UAI 2025

229 papers

MSP-SR: Multi-Stage Probabilistic Generative Super Resolution with Scarce High-Resolution Data

Ruike Zhu, Matthew Charles Weston, Hanwen Zhang, Arindam Banerjee

Learning to Sample in Stochastic Optimization

Sijia Zhou, Yunwen Lei, Ata Kaban

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning

Xue Zhou, Dapeng Man, Chen Xu, Fanyi Zeng, Tao Liu

Towards Provably Efficient Learning of Imperfect Information Extensive-Form Games with Linear Function Approximation

Canzhe Zhao, Shuze Chen, Weiming Liu, Haobo Fu, Qiang Fu

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation

Runze Zhao, Yue Yu, Adams Yiyue Zhu, chen yang, Dongruo Zhou

Finding Interior Optimum of Black-box Constrained Objective with Bayesian Optimization

Fengxue Zhang, Yuxin Chen

Residual Reweighted Conformal Prediction for Graph Neural Networks

Zheng Zhang, Jie Bao, Zhixin Zhou, Nicolo Colombo, Lixin Cheng

Near-Optimal Regret Bounds for Federated Multi-armed Bandits with Fully Distributed Communication

Haoran Zhang, Xuchuang Wang, Hao-Xu Chen, Hao Qiu, Lin Yang

Improving Adversarial Transferability via Decision Boundary Adaptation

Jiayu Zhang, Zhiyu Zhu, Zhibo Jin, Xinyi Wang, Huaming Chen

Causal Eligibility Traces for Confounding Robust Off-Policy Evaluation

Junzhe Zhang, Elias Bareinboim

Instance-Wise Monotonic Calibration by Constrained Transformation

Yunrui Zhang, Gustavo Enrique Batista, Salil S. Kanhere

Learning to Stabilize Unknown LTI Systems on a Single Trajectory under Stochastic Noise

Ziyi Zhang, Yorie Nakahira, Guannan Qu

Label Distribution Learning using the Squared Neural Family on the Probability Simplex

Daokun Zhang, Russell Tsuchida, Dino Sejdinovic

Complete Characterization for Adjustment in Summary Causal Graphs of Time Series

Clément Yvernes, Emilie Devijver, Eric Gaussier

Corruption-Robust Variance-aware Algorithms for Generalized Linear Bandits under Heavy-tailed Rewards

Qingyuan Yu, Euijin Baek, Xiang Li, Qiang Sun

How Likely Are Two Voting Rules Different?

Ziqi Yu, Lirong Xia, Qishen Han, Chengkai Zhang

$σ$-Maximal Ancestral Graphs

Binghua Yao, Joris Marten Mooij

Flow-Based Delayed Hawkes Process

Chao Yang, Wendi Ren, Shuang Li

MSCGrapher: Learning Multi-Scale Dynamic Correlations for Multivariate Time Series Forecasting

Xian Yang, Zhenguo Zhang, Shihao Lu

Best Arm Identification with Possibly Biased Offline Data

Le Yang, Vincent Y. F. Tan, Wang Chi Cheung

Full Network Capacity Framework for Sample-Efficient Deep Reinforcement Learning

Wentao Yang, Xinyue Liu, Yunlong Gao, Wenxin Liang, Linlin Zong

Dependent Randomized Rounding for Budget Constrained Experimental Design

Khurram Yamin, Edward Kennedy, Bryan Wilder

Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling

Jian Xu, Shian Du, Junmei Yang, Qianli Ma, Delu Zeng

Learning Multi-interest Embedding with Dynamic Graph Cluster for Sequention Recommendation

Xiao Chunjing, Ranhao Guo, Zhang Yongwang, Xiaoming Wu

The Consistency Hypothesis in Uncertainty Quantification for Large Language Models

Quan Xiao, Debarun Bhattacharjya, Balaji Ganesan, Radu Marinescu, Katya Mirylenka

FlightPatchNet: Multi-Scale Patch Network with Differential Coding for Short-Term Flight Trajectory Prediction

Lan Wu, Xuebin Wang, Ruijuan Chu, Guangyi Liu, Jing Zhang

Group-Agent Reinforcement Learning with Heterogeneous Agents

Kaiyue Wu, Xiao-Jun Zeng, Tingting Mu

Online Generalized Magician’s Problem with Multiple Workers

Ruoyu Wu, Wei Bao, Ben Liang, Liming Ge

Geodesic Slice Sampler for Multimodal Distributions with Strong Curvature

Bernardo Williams, Hanlin Yu, Hoang Phuc Hau Luu, Georgios Arvanitidis, Arto Klami

A Mirror Descent Perspective of Smoothed Sign Descent

Shuyang Wang, Diego Klabjan

Informative Synthetic Data Generation for Thorax Disease Classification

Yancheng Wang, Rajeev Goel, Marko Jojic, Alvin C. Silva, Teresa Wu

MutualNeRF: Improve the Performance of NeRF under Limited Samples with Mutual Information Theory

Zifan Wang, Jingwei Li, Yitang Li, Yunze Liu

VADIS: Investigating Inter-View Representation Biases for Multi-View Partial Multi-Label Learning

Jie Wang, Ning Xu, Xin Geng

A Parallel Network for LRCT Segmentation and Uncertainty Mitigation with Fuzzy Sets

Shiyi Wang, Yang Nan, Xiaodan Xing, Yingying Fang, Simon Lf Walsh

Nonparametric Bayesian Multi-Facet Clustering for Longitudinal Data

Luwei Wang, Kieran Richards, Sohan Seth

Targeted Learning for Variable Importance

Xiaohan Wang, Yunzhe Zhou, Giles Hooker

A Trajectory-Based Bayesian Approach to Multi-Objective Hyperparameter Optimization with Epoch-Aware Trade-Offs

Wenyu Wang, Zheyi Fan, Szu Hui Ng

Hindsight Merging: Diverse Data Generation with Language Models

Veniamin Veselovsky, Benedikt Stroebl, Gianluca Bencomo, Dilip Arumugam, Lisa Schut

Offline Changepoint Detection With Gaussian Processes

Janneke Verbeek, Tom Heskes, Yuliya Shapovalova

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models

Aishwarya Venkataramanan, Paul Bodesheim, Joachim Denzler

EERO: Early Exit with Reject Option for Efficient Classification with limited budget

Florian Valade, Mohamed Hebiri, Paul Gay

Black-box Optimization with Unknown Constraints via Overparameterized Deep Neural Networks

Dat Phan Trong, Hung The Tran, Sunil Gupta

Optimal Transport Alignment of User Preferences from Ratings and Texts

Nhu-Thuat Tran, Hady W. Lauw

HDP-Flow: Generalizable Bayesian Nonparametric Model for Time Series State Discovery

Sana Tonekaboni, Tina Behrouzi, Addison Weatherhead, Emily Fox, David Blei

On Continuous Monitoring of Risk Violations under Unknown Shift

Alexander Timans, Rajeev Verma, Eric Nalisnick, Christian A. Naesseth

Adversarial Training May Induce Deteriorating Distributions

Runzhi Tian, Yongyi Mao

A Unified Data Representation Learning for Non-parametric Two-sample Testing

Xunye Tian, Liuhua Peng, Zhijian Zhou, Mingming Gong, Arthur Gretton

Metric Learning in an RKHS

Gokcan Tatli, Yi Chen, Blake Mason, Robert D Nowak, Ramya Korlakai Vinayak

InfoDPCCA: Information-Theoretic Dynamic Probabilistic Canonical Correlation Analysis

Shiqin Tang, Shujian Yu

FALCON: Adaptive Cross-Domain APT Attack Investigation with Federated Causal Learning

Jialu Tang, Yali Gao, Xiaoyong Li, Jiawei Li, Shui Yu

Transparent Trade-offs between Properties of Explanations

Hiwot Belay Tadesse, Alihan Hüyük, Yaniv Yacoby, Weiwei Pan, Finale Doshi-velez

Adaptive Human-Robot Collaboration using Type-Based IRL

Prasanth Sengadu Suresh, Prashant Doshi, Bikramjit Banerjee

On Constant Regret for Low-Rank MDPs

Alexander Sturm, Sebastian Tschiatschek

Nonparametric Bayesian inference of item-level features in classifier combination

Patrick Stinson, Nikolaus Kriegeskorte

Pure and Strong Nash Equilibrium Computation in Compactly Representable Aggregate Games

Jared Soundy, Mohammad T. Irfan, Hau Chan

RDI: An adversarial robustness evaluation metric for deep neural networks based on model statistical features

Jialei Song, Xingquan Zuo, Feiyang Wang, Hai Huang, Tianle Zhang

Privacy-Preserving Neural Processes for Probabilistic User Modeling

Amir Sonee, HARIPRIYA HARIKUMAR, Alex Hämäläinen, Lukas Prediger, Samuel Kaski

Proxy-informed Bayesian transfer learning with unknown sources

Sabina J. Sloman, Julien Martinelli, Samuel Kaski

Approximate Bayesian Inference via Bitstring Representations

Aleksanteri Sladek, Martin Trapp, Arno Solin

Learning from Label Proportions and Covariate-shifted Instances

Sagalpreet Singh, Navodita Sharma, Shreyas Havaldar, Rishi Saket, Aravindan Raghuveer

Truthful Elicitation of Imprecise Forecasts

Anurag Singh, Siu Lun Chau, Krikamol Muandet

The Causal Information Bottleneck and Optimal Causal Variable Abstractions

Francisco N. F. Q. Simoes, Mehdi Dastani, Thijs van Ommen

Critical Influence of Overparameterization on Sharpness-aware Minimization

Sungbin Shin, Dongyeop Lee, Maksym Andriushchenko, Namhoon Lee

Minimax Optimal Nonsmooth Nonparametric Regression via Fractional Laplacian Eigenmaps

Zhaoyang Shi, Krishna Balasubramanian, Wolfgang Polonik

Learning Robust XGBoost Ensembles for Regression Tasks

Atri Vivek Sharma, Panagiotis Kouvaros, Alessio Lomuscio

Experimentation under Treatment Dependent Network Interference

Shiv Shankar, Ritwik Sinha, Madalina Fiterau

Divide and Orthogonalize: Efficient Continual Learning with Local Model Space Projection

Jin Shang, Simone Shao, Tian Tong, Fan Yang, Yetian Chen

Reparameterizing Hybrid Markov Logic Networks to handle Covariate-Shift in Representations

Anup Shakya, Abisha Thapa Magar, Somdeb Sarkhel, Deepak Venugopal

Conformal Prediction Sets for Deep Generative Models via Reduction to Conformal Regression

Hooman Shahrokhi, Devjeet Raj Roy, Yan Yan, Venera Arnaoudova, Jana Doppa

Scaling Probabilistic Circuits via Data Partitioning

Jonas Seng, Florian Peter Busch, Pooja Prasad, Devendra Singh Dhami, Martin Mundt

Revisiting the Equivalence of Bayesian Neural Networks and Gaussian Processes: On the Importance of Learning Activations

Marcin Sendera, Amin Sorkhei, Tomasz Kuśmierczyk

Distributionally and Adversarially Robust Logistic Regression via Intersecting Wasserstein Balls

Aras Selvi, Eleonora Kreacic, Mohsen Ghassemi, Vamsi K. Potluru, Tucker Balch

On Information-Theoretic Measures of Predictive Uncertainty

Kajetan Schweighofer, Lukas Aichberger, Mykyta Ielanskyi, Sepp Hochreiter

Scalable Bayesian Low-Rank Adaptation of Large Language Models via Stochastic Variational Subspace Inference

Colin Samplawski, Adam D. Cobb, Manoj Acharya, Ramneet Kaur, Susmit Jha

What is the Right Notion of Distance between Predict-then-Optimize Tasks?

Paula Rodriguez-Diaz, Lingkai Kong, Kai Wang, David Alvarez-Melis, Milind Tambe

Learning with Confidence

Oliver Ethan Richardson

COS-DPO: Conditioned One-Shot Multi-Objective Fine-Tuning Framework

Yinuo Ren, Tesi Xiao, Michael Shavlovsky, Lexing Ying, Holakou Rahmanian

FeDCM: Federated Learning of Deep Causal Generative Models

Md Musfiqur Rahman, Murat Kocaoglu

Enhanced Equilibria-Solving via Private Information Pre-Branch Structure in Adversarial Team Games

Chen Qiu, Haobo Fu, Kai Li, Jiajia Zhang, Xuan Wang

Multi-Label Bayesian Active Learning with Inter-Label Relationships

Yuanyuan Qi, Jueqing Lu, Xiaohao Yang, Joanne Enticott, Lan Du

Are You Doing Better Than Random Guessing? A Call for Using Negative Controls When Evaluating Causal Discovery Algorithms

Anne Helby Petersen

A Trust-Region Method for Graphical Stein Variational Inference

Liam Pavlovic, David M Rosen

Probabilistic Graph Circuits: Deep Generative Models for Tractable Probabilistic Inference over Graphs

Milan Papez, Martin Rektoris, Vaclav Smidl, Tomas Pevny

Exploring Exploration in Bayesian Optimization

Leonard Papenmeier, Nuojin Cheng, Stephen Becker, Luigi Nardi

Andrei Panferov, Yury Demidovich, Ahmad Rammal, Peter Richtarik

Concept Forgetting via Label Annealing

Subhodip Panda, Ananda Theertha Suresh, Atri Guha, Prathosh AP

An Information-theoretic Perspective of Hierarchical Clustering on Graphs

Yicheng Pan, Bingchen Fan, Pengyu Long, Feng Zheng

Probability-Raising Causality for Uncertain Parametric Markov Decision Processes with PAC Guarantees

Ryohei Oura, Yuji Ito

Do Vendi Scores Converge with Finite Samples? Truncated Vendi Score for Finite-Sample Convergence Guarantees

Azim Ospanov, Farzan Farnia

Discriminative ordering through ensemble consensus

Louis Ohl, Fredrik Lindsten

i$^2$VAE: Interest Information Augmentation with Variational Regularizers for Cross-Domain Sequential Recommendation

Xuying Ning, Wujiang Xu, Tianxin Wei, Xiaolei Liu

Bayesian Optimization over Bounded Domains with the Beta Product Kernel

Huy Hoang Nguyen, Han Zhou, Matthew B. Blaschko, Aleksei Tiulpin

Stochastic Embeddings : A Probabilistic and Geometric Analysis of Out-of-Distribution Behavior

Anthony Nguyen, Emanuel Aldea, Sylvie Le Hégarat-Mascle, Renaud Lustrat

Multiple Wasserstein Gradient Descent Algorithm for Multi-Objective Distributional Optimization

Hai Dai Nguyen, Hiroshi Mamitsuka, Atsuyoshi Nakamura

Temperature Optimization for Bayesian Deep Learning

Kenyon Ng, Chris van der Heide, Liam Hodgkinson, Susan Wei

Relational Causal Discovery with Latent Confounders

Matteo Negro, Andrea Piras, Ragib Ahsan, David Arbour, Elena Zheleva

When Extragradient Meets PAGE: Bridging Two Giants to Boost Variational Inequalities

Gleb Molodtsov, Valery Parfenov, Egor Petrov, Evseev Grigoriy, Daniil Medyakov

SpinSVAR: Estimating Structural Vector Autoregression Assuming Sparse Input

Panagiotis Misiakos, Markus Püschel

ODD: Overlap-aware Estimation of Model Performance under Distribution Shift

Aayush Mishra, Anqi Liu

A Quantum Information Theoretic Approach to Tractable Probabilistic Models

Pedro Zuidberg Dos Martires