AISTATS 2025

583 papers

ScoreFusion: Fusing Score-based Generative Models via Kullback–Leibler Barycenters

Hao Liu, Tony Junze Ye, Jose Blanchet, Nian Si

Active Feature Acquisition for Personalised Treatment Assignment

Julianna Piskorz, Nicolás Astorga, Jeroen Berrevoets, Mihaela van der Schaar

MODL: Multilearner Online Deep Learning

Antonios Valkanas, Boris N. Oreshkin, Mark Coates

Behavior-Inspired Neural Networks for Relational Inference

Yulong Yang, Bowen Feng, Keqin Wang, Naomi Leonard, Adji Bousso Dieng

Logarithmic Neyman Regret for Adaptive Estimation of the Average Treatment Effect

Ojash Neopane, Aaditya Ramdas, Aarti Singh

Time-varying Gaussian Process Bandits with Unknown Prior

Juliusz Ziomek, Masaki Adachi, Michael A Osborne

Characterizing the Accuracy-Communication-Privacy Trade-off in Distributed Stochastic Convex Optimization

Sudeep Salgia, Nikola Pavlovic, Yuejie Chi, Qing Zhao

Synthetic Potential Outcomes and Causal Mixture Identifiability

Bijan Mazaheri, Chandler Squires, Caroline Uhler

Signal Recovery from Random Dot-Product Graphs under Local Differential Privacy

Siddharth Vishwanath, Jonathan Hehir

Cross-modality Matching and Prediction of Perturbation Responses with Labeled Gromov-Wasserstein Optimal Transport

Jayoung Ryu, Charlotte Bunne, Luca Pinello, Aviv Regev, Romain Lopez

Linear Submodular Maximization with Bandit Feedback

Wenjing Chen, Victoria G. Crawford

Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orders

Christian Toth, Christian Knoll, Franz Pernkopf, Robert Peharz

From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior Approximation

Wenyuan Zhao, Haoyuan Chen, Tie Liu, Rui Tuo, Chao Tian

Robust Estimation in metric spaces: Achieving Exponential Concentration with a Fréchet Median

Jakwang Kim, Jiyoung Park, Anirban Bhattacharya

Variance-Aware Linear UCB with Deep Representation for Neural Contextual Bandits

Ha Manh Bui, Enrique Mallada, Anqi Liu

Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence

Berfin Simsek, Amire Bendjeddou, Daniel Hsu

Local Stochastic Sensitivity Analysis For Dynamical Systems

Nishant Panda, Jehanzeb H Chaudhry, Natalie Klein, James Carzon, Troy Butler

The cost of local and global fairness in Federated Learning

Yuying Duan, Gelei Xu, Yiyu Shi, Michael Lemmon

Approximate Equivariance in Reinforcement Learning

Jung Yeon Park, Sujay Bhatt, Sihan Zeng, Lawson L.S. Wong, Alec Koppel

Achieving Õ(√T) Regret in Average-Reward POMDPs with Known Observation Models

Alessio Russo, Alberto Maria Metelli, Marcello Restelli

InfoNCE: Identifying the Gap Between Theory and Practice

Evgenia Rusak, Patrik Reizinger, Attila Juhos, Oliver Bringmann, Roland S. Zimmermann

Double Debiased Machine Learning for Mediation Analysis with Continuous Treatments

Houssam Zenati, Judith Abécassis, Julie Josse, Bertrand Thirion

Deep Generative Quantile Bayes

Jungeum Kim, Percy S. Zhai, Veronika Ročková

Learning High-dimensional Gaussians from Censored Data

Arnab Bhattacharyya, Constantinos Costis Daskalakis, Themis Gouleakis, Yuhao Wang

All or None: Identifiable Linear Properties of Next-Token Predictors in Language Modeling

Emanuele Marconato, Sébastien Lachapelle, Sebastian Weichwald, Luigi Gresele

Tight Analysis of Difference-of-Convex Algorithm (DCA) Improves Convergence Rates for Proximal Gradient Descent

Teodor Rotaru, Panagiotis Patrinos, François Glineur

Visualizing token importance for black-box language models

Paulius Rauba, Qiyao Wei, Mihaela van der Schaar

Privacy in Metalearning and Multitask Learning: Modeling and Separations

Maryam Aliakbarpour, Konstantina Bairaktari, Adam Smith, Marika Swanberg, Jonathan Ullman

Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs

Enea Monzio Compagnoni, Rustem Islamov, Frank Norbert Proske, Aurelien Lucchi

Bayesian Principles Improve Prompt Learning In Vision-Language Models

Mingyu Kim, Jongwoo Ko, Mijung Park

Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy

Maryam Aliakbarpour, Syomantak Chaudhuri, Thomas Courtade, Alireza Fallah, Michael Jordan

Estimating the Spectral Moments of the Kernel Integral Operator from Finite Sample Matrices

Chanwoo Chun, SueYeon Chung, Daniel Lee

qPOTS: Efficient Batch Multiobjective Bayesian Optimization via Pareto Optimal Thompson Sampling

Ashwin Renganathan, Kade Carlson

Sampling from the Random Linear Model via Stochastic Localization Up to the AMP Threshold

Han Cui, Zhiyuan Yu, Jingbo Liu

Theoretical Analysis of Leave-one-out Cross Validation for Non-differentiable Penalties under High-dimensional Settings

Haolin Zou, Arnab Auddy, Kamiar Rahnama Rad, Arian Maleki

SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity

Peihao Wang, Zhiwen Fan, Dejia Xu, Dilin Wang, Sreyas Mohan

Theory of Agreement-on-the-Line in Linear Models and Gaussian Data

Christina Baek, Aditi Raghunathan, J Zico Kolter

Distributional Off-policy Evaluation with Bellman Residual Minimization

Sungee Hong, Zhengling Qi, Raymond K. W. Wong

Bridging the Theoretical Gap in Randomized Smoothing

Blaise Delattre, Paul Caillon, Quentin Barthélemy, Erwan Fagnou, Alexandre Allauzen

DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient Flows

Jonathan Geuter, Clément Bonet, Anna Korba, David Alvarez-Melis

The Size of Teachers as a Measure of Data Complexity: PAC-Bayes Excess Risk Bounds and Scaling Laws

Gintare Karolina Dziugaite, Daniel M. Roy

Analysis of Two-Stage Rollout Designs with Clustering for Causal Inference under Network Interference

Mayleen Cortez-Rodriguez, Matthew Eichhorn, Christina Yu

Application of Structured State Space Models to High energy physics with locality sensitive hashing

Cheng Jiang, Sitian Qian

Scalable Implicit Graphon Learning

Ali Azizpour, Nicolas Zilberstein, Santiago Segarra

Fast Convergence of Softmax Policy Mirror Ascent

Reza Asad, Reza Babanezhad Harikandeh, Issam H. Laradji, Nicolas Le Roux, Sharan Vaswani

Task Shift: From Classification to Regression in Overparameterized Linear Models

Tyler LaBonte, Kuo-Wei Lai, Vidya Muthukumar

Feasible Learning

Juan Ramirez, Ignacio Hounie, Juan Elenter, Jose Gallego-Posada, Meraj Hashemizadeh

Steinmetz Neural Networks for Complex-Valued Data

Shyam Venkatasubramanian, Ali Pezeshki, Vahid Tarokh

Near-Optimal Sample Complexity for Iterated CVaR Reinforcement Learning with a Generative Model

Zilong Deng, Simon Khan, Shaofeng Zou

Max-Rank: Efficient Multiple Testing for Conformal Prediction

Alexander Timans, Christoph-Nikolas Straehle, Kaspar Sakmann, Christian A. Naesseth, Eric Nalisnick

Learning Geometrically-Informed Lyapunov Functions with Deep Diffeomorphic RBF Networks

Samuel Tesfazgi, Leonhard Sprandl, Sandra Hirche

Semiparametric conformal prediction

Ji Won Park, Kyunghyun Cho

Risk-sensitive Bandits: Arm Mixture Optimality and Regret-efficient Algorithms

Meltem Tatlı, Arpan Mukherjee, Prashanth L. A., Karthikeyan Shanmugam, Ali Tajer

InnerThoughts: Disentangling Representations and Predictions in Large Language Models

Didier Chetelat, Joseph Cotnareanu, Rylee Thompson, Yingxue Zhang, Mark Coates

Adapting to Online Distribution Shifts in Deep Learning: A Black-Box Approach

Dheeraj Baby, Boran Han, SHUAI ZHANG, Cuixiong Hu, Bernie Wang

Tighter Confidence Bounds for Sequential Kernel Regression

Hamish Flynn, David Reeb

Stochastic Compositional Minimax Optimization with Provable Convergence Guarantees

Yuyang Deng, Fuli Qiao, Mehrdad Mahdavi

RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning Tasks

Eduard Tulchinskii, Daria Voronkova, Ilya Trofimov, Evgeny Burnaev, Serguei Barannikov

Multi-marginal Schrödinger Bridges with Iterative Reference Refinement

Yunyi Shen, Renato Berlinghieri, Tamara Broderick

Primal-Dual Spectral Representation for Off-policy Evaluation

Yang Hu, Tianyi Chen, Na Li, Kai Wang, Bo Dai

On Subjective Uncertainty Quantification and Calibration in Natural Language Generation

Ziyu Wang, Christopher C. Holmes

Your copula is a classifier in disguise: classification-based copula density estimation

David Huk, Mark Steel, Ritabrata Dutta

Two-Timescale Linear Stochastic Approximation: Constant Stepsizes Go a Long Way

Jeongyeol Kwon, Luke Dotson, Yudong Chen, Qiaomin Xie

SemlaFlow – Efficient 3D Molecular Generation with Latent Attention and Equivariant Flow Matching

Ross Irwin, Alessandro Tibo, Jon Paul Janet, Simon Olsson

Scalable Out-of-Distribution Robustness in the Presence of Unobserved Confounders

Parjanya Prajakta Prashant, Seyedeh Baharan Khatami, Bruno Ribeiro, Babak Salimi

Causal Discovery-Driven Change Point Detection in Time Series

Shanyun Gao, Raghavendra Addanki, Tong Yu, Ryan A. Rossi, Murat Kocaoglu

Copula Based Trainable Calibration Error Estimator of Multi-Label Classification with Label Interdependencies

Arkapal Panda, Utpal Garain

Kernel Single Proxy Control for Deterministic Confounding

Liyuan Xu, Arthur Gretton

Learning Visual-Semantic Subspace Representations

Gabriel Moreira, Manuel Marques, Joao Costeira, Alexander G Hauptmann

Stochastic Approximation with Unbounded Markovian Noise: A General-Purpose Theorem

Shaan Ul Haque, Siva Theja Maguluri

Conditioning diffusion models by explicit forward-backward bridging

Adrien Corenflos, Zheng Zhao, Thomas B. Schön, Simo Särkkä, Jens Sjölund

Causal Representation Learning from General Environments under Nonparametric Mixing

Ignavier Ng, Shaoan Xie, Xinshuai Dong, Peter Spirtes, Kun Zhang

On the Computational Tractability of the (Many) Shapley Values

Reda Marzouk, Shahaf Bassan, Guy Katz, De la Higuera

Unveiling the Role of Randomization in Multiclass Adversarial Classification: Insights from Graph Theory

Lucas Gnecco Heredia, Matteo Sammut, Muni Sreenivas Pydi, Rafael Pinot, Benjamin Negrevergne

A Novel Convex Gaussian Min Max Theorem for Repeated Features

David Bosch, Ashkan Panahi

Theoretically Grounded Pruning of Large Ground Sets for Constrained, Discrete Optimization

Ankur Nath, Alan Kuhnle

LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits

Masahiro Kato, Shinji Ito

Gated Recurrent Neural Networks with Weighted Time-Delay Feedback

N. Benjamin Erichson, Soon Hoe Lim, Michael W. Mahoney

Offline RL via Feature-Occupancy Gradient Ascent

Gergely Neu, Nneka Okolo

Density-Dependent Group Testing

Rahil Morjaria, Saikiran Bulusu, Venkata Gandikota, Sidharth Jaggi

Survival Models: Proper Scoring Rule and Stochastic Optimization with Competing Risks

Julie Alberge, Vincent Maladiere, Olivier Grisel, Judith Abécassis, Gael Varoquaux

Weighted Sum of Gaussian Process Latent Variable Models

James A C Odgers, Ruby Sedgwick, Chrysoula Dimitra Kappatou, Ruth Misener, Sarah Lucie Filippi

Towards Cost Sensitive Decision Making

Yang Li, Junier Oliva

A Safe Exploration Approach to Constrained Markov Decision Processes

Tingting Ni, Maryam Kamgarpour

Rethinking Neural-based Matrix Inversion: Why can’t, and Where can

Yuliang Ji, Jian Wu, Yuanzhe Xi

Nonparametric estimation of Hawkes processes with RKHSs

Anna Bonnet, Maxime Sangnier

Natural Language Counterfactual Explanations for Graphs Using Large Language Models

Flavio Giorgi, Cesare Campagnano, Fabrizio Silvestri, Gabriele Tolomei

Infinite-dimensional Diffusion Bridge Simulation via Operator Learning

Gefan Yang, Elizabeth Louise Baker, Michael Lind Severinsen, Christy Anna Hipsley, Stefan Sommer

Theoretical Convergence Guarantees for Variational Autoencoders

Sobihan Surendran, Antoine Godichon-Baggioni, Sylvain Le Corff

Diffusion Models under Group Transformations

Haoye Lu, Spencer Szabados, Yaoliang Yu

Anytime-Valid A/B Testing of Counting Processes

Michael Lindon, Nathan Kallus

Fairness Risks for Group-Conditionally Missing Demographics

Kaiqi Jiang, Wenzhe Fan, Mao Li, Xinhua Zhang

TVineSynth: A Truncated C-Vine Copula Generator of Synthetic Tabular Data to Balance Privacy and Utility

Elisabeth Griesbauer, Claudia Czado, Arnoldo Frigessi, Ingrid Hobæk Haff

Large Covariance Matrix Estimation With Nonnegative Correlations

Yixin Yan, QIAO YANG, Ziping Zhao

High-Dimensional Differential Parameter Inference in Exponential Family using Time Score Matching

Daniel James Williams, Leyang Wang, Qizhen Ying, Song Liu, Mladen Kolar

A graphical global optimization framework for parameter estimation of statistical models with nonconvex regularization functions

Danial Davarnia, Mohammadreza Kiaghadi

A Theoretical Understanding of Chain-of-Thought: Coherent Reasoning and Error-Aware Demonstration

Yingqian Cui, Pengfei He, Xianfeng Tang, Qi He, Chen Luo

Variational Inference in Location-Scale Families: Exact Recovery of the Mean and Correlation Matrix

Charles Margossian, Lawrence K. Saul

Optimal Time Complexity Algorithms for Computing General Random Walk Graph Kernels on Sparse Graphs

Krzysztof Marcin Choromanski, Isaac Reid, Arijit Sehanobish, Kumar Avinava Dubey

Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis

Rémi Khellaf, Aurélien Bellet, Julie Josse