AISTATS 2025

583 papers

Enhanced Adaptive Gradient Algorithms for Nonconvex-PL Minimax Optimization

Feihu Huang, Chunyu Xuan, Xinrui Wang, Siqi Zhang, Songcan Chen

FreqMoE: Enhancing Time Series Forecasting through Frequency Decomposition Mixture of Experts

Ziqi Liu

Order-Optimal Regret with Novel Policy Gradient Approaches in Infinite-Horizon Average Reward MDPs

Swetha Ganesh, Washim Uddin Mondal, Vaneet Aggarwal

Separation-Based Distance Measures for Causal Graphs

Jonas Wahl, Jakob Runge

Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks

Felix Jimenez, Matthias Katzfuss

Sparse Causal Effect Estimation using Two-Sample Summary Statistics in the Presence of Unmeasured Confounding

Shimeng Huang, Niklas Pfister, Jack Bowden

Covariance Selection over Networks

Wenfu Xia, Fengpei Li, Ying Sun, Ziping Zhao

Online-to-PAC generalization bounds under graph-mixing dependencies

Baptiste Abélès, Gergely Neu, Eugenio Clerico

Information Transfer Across Clinical Tasks via Adaptive Parameter Optimisation

Anshul Thakur, Elena Gal, Soheila Molaei, Xiao Gu, Patrick Schwab

Hyperbolic Prototypical Entailment Cones for Image Classification

Samuele Fonio, Roberto Esposito, Marco Aldinucci

Do Regularization Methods for Shortcut Mitigation Work As Intended?

Haoyang Hong, Ioanna Papanikolaou, Sonali Parbhoo

SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph

Mátyás Schubert, Tom Claassen, Sara Magliacane

Narrowing the Gap between Adversarial and Stochastic MDPs via Policy Optimization

Daniil Tiapkin, Evgenii Chzhen, Gilles Stoltz

A Differential Inclusion Approach for Learning Heterogeneous Sparsity in Neuroimaging Analysis

Wenjing Han, Yueming Wu, Xinwei Sun, Lingjing Hu, Yizhou Wang

Parallel Backpropagation for Inverse of a Convolution with Application to Normalizing Flows

Sandeep Nagar, Girish Varma

Independent Learning in Performative Markov Potential Games

Rilind Sahitaj, Paulius Sasnauskas, Yiğit Yalın, Debmalya Mandal, Goran Radanovic

Global Ground Metric Learning with Applications to scRNA data

Damin Kühn, Michael T Schaub

Sample Compression Unleashed: New Generalization Bounds for Real Valued Losses

Mathieu Bazinet, Valentina Zantedeschi, Pascal Germain

Tensor Network Based Feature Learning Model

Albert Saiapin, Kim Batselier

Fixed-Budget Change Point Identification in Piecewise Constant Bandits

Joseph Lazzaro, Ciara Pike-Burke

Composition and Control with Distilled Energy Diffusion Models and Sequential Monte Carlo

James Thornton, Louis Béthune, Ruixiang ZHANG, Arwen Bradley, Preetum Nakkiran

Powerful batch conformal prediction for classification

Ulysse Gazin, Ruth Heller, Etienne Roquain, Aldo Solari

On the Identifiability of Causal Abstractions

Xiusi Li, Sékou-Oumar Kaba, Siamak Ravanbakhsh

Performative Reinforcement Learning with Linear Markov Decision Process

Debmalya Mandal, Goran Radanovic

Differential Privacy in Distributed Learning: Beyond Uniformly Bounded Stochastic Gradients

Yue Huang, Jiaojiao Zhang, Qing Ling

Statistical Test for Auto Feature Engineering by Selective Inference

Tatsuya Matsukawa, Tomohiro Shiraishi, Shuichi Nishino, Teruyuki Katsuoka, Ichiro Takeuchi

Sketch-and-Project Meets Newton Method: Global $O(1/k^2)$ Convergence with Low-Rank Updates

Slavomír Hanzely

TRADE: Transfer of Distributions between External Conditions with Normalizing Flows

Stefan Wahl, Armand Rousselot, Felix Draxler, Ullrich Koethe

What Ails Generative Structure-based Drug Design: Expressivity is Too Little or Too Much?

Rafal Karczewski, Samuel Kaski, Markus Heinonen, Vikas K Garg

Safety in the Face of Adversity: Achieving Zero Constraint Violation in Online Learning with Slowly Changing Constraints

Bassel Hamoud, Ilnura Usmanova, Kfir Yehuda Levy

Infinite Width Limits of Self Supervised Neural Networks

Maximilian Fleissner, Gautham Govind Anil, Debarghya Ghoshdastidar

Continuous Structure Constraint Integration for Robust Causal Discovery

Lyuzhou Chen, Taiyu Ban, Derui Lyu, Yijia Sun, Kangtao Hu

Koopman-Equivariant Gaussian Processes

Petar Bevanda, Max Beier, Alexandre Capone, Stefan Georg Sosnowski, Sandra Hirche

Multimodal Learning with Uncertainty Quantification based on Discounted Belief Fusion

Grigor Bezirganyan, Sana Sellami, Laure Berti-Equille, Sébastien Fournier

Memorization in Attention-only Transformers

Léo Dana, Muni Sreenivas Pydi, Yann Chevaleyre

Out-of-distribution robustness for multivariate analysis via causal regularisation

Homer Durand, Gherardo Varando, Nathan Mankovich, Gustau Camps-Valls

Differentiable Causal Structure Learning with Identifiability by NOTIME

Jeroen Berrevoets, Jakob Raymaekers, Mihaela van der Schaar, Tim Verdonck, Ruicong Yao

High Dimensional Bayesian Optimization using Lasso Variable Selection

Vu Viet Hoang, Hung The Tran, Sunil Gupta, Vu Nguyen

Riemann$^2$: Learning Riemannian Submanifolds from Riemannian Data

Leonel Rozo, Miguel González-Duque, Noémie Jaquier, Søren Hauberg

Causal discovery in mixed additive noise models

Ruicong Yao, Tim Verdonck, Jakob Raymaekers

HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing Risks

Xin Liu, Weijia Zhang, Min-ling Zhang

Stochastic Gradient Descent for Bézier Simplex Representation of Pareto Set in Multi-Objective Optimization

Yasunari Hikima, Ken Kobayashi, Akinori Tanaka, Akiyoshi Sannai, Naoki Hamada

Subspace Recovery in Winsorized PCA: Insights into Accuracy and Robustness

Sangil Han, Kyoowon Kim, Sungkyu Jung

Meta-learning Task-specific Regularization Weights for Few-shot Linear Regression

Tomoharu Iwata, Atsutoshi Kumagai, Yasutoshi Ida

Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems

Da Long, Zhitong Xu, Qiwei Yuan, Yin Yang, Shandian Zhe

Domain Adaptation and Entanglement: an Optimal Transport Perspective

Okan Koc, Alexander Soen, Chao-Kai Chiang, Masashi Sugiyama

ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning

Kaan Ozkara, Bruce Huang, Ruida Zhou, Suhas Diggavi

HR-Bandit: Human-AI Collaborated Linear Recourse Bandit

Junyu Cao, Ruijiang Gao, Esmaeil Keyvanshokooh

Meta-learning from Heterogeneous Tensors for Few-shot Tensor Completion

Tomoharu Iwata, Atsutoshi Kumagai

Functional Stochastic Gradient MCMC for Bayesian Neural Networks

Mengjing Wu, Junyu Xuan, Jie Lu

Reinforcement Learning for Infinite-Horizon Average-Reward Linear MDPs via Approximation by Discounted-Reward MDPs

Kihyuk Hong, Woojin Chae, Yufan Zhang, Dabeen Lee, Ambuj Tewari

Sampling in High-Dimensions using Stochastic Interpolants and Forward-Backward Stochastic Differential Equations

Anand Jerry George, Nicolas Macris

Multi-level Advantage Credit Assignment for Cooperative Multi-Agent Reinforcement Learning

Xutong Zhao, Yaqi Xie

Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetics in Hyperbolic Space

Alex Chen, Philippe Chlenski, Kenneth Munyuza, Antonio Khalil Moretti, Christian A. Naesseth

New User Event Prediction Through the Lens of Causal Inference

Henry Yuchi, Shixiang Zhu, Li Dong, Yigit M. Arisoy, Matthew C. Spencer

Understanding Inverse Reinforcement Learning under Overparameterization: Non-Asymptotic Analysis and Global Optimality

Ruijia Zhang, Siliang Zeng, Chenliang Li, Alfredo Garcia, Mingyi Hong

Decision-Point Guided Safe Policy Improvement

Abhishek Sharma, Leo Benac, Sonali Parbhoo, Finale Doshi-velez

Transfer Learning for High-dimensional Reduced Rank Time Series Models

Mingliang Ma, Abolfazl Safikhani

Recurrent Neural Goodness-of-Fit Test for Time Series

Aoran Zhang, Wenbin Zhou, Liyan Xie, Shixiang Zhu

Permutation Invariant Functions: Statistical Testing, Density Estimation, and Metric Entropy

Wee Chaimanowong, Ying Zhu

Decoupling epistemic and aleatoric uncertainties with possibility theory

Nong Minh Hieu, Jeremie Houssineau, Neil K. Chada, Emmanuel Delande

On Preference-based Stochastic Linear Contextual Bandits with Knapsacks

Xin Liu

Is Gibbs sampling faster than Hamiltonian Monte Carlo on GLMs?

Son Luu, Zuheng Xu, Nikola Surjanovic, Miguel Biron-Lattes, Trevor Campbell

Computing high-dimensional optimal transport by flow neural networks

Chen Xu, Xiuyuan Cheng, Yao Xie

Memory-Efficient Optimization with Factorized Hamiltonian Descent

Son Nguyen, Lizhang Chen, Bo Liu, Qiang Liu

Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer Perceptrons

Wei Huang, Yuan Cao, Haonan Wang, Xin Cao, Taiji Suzuki

Evidential Uncertainty Probes for Graph Neural Networks

Linlin Yu, Kangshuo Li, Pritom Kumar Saha, Yifei Lou, Feng Chen

A Tight Regret Analysis of Non-Parametric Repeated Contextual Brokerage

Francois Bachoc, Tommaso Cesari, Roberto Colomboni

Protein Fitness Landscape: Spectral Graph Theory Perspective

Hao Zhu, Daniel M. Steinberg, Piotr Koniusz

Approximate Global Convergence of Independent Learning in Multi-Agent Systems

Ruiyang Jin, Zaiwei Chen, Yiheng Lin, Jie Song, Adam Wierman

Optimistic Safety for Online Convex Optimization with Unknown Linear Constraints

Spencer Hutchinson, Tianyi Chen, Mahnoosh Alizadeh

Calm Composite Losses: Being Improper Yet Proper Composite

Han Bao, Nontawat Charoenphakdee

Statistical Learning of Distributionally Robust Stochastic Control in Continuous State Spaces

Shengbo Wang, Nian Si, Jose Blanchet, Zhengyuan Zhou

A Safe Bayesian Learning Algorithm for Constrained MDPs with Bounded Constraint Violation

Krishna C Kalagarla, Rahul Jain, Pierluigi Nuzzo

Causal Discovery on Dependent Binary Data

Alex Chen, Qing Zhou

Understanding GNNs and Homophily in Dynamic Node Classification

Michael Ito, Danai Koutra, Jenna Wiens

Learning Laplacian Positional Encodings for Heterophilous Graphs

Michael Ito, Jiong Zhu, Dexiong Chen, Danai Koutra, Jenna Wiens

Flexible and Efficient Probabilistic PDE Solvers through Gaussian Markov Random Fields

Tim Weiland, Marvin Pförtner, Philipp Hennig

Learning Infinite-Horizon Average-Reward Linear Mixture MDPs of Bounded Span

Woojin Chae, Kihyuk Hong, Yufan Zhang, Ambuj Tewari, Dabeen Lee

Geometric Collaborative Filtering with Convergence

Hisham Husain, Julien Monteil

Spectral Representation for Causal Estimation with Hidden Confounders

Haotian Sun, Antoine Moulin, Tongzheng Ren, Arthur Gretton, Bo Dai

When Can We Solve the Weighted Low Rank Approximation Problem in Truly Subquadratic Time?

Chenyang Li, Yingyu Liang, Zhenmei Shi, Zhao Song

Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector

Andi Zhang, Tim Z. Xiao, Weiyang Liu, Robert Bamler, Damon Wischik

Is Prior-Free Black-Box Non-Stationary Reinforcement Learning Feasible?

Argyrios Gerogiannis, Yu-Han Huang, Venugopal Veeravalli

Robust Multi-fidelity Bayesian Optimization with Deep Kernel and Partition

Fengxue Zhang, Thomas Desautels, Yuxin Chen

Sparse Activations as Conformal Predictors

Margarida M Campos, João Cálem, Sophia Sklaviadis, Mario A. T. Figueiredo, Andre Martins

Q-learning for Quantile MDPs: A Decomposition, Performance, and Convergence Analysis

Jia Lin Hau, Erick Delage, Esther Derman, Mohammad Ghavamzadeh, Marek Petrik

Pick-to-Learn and Self-Certified Gaussian Process Approximations

Daniel Marks, Dario Paccagnan

Looped ReLU MLPs May Be All You Need as Practical Programmable Computers

Yingyu Liang, Zhizhou Sha, Zhenmei Shi, Zhao Song, Yufa Zhou

A Multi-Task Learning Approach to Linear Multivariate Forecasting

Liran Nochumsohn, Hedi Zisling, Omri Azencot

Robust Kernel Hypothesis Testing under Data Corruption

Antonin Schrab, Ilmun Kim

Parabolic Continual Learning

Haoming Yang, Ali Hasan, Vahid Tarokh

Improving N-Glycosylation and Biopharmaceutical Production Predictions Using AutoML-Built Residual Hybrid Models

Pedro Seber, Richard Braatz

Differentially Private Graph Data Release: Inefficiencies & Unfairness

Ferdinando Fioretto, Diptangshu Sen, Juba Ziani

Sampling From Multiscale Densities With Delayed Rejection Generalized Hamiltonian Monte Carlo

Gilad Turok, Chirag Modi, Bob Carpenter

Synthesis and Analysis of Data as Probability Measures With Entropy-Regularized Optimal Transport

Brendan Mallery, James M. Murphy, Shuchin Aeron

Asynchronous Decentralized Optimization with Constraints: Achievable Speeds of Convergence for Directed Graphs

Firooz Shahriari-Mehr, Ashkan Panahi

Restructuring Tractable Probabilistic Circuits

Honghua Zhang, Benjie Wang, Marcelo Arenas, Guy Van den Broeck

Analyzing the Role of Permutation Invariance in Linear Mode Connectivity

Keyao Zhan, Puheng Li, Lei Wu

An Iterative Algorithm for Rescaled Hyperbolic Functions Regression

Yeqi Gao, Zhao Song, Junze Yin