UAI 2026

330 papers

FraudGNAM: Inherently Interpretable Spectral GNN for Graph Fraud Detection

Suan Lee, Jinho Kim

Interpretable Spatial-Temporal Forecasting via Additive Neural Decomposition and Knowledge Distillation

Suan Lee, Jinho Kim

Nonlocal Bayesian Modeling of Continuous Spatio-Temporal Dynamics

Jaeyeong Lee, Heeyoung Kim

High-Order Markov Blanket Discovery via a k-Order Relaxation of the Faithfulness Assumption

Loong Kuan Lee, Ragavi Krishnamoorthy, Nico Piatkowski

Provably Correct k-Means Clustering of Persistence Diagrams

Hajin Lee, Kwangho Kim

Efficient Confidence Set Enumeration for Multi-label Conformal Classification

Arthur Ledaguenel, Florent Capelli, Jean-Marie Lagniez

Task-Free Continual Learning via Order-Invariant Linearized Adaptation and Density-Guided Adapter Routing

Hang Thi-Thuy Le, Nam-Quan Nguyen, Lam-Huy Nguyen, Dien Dinh, Minh Hoang

Robustness Quantification for Discriminative Models: a New Robustness Metric and its Application to Dynamic Classifier Selection

Rodrigo F L Lassance, Jasper De Bock

KMM-CP: Practical Conformal Prediction under Covariate Shift via Selective Kernel Mean Matching

Siddhartha Laghuvarapu, Rohan Deb, Jimeng Sun

Controlling Path Dependence in Gradient Ascent Unlearning through Forget-Set Ordering

Varun Sampath Kumar, Esmaeil S. Nadimi, Vinay Chakravarthi Gogineni

Decomposing Ensemble Spread in Lorenz ’96 with Learned Stochastic Parameterizations

Birgit Kühbacher, Daan Crommelin, Niki Kilbertus

Who to Trust? Aggregating Client Predictions in Federated Distillation.

Viktor Kovalchuk, Denis Son, Arman Bolatov, Mohsen Guizani, Samuel Horváth

Controlling Uncertainty and Hallucination Risk in Multi-Agent Fact Verification

Adam Kostka, Jaroslaw A. Chudziak

Maximally Robust Satisficing Bayesian Optimization

Samuli Kinnunen, Petrus Mikkola, Antti Niskanen, Arto Klami

Same Benchmark, Same Subspace: Task-Selective Convergence in LLM Representations

JaeSeong Kim, Suan Lee

Model Merging is Secretly Certifiable: Non-Vacuous Generalisation Bounds for Low-Shot Learning

Taehoon Kim, Henry Gouk, Minyoung Kim, Timothy Hospedales

On Causal Representation Learning with Internal Auxiliaries

Kwonho Kim, Heejeong Nam, Inwoo Hwang, Sanghack Lee

Residual Koopman Spectral Profiling for Predicting and Preventing Transformer Training Instability

Bum Jun Kim, Shohei Taniguchi, Makoto Kawano, Yusuke Iwasawa, Yutaka Matsuo

A Model-Free Universal AI

Yegon Kim, Juho Lee

Beyond Bounds: Quantifying the Probability of Counterfactual Fairness

Taehan Kim, Minyoung Cho, Sanghack Lee

Don’t Test What You Can Deduce: Causal Discovery with Logical Inference

Jonghwan Kim, Sanghack Lee

Capacity and Redundancy Trade-offs in Multi-Task Learning

Asif Khan

Conformal Risk Sharing: Certified Cost Allocation with Participation Guarantees

Ieva Kazlauskaite

Adaptive Cumulative Mass Calibration with Conformal Prediction

Daniil Kazantsev, Eric Moulines, Maxim Panov, Nikita Kotelevskii, Mohsen Guizani

Joint MDPs and Reinforcement Learning in Coupled-Dynamics Environments

Ege Can Kaya, Mahsa Ghasemi, Abolfazl Hashemi

Stochastic Dominance Driven First-Order Policy Optimization for Multi-Objective Reinforcement Learning

Ege Can Kaya, Kadierdan Kaheman, Jason M Cloud, Abolfazl Hashemi

General Bayesian Policy Learning

Masahiro Kato

Likelihood hacking in probabilistic program synthesis

Jacek Karwowski, Younesse Kaddar, Zihuiwen Ye, Esmeralda S. Whitammer, Sam Staton

COBALT: Censored Optimization and Bayesian Active Learning Techniques

Andrea Karlova, Rishabh Kabra, Daniel Augusto de Souza, Brooks Paige

Conditional Diffusion Models for Imbalanced Tabular Regression

Nathaniel Kang

Information-Theoretic Bayesian Optimization for Bilevel Optimization Problems

Takuya Kanayama, Yuki Ito, Tomoyuki Tamura, Masayuki Karasuyama

Who Guards the Guardians? The Challenges of Evaluating Identifiability of Learned Representations

Shruti Joshi, Théo Saulus, Wieland Brendel, Philippe Brouillard, Dhanya Sridhar

PRISM: Calibrated Bayesian Fusion and Auditable Attribution for Reliable LLM Event Prediction from Text

Chengyuan Jin, Daojian Zeng, Kang Liu, Jun Zhao, Yubo Chen

Improving the Convergence of Private Shuffled Gradient Methods with Public Data

Shuli Jiang, PRANAY SHARMA, Zhiwei Steven Wu, Gauri Joshi

Score-Based Diffusion Priors for Adaptive Conformal Inference under Distribution Shift

Xiangyu Jiang

Calibration-Aware Online Adaptation under Label Shift

Jiun Jeong, Byeongwoo An, Gi-Soo Kim, Kyubo Shin

Optimal Conformal Prediction under Epistemic Uncertainty

Alireza Javanmardi, Soroush H. Zargarbashi, Santo M. A. R. Thies, Willem Waegeman, Aleksandar Bojchevski

Graph-Dependent Regret Bounds in Multi-Armed Bandits with Interference

Fateme Jamshidi, Mohammad Shahverdikondori, Negar Kiyavash

Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions

Rustem Islamov, Grigory Malinovsky, Alexander Gaponov, Aurelien Lucchi, Peter Richtarik

Achieving Alignment Through Adaptive Play: Helping Optimize Objectives Without Observing Them

Jason T. Isa, Samuel Burden, Lillian J. Ratliff

Probabilistic Edge Modulation for High-Dimensional Causal Discovery

Seyong Hwang, Kyoungjae Lee, Sunmin Oh, Gunwoong Park

Balancing Expressivity and Learnability in Quantum Kernel Bandit Optimization

Yuqi Huang, Vincent Y. F. Tan, Sharu Theresa Jose

Model-Agnostic Online Certificate-Driven Calibration for Time Series Forecasting Under Distribution Shift

Chenfeng Huang, Zixuan Ma, George Michailidis

From Global to Factor-Wise Expert Composition in Discrete Diffusion Models

Haozhe Huang, Yudong Xu, Abhijoy Mandal, Alan Aspuru-Guzik

Policy-Based Trajectory Clustering in Offline Reinforcement Learning

Xinqi Wang, Simon Shaolei Du, Hao Hu

From Bayes’ Rule to Bayes Rules: Information Processing, Bayes’ Theorem, and Imprecise Probabilities

Jeremie Houssineau, Badr-Eddine Chérief-Abdellatif

Generalized Distribution-Free Semi-Supervised Learning with Risk Rewrite

Yushi Hirose, Hiroo Irobe, Takafumi Kanamori

EpiRDN: A Learnable Anisotropic Reaction-Diffusion Network for Epidemic Time Series Prediction

Asela Hevapathige

Identifying Labeling Mechanism in Positive–Unlabeled Learning under Unknown Class Prior

Siying He, Weijuan Liang, Jiatong Liu

Identification and Bounding of Central Moments of Causal Effects Using Marginal Moments Information

Naoya Hashimoto, Yuta Kawakami, Jin Tian

Learning Max-Stable Representations that Extrapolate

Ali Hasan, Patrick Kendal Kuiper, Yuting Ng, Jose Blanchet, Vahid Tarokh

Learning Bayesian and Markov Networks with an Unreliable Oracle

Juha Harviainen, Pekka Parviainen, Vidya Sagar Sharma

Should You Use Your Large Language Model to Explore or Exploit?

Keegan Harris, Aleksandrs Slivkins

Robust Bayesian Decision Making under Adversarial Uncertainty

HARIPRIYA HARIKUMAR, Sammie Katt, Yasir Zubayr Barlas, Samuel Kaski

Provable Subspace Identification of Nonlinear Multi-view CCA

Zhiwei Han, Stefan Matthes, Hao Shen

Scalable Model-Assisted Multi-Target Estimation in Large Image Collections

Max Hamilton, Jinlin Lai, Daniel Sheldon, Subhransu Maji

A Probabilistic Circuit Framework for Interpretable Graph PU Learning

Sagad Hamid, Dooho Lee, Myeong Kong, Tanya Braun, Jaemin Yoo

Reliable Conformal Prediction for Ordinal Classification Using the Ranked Probability Score

Stefan Haas, Luca Killmaier, Alireza Javanmardi, Eyke Hüllermeier

Proximal Identification and Estimation in Front-Door Causal Structures with Unobserved Confounding of the Mediator

Helen Guo, Beatrix Yaxin Wen, Ilya Shpitser

Provably Efficient Reinforcement Learning in Continuous-Time Episodic MDPs with Poisson Decision Epochs

Kenny Guo, Valentio Iverson, Sahan Wijetunga, William Chang

The Price of Valid Inference After Causal Discovery

Dongxin Guo, Jikun Wu, Siu Ming Yiu

Detecting Out of Distribution Samples using Class Centered Residual Energy in the Discarded PCA Subspace with Weight Alignment

Shreen Gul, Mohamed Elmahallawy, Ardhendu Tripathy, Sanjay Madria

Eigenvalue Calibration for Semantic Embeddings of Large Language Models

Sebastian G. Gruber, Nassim Walha, Francis Bach, Florian Buettner

Gaussian Graphical Learning via PSD Constraint for Blockwise Missing Multimodal Data

Joseph L. Graves, Yufeng Liu, Elio Zhang, Seong-Tae Kim, for the Alzheimer’s Disease Neuroimaging Initiative

Quantification of Credal Uncertainty: A Distance-Based Approach

Xabier Gonzalez-Garcia, Siu Lun Chau, Julian Rodemann, Michele Caprio, Krikamol Muandet

Fast Best-in-Class Regret for Contextual Bandits

Samuel Girard, Aurelien Bibaut, Jill-Jenn Vie, Arthur Gretton, Nathan Kallus

Implicit Learning for Reasoning in First-Order Probabilistic Logic

Luise Ge, Brendan Juba, Kris Nilsson, Alison Shao

Conformal Online Model Aggregation

Matteo Gasparin, Aaditya Ramdas

Advances in PAC-Bayesian certification of deep neural networks: tighter closed-form inequalities and optimization of bounds on non-differentiable losses

Diego García-Pérez, Emilio Parrado-hernández, John Shawe-Taylor

Perfect Matching Map Recovery Under Unknown Scalar Affine Transformation

Tigran Galstyan, Avetik Karagulyan, Arshak Minasyan

Bregman contrastive estimation as a general framework for learning unnormalized models: Efficiency, robustness and optimal noise

Yuto Fujii, Hiroaki Sasaki, Takafumi Kanamori

Explainable Clustering of Mixture Models

Maximilian Fleissner, Maedeh Zarvandi, Debarghya Ghoshdastidar

Average Controlled and Average Natural Micro Direct Effects in Summary Causal Graphs

Simon Ferreira, Charles K. Assaad

Mitigating Spurious Correlations with Memorization-Guided Dataset De-Biasing

Arda Fazla, Abolfazl Hashemi

Poisson–Gamma Modeling of Inter-Relational Dependencies in Dynamic Knowledge Graphs

Nan Fang, Yijun Wang, Hao Liao, Sikun Yang

How PC-based Methods Err: Towards Better Reporting of Assumption Violations and Small Sample Errors

Sofia Faltenbacher, Jonas Wahl, Rebecca Jean Herman, Jakob Runge

One Model to Rule Them All: Canonically Gluing Causal Models Along Shared Substructures

Markus Englberger, Devendra Singh Dhami

Neural Diffusion Intensity Models for Point Process Data

Xinlong Du, Harsha Honnappa, Vinayak Rao

Sparse Action-Dependent Policy Iteration under Coordination Structures: Convergence and Optimality

Jianglin Ding, Jingcheng Tang, Gangshan Jing

Communication-Efficient Distributed Training for Collaborative Flat Optima Recovery in Deep Learning

Tolga Dimlioglu, Anna Ewa Choromanska

Ensemble Diversity Optimization for Subjective Supervision

Xia Cui, Ziyi Huang, Nishanthi Rupika Abeynayake

Which Directions Matter? Sparse Design for Affine Robust Optimization

Pedro Chumpitaz-Flores, My Duong, Juan S. Borrero, Kaixun Hua

Canonical Domain Reduction for Partial Counterfactual Identification

Yesong Choe, Yeahoon Kwon, Min Woo Park, Sanghack Lee

Fairness under Graph Uncertainty: Achieving Interventional Fairness with Partially Known Causal Graphs over Clusters of Variables

Yoichi Chikahara

Policy Optimization for Adversarial Linear Mixture MDPs with Unknown Transitions and Bandit Feedback

Yutian Cheng, Canzhe Zhao, Shuai Li

Hyperbolic Belief Propagation

Zehua Cheng, Wei Dai, Jiahao Sun

On the Sublinear Regret of Continuous K-Max Bandits

Yu Chen, Siwei Wang, Longbo Huang, Wei Chen

On the Granularity of Causal Effect Identifiability

Yizuo Chen, Adnan Darwiche

Finite-Sample Regret Analysis of Nash Q-Learning with Random-Feature Approximation

Yongshan Chen, Yuchen Hou, Zhuowen Zou, Calvin Yeung, Mohsen Imani

Interactive Multi-Objective Probabilistic Preference Learning with Soft and Hard Bounds

Edward Chen, Sang T. Truong, Natalie Dullerud, Sanmi Koyejo, Carlos Guestrin

SteinGate: Tail-Sensitive Safe Reinforcement Learning via Stein Discrepancy

Yassine Chemingui, Chenhua Fan, Honghao Wei, Janardhan Rao Doppa

Linear Regression with Heteroskedastic Errors

Siddhant Chaudhary, Aditya Bhaskara

Inference for quantile-parametrized families via CDF confidence bands

Srijan Chattopadhyay, Siddhaarth Sarkar, Arun K. Kuchibhotla

Robust Constrained Markov Games: Multi-Agent Decision-Making under Model Uncertainty and Constraints

Ningkang Chang, Chenyu Xu, Ziying Jia, Yue Wang, Sihong He

Group-Fair Allocations of Contiguous Blocks of Indivisible Items

Hau Chan, Minming Li, Yingchao Zhao, Shangkun Zheng

Neural Routed Boosting: Robust Learning against Heteroscedastic Noise

Puspak Chakraborty, Arun Rajkumar

Robust Predictive Uncertainty and Double Descent in Contaminated Bayesian Random Features

Michele Caprio, Katerina Papagiannouli, Siu Lun Chau, Sayan Mukherjee

Provably Efficient Personalized Multi-Objective Bandits with Proactive Conversational Queries

Linfeng Cao, Ming Shi, Ness Shroff

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression

Christopher Bülte, Yusuf Sale, Timo Löhr, Paul Hofman, Gitta Kutyniok

Uncertainty Quantification for Regression: A Unified Framework based on kernel scores

Christopher Bülte, Yusuf Sale, Gitta Kutyniok, Eyke Hüllermeier