AISTATS 2025

583 papers

To Give or Not to Give? The Impacts of Strategically Withheld Recourse

Yatong Chen, Andrew Estornell, Yevgeniy Vorobeychik, Yang Liu

The Polynomial Iteration Complexity for Variance Exploding Diffusion Models: Elucidating SDE and ODE Samplers

Ruofeng Yang, Bo Jiang, Shuai Li

Bridging Multiple Worlds: Multi-marginal Optimal Transport for Causal Partial-identification Problem

Zijun Gao, Shu Ge, Jian Qian

Steering No-Regret Agents in MFGs under Model Uncertainty

Leo Widmer, Jiawei Huang, Niao He

Amortized Probabilistic Conditioning for Optimization, Simulation and Inference

Paul Edmund Chang, Nasrulloh Ratu Bagus Satrio Loka, Daolang Huang, Ulpu Remes, Samuel Kaski

On the Sample Complexity of Next-Token Prediction

Oğuz Kaan Yüksel, Nicolas Flammarion

A Generalized Theory of Mixup for Structure-Preserving Synthetic Data

Chungpa Lee, Jongho Im, Joseph H.T. Kim

FedBaF: Federated Learning Aggregation Biased by a Foundation Model

Jong-Ik Park, Srinivasa Pranav, Jose M F Moura, Carlee Joe-Wong

Learning the Pareto Front Using Bootstrapped Observation Samples

Wonyoung Kim, Garud Iyengar, Assaf Zeevi

Harnessing Causality in Reinforcement Learning with Bagged Decision Times

Daiqi Gao, Hsin-Yu Lai, Predrag Klasnja, Susan Murphy

Prediction-Centric Uncertainty Quantification via MMD

Zheyang Shen, Jeremias Knoblauch, Samuel Power, Chris J. Oates

Reinforcement Learning for Adaptive MCMC

Congye Wang, Wilson Ye Chen, Heishiro Kanagawa, Chris J. Oates

Reliable and Scalable Variable Importance Estimation via Warm-start and Early Stopping

Zexuan Sun, Garvesh Raskutti

Locally Private Sampling with Public Data

Behnoosh Zamanlooy, Mario Diaz, Shahab Asoodeh

Data Reconstruction Attacks and Defenses: A Systematic Evaluation

Sheng Liu, Zihan Wang, Yuxiao Chen, Qi Lei

On the Geometry and Optimization of Polynomial Convolutional Networks

Vahid Shahverdi, Giovanni Luca Marchetti, Kathlén Kohn

Empirical Error Estimates for Graph Sparsification

Siyao Wang, Miles E. Lopes

Selecting the Number of Communities for Weighted Degree-Corrected Stochastic Block Models

Yucheng Liu, Xiaodong Li

Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal Learning

Jiaru Zhang, Rui Ding, Qiang Fu, Huang Bojun, Zizhen Deng

Epistemic Uncertainty and Excess Risk in Variational Inference

Futoshi Futami

Bridging Domains with Approximately Shared Features

Ziliang Samuel Zhong, Xiang Pan, Qi Lei

Scalable spectral representations for multiagent reinforcement learning in network MDPs

Zhaolin Ren, Runyu Zhang, Bo Dai, Na Li

Elastic Representation: Mitigating Spurious Correlations for Group Robustness

Tao Wen, Zihan Wang, Quan Zhang, Qi Lei

Signature Isolation Forest

Marta Campi, Guillaume Staerman, Gareth W. Peters, Tomoko Masui

Fourier Circuits in Neural Networks and Transformers: A Case Study of Modular Arithmetic with Multiple Inputs

Chenyang Li, Yingyu Liang, Zhenmei Shi, Zhao Song, Tianyi Zhou

Adaptive Extragradient Methods for Root-finding Problems under Relaxed Assumptions

Yang Luo, Michael J O’Neill

On the Asymptotic Mean Square Error Optimality of Diffusion Models

Benedikt Fesl, Benedikt Böck, Florian Strasser, Michael Baur, Michael Joham

Signed Graph Autoencoder for Explainable and Polarization-Aware Network Embeddings

Nikolaos Nakis, Chrysoula Kosma, Giannis Nikolentzos, Michail Chatzianastasis, Iakovos Evdaimon

A Theoretical Framework for Preventing Class Collapse in Supervised Contrastive Learning

Chungpa Lee, Jeongheon Oh, Kibok Lee, Jy-yong Sohn

Function-Space MCMC for Bayesian Wide Neural Networks

Lucia Pezzetti, Stefano Favaro, Stefano Peluchetti

Ant Colony Sampling with GFlowNets for Combinatorial Optimization

Minsu Kim, Sanghyeok Choi, Hyeonah Kim, Jiwoo Son, Jinkyoo Park

HAVER: Instance-Dependent Error Bounds for Maximum Mean Estimation and Applications to Q-Learning and Monte Carlo Tree Search

Tuan Nguyen, Jay Barrett, Kwang-Sung Jun

Inverse Optimization with Prediction Market: A Characterization of Scoring Rules for Elciting System States

Han Bao, Shinsaku Sakaue

Scalable Inference for Bayesian Multinomial Logistic-Normal Dynamic Linear Models

Manan Saxena, Tinghua Chen, Justin D Silverman

Improving Pre-trained Self-Supervised Embeddings Through Effective Entropy Maximization

Deep Chakraborty, Yann LeCun, Tim G. J. Rudner, Erik Learned-Miller

Nonparametric Factor Analysis and Beyond

Yujia Zheng, Yang Liu, Jiaxiong Yao, Yingyao Hu, Kun Zhang

No-Regret Bayesian Optimization with Stochastic Observation Failures

Shogo Iwazaki, Tomohiko Tanabe, Mitsuru Irie, Shion Takeno, Kota Matsui

Near-Optimal Algorithm for Non-Stationary Kernelized Bandits

Shogo Iwazaki, Shion Takeno

Some Targets Are Harder to Identify than Others: Quantifying the Target-dependent Membership Leakage

Achraf Azize, Debabrota Basu

Nyström Kernel Stein Discrepancy

Florian Kalinke, Zoltan Szabo, Bharath Sriperumbudur

Prior-Dependent Allocations for Bayesian Fixed-Budget Best-Arm Identification in Structured Bandits

Nicolas Nguyen, Imad Aouali, András György, Claire Vernade

Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation

Diantong Li, Fengxue Zhang, Chong Liu, Yuxin Chen

Infinite-Horizon Reinforcement Learning with Multinomial Logit Function Approximation

Jaehyun Park, Junyeop Kwon, Dabeen Lee

Strong Screening Rules for Group-based SLOPE Models

Fabio Feser, Marina Evangelou

Learning in Herding Mean Field Games: Single-Loop Algorithm with Finite-Time Convergence Analysis

Sihan Zeng, Sujay Bhatt, Alec Koppel, Sumitra Ganesh

Generalization Lower Bounds for GD and SGD in Smooth Stochastic Convex Optimization

Peiyuan Zhang, Jiaye Teng, Jingzhao Zhang

Disentangling impact of capacity, objective, batchsize, estimators, and step-size on flow VI

Abhinav Agrawal, Justin Domke

Approximate information maximization for bandit games

Alex Barbier Chebbah, Christian L. Vestergaard, Jean-Baptiste Masson, Etienne Boursier

A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasets

Ossi Räisä, Antti Honkela

Adversarial Training in High-Dimensional Regression: Generated Data and Neural Networks

Yue Xing

Almost linear time differentially private release of synthetic graphs

Zongrui Zou, Jingcheng Liu, Jalaj Upadhyay

Accelerated Methods for Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties

David Martínez-Rubio, Christophe Roux, Christopher Criscitiello, Sebastian Pokutta

The Pivoting Framework: Frank-Wolfe Algorithms with Active Set Size Control

Mathieu Besançon, Sebastian Pokutta, Elias Samuel Wirth

Choice is what matters after Attention

Chenhan Fu, Guoming Wang, Juncheng Li, Rongxing Lu, Siliang Tang

Density Ratio Estimation via Sampling along Generalized Geodesics on Statistical Manifolds

Masanari Kimura, Howard Bondell

The Local Learning Coefficient: A Singularity-Aware Complexity Measure

Edmund Lau, Zach Furman, George Wang, Daniel Murfet, Susan Wei

Bayesian Gaussian Process ODEs via Double Normalizing Flows

Jian Xu, Shian Du, Junmei Yang, Xinghao Ding, Delu Zeng

Harnessing the Power of Vicinity-Informed Analysis for Classification under Covariate Shift

Mitsuhiro Fujikawa, Youhei Akimoto, Jun Sakuma, Kazuto Fukuchi

Conditional Generative Learning from Invariant Representations in Multi-Source: Robustness and Efficiency

Guojun Zhu, Sanguo Zhang, Mingyang Ren

$β$-th order Acyclicity Derivatives for DAG Learning

Madhumitha Shridharan, Garud Iyengar

Relating Piecewise Linear Kolmogorov Arnold Networks to ReLU Networks

Nandi Schoots, Mattia Jacopo Villani, Niels uit de Bos

Estimation of Large Zipfian Distributions with Sort and Snap

Peter Matthew Jacobs, Anirban Bhattacharya, Debdeep Pati, Lekha Patel, Jeff M. Phillips

A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning

Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Avila Pires, Yunhao Tang, Clare Lyle

S-CFE: Simple Counterfactual Explanations

Shpresim Sadiku, Moritz Wagner, Sai Ganesh Nagarajan, Sebastian Pokutta

Optimising Clinical Federated Learning through Mode Connectivity-based Model Aggregation

Anshul Thakur, Soheila Molaei, Patrick Schwab, Danielle Belgrave, Kim Branson

Fine-Tuning with Uncertainty-Aware Priors Makes Vision and Language Foundation Models More Reliable

Tim G. J. Rudner, Xiang Pan, Yucen Lily Li, Ravid Shwartz-Ziv, Andrew Gordon Wilson

On the Convergence of Locally Adaptive and Scalable Diffusion-Based Sampling Methods for Deep Bayesian Neural Network Posteriors

Tim Rensmeyer, Oliver Niggemann

Bayesian Off-Policy Evaluation and Learning for Large Action Spaces

Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba

Credal Two-Sample Tests of Epistemic Uncertainty

Siu Lun Chau, Antonin Schrab, Arthur Gretton, Dino Sejdinovic, Krikamol Muandet

Efficient Estimation of a Gaussian Mean with Local Differential Privacy

Kalinin Nikita, Lukas Steinberger

ClusterSC: Advancing Synthetic Control with Donor Selection

Saeyoung Rho, Andrew Tang, Noah Bergam, Rachel Cummings, Vishal Misra

Bayesian Inference in Recurrent Explicit Duration Switching Linear Dynamical Systems

Mikołaj Słupiński

Flexible Copula-Based Mixed Models in Deep Learning: A Scalable Approach to Arbitrary Marginals

Giora Simchoni, Saharon Rosset

Randomized Iterative Solver as Iterative Refinement: A Simple Fix Towards Backward Stability

Ruihan Xu, Yiping Lu

Lower Bounds for Time-Varying Kernelized Bandits

Xu Cai, Jonathan Scarlett

A Family of Distributions of Random Subsets for Controlling Positive and Negative Dependence

Takahiro Kawashima, Hideitsu Hino

Locally Private Estimation with Public Features

Yuheng Ma, Ke Jia, Hanfang Yang

Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel–Young Loss Perspective and Gap-Dependent Regret Analysis

Shinsaku Sakaue, Han Bao, Taira Tsuchiya

Generalized Criterion for Identifiability of Additive Noise Models Using Majorization

Aramayis Dallakyan, Yang Ni

Cost-aware simulation-based inference

Ayush Bharti, Daolang Huang, Samuel Kaski, François-Xavier Briol

Automatically Adaptive Conformal Risk Control

Vincent Blot, Anastasios Nikolas Angelopoulos, Michael Jordan, Nicolas J-B. Brunel

Paths and Ambient Spaces in Neural Loss Landscapes

Daniel Dold, Julius Kobialka, Nicolai Palm, Emanuel Sommer, David Rügamer

Additive Model Boosting: New Insights and Path(ologie)s

Rickmer Schulte, David Rügamer