UAI 2024

200 papers

\ensuremathα-Former: Local-Feature-Aware (L-FA) Transformer Zhi Xu, Bin Sun, Yue Bai, Yun Fu

$χ$SPN: Characteristic Interventional Sum-Product Networks for Causal Inference in Hybrid Domains Harsh Poonia, Moritz Willig, Zhongjie Yu, Matej Ze\vcević, Kristian Kersting, Devendra Singh Dhami

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A General Identification Algorithm for Data Fusion Problems Under Systematic Selection Jaron Jia Rong Lee, AmirEmad Ghassami, Ilya Shpitser

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A Generalized Bayesian Approach to Distribution-on-Distribution Regression Tin Lok James Ng

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A Global Markov Property for Solutions of Stochastic Difference Equations and the Corresponding Full Time Graphs Tom Hochsprung, Jakob Runge, Andreas Gerhardus

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A Graph Theoretic Approach for Preference Learning with Feature Information Aadirupa Saha, Arun Rajkumar

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A Homogenization Approach for Gradient-Dominated Stochastic Optimization Jiyuan Tan, Chenyu Xue, Chuwen Zhang, Qi Deng, Dongdong Ge, Yinyu Ye

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Active Learning Framework for Incomplete Networks Tung Khong, Cong Tran, Cuong Pham

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Adaptive SoftMax Trees for Many-Class Classification Rasul Kairgeldin, Magzhan Gabidolla, Miguel Carreira-Perpiñán

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Adaptive Time-Stepping Schedules for Diffusion Models Yuzhu Chen, Fengxiang He, Shi Fu, Xinmei Tian, Dacheng Tao

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Adjustment Identification Distance: A Gadjid for Causal Structure Learning Leonard Henckel, Theo Würtzen, Sebastian Weichwald

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Amortized Variational Inference: When and Why? Charles C. Margossian, David M. Blei

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Analysis of Bootstrap and Subsampling in High-Dimensional Regularized Regression Lucas Clarté, Adrien Vandenbroucque, Guillaume Dalle, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

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Anomaly Detection with Variance Stabilized Density Estimation Amit Rozner, Barak Battash, Henry Li, Lior Wolf, Ofir Lindenbaum

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Approximate Bayesian Computation with Path Signatures Joel Dyer, Patrick Cannon, Sebastian M. Schmon

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Approximate Kernel Density Estimation Under Metric-Based Local Differential Privacy Yi Zhou, Yanhao Wang, Long Teng, Qiang Huang, Cen Chen

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Approximation Algorithms for Observer Aware MDPs Shuwa Miura, Olivier Buffet, Shlomo Zilberstein

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AutoDrop: Training Deep Learning Models with Automatic Learning Rate Drop Jing Wang, Yunfei Teng, Anna Choromanska

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BanditQ:Fair Bandits with Guaranteed Rewards Abhishek Sinha

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Bandits with Knapsacks and Predictions Davide Drago, Andrea Celli, Marek Elias

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Base Models for Parabolic Partial Differential Equations Xingzi Xu, Ali Hasan, Jie Ding, Vahid Tarokh

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Bayesian Active Learning in the Presence of Nuisance Parameters Sabina J. Sloman, Ayush Bharti, Julien Martinelli, Samuel Kaski

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Bayesian Pseudo-Coresets via Contrastive Divergence Piyush Tiwary, Kumar Shubham, Vivek V. Kashyap, A. P. Prathosh

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BEARS Make Neuro-Symbolic Models Aware of Their Reasoning Shortcuts Emanuele Marconato, Samuele Bortolotti, Emile Krieken, Antonio Vergari, Andrea Passerini, Stefano Teso

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Beyond Dirichlet-Based Models: When Bayesian Neural Networks Meet Evidential Deep Learning Hanjing Wang, Qiang Ji

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Bias-Aware Boolean Matrix Factorization Using Disentangled Representation Learning Xiao Wang, Jia Wang, Tong Zhao, Yijie Wang, Nan Zhang, Yong Zang, Sha Cao, Chi Zhang

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Bootstrap Your Conversions: Thompson Sampling for Partially Observable Delayed Rewards Marco Gigli, Fabio Stella

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Bounding Causal Effects with Leaky Instruments David Watson, Jordan Penn, Lee Gunderson, Gecia Bravo-Hermsdorff, Afsaneh Mastouri, Ricardo Silva

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Calibrated and Conformal Propensity Scores for Causal Effect Estimation Shachi Deshpande, Volodymyr Kuleshov

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Can We Defend Against the Unknown? an Empirical Study About Threshold Selection for Neural Network Monitoring Khoi Tran Dang, Kevin Delmas, Jérémie Guiochet, Joris Guérin

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Causal Discovery with Deductive Reasoning: One Less Problem Jonghwan Kim, Inwoo Hwang, Sanghack Lee

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Causally Abstracted Multi-Armed Bandits Fabio Massimo Zennaro, Nicholas Bishop, Joel Dyer, Yorgos Felekis, Anisoara Calinescu, Michael Wooldridge, Theodoros Damoulas

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Center-Based Relaxed Learning Against Membership Inference Attacks Xingli Fang, Jung-Eun Kim

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Characterising Interventions in Causal Games Manuj Mishra, James Fox, Michael Wooldridge

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Characterizing Data Point Vulnerability as Average-Case Robustness Tessa Han, Suraj Srinivas, Himabindu Lakkaraju

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Cold-Start Recommendation by Personalized Embedding Region Elicitation Hieu Trung Nguyen, Duy Nguyen, Khoa Doan, Viet Anh Nguyen

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Common Event Tethering to Improve Prediction of Rare Clinical Events Quinn Lanners, Qin Weng, Marie-Louise Meng, Matthew M. Engelhard

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Computing Low-Entropy Couplings for Large-Support Distributions Samuel Sokota, Dylan Sam, Christian Witt, Spencer Compton, Jakob Foerster, J. Zico Kolter

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Conditional Bayesian Quadrature Zonghao Chen, Masha Naslidnyk, Arthur Gretton, Francois-Xavier Briol

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Consistency Regularization for Domain Generalization with Logit Attribution Matching Han Gao, Kaican Li, Weiyan Xie, Zhi Lin, Yongxiang Huang, Luning Wang, Caleb Cao, Nevin Zhang

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ContextFlow++: Generalist-Specialist Flow-Based Generative Models with Mixed-Variable Context Encoding Denis Gudovskiy, Tomoyuki Okuno, Yohei Nakata

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Convergence Behavior of an Adversarial Weak Supervision Method Steven An, Sanjoy Dasgupta

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Cooperative Meta-Learning with Gradient Augmentation Jongyun Shin, Seungjin Han, Jangho Kim

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Cost-Sensitive Uncertainty-Based Failure Recognition for Object Detection Moussa Kassem-Sbeyti, Michelle Karg, Christian Wirth, Nadja Klein, Sahin Albayrak

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CSS: Contrastive Semantic Similarities for Uncertainty Quantification of LLMs Shuang Ao, Stefan Rueger, Advaith Siddharthan

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DataSP: A Differential All-to-All Shortest Path Algorithm for Learning Costs and Predicting Paths with Context Alan Lahoud, Erik Schaffernicht, Johannes Stork

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Decentralized Online Learning in General-Sum Stackelberg Games Yaolong Yu, Haipeng Chen

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Decentralized Two-Sided Bandit Learning in Matching Market Yirui Zhang, Zhixuan Fang

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Decision-Focused Evaluation of Worst-Case Distribution Shift Kevin Ren, Yewon Byun, Bryan Wilder

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Detecting Critical Treatment Effect Bias in Small Subgroups Piersilvio De Bartolomeis, Javier Abad, Konstantin Donhauser, Fanny Yang

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Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation Yoichi Chikahara, Kansei Ushiyama

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Differentially Private No-Regret Exploration in Adversarial Markov Decision Processes Shaojie Bai, Lanting Zeng, Chengcheng Zhao, Xiaoming Duan, Mohammad Sadegh Talebi, Peng Cheng, Jiming Chen

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Dirichlet Continual Learning: Tackling Catastrophic Forgetting in NLP Min Zeng, Haiqin Yang, Wei Xue, Qifeng Liu, Yike Guo

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Discrete Probabilistic Inference as Control in Multi-Path Environments Tristan Deleu, Padideh Nouri, Nikolay Malkin, Doina Precup, Yoshua Bengio

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DistriBlock: Identifying Adversarial Audio Samples by Leveraging Characteristics of the Output Distribution Matı́as Pizarro, Dorothea Kolossa, Asja Fisher

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Distributionally Robust Optimization as a Scalable Framework to Characterize Extreme Value Distributions Patrick Kuiper, Ali Hasan, Wenhao Yang, Yuting Ng, Hoda Bidkhori, Jose Blanchet, Vahid Tarokh

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Domain Adaptation with Cauchy-Schwarz Divergence Wenzhe Yin, Shujian Yu, Yicong Lin, Jie Liu, Jan-Jakob Sonke, Efstratios Gavves

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Early-Exit Neural Networks with Nested Prediction Sets Metod Jazbec, Patrick Forré, Stephan Mandt, Dan Zhang, Eric Nalisnick

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Efficient Interactive Maximization of BP and Weakly Submodular Objectives Adhyyan Narang, Omid Sadeghi, Lillian Ratliff, Maryam Fazel, Jeff Bilmes

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Efficient Monte Carlo Tree Search via On-the-Fly State-Conditioned Action Abstraction Yunhyeok Kwak, Inwoo Hwang, Dooyoung Kim, Sanghack Lee, Byoung-Tak Zhang

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Efficiently Deciding Algebraic Equivalence of Bow-Free Acyclic Path Diagrams Thijs Ommen

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End-to-End Conditional Robust Optimization Abhilash Reddy Chenreddy, Erick Delage

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End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty My H. Dinh, James Kotary, Ferdinando Fioretto

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Enhancing Patient Recruitment Response in Clinical Trials: An Adaptive Learning Framework Xinying Fang, Shouhao Zhou

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EntProp: High Entropy Propagation for Improving Accuracy and Robustness Shohei Enomoto

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Equilibrium Computation in Multidimensional Congestion Games: CSP and Learning Dynamics Approaches Mohammad T. Irfan, Hau Chan, Jared Soundy

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Evaluating Bayesian Deep Learning for Radio Galaxy Classification Devina Mohan, Anna M. M. Scaife

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Exploring High-Dimensional Search Space via Voronoi Graph Traversing Aidong Zhao, Xuyang Zhao, Tianchen Gu, Zhaori Bi, Xinwei Sun, Changhao Yan, Fan Yang, Dian Zhou, Xuan Zeng

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Extremely Greedy Equivalence Search Achille Nazaret, David Blei

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Fair Active Learning in Low-Data Regimes Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin Jamieson

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Fast Interactive Search Under a Scale-Free Comparison Oracle Daniyar Chumbalov, Lars Klein, Lucas Maystre, Matthias Grossglauser

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Fast Reliability Estimation for Neural Networks with Adversarial Attack-Driven Importance Sampling Karim Tit, Teddy Furon

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Faster Perfect Sampling of Bayesian Network Structures Juha Harviainen, Mikko Koivisto

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FedAST: Federated Asynchronous Simultaneous Training Baris Askin, Pranay Sharma, Carlee Joe-Wong, Gauri Joshi

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Finite-Time Analysis of Three-Timescale Constrained Actor-Critic and Constrained Natural Actor-Critic Algorithms. Prashansa Panda, Shalabh Bhatnagar

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Functional Wasserstein Bridge Inference for Bayesian Deep Learning Mengjing Wu, Junyu Xuan, Jie Lu

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Functional Wasserstein Variational Policy Optimization Junyu Xuan, Mengjing Wu, Zihe Liu, Jie Lu

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GCVR: Reconstruction from Cross-View Enable Sufficient and Robust Graph Contrastive Learning Qianlong Wen, Zhongyu Ouyang, Chunhui Zhang, Yiyue Qian, Chuxu Zhang, Yanfang Ye

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General Markov Model for Solving Patrolling Games Andrzej Nagórko, Marcin Waniek, Małgorzata Róg, Michał Godziszewski, Barbara Rosiak, Tomasz Paweł Michalak

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Generalization and Learnability in Multiple Instance Regression Kushal Chauhan, Rishi Saket, Lorne Applebaum, Ashwinkumar Badanidiyuru, Chandan Giri, Aravindan Raghuveer

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Generalized Expected Utility as a Universal Decision Rule – A Step Forward Hélène Fargier, Pierre Pomeret-Coquot

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GeONet: A Neural Operator for Learning the Wasserstein Geodesic Andrew Gracyk, Xiaohui Chen

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Gradient Descent in Matrix Factorization: Understanding Large Initialization Hengchao Chen, Xin Chen, Mohamad Elmasri, Qiang Sun

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Graph Contrastive Learning Under Heterophily via Graph Filters Wenhan Yang, Baharan Mirzasoleiman

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Graph Feedback Bandits with Similar Arms Han Qi, Guo Fei, Li Zhu

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Group Fairness in Predict-Then-Optimize Settings for Restless Bandits Shresth Verma, Yunfan Zhao, Sanket Shah, Niclas Boehmer, Aparna Taneja, Milind Tambe

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Guaranteeing Robustness Against Real-World Perturbations in Time Series Classification Using Conformalized Randomized Smoothing Nicola Franco, Jakob Spiegelberg, Jeanette Miriam Lorenz, Stephan Günnemann

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Hidden Population Estimation with Indirect Inference and Auxiliary Information Justin Weltz, Eric Laber, Alexander Volfovsky

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How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression Lucas Kook, Chris Kolb, Philipp Schiele, Daniel Dold, Marcel Arpogaus, Cornelius Fritz, Philipp Baumann, Philipp Kopper, Tobias Pielok, Emilio Dorigatti, David Rügamer

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How to Fix a Broken Confidence Estimator: Evaluating Post-Hoc Methods for Selective Classification with Deep Neural Networks Luı́s Felipe Cattelan, Danilo Silva

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Hybrid CtrlFormer: Learning Adaptive Search Space Partition for Hybrid Action Control via Transformer-Based Monte Carlo Tree Search Jiashun Liu, Xiaotian Hao, Jianye Hao, Yan Zheng, Yujing Hu, Changjie Fan, Tangjie Lv, Zhipeng Hu

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Identifiability of Total Effects from Abstractions of Time Series Causal Graphs Charles K. Assaad, Emilie Devijver, Eric Gaussier, Gregor Goessler, Anouar Meynaoui

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Identification and Estimation of Conditional Average Partial Causal Effects via Instrumental Variable Yuta Kawakami, Manabu Kuroki, Jin Tian

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Identifying Causal Changes Between Linear Structural Equation Models Vineet Malik, Kevin Bello, Asish Ghoshal, Jean Honorio

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Identifying Homogeneous and Interpretable Groups for Conformal Prediction Natalia Martinez Gil, Dhaval Patel, Chandra Reddy, Giri Ganapavarapu, Roman Vaculin, Jayant Kalagnanam

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ILP-FORMER: Solving Integer Linear Programming with Sequence to Multi-Label Learning Shufeng Kong, Caihua Liu, Carla Gomes

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Inference for Optimal Linear Treatment Regimes in Personalized Decision-Making Yuwen Cheng, Shu Yang

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Inference in Probabilistic Answer Set Programs with Imprecise Probabilities via Optimization Damiano Azzolini, Fabrizio Riguzzi

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Invariant Causal Prediction with Local Models Alexander Mey, Rui M. Castro

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Investigating the Impact of Model Width and Density on Generalization in Presence of Label Noise Yihao Xue, Kyle Whitecross, Baharan Mirzasoleiman

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Iterated INLA for State and Parameter Estimation in Nonlinear Dynamical Systems Rafael Anderka, Marc Peter Deisenroth, So Takao

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Knowledge Intensive Learning of Credal Networks Saurabh Mathur, Alessandro Antonucci, Sriraam Natarajan

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Label Consistency-Based Worker Filtering for Crowdsourcing Jiao Li, Liangxiao Jiang, Chaoqun Li, Wenjun Zhang

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Label-Wise Aleatoric and Epistemic Uncertainty Quantification Yusuf Sale, Paul Hofman, Timo Löhr, Lisa Wimmer, Thomas Nagler, Eyke Hüllermeier

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Last-Iterate Convergence Separation Between Extra-Gradient and Optimism in Constrained Periodic Games Yi Feng, Ping Li, Ioannis Panageas, Xiao Wang

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Latent Representation Entropy Density for Distribution Shift Detection Fabio Arnez, Daniel Alfonso Montoya Vasquez, Ansgar Radermacher, François Terrier

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Learning Accurate and Interpretable Decision Trees Maria-Florina Balcan, Dravyansh Sharma

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Learning Causal Abstractions of Linear Structural Causal Models Riccardo Massidda, Sara Magliacane, Davide Bacciu

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Learning Distributionally Robust Tractable Probabilistic Models in Continuous Domains Hailiang Dong, James Amato, Vibhav Gogate, Nicholas Ruozzi

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Learning from Crowds with Dual-View K-Nearest Neighbor Jiao Li, Liangxiao Jiang, Xue Wu, Wenjun Zhang

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Learning Relevant Contextual Variables Within Bayesian Optimization Julien Martinelli, Ayush Bharti, Armi Tiihonen, S. T. John, Louis Filstroff, Sabina J. Sloman, Patrick Rinke, Samuel Kaski

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Learning to Rank for Active Learning via Multi-Task Bilevel Optimization Zixin Ding, Si Chen, Ruoxi Jia, Yuxin Chen

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Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problem Cong Zhang, Zhiguang Cao, Yaoxin Wu, Wen Song, Jing Sun

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Linear Opinion Pooling for Uncertainty Quantification on Graphs Clemens Damke, Eyke Hüllermeier

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Linearly Constrained Gaussian Processes Are SkewGPs: Application to Monotonic Preference Learning and Desirability Alessio Benavoli, Dario Azzimonti

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Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs Jacqueline Maasch, Weishen Pan, Shantanu Gupta, Volodymyr Kuleshov, Kyra Gan, Fei Wang

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Localised Natural Causal Learning Algorithms for Weak Consistency Conditions Kai Teh, Kayvan Sadeghi, Terry Soo

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Low-Rank Matrix Bandits with Heavy-Tailed Rewards Yue Kang, Cho-Jui Hsieh, Thomas Chun Man Lee

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Masking the Unknown: Leveraging Masked Samples for Enhanced Data Augmentation Xun Yao, Zijian Huang, Xinrong Hu, Jie Yang, Yi Guo

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Memorization Capacity for Additive Fine-Tuning with Small ReLU Networks Jy-yong Sohn, Dohyun Kwon, Seoyeon An, Kangwook Lee

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MetaCOG: A Heirarchical Probabilistic Model for Learning Meta-Cognitive Visual Representations Marlene Berke, Zhangir Azerbayev, Mario Belledonne, Zenna Tavares, Julian Jara-Ettinger

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Metric Learning from Limited Pairwise Preference Comparisons Zhi Wang, Geelon So, Ramya Korlakai Vinayak

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Mitigating Overconfidence in Out-of-Distribution Detection by Capturing Extreme Activations Mohammad Azizmalayeri, Ameen Abu-Hanna, Giovanni Cinà

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Model-Free Robust Reinforcement Learning with Sample Complexity Analysis Yudan Wang, Shaofeng Zou, Yue Wang

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Multi-Fidelity Bayesian Optimization with Multiple Information Sources of Input-Dependent Fidelity Mingzhou Fan, Byung-Jun Yoon, Edward Dougherty, Nathan Urban, Francis Alexander, Raymundo Arróyave, Xiaoning Qian

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Multi-Layer Random Features and the Approximation Power of Neural Networks Rustem Takhanov

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Multi-Relational Structural Entropy Yuwei Cao, Hao Peng, Angsheng Li, Chenyu You, Zhifeng Hao, Philip S. Yu

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Neighbor Similarity and Multimodal Alignment Based Product Recommendation Study Zhiqiang Zhang, Yongqiang Jiang, Qian Gao, Zhipeng Wang

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Neural Active Learning Meets the Partial Monitoring Framework Maxime Heuillet, Ola Ahmad, Audrey Durand

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Neural Architecture Search Finds Robust Models by Knowledge Distillation Utkarsh Nath, Yancheng Wang, Yingzhen Yang

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Neural Optimal Transport with Lagrangian Costs Aram-Alexandre Pooladian, Carles Domingo-Enrich, Ricky T. Q. Chen, Brandon Amos

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No-Regret Learning of Nash Equilibrium for Black-Box Games via Gaussian Processes Minbiao Han, Fengxue Zhang, Yuxin Chen

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Non-Stationary Domain Generalization: Theory and Algorithm Thai-Hoang Pham, Xueru Zhang, Ping Zhang

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Normalizing Flows for Conformal Regression Nicolò Colombo

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Offline Bayesian Aleatoric and Epistemic Uncertainty Quantification and Posterior Value Optimisation in Finite-State MDPs Filippo Valdettaro, Aldo Faisal

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Offline Reward Perturbation Boosts Distributional Shift in Online RL Zishun Yu, Siteng Kang, Xinhua Zhang

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On Convergence of Federated Averaging Langevin Dynamics Wei Deng, Qian Zhang, Yian Ma, Zhao Song, Guang Lin

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On Hardware-Efficient Inference in Probabilistic Circuits Lingyun Yao, Martin Trapp, Jelin Leslin, Gaurav Singh, Peng Zhang, Karthekeyan Periasamy, Martin Andraud

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On Overcoming Miscalibrated Conversational Priors in LLM-Based ChatBots Christine Herlihy, Jennifer Neville, Tobias Schnabel, Adith Swaminathan

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On the Capacitated Facility Location Problem with Scarce Resources Gennaro Auricchio, Harry J. Clough, Jie Zhang

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On the Convergence of Hierarchical Federated Learning with Partial Worker Participation Xiaohan Jiang, Hongbin Zhu

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On the Inductive Biases of Demographic Parity-Based Fair Learning Algorithms Haoyu Lei, Amin Gohari, Farzan Farnia

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One Shot Inverse Reinforcement Learning for Stochastic Linear Bandits Etash Guha, Jim James, Krishna Acharya, Vidya Muthukumar, Ashwin Pananjady

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Online Policy Optimization for Robust Markov Decision Process Jing Dong, Jingwei Li, Baoxiang Wang, Jingzhao Zhang

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Optimistic Regret Bounds for Online Learning in Adversarial Markov Decision Processes Sang Bin Moon, Abolfazl Hashemi

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Optimization Framework for Semi-Supervised Attributed Graph Coarsening Manoj Kumar, Subhanu Halder, Archit Kane, Ruchir Gupta, Sandeep Kumar

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Optimizing Language Models for Human Preferences Is a Causal Inference Problem Victoria Lin, Eli Ben-Michael, Louis-Philippe Morency

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Partial Identification of the Maximum Mean Discrepancy with Mismeasured Data Ron Nafshi, Maggie Makar

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Partial Identification with Proxy of Latent Confoundings via Sum-of-Ratios Fractional Programming Zhiheng Zhang, Xinyan Su

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Patch-Prompt Aligned Bayesian Prompt Tuning for Vision-Language Models Xinyang Liu, Dongsheng Wang, Bowei Fang, Miaoge Li, Yishi Xu, Zhibin Duan, Bo Chen, Mingyuan Zhou

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Performative Reinforcement Learning in Gradually Shifting Environments Ben Rank, Stelios Triantafyllou, Debmalya Mandal, Goran Radanovic

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Pix2Code: Learning to Compose Neural Visual Concepts as Programs Antonia Wüst, Wolfgang Stammer, Quentin Delfosse, Devendra Singh Dhami, Kristian Kersting

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Polynomial Semantics of Tractable Probabilistic Circuits Oliver Broadrick, Honghua Zhang, Guy Broeck

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Posterior Inference on Shallow Infinitely Wide Bayesian Neural Networks Under Weights with Unbounded Variance Jorge Loria, Anindya Bhadra

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Power Mean Estimation in Stochastic Monte-Carlo Tree Search Tuan Dam, Odalric-Ambrym Maillard, Emilie Kaufmann

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Privacy-Aware Randomized Quantization via Linear Programming Zhongteng Cai, Xueru Zhang, Mohammad Mahdi Khalili

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Probabilistic Reconciliation of Mixed-Type Hierarchical Time Series Lorenzo Zambon, Dario Azzimonti, Nicolò Rubattu, Giorgio Corani

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Probabilities of Causation for Continuous and Vector Variables Yuta Kawakami, Manabu Kuroki, Jin Tian

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Products, Abstractions and Inclusions of Causal Spaces Simon Buchholz, Junhyung Park, Bernhard Schölkopf

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Publishing Number of Walks and Katz Centrality Under Local Differential Privacy Louis Betzer, Vorapong Suppakitpaisarn, Quentin Hillebrand

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Pure Exploration in Asynchronous Federated Bandits Zichen Wang, Chuanhao Li, Chenyu Song, Lianghui Wang, Quanquan Gu, Huazheng Wang

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Quantifying Local Model Validity Using Active Learning Sven Lämmle, Can Bogoclu, Robert Vosshall, Anselm Haselhoff, Dirk Roos

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Quantifying Representation Reliability in Self-Supervised Learning Models Young-Jin Park, Hao Wang, Shervin Ardeshir, Navid Azizan

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Quantization of Large Language Models with an Overdetermined Basis Daniil Merkulov, Daria Cherniuk, Alexander Rudikov, Ivan Oseledets, Ekaterina Muravleva, Aleksandr Mikhalev, Boris Kashin

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QuantProb: Generalizing Probabilities Along with Predictions for a Pre-Trained Classifier Aditya Challa, Soma Dhavala, Snehanshu Saha

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Quantum Kernelized Bandits Yasunari Hikima, Kazunori Murao, Sho Takemori, Yuhei Umeda

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Random Linear Projections Loss for Hyperplane-Based Optimization in Neural Networks Shyam Venkatasubramanian, Ahmed Aloui, Vahid Tarokh

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RE-SORT: Removing Spurious Correlation in Multilevel Interaction for CTR Prediction Songli Wu, Liang Du, Jiaqi Yang, Yuai Wang, Dechuan Zhan, Shuang Zhao, Zixun Sun

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Recursively-Constrained Partially Observable Markov Decision Processes Qi Heng Ho, Tyler Becker, Benjamin Kraske, Zakariya Laouar, Martin Feather, Federico Rossi, Morteza Lahijanian, Zachary Sunberg

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Reflected Schrödinger Bridge for Constrained Generative Modeling Wei Deng, Yu Chen, Nicole Tianjiao Yang, Hengrong Du, Qi Feng, Ricky Tian Qi Chen

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Response Time Improves Gaussian Process Models for Perception and Preferences Michael Shvartsman, Benjamin Letham, Eytan Bakshy, Stephen Keeley

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Revisiting Convergence of AdaGrad with Relaxed Assumptions Yusu Hong, Junhong Lin

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Revisiting Kernel Attention with Correlated Gaussian Process Representation Long Minh Bui, Tho Tran Huu, Duy Dinh, Tan Minh Nguyen, Trong Nghia Hoang

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Robust Entropy Search for Safe Efficient Bayesian Optimization Dorina Weichert, Alexander Kister, Sebastian Houben, Patrick Link, Gunar Ernis

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Sample Average Approximation for Black-Box Variational Inference Javier Burroni, Justin Domke, Daniel Sheldon

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Shedding Light on Large Generative Networks: Estimating Epistemic Uncertainty in Diffusion Models Lucas Berry, Axel Brando, David Meger

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SMuCo: Reinforcement Learning for Visual Control via Sequential Multi-View Total Correlation Tong Cheng, Hang Dong, Lu Wang, Bo Qiao, Qingwei Lin, Saravan Rajmohan, Thomas Moscibroda

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Sound Heuristic Search Value Iteration for Undiscounted POMDPs with Reachability Objectives Qi Heng Ho, Martin Feather, Federico Rossi, Zachary Sunberg, Morteza Lahijanian

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Statistical and Causal Robustness for Causal Null Hypothesis Tests Junhui Yang, Rohit Bhattacharya, Youjin Lee, Ted Westling

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Stein Random Feature Regression Houston Warren, Rafael Oliveira, Fabio Ramos

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Support Recovery in Sparse PCA with General Missing Data Hanbyul Lee, Qifan Song, Jean Honorio

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Targeted Reduction of Causal Models Armin Kekić, Bernhard Schölkopf, Michel Besserve

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The Real Deal Behind the Artificial Appeal: Inferential Utility of Tabular Synthetic Data Alexander Decruyenaere, Heidelinde Dehaene, Paloma Rabaey, Christiaan Polet, Johan Decruyenaere, Stijn Vansteelandt, Thomas Demeester

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To Smooth a Cloud or to Pin It Down: Expressiveness Guarantees and Insights on Score Matching in Denoising Diffusion Models Teodora Reu, Francisco Vargas, Anna Kerekes, Michael Bronstein

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Towards Bounding Causal Effects Under Markov Equivalence Alexis Bellot

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Towards Minimax Optimality of Model-Based Robust Reinforcement Learning Pierre Clavier, Erwan Le Pennec, Matthieu Geist

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Towards Representation Learning for Weighting Problems in Design-Based Causal Inference Oscar Clivio, Avi Feller, Chris Holmes

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Towards Scalable Bayesian Transformers: Investigating Stochastic Subset Selection for NLP Peter Johannes Tejlgaard Kampen, Gustav Ragnar Stoettrup Als, Michael Riis Andersen

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Transductive and Inductive Outlier Detection with Robust Autoencoders Ofir Lindenbaum, Yariv Aizenbud, Yuval Kluger

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Trusted Re-Weighting for Label Distribution Learning Zhuoran Zheng, Chen Wu, Yeying Jin, Xiuyi Jia

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Two Facets of SDE Under an Information-Theoretic Lens: Generalization of SGD via Training Trajectories and via Terminal States Ziqiao Wang, Yongyi Mao

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Uncertainty Estimation with Recursive Feature Machines Daniel Gedon, Amirhesam Abedsoltan, Thomas B. Schön, Mikhail Belkin

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Understanding Pathologies of Deep Heteroskedastic Regression Eliot Wong-Toi, Alex Boyd, Vincent Fortuin, Stephan Mandt

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Unified PAC-Bayesian Study of Pessimism for Offline Policy Learning with Regularized Importance Sampling Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba

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Unsupervised Feature Selection Towards Pattern Discrimination Power Wangduk Seo, Jaesung Lee

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Using Autodiff to Estimate Posterior Moments, Marginals and Samples Sam Bowyer, Thomas Heap, Laurence Aitchison

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Value-Based Abstraction Functions for Abstraction Sampling Bobak Pezeshki, Kalev Kask, Alexander Ihler, Rina Dechter

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Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions Mai Elkady, Thu Bui, Bruno Ribeiro, David Inouye

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Walking the Values in Bayesian Inverse Reinforcement Learning Ondrej Bajgar, Alessandro Abate, Konstantinos Gatsis, Michael Osborne

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Zero Inflation as a Missing Data Problem: A Proxy-Based Approach Trung Phung, Jaron Lee, Opeyemi Oladapo-Shittu, Eili Klein, Ayse Gurses, Susan Hannum, Kimberly Weems, Jill Marsteller, Sara Cosgrove, Sara Keller, Ilya Shpitser

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