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THU 2 MAY
7:30 a.m.
Registration Check-in Desk
(ends 5:00 PM)
8:45 a.m.
Remarks:
Opening Remarks
(ends 9:00 AM)
9 a.m.
Invited Talk:
Invited Talk - Matt Hoffman
Matthew D. Hoffman
(ends 10:00 AM)
10 a.m.
Break:
Morning Coffee Break
(ends 10:30 AM)
10:30 a.m.
Oral: RL & Optimization [10:30-11:30]
Orals 10:30-11:30
[10:30] Conformal Contextual Robust Optimization
[10:30] Near-Optimal Policy Optimization for Correlated Equilibrium in General-Sum Markov Games
[10:30] Model-based Policy Optimization under Approximate Bayesian Inference
[10:30] Online Learning of Decision Trees with Thompson Sampling
(ends 11:30 AM)
11:30 a.m.
D&I Panel:
Establishing your own research path in the age of big compute
(ends 12:30 PM)
12:30 p.m.
Lunch Break on your own:
Lunch - retail
(ends 2:00 PM)
2 p.m.
Oral: Optimization [2:00-3:15]
Orals 2:00-3:15
[2:00] The sample complexity of ERMs in stochastic convex optimization
[2:00] Stochastic Methods in Variational Inequalities: Ergodicity, Bias and Refinements
[2:00] Absence of spurious solutions far from ground truth: A low-rank analysis with high-order losses
[2:00] Learning-Based Algorithms for Graph Searching Problems
[2:00] Graph Partitioning with a Move Budget
(ends 3:15 PM)
3:15 p.m.
Break:
Afternoon Coffee Break
(ends 3:45 PM)
3:45 p.m.
Oral: Probabilistic Methods [3:45-5:00]
Orals 3:45-5:00
[3:45] Neural McKean-Vlasov Processes: Distributional Dependence in Diffusion Processes
[3:45] Reparameterized Variational Rejection Sampling
[3:45] Intrinsic Gaussian Vector Fields on Manifolds
[3:45] Generative Flow Networks as Entropy-Regularized RL
[3:45] Robust Approximate Sampling via Stochastic Gradient Barker Dynamics
(ends 5:00 PM)
5 p.m.
Poster Session 1 [5:00-5:30]
Posters 5:00-5:30
Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models
Scalable Higher-Order Tensor Product Spline Models
Fast Dynamic Sampling for Determinantal Point Processes
Optimal Zero-Shot Detector for Multi-Armed Attacks
Conformal Contextual Robust Optimization
From Data Imputation to Data Cleaning --- Automated Cleaning of Tabular Data Improves Downstream Predictive Performance
On-Demand Federated Learning for Arbitrary Target Class Distributions
Manifold-Aligned Counterfactual Explanations for Neural Networks
On the (In)feasibility of ML Backdoor Detection as an Hypothesis Testing Problem
Simple and scalable algorithms for cluster-aware precision medicine
HintMiner: Automatic Question Hints Mining From Q\&A Web Posts with Language Model via Self-Supervised Learning
Enhancing In-context Learning via Linear Probe Calibration
TenGAN: Pure Transformer Encoders Make an Efficient Discrete GAN for De Novo Molecular Generation
Best Arm Identification with Resource Constraints
LEDetection: A Simple Framework for Semi-Supervised Few-Shot Object Detection
Fusing Individualized Treatment Rules Using Secondary Outcomes
A White-Box False Positive Adversarial Attack Method on Contrastive Loss Based Offline Handwritten Signature Verification Models
When No-Rejection Learning is Consistent for Regression with Rejection
Efficient Graph Laplacian Estimation by Proximal Newton
Simulation-Free Schrödinger Bridges via Score and Flow Matching
Federated Learning For Heterogeneous Electronic Health Records Utilising Augmented Temporal Graph Attention Networks
Cross-model Mutual Learning for Exemplar-based Medical Image Segmentation
Provable local learning rule by expert aggregation for a Hawkes network
Ethics in Action: Training Reinforcement Learning Agents for Moral Decision-making In Text-based Adventure Games
A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning
Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpoint
Safe and Interpretable Estimation of Optimal Treatment Regimes
MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization
Bayesian Online Learning for Consensus Prediction
BlockBoost: Scalable and Efficient Blocking through Boosting
Estimating treatment effects from single-arm trials via latent-variable modeling
ALAS: Active Learning for Autoconversion Rates Prediction from Satellite Data
Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation
Shape Arithmetic Expressions: Advancing Scientific Discovery Beyond Closed-Form Equations
Attention-based Multi-instance Mixed Models
On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation
Learning to Rank for Optimal Treatment Allocation Under Resource Constraints
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine Learning
Contextual Bandits with Budgeted Information Reveal
Holographic Global Convolutional Networks for Long-Range Prediction Tasks in Malware Detection
Deep Learning-Based Alternative Route Computation
Uncertainty-aware Continuous Implicit Neural Representations for Remote Sensing Object Counting
Towards a Complete Benchmark on Video Moment Localization
SADI: Similarity-Aware Diffusion Model-Based Imputation for Incomplete Temporal EHR Data
The Effective Number of Shared Dimensions Between Paired Datasets
Auditing Fairness under Unobserved Confounding
Sample Efficient Learning of Factored Embeddings of Tensor Fields
Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach
Density Uncertainty Layers for Reliable Uncertainty Estimation
Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?
Neural McKean Vlasov Processes: Distributional Dependence in Diffusion Models
DNNLasso: Scalable Graph Learning for Matrix-Variate Data
Error bounds for any regression model using Gaussian processes with gradient information
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes
Looping in the Human: Collaborative and Explainable Bayesian Optimization
VEC-SBM: Optimal Community Detection with Vectorial Edges Covariates
Theory-guided Message Passing Neural Network for Probabilistic Inference
Reparameterized Variational Rejection Sampling
Riemannian Laplace Approximation with the Fisher Metric
Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support
Categorical Generative Model Evaluation via Synthetic Distribution Coarsening
Data-Adaptive Probabilistic Likelihood Approximation for Ordinary Differential Equations
Intrinsic Gaussian Vector Fields on Manifolds
A Unifying Variational Framework for Gaussian Process Motion Planning
Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning
Subsampling Error in Stochastic Gradient Langevin Diffusions
Robust Offline Reinforcement Learning with Heavy-Tailed Rewards
Self-Supervised Quantization-Aware Knowledge Distillation
Joint control variate for faster black-box variational inference
Adaptivity of Diffusion Models to Manifold Structures
Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean
Diagonalisation SGD: Fast \& Convergent SGD for Non-Differentiable Models via Reparameterisation and Smoothing
Tuning-Free Maximum Likelihood Training of Latent Variable Models via Coin Betting
Bayesian Semi-structured Subspace Inference
Structural perspective on constraint-based learning of Markov networks
Variational Gaussian Process Diffusion Processes
Scalable Meta-Learning with Gaussian Processes
Prior-dependent analysis of posterior sampling reinforcement learning with function approximation
Training Implicit Generative Models via an Invariant Statistical Loss
GmGM: a fast multi-axis Gaussian graphical model
Learning Sparse Codes with Entropy-Based ELBOs
Robust Approximate Sampling via Stochastic Gradient Barker Dynamics
Probabilistic Integral Circuits
Backward Filtering Forward Deciding in Linear Non-Gaussian State Space Models
Stochastic Approximation with Biased MCMC for Expectation Maximization
Mixed variational flows for discrete variables
Minimizing Convex Functionals over Space of Probability Measures via KL Divergence Gradient Flow
Surrogate Bayesian Networks for Approximating Evolutionary Games
Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias
Large-Scale Gaussian Processes via Alternating Projection
Clustering Items From Adaptively Collected Inconsistent Feedback
Fair k-center Clustering with Outliers
Deep anytime-valid hypothesis testing
Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters
A General Algorithm for Solving Rank-one Matrix Sensing
Recovery Guarantees for Distributed-OMP
Making Better Use of Unlabelled Data in Bayesian Active Learning
BOBA: Byzantine-Robust Federated Learning with Label Skewness
Autoregressive Bandits
MINTY: Rule-based models that minimize the need for imputing features with missing values
Graph Machine Learning through the Lens of Bilevel Optimization
Towards Achieving Sub-linear Regret and Hard Constraint Violation in Model-free RL
Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization
Scalable Algorithms for Individual Preference Stable Clustering
Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles
Adaptive Experiment Design with Synthetic Controls
Online non-parametric likelihood-ratio estimation by Pearson-divergence functional minimization
Scalable Learning of Item Response Theory Models
Corruption-Robust Offline Two-Player Zero-Sum Markov Games
Quantized Fourier and Polynomial Features for more Expressive Tensor Network Models
Fair Soft Clustering
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels
MMD-based Variable Importance for Distributional Random Forest
Better Batch for Deep Probabilistic Time Series Forecasting
Adaptive Parametric Prototype Learning for Cross-Domain Few-Shot Classification
Online Calibrated and Conformal Prediction Improves Bayesian Optimization
Offline Policy Evaluation and Optimization Under Confounding
Asynchronous Randomized Trace Estimation
FedFisher: Leveraging Fisher Information for One-Shot Federated Learning
Feasible $Q$-Learning for Average Reward Reinforcement Learning
Smoothness-Adaptive Dynamic Pricing with Nonparametric Demand Learning
Sample Complexity Characterization for Linear Contextual MDPs
Queuing dynamics of asynchronous Federated Learning
Learning Populations of Preferences via Pairwise Comparison Queries
Distributionally Robust Model-based Reinforcement Learning with Large State Spaces
A Neural Architecture Predictor based on GNN-Enhanced Transformer
Robust Data Clustering with Outliers via Transformed Tensor Low-Rank Representation
Regret Bounds for Risk-sensitive Reinforcement Learning with Lipschitz Dynamic Risk Measures
CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference
Multitask Online Learning: Listen to the Neighborhood Buzz
Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model
Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks
Classifier Calibration with ROC-Regularized Isotonic Regression
RL in Markov Games with Independent Function Approximation: Improved Sample Complexity Bound under the Local Access Model
Multiclass Learning from Noisy Labels for Non-decomposable Performance Measures
Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels
Efficient Model-Based Concave Utility Reinforcement Learning through Greedy Mirror Descent
GRAWA: Gradient-based Weighted Averaging for Distributed Training of Deep Learning Models
A Doubly Robust Approach to Sparse Reinforcement Learning
Analysis of Kernel Mirror Prox for Measure Optimization
Online Learning in Contextual Second-Price Pay-Per-Click Auctions
Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients
Information Theoretically Optimal Sample Complexity of Learning Dynamical Directed Acyclic Graphs
Local Causal Discovery with Linear non-Gaussian Cyclic Models
An Improved Algorithm for Learning Drifting Discrete Distributions
An Online Bootstrap for Time Series
Achieving Fairness through Separability: A Unified Framework for Fair Representation Learning
Is this model reliable for everyone? Testing for strong calibration
Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations
Emergent specialization from participation dynamics and multi-learner retraining
Optimal Sparse Survival Trees
Explanation-based Training with Differentiable Insertion/Deletion Metric-aware Regularizers
Online Distribution Learning with Local Privacy Constraints
The Risks of Recourse in Binary Classification
Quantifying Uncertainty in Natural Language Explanations of Large Language Models
Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective
Effect of Ambient-Intrinsic Dimension Gap on Adversarial Vulnerability
Fast and Adversarial Robust Kernelized SDU Learning
Uncertainty Matters: Stable Conclusions under Unstable Assessment of Fairness Results
Analyzing Explainer Robustness via Probabilistic Lipschitzness of Prediction Functions
Certified private data release for sparse Lipschitz functions
Analysis of Privacy Leakage in Federated Large Language Models
Confident Feature Ranking
Taming False Positives in Out-of-Distribution Detection with Human Feedback
On the Privacy of Selection Mechanisms with Gaussian Noise
Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry
Solving Attention Kernel Regression Problem via Pre-conditioner
Thompson Sampling Itself is Differentially Private
Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes
DAGnosis: Localized Identification of Data Inconsistencies using Structures
Interpretability Guarantees with Merlin-Arthur Classifiers
Tackling the XAI Disagreement Problem with Regional Explanations
Sparse and Faithful Explanations Without Sparse Models
Enhancing Distributional Stability among Sub-populations
Agnostic Multi-Robust Learning using ERM
Imposing Fairness Constraints in Synthetic Data Generation
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data
DHMConv: Directed Hypergraph Momentum Convolution Framework
Estimation of partially known Gaussian graphical models with score-based structural priors
Data-Driven Online Model Selection With Regret Guarantees
Monitoring machine learning-based risk prediction algorithms in the presence of performativity
Directed Hypergraph Representation Learning for Link Prediction
(ends 5:30 PM)
6 p.m.
Affinity Event:
LatinX Social
(ends 8:00 PM)

FRI 3 MAY
7 a.m.
Registration Check-in Desk
(ends 5:00 PM)
8 a.m.
Mentoring Event (D&I):
Mementor Event
(ends 9:00 AM)
9 a.m.
Invited Talk:
Conformal online model aggregation
Aaditya Ramdas
(ends 10:00 AM)
10 a.m.
Break:
Morning Coffee Break
(ends 10:30 AM)
10:30 a.m.
Oral: Bandit & Causality [10:30-11:30]
Orals 10:30-11:30
[10:30] Positivity-free Policy Learning with Observational Data
[10:30] Best-of-Both-Worlds Algorithms for Linear Contextual Bandits
[10:30] Policy Learning for Localized Interventions from Observational Data
[10:30] Exploration via linearly perturbed loss minimisation
(ends 11:30 AM)
11:30 a.m.
Oral: Causality [11:30-12:30]
Orals 11:30-12:30
[11:30] Membership Testing in Markov Equivalence Classes via Independence Queries
[11:30] Causal Modeling with Stationary Diffusions
[11:30] On the Misspecification of Linear Assumptions in Synthetic Controls
[11:30] General Identifiability and Achievability for Causal Representation Learning
(ends 12:30 PM)
12:30 p.m.
Lunch Break on your own:
Lunch - retail
(ends 2:00 PM)
Mentoring Event (D&I):
Mementor Event
(ends 2:00 PM)
2 p.m.
Test Of Time:
ToT Award
(ends 3:00 PM)
3 p.m.
Oral: Applications [3:00-3:30]
Equivariant bootstrapping for uncertainty quantification in imaging inverse problems
Mixed Models with Multiple Instance Learning
(ends 3:30 PM)
3:15 p.m.
Break:
Afternoon Coffee Break
(ends 3:45 PM)
4 p.m.
Oral: General Machine Learning [4:00-5:00]
Orals 4:00-5:00
[4:00] End-to-end Feature Selection Approach for Learning Skinny Trees
[4:00] Probabilistic Modeling for Sequences of Sets in Continuous-Time
[4:00] Learning to Defer to a Population: A Meta-Learning Approach
[4:00] An Impossibility Theorem for Node Embedding
(ends 5:00 PM)
5 p.m.
Poster Session 2 [5:00-5:30]
Posters 5:00-5:30
Learning Safety Constraints from Demonstrations with Unknown Rewards
Equivariant bootstrapping for uncertainty quantification in imaging inverse problems
Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification
Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels
Nonparametric Automatic Differentiation Variational Inference with Spline Approximation
Robust variance-regularized risk minimization with concomitant scaling
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits
Fixed-Budget Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit
Generalization Bounds for Label Noise Stochastic Gradient Descent
Importance Matching Lemma for Lossy Compression with Side Information
Central Limit Theorem for Two-Timescale Stochastic Approximation with Markovian Noise: Theory and Applications
Transductive conformal inference with adaptive scores
Learning Latent Partial Matchings with Gumbel-IPF Networks
Integrating Uncertainty Awareness into Conformalized Quantile Regression
On the Expected Size of Conformal Prediction Sets
Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation
Model-Based Best Arm Identification for Decreasing Bandits
A/B Testing and Best-arm Identification for Linear Bandits with Robustness to Non-stationarity
Causal Discovery under Off-Target Interventions
Conformalized Deep Splines for Optimal and Efficient Prediction Sets
Optimal Exploration is no harder than Thompson Sampling
Sample-Efficient Personalization: Modeling User Parameters as Low Rank Plus Sparse Components
Robust SVD Made Easy: A fast and reliable algorithm for large-scale data analysis
Bures-Wasserstein Means of Graphs
Tight Verification of Probabilistic Robustness in Bayesian Neural Networks
Positivity-free Policy Learning with Observational Data
Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling
Causal Modeling with Stationary Diffusions
Vector Quantile Regression on Manifolds
Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games
On Convergence in Wasserstein Distance and f-divergence Minimization Problems
Near Optimal Adversarial Attacks on Stochastic Bandits and Defenses with Smoothed Responses
Learning Extensive-Form Perfect Equilibria in Two-Player Zero-Sum Sequential Games
Online multiple testing with e-values
User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates
Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical Learning
Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors
Privacy-Preserving Decentralized Actor-Critic for Cooperative Multi-Agent Reinforcement Learning
Double InfoGAN for Contrastive Analysis
P-tensors: a General Framework for Higher Order Message Passing in Subgraph Neural Networks
Testing Generated Distributions in GANs to Penalize Mode Collapse
Adaptive Compression in Federated Learning via Side Information
Compression with Exact Error Distribution for Federated Learning
Mechanics of Next Token Prediction with Self-Attention
E(3)-Equivariant Mesh Neural Networks
A Unified Framework for Discovering Discrete Symmetries
Unsupervised Novelty Detection in Pretrained Representation Space with Locally Adapted Likelihood Ratio
Multi-Dimensional Hyena for Spatial Inductive Bias
On the Model-Misspecification in Reinforcement Learning
Data-Efficient Contrastive Language-Image Pretraining: Prioritizing Data Quality over Quantity
Efficient Low-Dimensional Compression of Overparameterized Models
Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees
Adaptive Discretization for Event PredicTion (ADEPT)
Sequence Length Independent Norm-Based Generalization Bounds for Transformers
Exploring the Power of Graph Neural Networks in Solving Linear Optimization Problems
Accuracy-Preserving Calibration via Statistical Modeling on Probability Simplex
Efficient Neural Architecture Design via Capturing Architecture-Performance Joint Distribution
Analysis of Using Sigmoid Loss for Contrastive Learning
On The Temporal Domain of Differential Equation Inspired Graph Neural Networks
Adaptive and non-adaptive minimax rates for weighted Laplacian-Eigenmap based nonparametric regression
Learning Dynamics in Linear VAE: Posterior Collapse Threshold, Superfluous Latent Space Pitfalls, and Speedup with KL Annealing
Understanding Progressive Training Through the Framework of Randomized Coordinate Descent
On the Theoretical Expressive Power and the Design Space of Higher-Order Graph Transformers
Free-form Flows: Make Any Architecture a Normalizing Flow
Monotone Operator Theory-Inspired Message Passing for Learning Long-Range Interaction on Graphs
Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers
Implicit Regularization in Deep Tucker Factorization: Low-Rankness via Structured Sparsity
Simulating weighted automata over sequences and trees with transformers
Functional Graphical Models: Structure Enables Offline Data-Driven Optimization
From Coupled Oscillators to Graph Neural Networks: Reducing Over-smoothing via a Kuramoto Model-based Approach
A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization
Fast 1-Wasserstein distance approximations using greedy strategies
Graph Partitioning with a Move Budget
AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms
Generalization Bounds of Nonconvex-(Strongly)-Concave Stochastic Minimax Optimization
Lower-level Duality Based Reformulation and Majorization Minimization Algorithm for Hyperparameter Optimization
Breaking the Heavy-Tailed Noise Barrier in Stochastic Optimization Problems
Adaptive Quasi-Newton and Anderson Acceleration Framework with Explicit Global (Accelerated) Convergence Rates
Learning-Based Algorithms for Graph Searching Problems
End-to-end Feature Selection Approach for Learning Skinny Trees
Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization
Fairness in Submodular Maximization over a Matroid Constraint
Submodular Minimax Optimization: Finding Effective Sets
Learning Sampling Policy to Achieve Fewer Queries for Zeroth-Order Optimization
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates
Risk Seeking Bayesian Optimization under Uncertainty for Obtaining Extremum
Absence of spurious solutions far from ground truth: A low-rank analysis with high-order losses
A 4-Approximation Algorithm for Min Max Correlation Clustering
An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization
Near-Optimal Convex Simple Bilevel Optimization with a Bisection Method
Multi-Level Symbolic Regression: Function Structure Learning for Multi-Level Data
Extragradient Type Methods for Riemannian Variational Inequality Problems
Optimising Distributions with Natural Gradient Surrogates
Lexicographic Optimization: Algorithms and Stability
Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression
Complexity of Single Loop Algorithms for Nonlinear Programming with Stochastic Objective and Constraints
Fixed-kinetic Neural Hamiltonian Flows for enhanced interpretability and reduced complexity
Online Learning of Decision Trees with Thompson Sampling
An Impossibility Theorem for Node Embedding
Improved Sample Complexity Analysis of Natural Policy Gradient Algorithm with General Parameterization for Infinite Horizon Discounted Reward Markov Decision Processes
Learning Fair Division from Bandit Feedback
Optimal Transport for Measures with Noisy Tree Metric
Probabilistic Calibration by Design for Neural Network Regression
A Scalable Algorithm for Individually Fair k-Means Clustering
Offline Primal-Dual Reinforcement Learning for Linear MDPs
Learning multivariate temporal point processes via the time-change theorem
Model-based Policy Optimization under Approximate Bayesian Inference
Training a Tucker Model With Shared Factors: a Riemannian Optimization Approach
Don't Be Pessimistic Too Early: Look K Steps Ahead!
Multi-Resolution Active Learning of Fourier Neural Operators
Identifiable Feature Learning for Spatial Data with Nonlinear ICA
Resilient Constrained Reinforcement Learning
DiffRed: Dimensionality reduction guided by stable rank
Learning to Defer to a Population: A Meta-Learning Approach
On learning history-based policies for controlling Markov decision processes
Length independent PAC-Bayes bounds for Simple RNNs
On the estimation of persistence intensity functions and linear representations of persistence diagrams
Dissimilarity Bandits
Consistent Optimal Transport with Empirical Conditional Measures
Horizon-Free and Instance-Dependent Regret Bounds for Reinforcement Learning with General Function Approximation
Learning Adaptive Kernels for Statistical Independence Tests
On the Nystr\"om Approximation for Preconditioning in Kernel Machines
Fitting ARMA Time Series Models without Identification: A Proximal Approach
Unsupervised Change Point Detection in Multivariate Time Series
A Bayesian Learning Algorithm for Unknown Zero-sum Stochastic Games with an Arbitrary Opponent
Near-Optimal Policy Optimization for Correlated Equilibrium in General-Sum Markov Games
Supervised Feature Selection via Ensemble Gradient Information from Sparse Neural Networks
Timing as an Action: Learning When to Observe and Act
How Good is a Single Basin?
Independent Learning in Constrained Markov Potential Games
Low-rank MDPs with Continuous Action Spaces
Learning Cartesian Product Graphs with Laplacian Constraints
Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems
Multi-resolution Time-Series Transformer for Long-term Forecasting
Consistency of Dictionary-Based Manifold Learning
Probabilistic Modeling for Sequences of Sets in Continuous-Time
Decentralized Multi-Level Compositional Optimization Algorithms with Level-Independent Convergence Rate
Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical Systems
A General Theoretical Paradigm to Understand Learning from Human Preferences
Near-Interpolators: Rapid Norm Growth and the Trade-Off between Interpolation and Generalization
On the Generalization Ability of Unsupervised Pretraining
Surrogate Active Subspaces for Jump-Discontinuous Functions
Continual Domain Adversarial Adaptation via Double-Head Discriminators
Mixture-of-Linear-Experts for Long-term Time Series Forecasting
Provable Policy Gradient Methods for Average-Reward Markov Potential Games
A Cubic-regularized Policy Newton Algorithm for Reinforcement Learning
Efficient Active Learning Halfspaces with Tsybakov Noise: A Non-convex Optimization Approach
Consistent Hierarchical Classification with A Generalized Metric
A/B testing under Interference with Partial Network Information
Personalized Federated X-armed Bandit
Comparing Comparators in Generalization Bounds
Ordinal Potential-based Player Rating
A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport
Maximum entropy GFlowNets with soft Q-learning
Identifying Copeland Winners in Dueling Bandits with Indifferences
Breaking isometric ties and introducing priors in Gromov-Wasserstein distances
Pure Exploration in Bandits with Linear Constraints
Multi-armed bandits with guaranteed revenue per arm
Constant or Logarithmic Regret in Asynchronous Multiplayer Bandits with Limited Communication
Robust Non-linear Normalization of Heterogeneous Feature Distributions with Adaptive Tanh-Estimators
Faster Convergence with MultiWay Preferences
Minimax optimal density estimation using a shallow generative model with a one-dimensional latent variable
Delegating Data Collection in Decentralized Machine Learning
Efficient Quantum Agnostic Improper Learning of Decision Trees
Graph fission and cross-validation
Meta Learning in Bandits within shared affine Subspaces
On Ranking-based Tests of Independence
Structured Transforms Across Spaces with Cost-Regularized Optimal Transport
Federated Linear Contextual Bandits with Heterogeneous Clients
Directional Optimism for Safe Linear Bandits
TransFusion: Covariate-Shift Robust Transfer Learning for High-Dimensional Regression
Exploration via linearly perturbed loss minimisation
Proximal Causal Inference for Synthetic Control with Surrogates
Oracle-Efficient Pessimism: Offline Policy Optimization In Contextual Bandits
The Solution Path of SLOPE
Graph Pruning for Enumeration of Minimal Unsatisfiable Subsets
Sharp error bounds for imbalanced classification: how many examples in the minority class?
Multi-Domain Causal Representation Learning via Weak Distributional Invariances
DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data
Robust Sparse Voting
Unified Transfer Learning in High-Dimensional Linear Regression
Hidden yet quantifiable: A lower bound for confounding strength using randomized trials
Distributionally Robust Quickest Change Detection using Wasserstein Uncertainty Sets
Information-theoretic Analysis of Bayesian Test Data Sensitivity
Filter, Rank, and Prune: Learning Linear Cyclic Gaussian Graphical Models
Efficient Data Valuation for Weighted Nearest Neighbor Algorithms
(ends 5:30 PM)

SAT 4 MAY
7 a.m.
Registration Check-in Desk
(ends 2:00 PM)
8 a.m.
Mentoring Event (D&I):
Mementor Event
(ends 9:00 AM)
9 a.m.
Invited Talk:
Learning with symmetries: eigenvectors, graph representations and sample complexity
Stefanie Jegelka
(ends 10:00 AM)
10 a.m.
Break:
Morning Coffee Break
(ends 10:30 AM)
10:30 a.m.
Oral: Deep Learning [10:30-11:30]
Orals 10:30-11:30
[10:30] Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors
[10:30] Functional Flow Matching
[10:30] Deep Classifier Mimicry without Data Access
[10:30] Multi-Resolution Active Learning of Fourier Neural Operators
(ends 11:30 AM)
11:30 a.m.
Oral: Statistics [11:30-12:30]
Orals 11:30-12:30
[11:30] Transductive conformal inference with adaptive scores
[11:30] Approximate Leave-one-out Cross Validation for Regression with $\ell_1$ Regularizers
[11:30] Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent
[11:30] Testing exchangeability by pairwise betting
(ends 12:30 PM)
12:30 p.m.
Lunch Break on your own:
Lunch - retail
(ends 2:00 PM)
Mentoring Event (D&I):
Mementor Event
(ends 2:00 PM)
2 p.m.
Oral: Trustworthy ML [2:00-3:00]
Orals 2:00-3:00
[2:00] Efficient Data Shapley for Weighted Nearest Neighbor Algorithms
[2:00] On Counterfactual Metrics for Social Welfare: Incentives, Ranking, and Information Asymmetry
[2:00] Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data
[2:00] Is this model reliable for everyone? Testing for strong calibration
(ends 3:00 PM)
3 p.m.
Poster Session 3 [3:00-5:30]
Posters 3:00-5:30
Online Bilevel Optimization: Regret Analysis of Online Alternating Gradient Methods
Quantifying intrinsic causal contributions via structure preserving interventions
Near-Optimal Pure Exploration in Matrix Games: A Generalization of Stochastic Bandits \& Dueling Bandits
Optimal Budgeted Rejection Sampling for Generative Models
Discriminator Guidance for Autoregressive Diffusion Models
Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks
Approximate Bayesian Class-Conditional Models under Continuous Representation Shift
Proving Linear Mode Connectivity of Neural Networks via Optimal Transport
Functional Flow Matching
DE-HNN: An effective neural model for Circuit Netlist representation
Towards Practical Non-Adversarial Distribution Matching
Tensor-view Topological Graph Neural Network
Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo
FALCON: FLOP-Aware Combinatorial Optimization for Neural Network Pruning
The effect of Leaky ReLUs on the training and generalization of overparameterized networks
Soft-constrained Schr\"odinger Bridge: a Stochastic Control Approach
Understanding the Generalization Benefits of Late Learning Rate Decay
BLIS-Net: Classifying and Analyzing Signals on Graphs
Two Birds with One Stone: Enhancing Uncertainty Quantification and Interpretability with Graph Functional Neural Process
Understanding Inverse Scaling and Emergence in Multitask Representation Learning
Deep Classifier Mimicry without Data Access
Data Driven Threshold and Potential Initialization for Spiking Neural Networks
Revisiting the Noise Model of Stochastic Gradient Descent
Strategic Usage in a Multi-Learner Setting
Random Oscillators Network for Time Series Processing
Non-Convex Joint Community Detection and Group Synchronization via Generalized Power Method
Fast Minimization of Expected Logarithmic Loss via Stochastic Dual Averaging
General Tail Bounds for Non-Smooth Stochastic Mirror Descent
Communication-Efficient Federated Learning With Data and Client Heterogeneity
Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence
Why is parameter averaging beneficial in SGD? An objective smoothing perspective
Stochastic Extragradient with Random Reshuffling: Improved Convergence for Variational Inequalities
Fast and Accurate Estimation of Low-Rank Matrices from Noisy Measurements via Preconditioned Non-Convex Gradient Descent
LP-based Construction of DC Decompositions for Efficient Inference of Markov Random Fields
On Parameter Estimation in Deviated Gaussian Mixture of Experts
THE SAMPLE COMPLEXITY OF ERM IN EUCLIDEAN STOCHASTIC CONVEX OPTIMIZATION
Multi-objective Optimization via Wasserstein-Fisher-Rao Gradient Flow
Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization
Informative Path Planning with Limited Adaptivity
Acceleration and Implicit Regularization in Gaussian Phase Retrieval
Stochastic Methods in Variational Inequalities: Ergodicity, Bias and Refinements
Weight-Sharing Regularization
First Passage Percolation with Queried Hints
Krylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate
Adaptive Federated Minimax Optimization with Lower Complexities
Adaptive Batch Sizes for Active Learning: A Probabilistic Numerics Approach
Sharpened Lazy Incremental Quasi-Newton Method
SDEs for Minimax Optimization
Parameter-Agnostic Optimization under Relaxed Smoothness
Stochastic Frank-Wolfe: Unified Analysis and Zoo of Special Cases
The Galerkin method beats Graph-Based Approaches for Spectral Algorithms
Contextual Directed Acyclic Graphs
Faithful graphical representations of local independence
On the connection between Noise-Contrastive Estimation and Contrastive Divergence
A Greedy Approximation for k-Determinantal Point Processes
Approximate Control for Continuous-Time POMDPs
Policy Evaluation for Reinforcement Learning from Human Feedback: A Sample Complexity Analysis
Symmetric Equilibrium Learning of VAEs
On Feynman–Kac training of partial Bayesian neural networks
No-Regret Algorithms for Safe Bayesian Optimization with Monotonicity Constraints
Variational Resampling
Score Operator Newton transport
Posterior Uncertainty Quantification in Neural Networks using Data Augmentation
Data-Driven Confidence Intervals with Optimal Rates for the Mean of Heavy-Tailed Distributions
Trigonometric Quadrature Fourier Features for Scalable Gaussian Process Regression
Cylindrical Thompson Sampling for High-Dimensional Bayesian Optimization
Benefits of Non-Linear Scale Parameterizations in Black Box Variational Inference through Smoothness Results and Gradient Variance Bounds
Conditional Adjustment in a Markov Equivalence Class
Equivalence Testing: The Power of Bounded Adaptivity
Optimal estimation of Gaussian (poly)trees
Inconsistency of Cross-Validation for Structure Learning in Gaussian Graphical Models
Hodge-Compositional Edge Gaussian Processes
On cyclical MCMC sampling
Adaptive importance sampling for heavy-tailed distributions via $\alpha$-divergence minimization
Efficient Variational Sequential Information Control
An Analytic Solution to Covariance Propagation in Neural Networks
Think Global, Adapt Local: Learning Locally Adaptive K-Nearest Neighbor Kernel Density Estimators
Sinkhorn Flow as Mirror Flow: A Continuous-Time Framework for Generalizing the Sinkhorn Algorithm
Near-optimal Per-Action Regret Bounds for Sleeping Bandits
Generative Flow Networks as Entropy-Regularized RL
Simulation-Based Stacking
Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inference
Coreset Markov chain Monte Carlo
Identifiability of Product of Experts Models
Fast Fourier Bayesian Quadrature
autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm
The AL$\ell_0$CORE Tensor Decomposition for Sparse Count Data
Warped Diffusion for Latent Differentiation Inference
Differentiable Rendering with Reparameterized Volume Sampling
Electronic Medical Records Assisted Digital Clinical Trial Design
Sample-efficient neural likelihood-free Bayesian inference of implicit HMMs
Conformalized Semi-supervised Random Forest for Classification and Abnormality Detection
Online learning in bandits with predicted context
Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methods
SPEED: Experimental Design for Policy Evaluation in Linear Heteroscedastic Bandits
Pessimistic Off-Policy Multi-Objective Optimization
Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures
Reward-Relevance-Filtered Linear Offline Reinforcement Learning
Stochastic Multi-Armed Bandits with Strongly Reward-Dependent Delays
Improved Algorithm for Adversarial Linear Mixture MDPs with Bandit Feedback and Unknown Transition
Learning the Pareto Set Under Incomplete Preferences: Pure Exploration in Vector Bandits
Multi-Agent Learning in Contextual Games under Unknown Constraints
Learning Unknown Intervention Targets in Structural Causal Models from Heterogeneous Data
Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent in High-Dimensional Least Squares
Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data
Consistent and Asymptotically Unbiased Estimation of Proper Calibration Errors
Identification and Estimation of ``Causes of Effects'' using Covariate-Mediator Information
Sequential learning of the Pareto front for multi-objective bandits
On the Impact of Overparameterization on the Training of a Shallow Neural Network in High Dimensions
Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles
On the Misspecification of Linear Assumptions in Synthetic Controls
Multi-Agent Bandit Learning through Heterogeneous Action Erasure Channels
Conditions on Preference Relations that Guarantee the Existence of Optimal Policies
Membership Testing in Markov Equivalence Classes via Independence Queries
General Identifiability and Achievability for Causal Representation Learning
Proxy Methods for Domain Adaptation
Self-Compatibility: Evaluating Causal Discovery without Ground Truth
Benchmarking Observational Studies with Experimental Data under Right-Censoring
Anytime-Constrained Reinforcement Learning
Causal Q-Aggregation for CATE Model Selection
Learning Policies for Localized Interventions from Observational Data
Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion
Gibbs-Based Information Criteria and the Over-Parameterized Regime
Think Before You Duel: Understanding Complexities of Preference Learning under Constrained Resources
Sum-max Submodular Bandits
Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input Uncertainty
To Pool or Not To Pool: Analyzing the Regularizing Effects of Group-Fair Training on Shared Models
Causal Bandits with General Causal Models and Interventions
On the price of exact truthfulness in incentive-compatible online learning with bandit feedback: a regret lower bound for WSU-UX
Faster Recalibration of an Online Predictor via Approachability
Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstention
Differentially Private Reward Estimation with Preference Feedback
Identifying Confounding from Causal Mechanism Shifts
Testing exchangeability by pairwise betting
Boundary-Aware Uncertainty for Feature Attribution Explainers
EM for Mixture of Linear Regression with Clustered Data
Federated Experiment Design under Distributed Differential Privacy
Can Probabilistic Feedback Drive User Impacts in Online Platforms?
Achieving Group Distributional Robustness and Minimax Group Fairness with Interpolating Classifiers
Pr{I}sing: Privacy-Preserving Peer Effect Estimation via {I}sing Model
Invariant Aggregator for Defending against Federated Backdoor Attacks
Unveiling Latent Causal Rules: A Temporal Point Process Approach for Abnormal Event Explanation
Identifying Spurious Biases Early in Training through the Lens of Simplicity Bias
Density-Regression: Efficient and Distance-aware Deep Regressor for Uncertainty Estimation under Distribution Shifts
The Relative Gaussian Mechanism and its Application to Private Gradient Descent
Approximate Leave-one-out Cross Validation for Regression with l1 Regularizers
Formal Verification of Unknown Stochastic Systems via Non-parametric Estimation
Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training
SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization
SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification
Differentially Private Conditional Independence Testing
Fair Machine Unlearning: Data Removal while Mitigating Disparities
On the Effect of Key Factors in Spurious Correlation: A theoretical Perspective
How does GPT-2 Predict Acronyms? Extracting and Understanding a Circuit via Mechanistic Interpretability
Learning Under Random Distributional Shifts
Towards Costless Model Selection in Contextual Bandits: A Bias-Variance Perspective
NoisyMix: Boosting Model Robustness to Common Corruptions
On the Vulnerability of Fairness Constrained Learning to Malicious Noise
Private Learning with Public Features
Computing epidemic metrics with edge differential privacy
Improving Robustness via Tilted Exponential Layer: A Communication-Theoretic Perspective
Non-vacuous Generalization Bounds for Adversarial Risk in Stochastic Neural Networks
Pathwise Explanation of ReLU Neural Networks
Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains
Mitigating Underfitting in Learning to Defer with Consistent Losses
Efficient Conformal Prediction under Data Heterogeneity
Bandit Pareto Set Identification: the Fixed Budget Setting
Restricted Isometry Property of Rank-One Measurements with Random Unit-Modulus Vectors
Any-dimensional equivariant neural networks
Multivariate Time Series Forecasting By Graph Attention Networks With Theoretical Guarantees
Spectrum Extraction and Clipping for Implicitly Linear Layers
Preventing Arbitrarily High Confidence on Far-Away Data in Point-Estimated Discriminative Neural Networks
Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers
Causally Inspired Regularization Enables Domain General Representations
XB-MAML: Learning Expandable Basis Parameters for Effective Meta-Learning with Wide Task Coverage
SDMTR: A Brain-inspired Transformer for Relation Inference
Towards Convergence Rates for Parameter Estimation in Gaussian-gated Mixture of Experts
FairRR: Pre-Processing for Group Fairness through Randomized Response
Asymptotic Characterisation of the Performance of Robust Linear Regression in the Presence of Outliers
(ends 5:30 PM)

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