UAI 2025

229 papers

Improved Variational Inference in Discrete VAEs using Error Correcting Codes

María Martínez-García, Grace Villacrés, David Mitchell, Pablo M. Olmos

Off-policy Predictive Control with Causal Sensitivity Analysis

Myrl G Marmarelis, Ali Hasan, Kamyar Azizzadenesheli, R. Michael Alvarez, Anima Anandkumar

MindFlayer SGD: Efficient Parallel SGD in the Presence of Heterogeneous and Random Worker Compute Times

Arto Maranjyan, Omar Shaikh Omar, Peter Richtarik

Testing Generalizability in Causal Inference

Daniel de Vassimon Manela, Linying Yang, Robin J. Evans

SALSA: A Secure, Adaptive and Label-Agnostic Scalable Algorithm for Machine Unlearning

Owais Makroo, Atif Hassan, Swanand Khare

Weak to Strong Learning from Aggregate Labels

Yukti Makhija, Rishi Saket

Multi-armed Bandits with Missing Outcomes

Ilia Mahrooghi, Mahshad Moradi, Sina Akbari, Negar Kiyavash

Federated Rényi Fair Inference in Federated Heterogeneous System

Zhiyong Ma, Yuanjie Shi, Yan Yan, Jian Chen

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold

Song Liu, Leyang Wang, Yakun Wang

Beyond Invisibility: Learning Robust Visible Watermarks for Stronger Copyright Protection

Tianci Liu, Tong Yang, Quan Zhang, Qi Lei

Periodical Moving Average Accelerates Gradient Accumulation for Post-Training

Yumou Liu, An Li, Chaojie Li, Fei Yu, Benyou Wang

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning

Zhuqing Liu, Chaosheng Dong, Michinari Momma, Simone Shao, Shaoyuan Xu

Letting Uncertainty Guide Your Multimodal Machine Translation

Wuyi Liu, Yue Gao, Yige Mao, Jing Zhao

DyGMAE: A Novel Dynamic Graph Masked Autoencoder for Link Prediction

Weixiong Liu, Junwei Cheng, Zhongyu Pan, Chaobo He, Quanlong Guan

Multi-group Uncertainty Quantification for Long-form Text Generation

Terrance Liu, Steven Wu

CP$^2$: Leveraging Geometry for Conformal Prediction via Canonicalization

Putri A Van der Linden, Alexander Timans, Erik J Bekkers

FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning

I-Cheng Lin, Osman Yagan, Carlee Joe-Wong

Flat Posterior Does Matter For Bayesian Model Averaging

Sungjun Lim, Jeyoon Yeom, Sooyon Kim, Hoyoon Byun, Jinho Kang

Dynamic Maintenance of Kernel Density Estimation Data Structure: From Practice to Theory

Jiehao Liang, Zhao Song, Zhaozhuo Xu, Junze Yin, Danyang Zhuo

Enhancing Uncertainty Quantification in Large Language Models through Semantic Graph Density

Zhaoye Li, Siyuan Shen, Wenjing Yang, Ruochun Jin, Huan Chen

Trading Off Voting Axioms for Privacy

Zhechen Li, Ao Liu, Lirong Xia, Yongzhi Cao, Hanpin Wang

Constraint-based Causal Discovery from a Collection of Conditioning Sets

Kenneth Lee, Bruno Ribeiro, Murat Kocaoglu

Adaptive Reward Design for Reinforcement Learning

Minjae Kwon, Ingy ElSayed-Aly, Lu Feng

A Probabilistic Neuro-symbolic Layer for Algebraic Constraint Satisfaction

Leander Kurscheidt, Paolo Morettin, Roberto Sebastiani, Andrea Passerini, Antonio Vergari

Beyond Sin-Squared Error: Linear Time Entrywise Uncertainty Quantification for Streaming PCA

Syamantak Kumar, Shourya Pandey, Purnamrita Sarkar

Budget Allocation Exploiting Label Correlation between Instances

Adithya Kulkarni, Mohna Chakraborty, Sihong Xie, Qi Li

An Optimal Algorithm for Strongly Convex Min-Min Optimization

Dmitry Kovalev, Alexander Gasnikov, Grigory Malinovsky

Probabilistic Explanations for Regression Models

Frédéric Koriche, Jean-Marie Lagniez, Chi Tran

Robust Optimization with Diffusion Models for Green Security

Lingkai Kong, Haichuan Wang, Yuqi Pan, Cheol Woo Kim, Mingxiao Song

Error Bounds for Physics-Informed Neural Networks in Fokker-Planck PDEs

Chun-Wei Kong, Luca Laurenti, Jay McMahon, Morteza Lahijanian

DF$^2$: Distribution-Free Decision-Focused Learning

Lingkai Kong, Wenhao Mu, Jiaming Cui, Yuchen Zhuang, B. Aditya Prakash

A Multivariate Unimodality Test Harnessing the Dip Statistic of Mahalanobis Distances Over Random Projections

Prodromos Kolyvakis, Aristidis Likas

Causal Effect Identification in Heterogeneous Environments from Higher-Order Moments

Yaroslav Kivva, Sina Akbari, Saber Salehkaleybar, Negar Kiyavash

Bayesian Optimization with Inexact Acquisition: Is Random Grid Search Sufficient?

Hwanwoo Kim, Chong Liu, Yuxin Chen

Collaborative Prediction: To Join or To Disjoin Datasets

Kyung Rok Kim, Yansong Wang, Xiaocheng Li, Guanting Chen

Efficiently Escaping Saddle Points for Policy Optimization

Mohammadsadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash, Niao He, Matthias Grossglauser

Accurate and Scalable Stochastic Gaussian Process Regression via Learnable Coreset-based Variational Inference

Mert Ketenci, Adler J Perotte, Noemie Elhadad, Inigo Urteaga

Enumerating Optimal Cost-Constrained Adjustment Sets

Batya Kenig

Explaining Negative Classifications of AI Models in Tumor Diagnosis

David A. Kelly, Hana Chockler, Nathan Blake

Decomposition of Probabilities of Causation with Two Mediators

Yuta Kawakami, Jin Tian

Moments of Causal Effects

Yuta Kawakami, Jin Tian

Adapting Prediction Sets to Distribution Shifts Without Labels

Kevin Kasa, Zhiyu Zhang, Heng Yang, Graham W. Taylor

ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression

Avetik Karagulyan, Peter Richtarik

Provably Adaptive Average Reward Reinforcement Learning for Metric Spaces

Avik Kar, Rahul Singh

Distributional Reinforcement Learning with Dual Expectile-Quantile Regression

Sami Jullien, Romain Deffayet, Jean-Michel Renders, Paul Groth, Maarten de Rijke

Best Possible Q-Learning

Jiechuan Jiang, Zongqing Lu

Coevolutionary Emergent Systems Optimization with Applications to Ultra-High-Dimensional Metasurface Design : OAM Wave Manipulation

Zhengxuan Jiang, Guowen Ding, Wen Jiang

Fast Calculation of Feature Contributions in Boosting Trees

Zhongli Jiang, Min Zhang, Dabao Zhang

Generative Uncertainty in Diffusion Models

Metod Jazbec, Eliot Wong-Toi, Guoxuan Xia, Dan Zhang, Eric Nalisnick

Lower Bounds on the Size of Markov Equivalence Classes

Erik L Jahn, Frederick Eberhardt, Leonard Schulman

Root Cause Analysis of Failures from Partial Causal Structures

Azam Ikram, Kenneth Lee, Shubham Agarwal, Shiv Kumar Saini, Saurabh Bagchi

FDR-SVM: A Federated Distributionally Robust Support Vector Machine via a Mixture of Wasserstein Balls Ambiguity Set

Michael Ibrahim, Heraldo Rozas, Nagi Gebraeel, Weijun Xie

Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis

Ruiquan Huang, Donghao Li, Chengshuai Shi, Cong Shen, Jing Yang

Simulation-Free Differential Dynamics Through Neural Conservation Laws

Mengjian Hua, Eric Vanden-Eijnden, Ricky T. Q. Chen

Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Models

Thi Kieu Khanh Ho, Narges Armanfard

LoSAM: Local Search in Additive Noise Models with Mixed Mechanisms and General Noise for Global Causal Discovery

Sujai Hiremath, Promit Ghosal, Kyra Gan

SPvR: Structured Pruning via Ranking

Atif Hassan, Jiaul H. Paik, Swanand Khare

RCAP: Robust, Class-Aware, Probabilistic Dynamic Dataset Pruning

Atif Hassan, Swanand Khare, Jiaul H. Paik

Quantum Speedups for Bayesian Network Structure Learning

Juha Harviainen, Kseniya Rychkova, Mikko Koivisto

Conformal Prediction without Nonconformity Scores

Jonas Hanselle, Alireza Javanmardi, Tobias Florin Oberkofler, Yusuf Sale, Eyke Hüllermeier

Contrast-CAT: Contrasting Activations for Enhanced Interpretability in Transformer-based Text Classifiers

Sungmin Han, Jeonghyun Lee, Sangkyun Lee

Learning Algorithms for Multiple Instance Regression

Aaryan Gupta, Rishi Saket

On the Privacy Risks of Spiking Neural Networks: A Membership Inference Analysis

Junyi Guan, Abhijith Sharma, Chong Tian, Salem Lahlou

Guaranteed Prediction Sets for Functional Surrogate Models

Ander Gray, Vignesh Gopakumar, Sylvain Rousseau, Sébastien Destercke

Efficient Algorithms for Logistic Contextual Slate Bandits with Bandit Feedback

Tanmay Goyal, Gaurav Sinha

Over the Top-1: Uncertainty-Aware Cross-Modal Retrieval with CLIP

Lluis Gomez

Optimal Zero-shot Regret Minimization for Selective Classification with Out-of-Distribution Detection

Eduardo Dadalto Camara Gomes, Marco Romanelli

Statistical Significance of Feature Importance Rankings

Jeremy Goldwasser, Giles Hooker

Nonlinear Causal Discovery for Grouped Data

Konstantin Göbler, Tobias Windisch, Mathias Drton

A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time

Yeqi Gao, Zhao Song, Weixin Wang, Junze Yin

Order-Optimal Global Convergence for Actor-Critic with General Policy and Neural Critic Parametrization

Swetha Ganesh, Jiayu Chen, Washim Uddin Mondal, Vaneet Aggarwal

Partial-Label Learning with Conformal Candidate Cleaning

Tobias Fuchs, Florian Kalinke

Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution Bundles

Pablo Flores, Olga Graf, Pavlos Protopapas, Karim Pichara

Generalised Probabilistic Modelling and Improved Uncertainty Estimation in Comparative LLM-as-a-judge

Yassir Fathullah, Mark Gales

Proximal Interacting Particle Langevin Algorithms

Paula Cordero Encinar, Francesca Romana Crucinio, Omer Deniz Akyildiz

Mixup Regularization: A Probabilistic Perspective

Yousef El-Laham, Niccolò Dalmasso, Svitlana Vyetrenko, Vamsi K. Potluru, Manuela Veloso

Toward Universal Laws of Outlier Propagation

Aram Ebtekar, Yuhao Wang, Dominik Janzing

Learning Causal Response Representations through Direct Effect Analysis

Homer Durand, Gherardo Varando, Gustau Camps-Valls

Probabilistic Semantics Guided Discovery of Approximate Functional Dependencies

Liang Duan, Xinran Wu, Xinhui Li, Lixing Yu, Kun Yue

Computationally Efficient Methods for Invariant Feature Selection with Sparsity

Jane Du, Arindam Banerjee

Tuning Algorithmic and Architectural Hyperparameters in Graph-Based Semi-Supervised Learning with Provable Guarantees

Ally Yalei Du, Eric Huang, Dravyansh Sharma

Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles

Mathias Drton, Marina Garrote-López, Niko Nikov, Elina Robeva, Y. Samuel Wang

Simulation-based Inference for High-dimensional Data using Surjective Sequential Neural Likelihood Estimation

Simon Dirmeier, Carlo Albert, Fernando Pérez-Cruz

Nearly Optimal Differentially Private ReLU Regression

Meng Ding, Mingxi Lei, Shaowei Wang, Tianhang Zheng, Di Wang

Valid Bootstraps for Network Embeddings with Applications to Network Visualisation

Emerald Dilworth, Ed Davis, Daniel John Lawson

Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning

Francesco Diana, André Nusser, CHUAN XU, Giovanni Neglia

Calibrated Regression Against An Adversary Without Regret

Shachi Deshpande, Charles Marx, Volodymyr Kuleshov

Optimal Submanifold Structure in Log-linear Models

Zhou Derun, Mahito Sugiyama

ELBO, regularized maximum likelihood, and their common one-sample approximation for training stochastic neural networks

Sina Däubener, Simon Damm, Asja Fischer

Online Learning with Stochastically Partitioning Experts

Puranjay Datta, Sharayu Moharir, Jaya Prakash Champati

The Relativity of Causal Knowledge

Gabriele D’Acunto, Claudio Battiloro

Measuring IIA Violations in Similarity Choices with Bayesian Models

Hugo Sales Correa, Suryanarayana Sankagiri, Daniel R. Figueiredo, Matthias Grossglauser

RL, but don’t do anything I wouldn’t do

Michael K. Cohen, Marcus Hutter, Yoshua Bengio, Stuart Russell

Building Conformal Prediction Intervals with Approximate Message Passing

Lucas Clarte, Lenka Zdeborová

Optimal Transport for Probabilistic Circuits

Adrian Ciotinga, YooJung Choi

Well-Defined Function-Space Variational Inference in Bayesian Neural Networks via Regularized KL-Divergence

Tristan Cinquin, Robert Bamler

Selective Blocking for Message-Passing Neural Networks on Heterophilic Graphs

Yoonhyuk Choi, Taewook Ko, Jiho Choi, Chong-Kwon Kim

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization

Yuen Chen, Haozhe Si, Guojun Zhang, Han Zhao

NRFlow: Towards Noise-Robust Generative Modeling via High-Order Mechanism

Bo Chen, Chengyue Gong, Xiaoyu Li, Yingyu Liang, Zhizhou Sha

Tuning-Free Coreset Markov Chain Monte Carlo via Hot DoG

Naitong Chen, Jonathan H. Huggins, Trevor Campbell