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ORCID41st UAI 2025: Rio de Janeiro, Brazil
- Silvia Chiappa, Sara Magliacane:
Conference on Uncertainty in Artificial Intelligence, Rio Othon Palace, Rio de Janeiro, Brazil, 21-25 July 2025. Proceedings of Machine Learning Research 286, PMLR 2025 - Sushant Agarwal, Yukti Makhija, Rishi Saket, Aravindan Raghuveer:
Aggregating Data for Optimal Learning. 1-30 - Johan de Aguas, Leonard Henckel, Johan Pensar, Guido Biele:
Causal Inference amid Missingness-Specific Independences and Mechanism Shifts. 31-44 - Ömer Faruk Akgül, Rajgopal Kannan, Viktor K. Prasanna:
Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information. 45-63 - Ahmed Aloui, Juncheng Dong, Cat Phuoc Le, Vahid Tarokh:
CATE Estimation With Potential Outcome Imputation From Local Regression. 64-90 - Ahmed Aloui, Juncheng Dong, Ali Hasan, Vahid Tarokh:
Conditional Average Treatment Effect Estimation Under Hidden Confounders. 91-110 - Sangho An, Jinwoo Kim, Keonho Lee, Jingang Huh, Chanwoong Kwak, Yujin Lee, Moonsub Jin, Jangho Kim:
Sparse Structure Exploration and Re-optimization for Vision Transformer. 111-131 - Roman Andriushchenko, Milan Ceska, Debraj Chakraborty, Sebastian Junges, Jan Kretínský, Filip Macák:
Symbiotic Local Search for Small Decision Tree Policies in MDPs. 132-148 - Gayathri Anil, Prashant Doshi, Daniel Redder, Adam Eck, Leen-Kiat Soh:
MOHITO: Multi-Agent Reinforcement Learning using Hypergraphs for Task-Open Systems. 149-171 - Ankur Ankan, Johannes Textor:
Expert-In-The-Loop Causal Discovery: Iterative Model Refinement Using Expert Knowledge. 172-183 - Pablo G. Arce, Roi Naveiro, David Ríos Insua:
Evasion Attacks Against Bayesian Predictive Models. 184-202 - Marcel Arpogaus, Thomas Kneib, Thomas Nagler, David Rügamer:
Hybrid Bernstein Normalizing Flows for Flexible Multivariate Density Regression with Interpretable Marginals. 203-222 - Ali Asadi, Krishnendu Chatterjee, Jakob de Raaij:
Lower Bound on Howard Policy Iteration for Deterministic Markov Decision Processes. 223-237 - Ali Asadi, Krishnendu Chatterjee, Raimundo Saona, Ali Shafiee:
Limit-sure Reachability for Small Memory Policies in POMDPs is NP-complete. 238-256 - Yoshua Bengio, Michael K. Cohen, Nikolay Malkin, Matt MacDermott, Damiano Fornasiere, Pietro Greiner, Younesse Kaddar:
Can a Bayesian Oracle Prevent Harm from an Agent? 257-270 - Sourbh Bhadane, Joris M. Mooij, Philip A. Boeken, Onno Zoeter:
Revisiting the Berkeley Admissions data: Statistical Tests for Causal Hypotheses. 271-295 - Jie Bian, Vincent Y. F. Tan:
Asymptotically Optimal Linear Best Feasible Arm Identification with Fixed Budget. 296-331 - Revoti Prasad Bora, Philipp Terhörst, Raymond N. J. Veldhuis, Raghavendra Ramachandra, Kiran Bylappa Raja:
BELIEF - Bayesian Sign Entropy Regularization for LIME Framework. 332-354 - Alexander Bork, Joost-Pieter Katoen, Tim Quatmann, Svenja Stein:
Multi-Cost-Bounded Reachability Analysis of POMDPs. 355-387 - Louenas Bounia:
Using Submodular Optimization to Approximate Minimum-Size Abductive Path Explanations for Tree-Based Models. 388-397 - Cornelius V. Braun, Robert Tjarko Lange, Marc Toussaint:
Stein Variational Evolution Strategies. 398-420 - Gecia Bravo Hermsdorff, Kayvan Sadeghi, Lee M. Gunderson:
Causal Models for Growing Networks. 421-442 - Luben M. C. Cabezas, Vagner S. Santos, Thiago Ramos, Rafael Izbicki:
Epistemic Uncertainty in Conformal Scores: A Unified Approach. 443-470 - Penglin Cai, Chi Zhang, Yuhui Fu, Haoqi Yuan, Zongqing Lu:
Creative Agents: Empowering Agents with Imagination for Creative Tasks. 471-496 - Jian-Feng Cai, Tong Wu, Ruizhe Xia:
Fast Non-convex Matrix Sensing with Optimal Sample Complexity. 497-520 - Zhongze Cai, Hansheng Jiang, Xiaocheng Li:
Out-of-distribution Robust Optimization. 521-539 - Emma Ceccherini, Ian Gallagher, Andrew Jones, Daniel John Lawson:
Unsupervised Attributed Dynamic Network Embedding with Stability Guarantees. 540-567 - Xiang Chen, Kun Yue, Liang Duan, Lixing Yu:
Improving Graph Contrastive Learning with Community Structure. 568-585 - Jacob M. Chen, Michael Oberst:
Just Trial Once: Ongoing Causal Validation of Machine Learning Models. 586-611 - Wenjing Chen, Shuo Xing, Victoria G. Crawford:
Adaptive Threshold Sampling for Pure Exploration in Submodular Bandits. 612-646 - Naitong Chen, Jonathan H. Huggins, Trevor Campbell:
Tuning-Free Coreset Markov Chain Monte Carlo via Hot DoG. 647-672 - Bo Chen, Chengyue Gong, Xiaoyu Li, Yingyu Liang, Zhizhou Sha, Zhenmei Shi, Zhao Song, Mingda Wan, Xugang Ye:
NRFlow: Towards Noise-Robust Generative Modeling via High-Order Mechanism. 673-704 - Yuen Chen, Haozhe Si, Guojun Zhang, Han Zhao:
Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization. 705-736 - Yoonhyuk Choi, Taewook Ko, Jiho Choi, Chong-Kwon Kim:
Selective Blocking for Message-Passing Neural Networks on Heterophilic Graphs. 737-751 - Tristan Cinquin, Robert Bamler:
Well-Defined Function-Space Variational Inference in Bayesian Neural Networks via Regularized KL-Divergence. 752-776 - Adrian Ciotinga, YooJung Choi:
Optimal Transport for Probabilistic Circuits. 777-797 - Lucas Clarté, Lenka Zdeborová:
Building Conformal Prediction Intervals with Approximate Message Passing. 798-820 - Michael K. Cohen, Marcus Hutter, Yoshua Bengio, Stuart Russell:
RL, but don't do anything I wouldn't do. 821-836 - Hugo Sales Correa, Suryanarayana Sankagiri, Daniel R. Figueiredo, Matthias Grossglauser:
Measuring IIA Violations in Similarity Choices with Bayesian Models. 837-862 - Gabriele D'Acunto, Claudio Battiloro:
The Relativity of Causal Knowledge. 863-881 - Puranjay Datta, Sharayu Moharir, Jaya Prakash Champati:
Online Learning with Stochastically Partitioning Experts. 882-896 - Sina Däubener, Simon Damm, Asja Fischer:
ELBO, regularized maximum likelihood, and their common one-sample approximation for training stochastic neural networks. 897-914 - Zhou Derun, Mahito Sugiyama:
Optimal Submanifold Structure in Log-linear Models. 915-932 - Shachi Deshpande, Charles Marx, Volodymyr Kuleshov:
Calibrated Regression Against An Adversary Without Regret. 933-958 - Francesco Diana, André Nusser, Chuan Xu, Giovanni Neglia:
Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning. 959-980 - Emerald Dilworth, Ed Davis, Daniel John Lawson:
Valid Bootstraps for Network Embeddings with Applications to Network Visualisation. 981-1002 - Meng Ding, Mingxi Lei, Shaowei Wang, Tianhang Zheng, Di Wang, Jinhui Xu:
Nearly Optimal Differentially Private ReLU Regression. 1003-1038 - Simon Dirmeier, Carlo Albert, Fernando Pérez-Cruz:
Simulation-based Inference for High-dimensional Data using Surjective Sequential Neural Likelihood Estimation. 1039-1063 - Mathias Drton, Marina Garrote-López, Niko Nikov, Elina Robeva, Y. Samuel Wang:
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles. 1064-1083 - Ally Yalei Du, Eric Huang, Dravyansh Sharma:
Tuning Algorithmic and Architectural Hyperparameters in Graph-Based Semi-Supervised Learning with Provable Guarantees. 1084-1111 - Jane Du, Arindam Banerjee:
Computationally Efficient Methods for Invariant Feature Selection with Sparsity. 1112-1120 - Liang Duan, Xinran Wu, Xinhui Li, Lixing Yu, Kun Yue:
Probabilistic Semantics Guided Discovery of Approximate Functional Dependencies. 1121-1134 - Homer Durand, Gherardo Varando, Gustau Camps-Valls:
Learning Causal Response Representations through Direct Effect Analysis. 1135-1166 - Aram Ebtekar, Yuhao Wang, Dominik Janzing:
Toward Universal Laws of Outlier Propagation. 1167-1183 - Yousef El-Laham, Niccolò Dalmasso, Svitlana Vyetrenko, Vamsi K. Potluru, Manuela Veloso:
Mixup Regularization: A Probabilistic Perspective. 1184-1219 - Paula Cordero-Encinar, Francesca R. Crucinio, Ömer Deniz Akyildiz:
Proximal Interacting Particle Langevin Algorithms. 1220-1265 - Yassir Fathullah, Mark J. F. Gales:
Generalised Probabilistic Modelling and Improved Uncertainty Estimation in Comparative LLM-as-a-judge. 1266-1288 - Pablo Flores, Olga Graf, Pavlos Protopapas, Karim Pichara:
Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution Bundles. 1289-1336 - Tobias Fuchs, Florian Kalinke:
Partial-Label Learning with Conformal Candidate Cleaning. 1337-1357 - Swetha Ganesh, Jiayu Chen, Washim Uddin Mondal, Vaneet Aggarwal:
Order-Optimal Global Convergence for Actor-Critic with General Policy and Neural Critic Parametrization. 1358-1380 - Yeqi Gao, Zhao Song, Weixin Wang, Junze Yin:
A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time. 1381-1452 - Konstantin Göbler, Tobias Windisch, Mathias Drton:
Nonlinear Causal Discovery for Grouped Data. 1453-1475 - Jeremy Goldwasser, Giles Hooker:
Statistical Significance of Feature Importance Rankings. 1476-1496 - Eduardo Dadalto Câmara Gomes, Marco Romanelli:
Optimal Zero-shot Regret Minimization for Selective Classification with Out-of-Distribution Detection. 1497-1520 - Lluis Gomez:
Over the Top-1: Uncertainty-Aware Cross-Modal Retrieval with CLIP. 1521-1532 - Tanmay Goyal, Gaurav Sinha:
Efficient Algorithms for Logistic Contextual Slate Bandits with Bandit Feedback. 1533-1568 - Ander Gray, Vignesh Gopakumar, Sylvain Rousseau, Sébastien Destercke:
Guaranteed Prediction Sets for Functional Surrogate Models. 1569-1585 - Junyi Guan, Abhijith Sharma, Chong Tian, Salem Lahlou:
On the Privacy Risks of Spiking Neural Networks: A Membership Inference Analysis. 1586-1599 - Aaryan Gupta, Rishi Saket:
Learning Algorithms for Multiple Instance Regression. 1600-1615 - Sungmin Han, Jeonghyun Lee, Sangkyun Lee:
Contrast-CAT: Contrasting Activations for Enhanced Interpretability in Transformer-based Text Classifiers. 1616-1625 - Jonas Hanselle, Alireza Javanmardi, Tobias Florin Oberkofler, Yusuf Sale, Eyke Hüllermeier:
Conformal Prediction without Nonconformity Scores. 1626-1639 - Juha Harviainen, Kseniya Rychkova, Mikko Koivisto:
Quantum Speedups for Bayesian Network Structure Learning. 1640-1647 - Atif Hassan, Swanand R. Khare, Jiaul H. Paik:
RCAP: Robust, Class-Aware, Probabilistic Dynamic Dataset Pruning. 1648-1662 - Atif Hassan, Jiaul H. Paik, Swanand R. Khare:
SPvR: Structured Pruning via Ranking. 1663-1676 - Sujai Hiremath, Promit Ghosal, Kyra Gan:
LoSAM: Local Search in Additive Noise Models with Mixed Mechanisms and General Noise for Global Causal Discovery. 1677-1709 - Thi Kieu Khanh Ho, Narges Armanfard:
Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Models. 1710-1729 - Mengjian Hua, Eric Vanden-Eijnden, Ricky T. Q. Chen:
Simulation-Free Differential Dynamics Through Neural Conservation Laws. 1730-1744 - Ruiquan Huang, Donghao Li, Chengshuai Shi, Cong Shen, Jing Yang:
Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis. 1745-1767 - Michael Ibrahim, Heraldo Rozas, Nagi Gebraeel, Weijun Xie:
FDR-SVM: A Federated Distributionally Robust Support Vector Machine via a Mixture of Wasserstein Balls Ambiguity Set. 1768-1793 - Azam Ikram, Kenneth Lee, Shubham Agarwal, Shiv Kumar Saini, Saurabh Bagchi, Murat Kocaoglu:
Root Cause Analysis of Failures from Partial Causal Structures. 1794-1818 - Erik Jahn, Frederick Eberhardt, Leonard J. Schulman:
Lower Bounds on the Size of Markov Equivalence Classes. 1819-1836 - Metod Jazbec, Eliot Wong-Toi, Guoxuan Xia, Dan Zhang, Eric T. Nalisnick, Stephan Mandt:
Generative Uncertainty in Diffusion Models. 1837-1858 - Zhongli Jiang, Min Zhang, Dabao Zhang:
Fast Calculation of Feature Contributions in Boosting Trees. 1859-1875 - Zhengxuan Jiang, Guowen Ding, Wen Jiang:
Coevolutionary Emergent Systems Optimization with Applications to Ultra-High-Dimensional Metasurface Design : OAM Wave Manipulation. 1876-1894 - Jiechuan Jiang, Zongqing Lu:
Best Possible Q-Learning. 1895-1908 - Sami Jullien, Romain Deffayet, Jean-Michel Renders, Paul Groth, Maarten de Rijke:
Distributional Reinforcement Learning with Dual Expectile-Quantile Regression. 1909-1923 - Avik Kar, Rahul Singh:
Provably Adaptive Average Reward Reinforcement Learning for Metric Spaces. 1924-1964 - Avetik G. Karagulyan, Peter Richtárik:
ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression. 1965-1989 - Kevin Kasa, Zhiyu Zhang, Heng Yang, Graham W. Taylor:
Adapting Prediction Sets to Distribution Shifts Without Labels. 1990-2010 - Yuta Kawakami, Jin Tian:
Moments of Causal Effects. 2011-2043 - Yuta Kawakami, Jin Tian:
Decomposition of Probabilities of Causation with Two Mediators. 2044-2068 - David A. Kelly, Hana Chockler, Nathan Blake:
Explaining Negative Classifications of AI Models in Tumor Diagnosis. 2069-2081 - Batya Kenig:
Enumerating Optimal Cost-Constrained Adjustment Sets. 2082-2100 - Mert Ketenci, Adler J. Perotte, Noémie Elhadad, Iñigo Urteaga:
Accurate and Scalable Stochastic Gaussian Process Regression via Learnable Coreset-based Variational Inference. 2101-2142 - Mohammadsadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash, Niao He, Matthias Grossglauser:
Efficiently Escaping Saddle Points for Policy Optimization. 2143-2162 - Kyung Rok Kim, Yansong Wang, Xiaocheng Li, Guanting Chen:
Collaborative Prediction: To Join or To Disjoin Datasets. 2163-2201 - Hwanwoo Kim, Chong Liu, Yuxin Chen:
Bayesian Optimization with Inexact Acquisition: Is Random Grid Search Sufficient? 2202-2222 - Yaroslav Kivva, Sina Akbari, Saber Salehkaleybar, Negar Kiyavash:
Causal Effect Identification in Heterogeneous Environments from Higher-Order Moments. 2223-2254 - Prodromos Kolyvakis, Aristidis Likas:
A Multivariate Unimodality Test Harnessing the Dip Statistic of Mahalanobis Distances Over Random Projections. 2255-2268 - Lingkai Kong, Wenhao Mu, Jiaming Cui, Yuchen Zhuang, B. Aditya Prakash, Bo Dai, Chao Zhang:
DF2: Distribution-Free Decision-Focused Learning. 2269-2290 - Chun-Wei Kong, Luca Laurenti, Jay W. McMahon, Morteza Lahijanian:
Error Bounds for Physics-Informed Neural Networks in Fokker-Planck PDEs. 2291-2324 - Lingkai Kong, Haichuan Wang, Yuqi Pan, Cheol Woo Kim, Mingxiao Song, Alayna Nguyen, Tonghan Wang, Haifeng Xu, Milind Tambe:
Robust Optimization with Diffusion Models for Green Security. 2325-2344 - Frédéric Koriche, Jean-Marie Lagniez, Chi Tran:
Probabilistic Explanations for Regression Models. 2345-2362 - Dmitry Kovalev, Alexander V. Gasnikov, Grigory Malinovsky:
An Optimal Algorithm for Strongly Convex Min-Min Optimization. 2363-2379 - Adithya Kulkarni, Mohna Chakraborty, Sihong Xie, Qi Li:
Budget Allocation Exploiting Label Correlation between Instances. 2380-2395 - Syamantak Kumar, Shourya Pandey, Purnamrita Sarkar:
Beyond Sin-Squared Error: Linear Time Entrywise Uncertainty Quantification for Streaming PCA. 2396-2430 - Leander Kurscheidt, Paolo Morettin, Roberto Sebastiani, Andrea Passerini, Antonio Vergari:
A Probabilistic Neuro-symbolic Layer for Algebraic Constraint Satisfaction. 2431-2471 - Minjae Kwon, Ingy Elsayed-Aly, Lu Feng:
Adaptive Reward Design for Reinforcement Learning. 2472-2485 - Kenneth Lee, Bruno Ribeiro, Murat Kocaoglu:
Constraint-based Causal Discovery from a Collection of Conditioning Sets. 2486-2516 - Zhechen Li, Ao Liu, Lirong Xia, Yongzhi Cao, Hanpin Wang:
Trading Off Voting Axioms for Privacy. 2517-2536 - Zhaoye Li, Siyuan Shen, Wenjing Yang, Ruochun Jin, Huan Chen, Ligong Cao, Jing Ren:
Enhancing Uncertainty Quantification in Large Language Models through Semantic Graph Density. 2537-2551 - Jiehao Liang, Zhao Song, Zhaozhuo Xu, Junze Yin, Danyang Zhuo:
Dynamic Maintenance of Kernel Density Estimation Data Structure: From Practice to Theory. 2552-2581 - Sungjun Lim, Jeyoon Yeom, Sooyon Kim, Hoyoon Byun, Jinho Kang, Yohan Jung, Jiyoung Jung, Kyungwoo Song:
Flat Posterior Does Matter For Bayesian Model Averaging. 2582-2617 - I-Cheng Lin, Osman Yagan, Carlee Joe-Wong:
FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning. 2618-2641 - Putri A. van der Linden, Alexander Timans, Erik J. Bekkers:
CP2: Leveraging Geometry for Conformal Prediction via Canonicalization. 2642-2658 - Terrance Liu, Steven Wu:
Multi-group Uncertainty Quantification for Long-form Text Generation. 2659-2684 - Weixiong Liu, Junwei Cheng, Zhongyu Pan, Chaobo He, Quanlong Guan:
DyGMAE: A Novel Dynamic Graph Masked Autoencoder for Link Prediction. 2685-2700 - Wuyi Liu, Yue Gao, Yige Mao, Jing Zhao:
Letting Uncertainty Guide Your Multimodal Machine Translation. 2701-2710 - Zhuqing Liu, Chaosheng Dong, Michinari Momma, Simone Shao, Shaoyuan Xu, Yan Gao, Haibo Yang, Jia Liu:
STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning. 2711-2747 - Yumou Liu, An Li, Chaojie Li, Fei Yu, Benyou Wang:
Periodical Moving Average Accelerates Gradient Accumulation for Post-Training. 2748-2768 - Tianci Liu, Tong Yang, Quan Zhang, Qi Lei:
Beyond Invisibility: Learning Robust Visible Watermarks for Stronger Copyright Protection. 2769-2785 - Song Liu, Leyang Wang, Yakun Wang:
Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold. 2786-2803 - Zhiyong Ma, Yuanjie Shi, Yan Yan, Jian Chen:
Federated Rényi Fair Inference in Federated Heterogeneous System. 2804-2843 - Ilia Mahrooghi, Mahshad Moradi, Sina Akbari, Negar Kiyavash:
Multi-armed Bandits with Missing Outcomes. 2844-2875 - Yukti Makhija, Rishi Saket:
Weak to Strong Learning from Aggregate Labels. 2876-2891 - Owais Makroo, Atif Hassan, Swanand R. Khare:
SALSA: A Secure, Adaptive and Label-Agnostic Scalable Algorithm for Machine Unlearning. 2892-2905 - Daniel de Vassimon Manela, Linying Yang, Robin J. Evans:
Testing Generalizability in Causal Inference. 2906-2927 - Arto Maranjyan, Omar Shaikh Omar, Peter Richtárik:
MindFlayer SGD: Efficient Parallel SGD in the Presence of Heterogeneous and Random Worker Compute Times. 2928-2957 - Myrl G. Marmarelis, Ali Hasan, Kamyar Azizzadenesheli, R. Michael Alvarez, Anima Anandkumar:
Off-policy Predictive Control with Causal Sensitivity Analysis. 2958-2972 - María Martínez-García, Grace Villacrés, David G. M. Mitchell, Pablo M. Olmos:
Improved Variational Inference in Discrete VAEs using Error Correcting Codes. 2973-3012 - Pedro Zuidberg Dos Martires:
A Quantum Information Theoretic Approach to Tractable Probabilistic Models. 3013-3030 - Aayush Mishra, Anqi Liu:
ODD: Overlap-aware Estimation of Model Performance under Distribution Shift. 3031-3047 - Panagiotis Misiakos, Markus Püschel:
SpinSVAR: Estimating Structural Vector Autoregression Assuming Sparse Input. 3048-3092 - Gleb Molodtsov, Valery Parfenov, Egor Petrov, Grigoriy Evseev, Daniil Medyakov, Aleksandr Beznosikov:
When Extragradient Meets PAGE: Bridging Two Giants to Boost Variational Inequalities. 3093-3122 - Matteo Negro, Andrea Piras, Ragib Ahsan, David Arbour, Elena Zheleva:
Relational Causal Discovery with Latent Confounders. 3123-3154 - Kenyon Ng, Chris van der Heide, Liam Hodgkinson, Susan Wei:
Temperature Optimization for Bayesian Deep Learning. 3155-3181 - Dai Hai Nguyen, Hiroshi Mamitsuka, Atsuyoshi Nakamura:
Multiple Wasserstein Gradient Descent Algorithm for Multi-Objective Distributional Optimization. 3182-3199 - Anthony Nguyen, Emanuel Aldea, Sylvie Le Hégarat-Mascle, Renaud Lustrat:
Stochastic Embeddings : A Probabilistic and Geometric Analysis of Out-of-Distribution Behavior. 3200-3220 - Huy Hoang Nguyen, Han Zhou, Matthew B. Blaschko, Aleksei Tiulpin:
Bayesian Optimization over Bounded Domains with the Beta Product Kernel. 3221-3234 - Xuying Ning, Wujiang Xu, Tianxin Wei, Xiaolei Liu:
i2VAE: Interest Information Augmentation with Variational Regularizers for Cross-Domain Sequential Recommendation. 3235-3251 - Louis Ohl, Fredrik Lindsten:
Discriminative ordering through ensemble consensus. 3252-3271 - Azim Ospanov, Farzan Farnia:
Do Vendi Scores Converge with Finite Samples? Truncated Vendi Score for Finite-Sample Convergence Guarantees. 3272-3299 - Ryohei Oura, Yuji Ito:
Probability-Raising Causality for Uncertain Parametric Markov Decision Processes with PAC Guarantees. 3300-3321 - Yicheng Pan, Bingchen Fan, Pengyu Long, Feng Zheng:
An Information-theoretic Perspective of Hierarchical Clustering on Graphs. 3322-3345 - Subhodip Panda, Ananda Theertha Suresh, Atri Guha, Prathosh A. P.:
Concept Forgetting via Label Annealing. 3346-3360 - Andrei Panferov, Yury Demidovich, Ahmad Rammal, Peter Richtárik:
Correlated Quantization for Faster Nonconvex Distributed Optimization. 3361-3387 - Leonard Papenmeier, Nuojin Cheng, Stephen Becker, Luigi Nardi:
Exploring Exploration in Bayesian Optimization. 3388-3415 - Milan Papez, Martin Rektoris, Václav Smídl, Tomás Pevný:
Probabilistic Graph Circuits: Deep Generative Models for Tractable Probabilistic Inference over Graphs. 3416-3450 - Liam Pavlovic, David M. Rosen:
A Trust-Region Method for Graphical Stein Variational Inference. 3451-3464 - Anne Helby Petersen:
Are You Doing Better Than Random Guessing? A Call for Using Negative Controls When Evaluating Causal Discovery Algorithms. 3465-3479 - Yuanyuan Qi, Jueqing Lu, Xiaohao Yang, Joanne Enticott, Lan Du:
Multi-Label Bayesian Active Learning with Inter-Label Relationships. 3480-3491 - Chen Qiu, Haobo Fu, Kai Li, Jiajia Zhang, Xuan Wang:
Enhanced Equilibria-Solving via Private Information Pre-Branch Structure in Adversarial Team Games. 3492-3506 - Md. Musfiqur Rahman, Murat Kocaoglu:
FeDCM: Federated Learning of Deep Causal Generative Models. 3507-3524 - Yinuo Ren, Tesi Xiao, Michael Shavlovsky, Lexing Ying, Holakou Rahmanian:
COS-DPO: Conditioned One-Shot Multi-Objective Fine-Tuning Framework. 3525-3551 - Oliver E. Richardson:
Learning with Confidence. 3552-3569 - Paula Rodriguez Diaz, Lingkai Kong, Kai Wang, David Alvarez-Melis, Milind Tambe:
What is the Right Notion of Distance between Predict-then-Optimize Tasks? 3570-3586 - Colin Samplawski, Adam D. Cobb, Manoj Acharya, Ramneet Kaur, Susmit Jha:
Scalable Bayesian Low-Rank Adaptation of Large Language Models via Stochastic Variational Subspace Inference. 3587-3604 - Kajetan Schweighofer, Lukas Aichberger, Mykyta Ielanskyi, Sepp Hochreiter:
On Information-Theoretic Measures of Predictive Uncertainty. 3605-3640 - Aras Selvi, Eleonora Kreacic, Mohsen Ghassemi, Vamsi K. Potluru, Tucker Balch, Manuela Veloso:
Distributionally and Adversarially Robust Logistic Regression via Intersecting Wasserstein Balls. 3641-3674 - Marcin Sendera, Amin Sorkhei, Tomasz Kusmierczyk:
Revisiting the Equivalence of Bayesian Neural Networks and Gaussian Processes: On the Importance of Learning Activations. 3675-3700 - Jonas Seng, Florian Peter Busch, Pooja Prasad, Devendra Singh Dhami, Martin Mundt, Kristian Kersting:
Scaling Probabilistic Circuits via Data Partitioning. 3701-3717 - Hooman Shahrokhi, Devjeet Raj Roy, Yan Yan, Venera Arnaoudova, Jana Doppa:
Conformal Prediction Sets for Deep Generative Models via Reduction to Conformal Regression. 3718-3748 - Anup Shakya, Abisha Thapa Magar, Somdeb Sarkhel, Deepak Venugopal:
Reparameterizing Hybrid Markov Logic Networks to handle Covariate-Shift in Representations. 3749-3765 - Jin Shang, Simone Shao, Tian Tong, Fan Yang, Yetian Chen, Yang Jiao, Jia Liu, Yan Gao:
Divide and Orthogonalize: Efficient Continual Learning with Local Model Space Projection. 3766-3786 - Shiv Shankar, Ritwik Sinha, Madalina Fiterau:
Experimentation under Treatment Dependent Network Interference. 3787-3808 - Atri Vivek Sharma, Panagiotis Kouvaros, Alessio Lomuscio:
Learning Robust XGBoost Ensembles for Regression Tasks. 3809-3825 - Zhaoyang Shi, Krishna Balasubramanian, Wolfgang Polonik:
Minimax Optimal Nonsmooth Nonparametric Regression via Fractional Laplacian Eigenmaps. 3826-3845 - Sungbin Shin, Dongyeop Lee, Maksym Andriushchenko, Namhoon Lee:
Critical Influence of Overparameterization on Sharpness-aware Minimization. 3846-3877 - Francisco Nunes Ferreira Quialheiro Simoes, Mehdi Dastani, Thijs van Ommen:
The Causal Information Bottleneck and Optimal Causal Variable Abstractions. 3878-3897 - Anurag Singh, Siu Lun Chau, Krikamol Muandet:
Truthful Elicitation of Imprecise Forecasts. 3898-3919 - Sagalpreet Singh, Navodita Sharma, Shreyas Havaldar, Rishi Saket, Aravindan Raghuveer:
Learning from Label Proportions and Covariate-shifted Instances. 3920-3938 - Aleksanteri M. Sladek, Martin Trapp, Arno Solin:
Approximate Bayesian Inference via Bitstring Representations. 3939-3957 - Sabina J. Sloman, Julien Martinelli, Samuel Kaski:
Proxy-informed Bayesian transfer learning with unknown sources. 3958-3978 - Amir Sonee, Haripriya Harikumar, Alex Hämäläinen, Lukas Prediger, Samuel Kaski:
Privacy-Preserving Neural Processes for Probabilistic User Modeling. 3979-3998 - Jialei Song, Xingquan Zuo, Feiyang Wang, Hai Huang, Tianle Zhang:
RDI: An adversarial robustness evaluation metric for deep neural networks based on model statistical features. 3999-4012 - Jared Soundy, Mohammad T. Irfan, Hau Chan:
Pure and Strong Nash Equilibrium Computation in Compactly Representable Aggregate Games. 4013-4033 - Patrick Stinson, Nikolaus Kriegeskorte:
Nonparametric Bayesian inference of item-level features in classifier combination. 4034-4043 - Alexander Sturm, Sebastian Tschiatschek:
On Constant Regret for Low-Rank MDPs. 4044-4079 - Prasanth Sengadu Suresh, Prashant Doshi, Bikramjit Banerjee:
Adaptive Human-Robot Collaboration using Type-Based IRL. 4080-4091 - Hiwot Belay Tadesse, Alihan Hüyük, Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez:
Transparent Trade-offs between Properties of Explanations. 4092-4112 - Jialu Tang, Yali Gao, Xiaoyong Li, Jiawei Li, Shui Yu, Binxing Fang:
FALCON: Adaptive Cross-Domain APT Attack Investigation with Federated Causal Learning. 4113-4131 - Shiqin Tang, Shujian Yu:
InfoDPCCA: Information-Theoretic Dynamic Probabilistic Canonical Correlation Analysis. 4132-4144 - Gokcan Tatli, Yi Chen, Blake Mason, Robert D. Nowak, Ramya Korlakai Vinayak:
Metric Learning in an RKHS. 4145-4164 - Xunye Tian, Liuhua Peng, Zhijian Zhou, Mingming Gong, Arthur Gretton, Feng Liu:
A Unified Data Representation Learning for Non-parametric Two-sample Testing. 4165-4184 - Runzhi Tian, Yongyi Mao:
Adversarial Training May Induce Deteriorating Distributions. 4185-4203 - Alexander Timans, Rajeev Verma, Eric T. Nalisnick, Christian A. Naesseth:
On Continuous Monitoring of Risk Violations under Unknown Shift. 4204-4226 - Sana Tonekaboni, Tina Behrouzi, Addison Weatherhead, Emily B. Fox, David M. Blei, Anna Goldenberg:
HDP-Flow: Generalizable Bayesian Nonparametric Model for Time Series State Discovery. 4227-4250 - Nhu-Thuat Tran, Hady W. Lauw:
Optimal Transport Alignment of User Preferences from Ratings and Texts. 4251-4265 - Dat Phan-Trong, Hung The Tran, Sunil Gupta:
Black-box Optimization with Unknown Constraints via Overparameterized Deep Neural Networks. 4266-4289 - Florian Valade, Mohamed Hebiri, Paul Gay:
EERO: Early Exit with Reject Option for Efficient Classification with limited budget. 4290-4308 - Aishwarya Venkataramanan, Paul Bodesheim, Joachim Denzler:
Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models. 4309-4328 - Janneke Verbeek, Tom Heskes, Yuliya Shapovalova:
Offline Changepoint Detection With Gaussian Processes. 4329-4348 - Veniamin Veselovsky, Benedikt Stroebl, Gianluca M. Bencomo, Dilip Arumugam, Lisa Schut, Arvind Narayanan, Thomas L. Griffiths:
Hindsight Merging: Diverse Data Generation with Language Models. 4349-4369 - Wenyu Wang, Zheyi Fan, Szu Hui Ng:
A Trajectory-Based Bayesian Approach to Multi-Objective Hyperparameter Optimization with Epoch-Aware Trade-Offs. 4370-4394 - Xiaohan Wang, Yunzhe Zhou, Giles Hooker:
Targeted Learning for Variable Importance. 4395-4410 - Luwei Wang, Kieran Richards, Sohan Seth:
Nonparametric Bayesian Multi-Facet Clustering for Longitudinal Data. 4411-4442 - Shiyi Wang, Yang Nan, Xiaodan Xing, Yingying Fang, Simon L. F. Walsh, Guang Yang:
A Parallel Network for LRCT Segmentation and Uncertainty Mitigation with Fuzzy Sets. 4443-4457 - Jie Wang, Ning Xu, Xin Geng:
VADIS: Investigating Inter-View Representation Biases for Multi-View Partial Multi-Label Learning. 4458-4471 - Zifan Wang, Jingwei Li, Yitang Li, Yunze Liu:
MutualNeRF: Improve the Performance of NeRF under Limited Samples with Mutual Information Theory. 4472-4488 - Yancheng Wang, Rajeev Goel, Marko Jojic, Alvin C. Silva, Teresa Wu, Yingzhen Yang:
Informative Synthetic Data Generation for Thorax Disease Classification. 4489-4514 - Shuyang Wang, Diego Klabjan:
A Mirror Descent Perspective of Smoothed Sign Descent. 4515-4542 - Bernardo Williams, Hanlin Yu, Hoang Phuc Hau Luu, Georgios Arvanitidis, Arto Klami:
Geodesic Slice Sampler for Multimodal Distributions with Strong Curvature. 4543-4564 - Ruoyu Wu, Wei Bao, Ben Liang, Liming Ge:
Online Generalized Magician's Problem with Multiple Workers. 4565-4596 - Kaiyue Wu, Xiao-Jun Zeng, Tingting Mu:
Group-Agent Reinforcement Learning with Heterogeneous Agents. 4597-4617 - Lan Wu, Xuebin Wang, Ruijuan Chu, Guangyi Liu, Jing Zhang, Linyu Wang:
FlightPatchNet: Multi-Scale Patch Network with Differential Coding for Short-Term Flight Trajectory Prediction. 4618-4635 - Quan Xiao, Debarun Bhattacharjya, Balaji Ganesan, Radu Marinescu, Katsiaryna Mirylenka, Nhan H. Pham, Michael R. Glass, Junkyu Lee:
The Consistency Hypothesis in Uncertainty Quantification for Large Language Models. 4636-4651 - Chunjing Xiao, Ranhao Guo, Zhang Yongwang, Xiaoming Wu:
Learning Multi-interest Embedding with Dynamic Graph Cluster for Sequention Recommendation. 4652-4662 - Jian Xu, Shian Du, Junmei Yang, Qianli Ma, Delu Zeng, John Paisley:
Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling. 4663-4680 - Khurram Yamin, Edward Kennedy, Bryan Wilder:
Dependent Randomized Rounding for Budget Constrained Experimental Design. 4681-4700 - Wentao Yang, Xinyue Liu, Yunlong Gao, Wenxin Liang, Linlin Zong, Guanglu Wang, Xianchao Zhang:
Full Network Capacity Framework for Sample-Efficient Deep Reinforcement Learning. 4701-4714 - Le Yang, Vincent Y. F. Tan, Wang Chi Cheung:
Best Arm Identification with Possibly Biased Offline Data. 4715-4730 - Xian Yang, Zhenguo Zhang, Shihao Lu:
MSCGrapher: Learning Multi-Scale Dynamic Correlations for Multivariate Time Series Forecasting. 4731-4751 - Chao Yang, Wendi Ren, Shuang Li:
Flow-Based Delayed Hawkes Process. 4752-4774 - Binghua Yao, Joris M. Mooij:
σ-Maximal Ancestral Graphs. 4775-4805 - Ziqi Yu, Lirong Xia, Qishen Han, Chengkai Zhang:
How Likely Are Two Voting Rules Different? 4806-4825 - Qingyuan Yu, Euijin Baek, Xiang Li, Qiang Sun:
Corruption-Robust Variance-aware Algorithms for Generalized Linear Bandits under Heavy-tailed Rewards. 4826-4843 - Clément Yvernes, Emilie Devijver, Éric Gaussier:
Complete Characterization for Adjustment in Summary Causal Graphs of Time Series. 4844-4871 - Daokun Zhang, Russell Tsuchida, Dino Sejdinovic:
Label Distribution Learning using the Squared Neural Family on the Probability Simplex. 4872-4888 - Ziyi Zhang, Yorie Nakahira, Guannan Qu:
Learning to Stabilize Unknown LTI Systems on a Single Trajectory under Stochastic Noise. 4889-4919 - Yunrui Zhang, Gustavo Enrique Batista, Salil S. Kanhere:
Instance-Wise Monotonic Calibration by Constrained Transformation. 4920-4932 - Junzhe Zhang, Elias Bareinboim:
Causal Eligibility Traces for Confounding Robust Off-Policy Evaluation. 4933-4942 - Jiayu Zhang, Zhiyu Zhu, Zhibo Jin, Xinyi Wang, Huaming Chen, Kim-Kwang Raymond Choo:
Improving Adversarial Transferability via Decision Boundary Adaptation. 4943-4958 - Haoran Zhang, Xuchuang Wang, Hao-Xu Chen, Hao Qiu, Lin Yang, Yang Gao:
Near-Optimal Regret Bounds for Federated Multi-armed Bandits with Fully Distributed Communication. 4959-4981 - Zheng Zhang, Jie Bao, Zhixin Zhou, Nicolò Colombo, Lixin Cheng, Rui Luo:
Residual Reweighted Conformal Prediction for Graph Neural Networks. 4982-4999 - Fengxue Zhang, Yuxin Chen:
Finding Interior Optimum of Black-box Constrained Objective with Bayesian Optimization. 5000-5029 - Runze Zhao, Yue Yu, Adams Yiyue Zhu, Chen Yang, Dongruo Zhou:
Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation. 5030-5057 - Canzhe Zhao, Shuze Chen, Weiming Liu, Haobo Fu, Qiang Fu, Shuai Li:
Towards Provably Efficient Learning of Imperfect Information Extensive-Form Games with Linear Function Approximation. 5058-5083 - Xue Zhou, Dapeng Man, Chen Xu, Fanyi Zeng, Tao Liu, Huan Wang, Shucheng He, Chaoyang Gao, Wu Yang:
Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning. 5084-5098 - Sijia Zhou, Yunwen Lei, Ata Kabán:
Learning to Sample in Stochastic Optimization. 5099-5115 - Ruike Zhu, Matthew Charles Weston, Hanwen Zhang, Arindam Banerjee:
MSP-SR: Multi-Stage Probabilistic Generative Super Resolution with Scarce High-Resolution Data. 5116-5134
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