Kernel QuantTree
FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels
Model-Free Robust Average-Reward Reinforcement Learning
Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic
Direct Parameterization of Lipschitz-Bounded Deep Networks
Fast Rates in Time-Varying Strongly Monotone Games
The Value of Out-of-Distribution Data
DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design
Everyone's Preference Changes Differently: A Weighted Multi-Interest Model For Retrieval
PFGM++: Unlocking the Potential of Physics-Inspired Generative Models
Algorithmic Collective Action in Machine Learning
Probabilistic Categorical Adversarial Attack and Adversarial Training
Generalization Analysis for Contrastive Representation Learning
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes
Federated Adversarial Learning: A Framework with Convergence Analysis
An Adaptive Entropy-Regularization Framework for Multi-Agent Reinforcement Learning
Solving Linear Programs with Fast Online Learning Algorithms
Robust Budget Pacing with a Single Sample
Poisoning Language Models During Instruction Tuning
RankMe: Assessing the Downstream Performance of Pretrained Self-Supervised Representations by Their Rank
Bootstrap in High Dimension with Low Computation
Hidden Symmetries of ReLU Networks
Learning to Initiate and Reason in Event-Driven Cascading Processes
Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World Modelling
QASA: Advanced Question Answering on Scientific Articles
Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes
Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning
Patch-level Contrastive Learning via Positional Query for Visual Pre-training
Learning to Optimize Differentiable Games
Generating Language Corrections for Teaching Physical Control Tasks
Behavior Contrastive Learning for Unsupervised Skill Discovery
On Penalty-based Bilevel Gradient Descent Method
Beyond Uniform Lipschitz Condition in Differentially Private Optimization
Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation
Efficient displacement convex optimization with particle gradient descent
Differentially Private Stochastic Convex Optimization under a Quantile Loss Function
Recasting Self-Attention with Holographic Reduced Representations
Coder Reviewer Reranking for Code Generation
Leveraging Proxy of Training Data for Test-Time Adaptation
Image Shortcut Squeezing: Countering Perturbative Availability Poisons with Compression
NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations
Effective and Efficient Structural Inference with Reservoir Computing
Cross-Modal Fine-Tuning: Align then Refine
Global Context Vision Transformers
GeCoNeRF: Few-shot Neural Radiance Fields via Geometric Consistency
Modality-Agnostic Variational Compression of Implicit Neural Representations
High Fidelity Image Counterfactuals with Probabilistic Causal Models
High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance
On the Complexity of Bayesian Generalization
Expectation-Complete Graph Representations with Homomorphisms
Shapley Based Residual Decomposition for Instance Analysis
Efficient Algorithms for Exact Graph Matching on Correlated Stochastic Block Models with Constant Correlation
Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss
Performative Reinforcement Learning
Last Switch Dependent Bandits with Monotone Payoff Functions
DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule
Trustworthy Policy Learning under the Counterfactual No-Harm Criterion
Are Gaussian Data All You Need? The Extents and Limits of Universality in High-Dimensional Generalized Linear Estimation
Certified Robust Neural Networks: Generalization and Corruption Resistance
Generalizing Neural Wave Functions
On the convergence of the MLE as an estimator of the learning rate in the Exp3 algorithm
Neural Markov Jump Processes
Learning Control-Oriented Dynamical Structure from Data
Bayesian Estimation of Differential Privacy
Eliminating Adversarial Noise via Information Discard and Robust Representation Restoration
The Numerical Stability of Hyperbolic Representation Learning
Improved Algorithms for White-Box Adversarial Streams
Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows
On the Convergence of Gradient Flow on Multi-layer Linear Models
Online Learning in Stackelberg Games with an Omniscient Follower
Surface Snapping Optimization Layer for Single Image Object Shape Reconstruction
Streaming Active Learning with Deep Neural Networks
Unit Scaling: Out-of-the-Box Low-Precision Training
Linear Causal Disentanglement via Interventions
Competing for Shareable Arms in Multi-Player Multi-Armed Bandits
MANSA: Learning Fast and Slow in Multi-Agent Systems
Thompson Sampling with Less Exploration is Fast and Optimal
SurCo: Learning Linear SURrogates for COmbinatorial Nonlinear Optimization Problems
Faster Rates of Convergence to Stationary Points in Differentially Private Optimization
Quantitative Universal Approximation Bounds for Deep Belief Networks
Vector-Valued Control Variates
Deep Anomaly Detection under Labeling Budget Constraints
Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series
Robust Weight Signatures: Gaining Robustness as Easy as Patching Weights?
Better Diffusion Models Further Improve Adversarial Training
Explaining Reinforcement Learning with Shapley Values
Randomized Schur Complement Views for Graph Contrastive Learning
Hierarchical Diffusion for Offline Decision Making
The Unreasonable Effectiveness of Few-shot Learning for Machine Translation
Understand and Modularize Generator Optimization in ELECTRA-style Pretraining
Distribution Free Domain Generalization
Secure Federated Correlation Test and Entropy Estimation
Computational Doob h-transforms for Online Filtering of Discretely Observed Diffusions
Rethinking Warm-Starts with Predictions: Learning Predictions Close to Sets of Optimal Solutions for Faster $\text{L}$-/$\text{L}^\natural$-Convex Function Minimization
Partially Observable Multi-agent RL with (Quasi-)Efficiency: The Blessing of Information Sharing
Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning
Implicit Regularization Leads to Benign Overfitting for Sparse Linear Regression
Is Consensus Acceleration Possible in Decentralized Optimization over Slowly Time-Varying Networks?
On Coresets for Clustering in Small Dimensional Euclidean spaces
Regret-Minimizing Double Oracle for Extensive-Form Games
GC-Flow: A Graph-Based Flow Network for Effective Clustering
Mitigating Memorization of Noisy Labels by Clipping the Model Prediction
Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models
The SSL Interplay: Augmentations, Inductive Bias, and Generalization
Emergence of Adaptive Circadian Rhythms in Deep Reinforcement Learning
Theory on Forgetting and Generalization of Continual Learning
Curriculum Co-disentangled Representation Learning across Multiple Environments for Social Recommendation
Building Neural Networks on Matrix Manifolds: A Gyrovector Space Approach
(ends 6:00 PM)
6 p.m.
Coffee Only Break
Oral C1 Supervised Learning [6:00-7:30]
Oral s 6:00-7:20
[6:00] Mimetic Initialization of Self-Attention Layers
[6:08] Difference of submodular minimization via DC programming
[6:16] Simplex Random Features
[6:24] Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks
[6:32] Tilted Sparse Additive Models
[6:40] Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape
[6:48] Hyena Hierarchy: Towards Larger Convolutional Language Models
[6:56] Direct Parameterization of Lipschitz-Bounded Deep Networks
[7:12] Subsample Ridge Ensembles: Equivalences and Generalized Cross-Validation
(ends 7:30 PM)
Oral C2 Time Series / Dynamics / Sequences [6:00-7:30]
Oral s 6:00-7:20
[6:00] Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series
[6:08] Self-Interpretable Time Series Prediction with Counterfactual Explanations
[6:16] Resurrecting Recurrent Neural Networks for Long Sequences
[6:24] Inferring Relational Potentials in Interacting Systems
[6:32] Memory-Based Dual Gaussian Processes for Sequential Learning
[6:40] H-Likelihood Approach to Deep Neural Networks with Temporal-Spatial Random Effects for High-Cardinality Categorical Features
[6:48] Generalized Teacher Forcing for Learning Chaotic Dynamics
[6:56] Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients
[7:04] Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere
[7:12] Learning Control-Oriented Dynamical Structure from Data
(ends 7:30 PM)
Oral C3 Multimodal and Pretaining [6:00-7:30]
Oral s 6:00-7:20
[6:00] Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding
[6:08] Calibrating Multimodal Learning
[6:16] StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis
[6:24] ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts
[6:32] Cross-Modal Fine-Tuning: Align then Refine
[6:40] Mu$^2$SLAM: Multitask, Multilingual Speech and Language Models
[6:48] Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time
[6:56] Pre-training for Speech Translation: CTC Meets Optimal Transport
[7:04] Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models
[7:12] Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes
(ends 7:30 PM)
Oral C4 Optimization [6:00-7:30]
Oral s 6:00-7:12
[6:00] Second-Order Optimization with Lazy Hessians
[6:08] Unifying Nesterov's Accelerated Gradient Methods for Convex and Strongly Convex Objective Functions
[6:16] Transformer-based Stagewise Decomposition for Large-Scale Multistage Stochastic Optimization
[6:24] Continuation Path Learning for Homotopy Optimization
[6:32] Over-parametrization via Lifting for Low-rank Matrix Sensing: Conversion of Spurious Solutions to Strict Saddle Points
[6:40] Buying Information for Stochastic Optimization
[6:48] A Fully First-Order Method for Stochastic Bilevel Optimization
[6:56] Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference
[7:04] Learning-Rate-Free Learning by D-Adaptation
(ends 7:30 PM)
Oral C5 Misc [6:00-7:30]
Oral s 6:00-7:04
[6:00] Learning Mixtures of Markov Chains and MDPs
[6:08] Uncertain Evidence in Probabilistic Models and Stochastic Simulators
[6:16] How Bad is Top-$K$ Recommendation under Competing Content Creators?
[6:24] Weighted Flow Diffusion for Local Graph Clustering with Node Attributes: an Algorithm and Statistical Guarantees
[6:32] Equivariant Polynomials for Graph Neural Networks
[6:40] Taming graph kernels with random features
[6:48] Robust Budget Pacing with a Single Sample
[6:56] Multicalibration as Boosting for Regression
(ends 7:30 PM)
Oral C6:
The Societal Impacts of AI
(ends 7:30 PM)
7:30 p.m.
Reception:
Closing Reception
(ends 8:45 PM)
8:45 p.m.
FRI 28 JUL
10 a.m.
11 a.m.
Registration
(ends 7:00 PM)
11:50 a.m.
Workshop:
2nd ICML Workshop on New Frontiers in Adversarial Machine Learning
(ends 8:00 PM)
11:55 a.m.
Workshop:
Workshop on Theory of Mind in Communicating Agents
(ends 8:10 PM)
noon
Workshop:
Structured Probabilistic Inference and Generative Modeling
(ends 8:00 PM)
Workshop:
New Frontiers in Learning, Control, and Dynamical Systems
(ends 7:00 PM)
Workshop:
2nd Workshop on Formal Verification of Machine Learning
(ends 8:00 PM)
Workshop:
Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities
(ends 8:00 PM)
Workshop:
HiLD: High-dimensional Learning Dynamics Workshop
(ends 8:00 PM)
Workshop:
The Synergy of Scientific and Machine Learning Modelling (SynS & ML) Workshop
(ends 8:00 PM)
Workshop:
PAC-Bayes Meets Interactive Learning
(ends 7:45 PM)
Workshop:
2nd Annual Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG-ML)
(ends 8:00 PM)
Workshop:
Challenges in Deployable Generative AI
(ends 8:00 PM)
Workshop:
Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators
(ends 8:00 PM)
Workshop:
The Many Facets of Preference-Based Learning
(ends 8:00 PM)
Workshop:
Knowledge and Logical Reasoning in the Era of Data-driven Learning
(ends 8:00 PM)
12:15 p.m.
Workshop:
3rd Workshop on Interpretable Machine Learning in Healthcare (IMLH)
(ends 8:00 PM)
Affinity Workshop:
4th Women in Machine Learning (WiML) Un-Workshop
(ends 7:45 PM)
1 p.m.
Coffee Break
3 p.m.
Lunch -(On Your Own)
6 p.m.
Coffee Break
SAT 29 JUL
11 a.m.
Registration
(ends 2:00 PM)
11:50 a.m.
Workshop:
The Second Workshop on Spurious Correlations, Invariance and Stability
(ends 8:00 PM)
11:55 a.m.
Workshop:
ES-FoMo: Efficient Systems for Foundation Models
(ends 12:00 AM)
noon
Workshop:
Machine Learning for Multimodal Healthcare Data
(ends 8:00 PM)
Workshop:
Interactive Learning with Implicit Human Feedback
(ends 8:00 PM)
Workshop:
“Could it have been different?” Counterfactuals in Minds and Machines
(ends 8:00 PM)
Workshop:
ICML 2023 Workshop on Computational Biology
(ends 8:00 PM)
Workshop:
Localized Learning: Decentralized Model Updates via Non-Global Objectives
(ends 8:00 PM)
Workshop:
Neural Conversational AI Workshop - What’s left to TEACH (Trustworthy, Enhanced, Adaptable, Capable and Human-centric) chatbots?
(ends 8:15 PM)
Workshop:
Generative AI and Law (GenLaw)
(ends 8:00 PM)
Workshop:
Artificial Intelligence & Human Computer Interaction
(ends 8:00 PM)
Workshop:
DMLR Workshop: Data-centric Machine Learning Research
(ends 8:00 PM)
Workshop:
Sampling and Optimization in Discrete Space
(ends 7:45 PM)
Workshop:
Duality Principles for Modern Machine Learning
(ends 8:00 PM)
Workshop:
2nd ICML Workshop on Machine Learning for Astrophysics
(ends 8:00 PM)
Workshop:
Neural Compression: From Information Theory to Applications
(ends 8:00 PM)
12:15 p.m.
Affinity Workshop:
Indigenous in AI Workshop
(ends 8:15 PM)
1 p.m.
Coffee Break
3 p.m.
Lunch -(On Your Own)
6 p.m.
Coffee Break