AISTATS 2025

583 papers

A Convex Relaxation Approach to Generalization Analysis for Parallel Positively Homogeneous Networks

Uday Kiran Reddy Tadipatri, Benjamin David Haeffele, Joshua Agterberg, René Vidal

The Strong Product Model for Network Inference without Independence Assumptions

Bailey Andrew, David Robert Westhead, Luisa Cutillo

Exposing Privacy Gaps: Membership Inference Attack on Preference Data for LLM Alignment

Qizhang Feng, Siva Rajesh Kasa, SANTHOSH KUMAR KASA, Hyokun Yun, Choon Hui Teo

Emergence of Globally Attracting Fixed Points in Deep Neural Networks With Nonlinear Activations

Amir Joudaki, Thomas Hofmann

Task-Driven Discrete Representation Learning

Long Tung Vuong

Clustering Context in Off-Policy Evaluation

Daniel Guzman Olivares, Philipp Schmidt, Jacek Golebiowski, Artur Bekasov

Mean-Field Microcanonical Gradient Descent

Marcus Häggbom, Morten Karlsmark, Joakim Andén

Every Call is Precious: Global Optimization of Black-Box Functions with Unknown Lipschitz Constants

Fares Fourati, Salma Kharrat, Vaneet Aggarwal, Mohamed-Slim Alouini

Training Neural Samplers with Reverse Diffusive KL Divergence

Jiajun He, Wenlin Chen, Mingtian Zhang, David Barber, José Miguel Hernández-Lobato

An Empirical Bernstein Inequality for Dependent Data in Hilbert Spaces and Applications

Erfan Mirzaei, Andreas Maurer, Vladimir R Kostic, Massimiliano Pontil

M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling

Xudong Sun, Nutan Chen, Alexej Gossmann, Yu Xing, Matteo Wohlrapp

Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory

Fabian Fumagalli, Maximilian Muschalik, Eyke Hüllermeier, Barbara Hammer, Julia Herbinger

Hypernym Bias: Unraveling Deep Classifier Training Dynamics through the Lens of Class Hierarchy

Roman Malashin, Yachnaya Valeria, Alexandr V. Mullin

Strategic Conformal Prediction

Daniel Csillag, Claudio Jose Struchiner, Guilherme Tegoni Goedert

AlleNoise - large-scale text classification benchmark dataset with real-world label noise

Alicja Rączkowska, Aleksandra Osowska-Kurczab, Jacek Szczerbiński, Kalina Jasinska-Kobus, Klaudia Nazarko

Certifiably Quantisation-Robust training and inference of Neural Networks

Hue Dang, Matthew Robert Wicker, Goetz Botterweck, Andrea Patane

Density Ratio-based Proxy Causal Learning Without Density Ratios

Bariscan Bozkurt, Ben Deaner, Dimitri Meunier, Liyuan Xu, Arthur Gretton

DPFL: Decentralized Personalized Federated Learning

Salma Kharrat, Marco Canini, Samuel Horváth

Analyzing Generative Models by Manifold Entropic Metrics

Daniel Galperin, Ullrich Koethe

On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients

Satish Kumar Keshri, Nazreen Shah, Ranjitha Prasad

Level Set Teleportation: An Optimization Perspective

Aaron Mishkin, Alberto Bietti, Robert M. Gower

Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayes Theory

Zhi Zhang, Chris Chow, Yasi Zhang, Yanchao Sun, Haochen Zhang

Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning

Boning Zhang, Dongzhu Liu, Osvaldo Simeone, Guanchu Wang, Dimitrios Pezaros

On Local Posterior Structure in Deep Ensembles

Mikkel Jordahn, Jonas Vestergaard Jensen, Mikkel N. Schmidt, Michael Riis Andersen

Refined Analysis of Constant Step Size Federated Averaging and Federated Richardson-Romberg Extrapolation

Paul Mangold, Alain Oliviero Durmus, Aymeric Dieuleveut, Sergey Samsonov, Eric Moulines

Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics

Daniel Paulin, Peter A. Whalley, Neil K. Chada, Benedict J. Leimkuhler

Counting Graphlets of Size k under Local Differential Privacy

Vorapong Suppakitpaisarn, Donlapark Ponnoprat, Nicha Hirankarn, Quentin Hillebrand

LITE: Efficiently Estimating Gaussian Probability of Maximality

Nicolas Menet, Jonas Hübotter, Parnian Kassraie, Andreas Krause

Noise-Aware Differentially Private Variational Inference

Talal Alrawajfeh, Joonas Jälkö, Antti Honkela

Wasserstein Distributionally Robust Bayesian Optimization with Continuous Context

Francesco Micheli, Efe C. Balta, Anastasios Tsiamis, John Lygeros

Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutions

Omer Noy Klein, Alihan Hüyük, Ron Shamir, Uri Shalit, Mihaela van der Schaar

Robust Classification by Coupling Data Mollification with Label Smoothing

Markus Heinonen, Ba-Hien Tran, Michael Kampffmeyer, Maurizio Filippone

Weighted Euclidean Distance Matrices over Mixed Continuous and Categorical Inputs for Gaussian Process Models

Mingyu Pu, Wang Songhao, Haowei Wang, Szu Hui Ng

Black-Box Uniform Stability for Non-Euclidean Empirical Risk Minimization

Simon Vary, David Martínez-Rubio, Patrick Rebeschini

Unconditionally Calibrated Priors for Beta Mixture Density Networks

Alix LHERITIER, Maurizio Filippone

Knowledge Graph Completion with Mixed Geometry Tensor Factorization

Viacheslav Yusupov, Maxim Rakhuba, Evgeny Frolov

Algorithmic Accountability in Small Data: Sample-Size-Induced Bias Within Classification Metrics

Jarren Briscoe, Garrett Kepler, Daryl Robert DeFord, Assefaw Gebremedhin

Mixed-Feature Logistic Regression Robust to Distribution Shifts

Qingshi Sun, Nathan Justin, Andres Gomez, Phebe Vayanos

Robust Offline Policy Learning with Observational Data from Multiple Sources

Aldo Gael Carranza, Susan Athey

Conformal Prediction Under Generalized Covariate Shift with Posterior Drift

Baozhen Wang, Xingye Qiao

Gaussian Mean Testing under Truncation

Clement Louis Canonne, Themis Gouleakis, Yuhao Wang, Qiping Yang

Deep Clustering via Probabilistic Ratio-Cut Optimization

Ayoub Ghriss, Claire Monteleoni

Learning Stochastic Nonlinear Dynamics with Embedded Latent Transfer Operators

Naichang Ke, Ryogo Tanaka, Yoshinobu Kawahara

Nonparametric Distributional Regression via Quantile Regression

Cheng Peng, Stan Uryasev

How Well Can Transformers Emulate In-Context Newton’s Method?

Angeliki Giannou, Liu Yang, Tianhao Wang, Dimitris Papailiopoulos, Jason D. Lee

Structure based SAT dataset for analysing GNN generalisation

Yi Fu, Anthony Tompkins, Yang Song, Maurice Pagnucco

Leveraging Frozen Batch Normalization for Co-Training in Source-Free Domain Adaptation

Xianwen Deng, Yijun Wang, Zhi Xue

A Robust Kernel Statistical Test of Invariance: Detecting Subtle Asymmetries

Ashkan Soleymani, Behrooz Tahmasebi, Stefanie Jegelka, Patrick Jaillet

Conditional simulation via entropic optimal transport: Toward non-parametric estimation of conditional Brenier maps

Ricardo Baptista, Aram-Alexandre Pooladian, Michael Brennan, Youssef Marzouk, Jonathan Niles-Weed

Graph-based Complexity for Causal Effect by Empirical Plug-in

Rina Dechter, Anna K Raichev, Jin Tian, Alexander Ihler

Regularity in Canonicalized Models: A Theoretical Perspective

Behrooz Tahmasebi, Stefanie Jegelka

Bilevel Reinforcement Learning via the Development of Hyper-gradient without Lower-Level Convexity

yan yang, Bin Gao, Ya-xiang Yuan

A Shapley-value Guided Rationale Editor for Rationale Learning

Zixin Kuang, Meng-Fen Chiang, Wang-Chien Lee

Advancing Fairness in Precision Medicine: A Universal Framework for Optimal Treatment Estimation in Censored Data

Hongni Wang, Junxi Zhang, Na Li, Linglong Kong, Bei Jiang

Invariant Link Selector for Spatial-Temporal Out-of-Distribution Problem

Katherine Tieu, Dongqi Fu, Jun Wu, Jingrui He

A Computation-Efficient Method of Measuring Dataset Quality based on the Coverage of the Dataset

Beomjun Kim, Jaehwan Kim, Kangyeon Kim, Sunwoo Kim, Heejin Ahn

Quantile Additive Trend Filtering

Zhi Zhang, Kyle Ritscher, OSCAR HERNAN MADRID PADILLA

I-trustworthy Models. A framework for trustworthiness evaluation of probabilistic classifiers

Ritwik Vashistha, Arya Farahi

Transfer Neyman-Pearson Algorithm for Outlier Detection

Mohammadreza Mousavi Kalan, Eitan J. Neugut, Samory Kpotufe

On the Consistent Recovery of Joint Distributions from Conditionals

Mahbod Majid, Rattana Pukdee, Vishwajeet Agrawal, Burak Varıcı, Pradeep Kumar Ravikumar

Order-Optimal Regret in Distributed Kernel Bandits using Uniform Sampling with Shared Randomness

Nikola Pavlovic, Sudeep Salgia, Qing Zhao

Testing Conditional Independence with Deep Neural Network Based Binary Expansion Testing (DeepBET)

Yang Yang, Kai Zhang, Ping-Shou Zhong

Variational Adversarial Training Towards Policies with Improved Robustness

Juncheng Dong, Hao-Lun Hsu, Qitong Gao, Vahid Tarokh, Miroslav Pajic

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting

Fuqiang Liu, Sicong Jiang, Luis Miranda-Moreno, Seongjin Choi, Lijun Sun

The Uniformly Rotated Mondrian Kernel

Calvin Osborne, Eliza O’Reilly

Learning to Forget: Bayesian Time Series Forecasting using Recurrent Sparse Spectrum Signature Gaussian Processes

Csaba Toth, Masaki Adachi, Michael A Osborne, Harald Oberhauser

Variational Schrödinger Momentum Diffusion

Kevin Rojas, Yixin Tan, Molei Tao, Yuriy Nevmyvaka, Wei Deng

Understanding the Learning Dynamics of LoRA: A Gradient Flow Perspective on Low-Rank Adaptation in Matrix Factorization

Ziqing Xu, Hancheng Min, Lachlan Ewen MacDonald, Jinqi Luo, Salma Tarmoun

Federated Communication-Efficient Multi-Objective Optimization

Baris Askin, PRANAY SHARMA, Gauri Joshi, Carlee Joe-Wong

Differentially Private Kernelized Contextual Bandits

Nikola Pavlovic, Sudeep Salgia, Qing Zhao

Beyond Size-Based Metrics: Measuring Task-Specific Complexity in Symbolic Regression

Krzysztof Kacprzyk, Mihaela van der Schaar

Optimal Stochastic Trace Estimation in Generative Modeling

Xinyang Liu, Hengrong Du, Wei Deng, Ruqi Zhang

Learning Pareto manifolds in high dimensions: How can regularization help?

Tobias Wegel, Filip Kovačević, Alexandru Tifrea, Fanny Yang

Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints

Lingkai Kong, Yuanqi Du, Wenhao Mu, Kirill Neklyudov, Valentin De Bortoli

Understanding the Effect of GCN Convolutions in Regression Tasks

Juntong Chen, Johannes Schmidt-Hieber, Claire Donnat, Olga Klopp

General Staircase Mechanisms for Optimal Differential Privacy

Alex Kulesza, Ananda Theertha Suresh, Yuyan Wang

Linearized Wasserstein Barycenters: Synthesis, Analysis, Representational Capacity, and Applications

Matthew Werenski, Brendan Mallery, Shuchin Aeron, James M. Murphy

Causal Temporal Regime Structure Learning

Abdellah Rahmani, Pascal Frossard

Faster WIND: Accelerating Iterative Best-of-N Distillation for LLM Alignment

Tong Yang, Jincheng Mei, Hanjun Dai, Zixin Wen, Shicong Cen

Beyond Discretization: Learning the Optimal Solution Path

Qiran Dong, Paul Grigas, Vishal Gupta

Stochastic Weight Sharing for Bayesian Neural Networks

Moule Lin, Shuhao Guan, Weipeng Jing, Goetz Botterweck, Andrea Patane

Batch, match, and patch: low-rank approximations for score-based variational inference

Chirag Modi, Diana Cai, Lawrence K. Saul

Understanding Expert Structures on Minimax Parameter Estimation in Contaminated Mixture of Experts

Fanqi Yan, Huy Nguyen, Le Quang Dung, Pedram Akbarian, nhật Hồ

Cost-Aware Optimal Pairwise Pure Exploration

Di Wu, Chengshuai Shi, Ruida Zhou, Cong Shen

Efficient and Asymptotically Unbiased Constrained Decoding for Large Language Models

Haotian Ye, Himanshu Jain, Chong You, Ananda Theertha Suresh, Haowei Lin

Quantifying Knowledge Distillation using Partial Information Decomposition

Pasan Dissanayake, Faisal Hamman, Barproda Halder, Ilia Sucholutsky, Qiuyi Zhang

SINE: Scalable MPE Inference for Probabilistic Graphical Models using Advanced Neural Embeddings

Shivvrat Arya, Tahrima Rahman, Vibhav Giridhar Gogate

Online Assortment and Price Optimization Under Contextual Choice Models

Yigit Efe Erginbas, Thomas Courtade, Kannan Ramchandran

Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent

Bo Chen, Xiaoyu Li, Yingyu Liang, Zhenmei Shi, Zhao Song

Keeping up with dynamic attackers: Certifying robustness to adaptive online data poisoning

Avinandan Bose, Laurent Lessard, Maryam Fazel, Krishnamurthy Dj Dvijotham

Corruption Robust Offline Reinforcement Learning with Human Feedback

Debmalya Mandal, Andi Nika, Parameswaran Kamalaruban, Adish Singla, Goran Radanovic

Offline Multi-task Transfer RL with Representational Penalization

Avinandan Bose, Simon Shaolei Du, Maryam Fazel

Posterior Mean Matching: Generative Modeling through Online Bayesian Inference

Sebastian Salazar, Michal Kucer, Yixin Wang, Emily Casleton, David Blei

Stochastic Rounding for LLM Training: Theory and Practice

Kaan Ozkara, Tao Yu, Youngsuk Park

Prepacking: A Simple Method for Fast Prefilling and Increased Throughput in Large Language Models

Siyan Zhao, Daniel Mingyi Israel, Guy Van den Broeck, Aditya Grover

M$^2$AD: Multi-Sensor Multi-System Anomaly Detection through Global Scoring and Calibrated Thresholding

Sarah Alnegheimish, Zelin He, Matthew Reimherr, Akash Chandrayan, Abhinav Pradhan

Cross-Modal Imputation and Uncertainty Estimation for Spatial Transcriptomics

Xiangyu Guo, Ricardo Henao

Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects

Seyedeh Baharan Khatami, Harsh Parikh, Haowei Chen, Sudeepa Roy, Babak Salimi

On the Power of Multitask Representation Learning with Gradient Descent

Qiaobo Li, Zixiang Chen, Yihe Deng, Yiwen Kou, Yuan Cao

Loss Gradient Gaussian Width based Generalization and Optimization Guarantees

Arindam Banerjee, Qiaobo Li, Yingxue Zhou