UAI 2025

229 papers

$σ$-Maximal Ancestral Graphs Binghua Yao, Joris Marten Mooij

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

A Mirror Descent Perspective of Smoothed Sign Descent Shuyang Wang, Diego Klabjan

A Multivariate Unimodality Test Harnessing the Dip Statistic of Mahalanobis Distances over Random Projections Prodromos Kolyvakis, Aristidis Likas

A Parallel Network for LRCT Segmentation and Uncertainty Mitigation with Fuzzy Sets Shiyi Wang, Yang Nan, Xiaodan Xing, Yingying Fang, Simon Lf Walsh, Guang Yang

A Probabilistic Neuro-Symbolic Layer for Algebraic Constraint Satisfaction Leander Kurscheidt, Paolo Morettin, Roberto Sebastiani, Andrea Passerini, Antonio Vergari

A Quantum Information Theoretic Approach to Tractable Probabilistic Models Pedro Zuidberg Dos Martires

A Trajectory-Based Bayesian Approach to Multi-Objective Hyperparameter Optimization with Epoch-Aware Trade-Offs Wenyu Wang, Zheyi Fan, Szu Hui Ng

A Trust-Region Method for Graphical Stein Variational Inference Liam Pavlovic, David M Rosen

A Unified Data Representation Learning for Non-Parametric Two-Sample Testing Xunye Tian, Liuhua Peng, Zhijian Zhou, Mingming Gong, Arthur Gretton, Feng Liu

Accurate and Scalable Stochastic Gaussian Process Regression via Learnable Coreset-Based Variational Inference Mert Ketenci, Adler J Perotte, Noémie Elhadad, Iñigo Urteaga

Adapting Prediction Sets to Distribution Shifts Without Labels Kevin Kasa, Zhiyu Zhang, Heng Yang, Graham W. Taylor

Adaptive Human-Robot Collaboration Using Type-Based IRL Prasanth Sengadu Suresh, Prashant Doshi, Bikramjit Banerjee

Adaptive Reward Design for Reinforcement Learning Minjae Kwon, Ingy ElSayed-Aly, Lu Feng

Adaptive Threshold Sampling for Pure Exploration in Submodular Bandits Wenjing Chen, Shuo Xing, Victoria G. Crawford

Adversarial Training May Induce Deteriorating Distributions Runzhi Tian, Yongyi Mao

Aggregating Data for Optimal Learning Sushant Agarwal, Yukti Makhija, Rishi Saket, Aravindan Raghuveer

An Information-Theoretic Perspective of Hierarchical Clustering on Graphs Yicheng Pan, Bingchen Fan, Pengyu Long, Feng Zheng

An Optimal Algorithm for Strongly Convex Min-Min Optimization Dmitry Kovalev, Alexander Gasnikov, Grigory Malinovsky

Approximate Bayesian Inference via Bitstring Representations Aleksanteri Sladek, Martin Trapp, Arno Solin

Are You Doing Better than Random Guessing? a Call for Using Negative Controls When Evaluating Causal Discovery Algorithms Anne Helby Petersen

Asymptotically Optimal Linear Best Feasible Arm Identification with Fixed Budget Jie Bian, Vincent Y. F. Tan

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

Bayesian Optimization over Bounded Domains with the Beta Product Kernel Huy Hoang Nguyen, Han Zhou, Matthew B. Blaschko, Aleksei Tiulpin

Bayesian Optimization with Inexact Acquisition: Is Random Grid Search Sufficient? Hwanwoo Kim, Chong Liu, Yuxin Chen

BELIEF - Bayesian Sign Entropy Regularization for LIME Framework Revoti Prasad Bora, Philipp Terhörst, Raymond Veldhuis, Raghavendra Ramachandra, Kiran Raja

Best Arm Identification with Possibly Biased Offline Data Le Yang, Vincent Y. F. Tan, Wang Chi Cheung

Best Possible Q-Learning Jiechuan Jiang, Zongqing Lu

Beyond Invisibility: Learning Robust Visible Watermarks for Stronger Copyright Protection Tianci Liu, Tong Yang, Quan Zhang, Qi Lei

Beyond Sin-Squared Error: Linear Time Entrywise Uncertainty Quantification for Streaming PCA Syamantak Kumar, Shourya Pandey, Purnamrita Sarkar

Black-Box Optimization with Unknown Constraints via Overparameterized Deep Neural Networks Dat Phan Trong, Hung The Tran, Sunil Gupta

Budget Allocation Exploiting Label Correlation Between Instances Adithya Kulkarni, Mohna Chakraborty, Sihong Xie, Qi Li

Building Conformal Prediction Intervals with Approximate Message Passing Lucas Clarté, Lenka Zdeborová

Calibrated Regression Against an Adversary Without Regret Shachi Deshpande, Charles Marx, Volodymyr Kuleshov

Can a Bayesian Oracle Prevent Harm from an Agent? Yoshua Bengio, Michael K. Cohen, Nikolay Malkin, Matt MacDermott, Damiano Fornasiere, Pietro Greiner, Younesse Kaddar

CATE Estimation with Potential Outcome Imputation from Local Regression Ahmed Aloui, Juncheng Dong, Cat Phuoc Le, Vahid Tarokh

Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles Mathias Drton, Marina Garrote-López, Niko Nikov, Elina Robeva, Y. Samuel Wang

Causal Effect Identification in Heterogeneous Environments from Higher-Order Moments Yaroslav Kivva, Sina Akbari, Saber Salehkaleybar, Negar Kiyavash

Causal Eligibility Traces for Confounding Robust Off-Policy Evaluation Junzhe Zhang, Elias Bareinboim

Causal Inference amid Missingness-Specific Independences and Mechanism Shifts Johan Aguas, Leonard Henckel, Johan Pensar, Guido Biele

Causal Models for Growing Networks Gecia Bravo-Hermsdorff, Kayvan Sadeghi, Lee M. Gunderson

Coevolutionary Emergent Systems Optimization with Applications to Ultra-High-Dimensional Metasurface Design : OAM Wave Manipulation Zhengxuan Jiang, Guowen Ding, Wen Jiang

Collaborative Prediction: To Join or to Disjoin Datasets Kyung Rok Kim, Yansong Wang, Xiaocheng Li, Guanting Chen

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning Xue Zhou, Dapeng Man, Chen Xu, Fanyi Zeng, Tao Liu, Huan Wang, Shucheng He, Chaoyang Gao, Wu Yang

Complete Characterization for Adjustment in Summary Causal Graphs of Time Series Clément Yvernes, Emilie Devijver, Eric Gaussier

Computationally Efficient Methods for Invariant Feature Selection with Sparsity Jane Du, Arindam Banerjee

Concept Forgetting via Label Annealing Subhodip Panda, Ananda Theertha Suresh, Atri Guha, Prathosh Ap

Conditional Average Treatment Effect Estimation Under Hidden Confounders Ahmed Aloui, Juncheng Dong, Ali Hasan, Vahid Tarokh

Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information Ömer Faruk Akgül, Rajgopal Kannan, Viktor Prasanna

Conformal Prediction Sets for Deep Generative Models via Reduction to Conformal Regression Hooman Shahrokhi, Devjeet Raj Roy, Yan Yan, Venera Arnaoudova, Jana Doppa

Conformal Prediction Without Nonconformity Scores Jonas Hanselle, Alireza Javanmardi, Tobias Florin Oberkofler, Yusuf Sale, Eyke Hüllermeier

Constraint-Based Causal Discovery from a Collection of Conditioning Sets Kenneth Lee, Bruno Ribeiro, Murat Kocaoglu

Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Models Thi Kieu Khanh Ho, Narges Armanfard

Contrast-CAT: Contrasting Activations for Enhanced Interpretability in Transformer-Based Text Classifiers Sungmin Han, Jeonghyun Lee, Sangkyun Lee

Correlated Quantization for Faster Nonconvex Distributed Optimization Andrei Panferov, Yury Demidovich, Ahmad Rammal, Peter Richtárik

Corruption-Robust Variance-Aware Algorithms for Generalized Linear Bandits Under Heavy-Tailed Rewards Qingyuan Yu, Euijin Baek, Xiang Li, Qiang Sun

COS-DPO: Conditioned One-Shot Multi-Objective Fine-Tuning Framework Yinuo Ren, Tesi Xiao, Michael Shavlovsky, Lexing Ying, Holakou Rahmanian

CP$^2$: Leveraging Geometry for Conformal Prediction via Canonicalization Putri A Linden, Alexander Timans, Erik J Bekkers

Creative Agents: Empowering Agents with Imagination for Creative Tasks Penglin Cai, Chi Zhang, Yuhui Fu, Haoqi Yuan, Zongqing Lu

Critical Influence of Overparameterization on Sharpness-Aware Minimization Sungbin Shin, Dongyeop Lee, Maksym Andriushchenko, Namhoon Lee

Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning Francesco Diana, André Nusser, Chuan Xu, Giovanni Neglia

Decomposition of Probabilities of Causation with Two Mediators Yuta Kawakami, Jin Tian

Dependent Randomized Rounding for Budget Constrained Experimental Design Khurram Yamin, Edward Kennedy, Bryan Wilder

DF$^2$: Distribution-Free Decision-Focused Learning Lingkai Kong, Wenhao Mu, Jiaming Cui, Yuchen Zhuang, B. Aditya Prakash, Bo Dai, Chao Zhang

Discriminative Ordering Through Ensemble Consensus Louis Ohl, Fredrik Lindsten

Distributional Reinforcement Learning with Dual Expectile-Quantile Regression Sami Jullien, Romain Deffayet, Jean-Michel Renders, Paul Groth, Maarten de Rijke

Distributionally and Adversarially Robust Logistic Regression via Intersecting Wasserstein Balls Aras Selvi, Eleonora Kreacic, Mohsen Ghassemi, Vamsi K. Potluru, Tucker Balch, Manuela Veloso

Divide and Orthogonalize: Efficient Continual Learning with Local Model Space Projection Jin Shang, Simone Shao, Tian Tong, Fan Yang, Yetian Chen, Yang Jiao, Jia Liu, Yan Gao

Do Vendi Scores Converge with Finite Samples? Truncated Vendi Score for Finite-Sample Convergence Guarantees Azim Ospanov, Farzan Farnia

DyGMAE: A Novel Dynamic Graph Masked Autoencoder for Link Prediction Weixiong Liu, Junwei Cheng, Zhongyu Pan, Chaobo He, Quanlong Guan

Dynamic Maintenance of Kernel Density Estimation Data Structure: From Practice to Theory Jiehao Liang, Zhao Song, Zhaozhuo Xu, Junze Yin, Danyang Zhuo

EERO: Early Exit with Reject Option for Efficient Classification with Limited Budget Florian Valade, Mohamed Hebiri, Paul Gay

Efficient Algorithms for Logistic Contextual Slate Bandits with Bandit Feedback Tanmay Goyal, Gaurav Sinha

Efficiently Escaping Saddle Points for Policy Optimization Mohammadsadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash, Niao He, Matthias Grossglauser

ELBO, Regularized Maximum Likelihood, and Their Common One-Sample Approximation for Training Stochastic Neural Networks Sina Däubener, Simon Damm, Asja Fischer

ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression Avetik Karagulyan, Peter Richtárik

Enhanced Equilibria-Solving via Private Information Pre-Branch Structure in Adversarial Team Games Chen Qiu, Haobo Fu, Kai Li, Jiajia Zhang, Xuan Wang

Enhancing Uncertainty Quantification in Large Language Models Through Semantic Graph Density Zhaoye Li, Siyuan Shen, Wenjing Yang, Ruochun Jin, Huan Chen, Ligong Cao, Jing Ren

Enumerating Optimal Cost-Constrained Adjustment Sets Batya Kenig

Epistemic Uncertainty in Conformal Scores: A Unified Approach Luben Miguel Cruz Cabezas, Vagner Silva Santos, Thiago Ramos, Rafael Izbicki

Error Bounds for Physics-Informed Neural Networks in Fokker-Planck PDEs Chun-Wei Kong, Luca Laurenti, Jay McMahon, Morteza Lahijanian

Evasion Attacks Against Bayesian Predictive Models Pablo G. Arce, Roi Naveiro, David Ríos Insua

Experimentation Under Treatment Dependent Network Interference Shiv Shankar, Ritwik Sinha, Madalina Fiterau

Expert-in-the-Loop Causal Discovery: Iterative Model Refinement Using Expert Knowledge Ankur Ankan, Johannes Textor

Explaining Negative Classifications of AI Models in Tumor Diagnosis David A. Kelly, Hana Chockler, Nathan Blake

Exploring Exploration in Bayesian Optimization Leonard Papenmeier, Nuojin Cheng, Stephen Becker, Luigi Nardi

FALCON: Adaptive Cross-Domain APT Attack Investigation with Federated Causal Learning Jialu Tang, Yali Gao, Xiaoyong Li, Jiawei Li, Shui Yu, Binxing Fang

Fast Calculation of Feature Contributions in Boosting Trees Zhongli Jiang, Min Zhang, Dabao Zhang

Fast Non-Convex Matrix Sensing with Optimal Sample Complexity Jian-Feng Cai, Tong Wu, Ruizhe Xia

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

FeDCM: Federated Learning of Deep Causal Generative Models Md Musfiqur Rahman, Murat Kocaoglu

Federated Rényi Fair Inference in Federated Heterogeneous System Zhiyong Ma, Yuanjie Shi, Yan Yan, Jian Chen

FedSPD: A Soft-Clustering Approach for Personalized Decentralized Federated Learning I-Cheng Lin, Osman Yagan, Carlee Joe-Wong

Finding Interior Optimum of Black-Box Constrained Objective with Bayesian Optimization Fengxue Zhang, Yuxin Chen

Flat Posterior Does Matter for Bayesian Model Averaging Sungjun Lim, Jeyoon Yeom, Sooyon Kim, Hoyoon Byun, Jinho Kang, Yohan Jung, Jiyoung Jung, Kyungwoo Song

FlightPatchNet: Multi-Scale Patch Network with Differential Coding for Short-Term Flight Trajectory Prediction Lan Wu, Xuebin Wang, Ruijuan Chu, Guangyi Liu, Jing Zhang, Linyu Wang

Flow-Based Delayed Hawkes Process Chao Yang, Wendi Ren, Shuang Li

Full Network Capacity Framework for Sample-Efficient Deep Reinforcement Learning Wentao Yang, Xinyue Liu, Yunlong Gao, Wenxin Liang, Linlin Zong, Guanglu Wang, Xianchao Zhang

Generalised Probabilistic Modelling and Improved Uncertainty Estimation in Comparative LLM-as-a-Judge Yassir Fathullah, Mark Gales

Generative Uncertainty in Diffusion Models Metod Jazbec, Eliot Wong-Toi, Guoxuan Xia, Dan Zhang, Eric Nalisnick, Stephan Mandt

Geodesic Slice Sampler for Multimodal Distributions with Strong Curvature Bernardo Williams, Hanlin Yu, Hoang Phuc Hau Luu, Georgios Arvanitidis, Arto Klami

Group-Agent Reinforcement Learning with Heterogeneous Agents Kaiyue Wu, Xiao-Jun Zeng, Tingting Mu

Guaranteed Prediction Sets for Functional Surrogate Models Ander Gray, Vignesh Gopakumar, Sylvain Rousseau, Sebastien Destercke

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Song Liu, Leyang Wang, Yakun Wang

HDP-Flow: Generalizable Bayesian Nonparametric Model for Time Series State Discovery Sana Tonekaboni, Tina Behrouzi, Addison Weatherhead, Emily Fox, David Blei, Anna Goldenberg

Hindsight Merging: Diverse Data Generation with Language Models Veniamin Veselovsky, Benedikt Stroebl, Gianluca Bencomo, Dilip Arumugam, Lisa Schut, Arvind Narayanan, Thomas L. Griffiths

How Likely Are Two Voting Rules Different? Ziqi Yu, Lirong Xia, Qishen Han, Chengkai Zhang

Hybrid Bernstein Normalizing Flows for Flexible Multivariate Density Regression with Interpretable Marginals Marcel Arpogaus, Thomas Kneib, Thomas Nagler, David Rügamer

i$^2$VAE: Interest Information Augmentation with Variational Regularizers for Cross-Domain Sequential Recommendation Xuying Ning, Wujiang Xu, Tianxin Wei, Xiaolei Liu

Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution Bundles Pablo Flores, Olga Graf, Pavlos Protopapas, Karim Pichara

Improved Variational Inference in Discrete VAEs Using Error Correcting Codes María Martínez-García, Grace Villacrés, David Mitchell, Pablo M. Olmos

Improving Adversarial Transferability via Decision Boundary Adaptation Jiayu Zhang, Zhiyu Zhu, Zhibo Jin, Xinyi Wang, Huaming Chen, Kim-Kwang Raymond Choo

Improving Graph Contrastive Learning with Community Structure Xiang Chen, Kun Yue, Liang Duan, Lixing Yu

InfoDPCCA: Information-Theoretic Dynamic Probabilistic Canonical Correlation Analysis Shiqin Tang, Shujian Yu

Informative Synthetic Data Generation for Thorax Disease Classification Yancheng Wang, Rajeev Goel, Marko Jojic, Alvin C. Silva, Teresa Wu, Yingzhen Yang

Instance-Wise Monotonic Calibration by Constrained Transformation Yunrui Zhang, Gustavo Enrique Batista, Salil S. Kanhere

Just Trial Once: Ongoing Causal Validation of Machine Learning Models Jacob M. Chen, Michael Oberst

Label Distribution Learning Using the Squared Neural Family on the Probability Simplex Daokun Zhang, Russell Tsuchida, Dino Sejdinovic

Learning Algorithms for Multiple Instance Regression Aaryan Gupta, Rishi Saket

Learning Causal Response Representations Through Direct Effect Analysis Homer Durand, Gherardo Varando, Gustau Camps-Valls

Learning from Label Proportions and Covariate-Shifted Instances Sagalpreet Singh, Navodita Sharma, Shreyas Havaldar, Rishi Saket, Aravindan Raghuveer

Learning Multi-Interest Embedding with Dynamic Graph Cluster for Sequention Recommendation Xiao Chunjing, Ranhao Guo, Zhang Yongwang, Xiaoming Wu

Learning Robust XGBoost Ensembles for Regression Tasks Atri Vivek Sharma, Panagiotis Kouvaros, Alessio Lomuscio

Learning to Sample in Stochastic Optimization Sijia Zhou, Yunwen Lei, Ata Kaban

Learning to Stabilize Unknown LTI Systems on a Single Trajectory Under Stochastic Noise Ziyi Zhang, Yorie Nakahira, Guannan Qu

Learning with Confidence Oliver Ethan Richardson

Letting Uncertainty Guide Your Multimodal Machine Translation Wuyi Liu, Yue Gao, Yige Mao, Jing Zhao

Limit-Sure Reachability for Small Memory Policies in POMDPs Is NP-Complete Ali Asadi, Krishnendu Chatterjee, Raimundo Saona, Ali Shafiee

LoSAM: Local Search in Additive Noise Models with Mixed Mechanisms and General Noise for Global Causal Discovery Sujai Hiremath, Promit Ghosal, Kyra Gan

Lower Bound on Howard Policy Iteration for Deterministic Markov Decision Processes Ali Asadi, Krishnendu Chatterjee, Jakob Raaij

Lower Bounds on the Size of Markov Equivalence Classes Erik L Jahn, Frederick Eberhardt, Leonard Schulman

Measuring IIA Violations in Similarity Choices with Bayesian Models Hugo Sales Correa, Suryanarayana Sankagiri, Daniel R. Figueiredo, Matthias Grossglauser

Metric Learning in an RKHS Gokcan Tatli, Yi Chen, Blake Mason, Robert D Nowak, Ramya Korlakai Vinayak

MindFlayer SGD: Efficient Parallel SGD in the Presence of Heterogeneous and Random Worker Compute Times Arto Maranjyan, Omar Shaikh Omar, Peter Richtárik

Minimax Optimal Nonsmooth Nonparametric Regression via Fractional Laplacian Eigenmaps Zhaoyang Shi, Krishna Balasubramanian, Wolfgang Polonik

Mixup Regularization: A Probabilistic Perspective Yousef El-Laham, Niccolo Dalmasso, Svitlana Vyetrenko, Vamsi K. Potluru, Manuela Veloso

MOHITO: Multi-Agent Reinforcement Learning Using Hypergraphs for Task-Open Systems Gayathri Anil, Prashant Doshi, Daniel Alan Redder, Adam Eck, Leen-Kiat Soh

Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization Yuen Chen, Haozhe Si, Guojun Zhang, Han Zhao

Moments of Causal Effects Yuta Kawakami, Jin Tian

MSCGrapher: Learning Multi-Scale Dynamic Correlations for Multivariate Time Series Forecasting Xian Yang, Zhenguo Zhang, Shihao Lu

MSP-SR: Multi-Stage Probabilistic Generative Super Resolution with Scarce High-Resolution Data Ruike Zhu, Matthew Charles Weston, Hanwen Zhang, Arindam Banerjee

Multi-Armed Bandits with Missing Outcomes Ilia Mahrooghi, Mahshad Moradi, Sina Akbari, Negar Kiyavash

Multi-Cost-Bounded Reachability Analysis of POMDPs Alexander Bork, Joost-Pieter Katoen, Tim Quatmann, Svenja Stein

Multi-Group Uncertainty Quantification for Long-Form Text Generation Terrance Liu, Steven Wu

Multi-Label Bayesian Active Learning with Inter-Label Relationships Yuanyuan Qi, Jueqing Lu, Xiaohao Yang, Joanne Enticott, Lan Du

Multiple Wasserstein Gradient Descent Algorithm for Multi-Objective Distributional Optimization Hai Dai Nguyen, Hiroshi Mamitsuka, Atsuyoshi Nakamura

MutualNeRF: Improve the Performance of NeRF Under Limited Samples with Mutual Information Theory Zifan Wang, Jingwei Li, Yitang Li, Yunze Liu

Near-Optimal Regret Bounds for Federated Multi-Armed Bandits with Fully Distributed Communication Haoran Zhang, Xuchuang Wang, Hao-Xu Chen, Hao Qiu, Lin Yang, Yang Gao

Nearly Optimal Differentially Private ReLU Regression Meng Ding, Mingxi Lei, Shaowei Wang, Tianhang Zheng, Di Wang, Jinhui Xu

Nonlinear Causal Discovery for Grouped Data Konstantin Göbler, Tobias Windisch, Mathias Drton

Nonparametric Bayesian Inference of Item-Level Features in Classifier Combination Patrick Stinson, Nikolaus Kriegeskorte

Nonparametric Bayesian Multi-Facet Clustering for Longitudinal Data Luwei Wang, Kieran Richards, Sohan Seth

NRFlow: Towards Noise-Robust Generative Modeling via High-Order Mechanism Bo Chen, Chengyue Gong, Xiaoyu Li, Yingyu Liang, Zhizhou Sha, Zhenmei Shi, Zhao Song, Mingda Wan, Xugang Ye

ODD: Overlap-Aware Estimation of Model Performance Under Distribution Shift Aayush Mishra, Anqi Liu

Off-Policy Predictive Control with Causal Sensitivity Analysis Myrl G Marmarelis, Ali Hasan, Kamyar Azizzadenesheli, R. Michael Alvarez, Anima Anandkumar

Offline Changepoint Detection with Gaussian Processes Janneke Verbeek, Tom Heskes, Yuliya Shapovalova

On Constant Regret for Low-Rank MDPs Alexander Sturm, Sebastian Tschiatschek

On Continuous Monitoring of Risk Violations Under Unknown Shift Alexander Timans, Rajeev Verma, Eric Nalisnick, Christian A. Naesseth

On Information-Theoretic Measures of Predictive Uncertainty Kajetan Schweighofer, Lukas Aichberger, Mykyta Ielanskyi, Sepp Hochreiter

On the Privacy Risks of Spiking Neural Networks: A Membership Inference Analysis Junyi Guan, Abhijith Sharma, Chong Tian, Salem Lahlou

Online Generalized Magician’s Problem with Multiple Workers Ruoyu Wu, Wei Bao, Ben Liang, Liming Ge

Online Learning with Stochastically Partitioning Experts Puranjay Datta, Sharayu Moharir, Jaya Prakash Champati

Optimal Submanifold Structure in Log-Linear Models Zhou Derun, Mahito Sugiyama

Optimal Transport Alignment of User Preferences from Ratings and Texts Nhu-Thuat Tran, Hady W. Lauw

Optimal Transport for Probabilistic Circuits Adrian Ciotinga, YooJung Choi

Optimal Zero-Shot Regret Minimization for Selective Classification with Out-of-Distribution Detection Eduardo Dadalto Câmara Gomes, Marco Romanelli

Order-Optimal Global Convergence for Actor-Critic with General Policy and Neural Critic Parametrization Swetha Ganesh, Jiayu Chen, Washim Uddin Mondal, Vaneet Aggarwal

Out-of-Distribution Robust Optimization Zhongze Cai, Hansheng Jiang, Xiaocheng Li

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

Partial-Label Learning with Conformal Candidate Cleaning Tobias Fuchs, Florian Kalinke

Periodical Moving Average Accelerates Gradient Accumulation for Post-Training Yumou Liu, An Li, Chaojie Li, Fei Yu, Benyou Wang

Privacy-Preserving Neural Processes for Probabilistic User Modeling Amir Sonee, Haripriya Harikumar, Alex Hämäläinen, Lukas Prediger, Samuel Kaski

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models Aishwarya Venkataramanan, Paul Bodesheim, Joachim Denzler

Probabilistic Explanations for Regression Models Frédéric Koriche, Jean-Marie Lagniez, Chi Tran

Probabilistic Graph Circuits: Deep Generative Models for Tractable Probabilistic Inference over Graphs Milan Papez, Martin Rektoris, Vaclav Smidl, Tomáš Pevný

Probabilistic Semantics Guided Discovery of Approximate Functional Dependencies Liang Duan, Xinran Wu, Xinhui Li, Lixing Yu, Kun Yue

Probability-Raising Causality for Uncertain Parametric Markov Decision Processes with PAC Guarantees Ryohei Oura, Yuji Ito

Provably Adaptive Average Reward Reinforcement Learning for Metric Spaces Avik Kar, Rahul Singh

Proximal Interacting Particle Langevin Algorithms Paula Cordero Encinar, Francesca Romana Crucinio, Omer Deniz Akyildiz

Proxy-Informed Bayesian Transfer Learning with Unknown Sources Sabina J. Sloman, Julien Martinelli, Samuel Kaski

Pure and Strong Nash Equilibrium Computation in Compactly Representable Aggregate Games Jared Soundy, Mohammad T. Irfan, Hau Chan

Quantum Speedups for Bayesian Network Structure Learning Juha Harviainen, Kseniya Rychkova, Mikko Koivisto

RCAP: Robust, Class-Aware, Probabilistic Dynamic Dataset Pruning Atif Hassan, Swanand Khare, Jiaul H. Paik

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

Relational Causal Discovery with Latent Confounders Matteo Negro, Andrea Piras, Ragib Ahsan, David Arbour, Elena Zheleva

Reparameterizing Hybrid Markov Logic Networks to Handle Covariate-Shift in Representations Anup Shakya, Abisha Thapa Magar, Somdeb Sarkhel, Deepak Venugopal

Residual Reweighted Conformal Prediction for Graph Neural Networks Zheng Zhang, Jie Bao, Zhixin Zhou, Nicolo Colombo, Lixin Cheng, Rui Luo

Revisiting the Berkeley Admissions Data: Statistical Tests for Causal Hypotheses Sourbh Bhadane, Joris Marten Mooij, Philip Boeken, Onno Zoeter

Revisiting the Equivalence of Bayesian Neural Networks and Gaussian Processes: On the Importance of Learning Activations Marcin Sendera, Amin Sorkhei, Tomasz Kuśmierczyk

RL, but Don’t Do Anything I Wouldn’t Do Michael K. Cohen, Marcus Hutter, Yoshua Bengio, Stuart Russell

Robust Optimization with Diffusion Models for Green Security Lingkai Kong, Haichuan Wang, Yuqi Pan, Cheol Woo Kim, Mingxiao Song, Alayna Nguyen, Tonghan Wang, Haifeng Xu, Milind Tambe

Root Cause Analysis of Failures from Partial Causal Structures Azam Ikram, Kenneth Lee, Shubham Agarwal, Shiv Kumar Saini, Saurabh Bagchi, Murat Kocaoglu

SALSA: A Secure, Adaptive and Label-Agnostic Scalable Algorithm for Machine Unlearning Owais Makroo, Atif Hassan, Swanand Khare

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Runze Zhao, Yue Yu, Adams Yiyue Zhu, Chen Yang, Dongruo Zhou

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

Scaling Probabilistic Circuits via Data Partitioning Jonas Seng, Florian Peter Busch, Pooja Prasad, Devendra Singh Dhami, Martin Mundt, Kristian Kersting

Selective Blocking for Message-Passing Neural Networks on Heterophilic Graphs Yoonhyuk Choi, Taewook Ko, Jiho Choi, Chong-Kwon Kim

Simulation-Based Inference for High-Dimensional Data Using Surjective Sequential Neural Likelihood Estimation Simon Dirmeier, Carlo Albert, Fernando Perez-Cruz

Simulation-Free Differential Dynamics Through Neural Conservation Laws Mengjian Hua, Eric Vanden-Eijnden, Ricky T. Q. Chen

Sparse Structure Exploration and Re-Optimization for Vision Transformer Sangho An, Jinwoo Kim, Keonho Lee, Jingang Huh, Chanwoong Kwak, Yujin Lee, Moonsub Jin, Jangho Kim

SpinSVAR: Estimating Structural Vector Autoregression Assuming Sparse Input Panagiotis Misiakos, Markus Püschel

SPvR: Structured Pruning via Ranking Atif Hassan, Jiaul H. Paik, Swanand Khare

Statistical Significance of Feature Importance Rankings Jeremy Goldwasser, Giles Hooker

Stein Variational Evolution Strategies Cornelius V. Braun, Robert Tjarko Lange, Marc Toussaint

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Zhuqing Liu, Chaosheng Dong, Michinari Momma, Simone Shao, Shaoyuan Xu, Yan Gao, Haibo Yang, Jia Liu

Stochastic Embeddings : A Probabilistic and Geometric Analysis of Out-of-Distribution Behavior Anthony Nguyen, Emanuel Aldea, Sylvie Le Hégarat-Mascle, Renaud Lustrat

Symbiotic Local Search for Small Decision Tree Policies in MDPs Roman Andriushchenko, Milan Ceska, Debraj Chakraborty, Sebastian Junges, Jan Kretinsky, Filip Macák

Targeted Learning for Variable Importance Xiaohan Wang, Yunzhe Zhou, Giles Hooker

Temperature Optimization for Bayesian Deep Learning Kenyon Ng, Chris Heide, Liam Hodgkinson, Susan Wei

Testing Generalizability in Causal Inference Daniel Vassimon Manela, Linying Yang, Robin J. Evans

The Causal Information Bottleneck and Optimal Causal Variable Abstractions Francisco N. F. Q. Simoes, Mehdi Dastani, Thijs Ommen

The Consistency Hypothesis in Uncertainty Quantification for Large Language Models Quan Xiao, Debarun Bhattacharjya, Balaji Ganesan, Radu Marinescu, Katya Mirylenka, Nhan H Pham, Michael Glass, Junkyu Lee

The Relativity of Causal Knowledge Gabriele D’Acunto, Claudio Battiloro

Toward Universal Laws of Outlier Propagation Aram Ebtekar, Yuhao Wang, Dominik Janzing

Towards Provably Efficient Learning of Imperfect Information Extensive-Form Games with Linear Function Approximation Canzhe Zhao, Shuze Chen, Weiming Liu, Haobo Fu, Qiang Fu, Shuai Li

Trading Off Voting Axioms for Privacy Zhechen Li, Ao Liu, Lirong Xia, Yongzhi Cao, Hanpin Wang

Transparent Trade-Offs Between Properties of Explanations Hiwot Belay Tadesse, Alihan Hüyük, Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez

Truthful Elicitation of Imprecise Forecasts Anurag Singh, Siu Lun Chau, Krikamol Muandet

Tuning Algorithmic and Architectural Hyperparameters in Graph-Based Semi-Supervised Learning with Provable Guarantees Ally Yalei Du, Eric Huang, Dravyansh Sharma

Tuning-Free Coreset Markov Chain Monte Carlo via Hot DoG Naitong Chen, Jonathan H. Huggins, Trevor Campbell

Unsupervised Attributed Dynamic Network Embedding with Stability Guarantees Emma Ceccherini, Ian Gallagher, Andrew Jones, Daniel John Lawson

Using Submodular Optimization to Approximate Minimum-Size Abductive Path Explanations for Tree-Based Models Louenas Bounia

VADIS: Investigating Inter-View Representation Biases for Multi-View Partial Multi-Label Learning Jie Wang, Ning Xu, Xin Geng

Valid Bootstraps for Network Embeddings with Applications to Network Visualisation Emerald Dilworth, Ed Davis, Daniel John Lawson

Variational Learning of Gaussian Process Latent Variable Models Through Stochastic Gradient Annealed Importance Sampling Jian Xu, Shian Du, Junmei Yang, Qianli Ma, Delu Zeng, John Paisley

Weak to Strong Learning from Aggregate Labels Yukti Makhija, Rishi Saket

Well-Defined Function-Space Variational Inference in Bayesian Neural Networks via Regularized KL-Divergence Tristan Cinquin, Robert Bamler

What Is the Right Notion of Distance Between Predict-Then-Optimize Tasks? Paula Rodriguez-Diaz, Lingkai Kong, Kai Wang, David Alvarez-Melis, Milind Tambe

When Extragradient Meets PAGE: Bridging Two Giants to Boost Variational Inequalities Gleb Molodtsov, Valery Parfenov, Egor Petrov, Evseev Grigoriy, Daniil Medyakov, Aleksandr Beznosikov