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ORCID40th UAI 2024: Barcelona, Spain
- Negar Kiyavash, Joris M. Mooij:
Uncertainty in Artificial Intelligence, 15-19 July 2024, Universitat Pompeu Fabra, Barcelona, Spain. Proceedings of Machine Learning Research 244, PMLR 2024 - Preface. i-xi
- Steven An, Sanjoy Dasgupta:
Convergence Behavior of an Adversarial Weak Supervision Method. 1-49 - Rafael Anderka, Marc Peter Deisenroth, So Takao:
Iterated INLA for State and Parameter Estimation in Nonlinear Dynamical Systems. 50-76 - Shuang Ao, Stefan Rueger, Advaith Siddharthan:
CSS: Contrastive Semantic Similarities for Uncertainty Quantification of LLMs. 77-87 - Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba:
Unified PAC-Bayesian Study of Pessimism for Offline Policy Learning with Regularized Importance Sampling. 88-109 - Fabio Arnez, Daniel Alfonso Montoya Vasquez, Ansgar Radermacher, François Terrier:
Latent Representation Entropy Density for Distribution Shift Detection. 110-137 - Baris Askin, Pranay Sharma, Carlee Joe-Wong, Gauri Joshi:
FedAST: Federated Asynchronous Simultaneous Training. 138-172 - Charles K. Assaad, Emilie Devijver, Éric Gaussier, Gregor Goessler, Anouar Meynaoui:
Identifiability of total effects from abstractions of time series causal graphs. 173-185 - Gennaro Auricchio, Harry J. Clough, Jie Zhang:
On the Capacitated Facility Location Problem with Scarce Resources. 186-202 - Mohammad Azizmalayeri, Ameen Abu-Hanna, Giovanni Cinà:
Mitigating Overconfidence in Out-of-Distribution Detection by Capturing Extreme Activations. 203-224 - Damiano Azzolini, Fabrizio Riguzzi:
Inference in Probabilistic Answer Set Programs with Imprecise Probabilities via Optimization. 225-234 - Shaojie Bai, Lanting Zeng, Chengcheng Zhao, Xiaoming Duan, Mohammad Sadegh Talebi, Peng Cheng, Jiming Chen:
Differentially Private No-regret Exploration in Adversarial Markov Decision Processes. 235-272 - Ondrej Bajgar, Alessandro Abate, Konstantinos Gatsis, Michael A. Osborne:
Walking the Values in Bayesian Inverse Reinforcement Learning. 273-287 - Maria-Florina Balcan, Dravyansh Sharma:
Learning Accurate and Interpretable Decision Trees. 288-307 - Alexis Bellot:
Towards Bounding Causal Effects under Markov Equivalence. 308-332 - Alessio Benavoli, Dario Azzimonti:
Linearly Constrained Gaussian Processes are SkewGPs: application to Monotonic Preference Learning and Desirability. 333-348 - Marlene Berke, Zhangir Azerbayev, Mario Belledonne, Zenna Tavares, Julian Jara-Ettinger:
MetaCOG: A Heirarchical Probabilistic Model for Learning Meta-Cognitive Visual Representations. 349-359 - Lucas Berry, Axel Brando, David Meger:
Shedding Light on Large Generative Networks: Estimating Epistemic Uncertainty in Diffusion Models. 360-376 - Louis Betzer, Vorapong Suppakitpaisarn, Quentin Hillebrand:
Publishing Number of Walks and Katz Centrality under Local Differential Privacy. 377-393 - Sam Bowyer, Thomas Heap, Laurence Aitchison:
Using Autodiff to Estimate Posterior Moments, Marginals and Samples. 394-417 - Oliver Broadrick, Honghua Zhang, Guy Van den Broeck:
Polynomial Semantics of Tractable Probabilistic Circuits. 418-429 - Simon Buchholz, Junhyung Park, Bernhard Schölkopf:
Products, Abstractions and Inclusions of Causal Spaces. 430-449 - Long Minh Bui, Tho Tran Huu, Duy Dinh, Tan Minh Nguyen, Trong Nghia Hoang:
Revisiting Kernel Attention with Correlated Gaussian Process Representation. 450-470 - Javier Burroni, Justin Domke, Daniel Sheldon:
Sample Average Approximation for Black-Box Variational Inference. 471-498 - Zhongteng Cai, Xueru Zhang, Mohammad Mahdi Khalili:
Privacy-Aware Randomized Quantization via Linear Programming. 499-516 - Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin Jamieson:
Fair Active Learning in Low-Data Regimes. 517-531 - Yuwei Cao, Hao Peng, Angsheng Li, Chenyu You, Zhifeng Hao, Philip S. Yu:
Multi-Relational Structural Entropy. 532-546 - Luís Felipe P. Cattelan, Danilo Silva:
How to Fix a Broken Confidence Estimator: Evaluating Post-hoc Methods for Selective Classification with Deep Neural Networks. 547-584 - Aditya Challa, Soma S. Dhavala, Snehanshu Saha:
QuantProb: Generalizing Probabilities along with Predictions for a Pre-trained Classifier. 585-602 - Kushal Chauhan, Rishi Saket, Lorne Applebaum, Ashwinkumar Badanidiyuru, Chandan Giri, Aravindan Raghuveer:
Generalization and Learnability in Multiple Instance Regression. 603-618 - Hengchao Chen, Xin Chen, Mohamad Elmasri, Qiang Sun:
Gradient descent in matrix factorization: Understanding large initialization. 619-647 - Zonghao Chen, Masha Naslidnyk, Arthur Gretton, François-Xavier Briol:
Conditional Bayesian Quadrature. 648-684 - Yuzhu Chen, Fengxiang He, Shi Fu, Xinmei Tian, Dacheng Tao:
Adaptive Time-Stepping Schedules for Diffusion Models. 685-697 - Tong Cheng, Hang Dong, Lu Wang, Bo Qiao, Qingwei Lin, Saravan Rajmohan, Thomas Moscibroda:
SMuCo: Reinforcement Learning for Visual Control via Sequential Multi-view Total Correlation. 698-717 - Yuwen Cheng, Shu Yang:
Inference for Optimal Linear Treatment Regimes in Personalized Decision-making. 718-735 - Abhilash Reddy Chenreddy, Erick Delage:
End-to-end Conditional Robust Optimization. 736-748 - Yoichi Chikahara, Kansei Ushiyama:
Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation. 749-762 - Daniyar Chumbalov, Lars Klein, Lucas Maystre, Matthias Grossglauser:
Fast Interactive Search under a Scale-Free Comparison Oracle. 763-786 - Lucas Clarté, Adrien Vandenbroucque, Guillaume Dalle, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová:
Analysis of Bootstrap and Subsampling in High-dimensional Regularized Regression. 787-819 - Pierre Clavier, Erwan Le Pennec, Matthieu Geist:
Towards Minimax Optimality of Model-based Robust Reinforcement Learning. 820-855 - Oscar Clivio, Avi Feller, Chris C. Holmes:
Towards Representation Learning for Weighting Problems in Design-Based Causal Inference. 856-880 - Nicolò Colombo:
Normalizing Flows for Conformal Regression. 881-893 - Tuan Dam, Odalric-Ambrym Maillard, Emilie Kaufmann:
Power Mean Estimation in Stochastic Monte-Carlo Tree Search. 894-918 - Clemens Damke, Eyke Hüllermeier:
Linear Opinion Pooling for Uncertainty Quantification on Graphs. 919-929 - Khoi Tran Dang, Kevin Delmas, Jérémie Guiochet, Joris Guérin:
Can we Defend Against the Unknown? An Empirical Study About Threshold Selection for Neural Network Monitoring. 930-942 - Piersilvio De Bartolomeis, Javier Abad, Konstantin Donhauser, Fanny Yang:
Detecting critical treatment effect bias in small subgroups. 943-965 - Alexander Decruyenaere, Heidelinde Dehaene, Paloma Rabaey, Christiaan Polet, Johan Decruyenaere, Stijn Vansteelandt, Thomas Demeester:
The Real Deal Behind the Artificial Appeal: Inferential Utility of Tabular Synthetic Data. 966-996 - Tristan Deleu, Padideh Nouri, Nikolay Malkin, Doina Precup, Yoshua Bengio:
Discrete Probabilistic Inference as Control in Multi-path Environments. 997-1021 - Wei Deng, Qian Zhang, Yian Ma, Zhao Song, Guang Lin:
On Convergence of Federated Averaging Langevin Dynamics. 1022-1054 - Wei Deng, Yu Chen, Nicole Tianjiao Yang, Hengrong Du, Qi Feng, Ricky Tian Qi Chen:
Reflected Schrödinger Bridge for Constrained Generative Modeling. 1055-1082 - Shachi Deshpande, Volodymyr Kuleshov:
Calibrated and Conformal Propensity Scores for Causal Effect Estimation. 1083-1111 - Zixin Ding, Si Chen, Ruoxi Jia, Yuxin Chen:
Learning to Rank for Active Learning via Multi-Task Bilevel Optimization. 1112-1128 - My H. Dinh, James Kotary, Ferdinando Fioretto:
End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty. 1129-1145 - Jing Dong, Jingwei Li, Baoxiang Wang, Jingzhao Zhang:
Online Policy Optimization for Robust Markov Decision Process. 1146-1175 - Hailiang Dong, James Amato, Vibhav Gogate, Nicholas Ruozzi:
Learning Distributionally Robust Tractable Probabilistic Models in Continuous Domains. 1176-1188 - Davide Drago, Andrea Celli, Marek Eliás:
Bandits with Knapsacks and Predictions. 1189-1206 - Joel Dyer, Patrick Cannon, Sebastian M. Schmon:
Approximate Bayesian Computation with Path Signatures. 1207-1231 - Mai Elkady, Thu Bui, Bruno Ribeiro, David I. Inouye:
Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions. 1232-1256 - Shohei Enomoto:
EntProp: High Entropy Propagation for Improving Accuracy and Robustness. 1257-1270 - Mingzhou Fan, Byung-Jun Yoon, Edward R. Dougherty, Nathan M. Urban, Francis J. Alexander, Raymundo Arróyave, Xiaoning Qian:
Multi-fidelity Bayesian Optimization with Multiple Information Sources of Input-dependent Fidelity. 1271-1293 - Xingli Fang, Jung-Eun Kim:
Center-Based Relaxed Learning Against Membership Inference Attacks. 1294-1306 - Xinying Fang, Shouhao Zhou:
Enhancing Patient Recruitment Response in Clinical Trials: an Adaptive Learning Framework. 1307-1322 - Hélène Fargier, Pierre Pomeret-Coquot:
Generalized Expected Utility as a Universal Decision Rule - A Step Forward. 1323-1338 - Yi Feng, Ping Li, Ioannis Panageas, Xiao Wang:
Last-iterate Convergence Separation between Extra-gradient and Optimism in Constrained Periodic Games. 1339-1370 - Nicola Franco, Jakob Spiegelberg, Jeanette Miriam Lorenz, Stephan Günnemann:
Guaranteeing Robustness Against Real-World Perturbations In Time Series Classification Using Conformalized Randomized Smoothing. 1371-1388 - Han Gao, Kaican Li, Weiyan Xie, Zhi Lin, Yongxiang Huang, Luning Wang, Caleb Chen Cao, Nevin L. Zhang:
Consistency Regularization for Domain Generalization with Logit Attribution Matching. 1389-1407 - Daniel Gedon, Amirhesam Abedsoltan, Thomas B. Schön, Mikhail Belkin:
Uncertainty Estimation with Recursive Feature Machines. 1408-1437 - Marco Gigli, Fabio Stella:
Bootstrap Your Conversions: Thompson Sampling for Partially Observable Delayed Rewards. 1438-1452 - Andrew Gracyk, Xiaohui Chen:
GeONet: a neural operator for learning the Wasserstein geodesic. 1453-1478 - Denis A. Gudovskiy, Tomoyuki Okuno, Yohei Nakata:
ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-variable Context Encoding. 1479-1490 - Etash Guha, Jim James, Krishna Acharya, Vidya Muthukumar, Ashwin Pananjady:
One Shot Inverse Reinforcement Learning for Stochastic Linear Bandits. 1491-1512 - Tessa Han, Suraj Srinivas, Himabindu Lakkaraju:
Characterizing Data Point Vulnerability as Average-Case Robustness. 1513-1540 - Minbiao Han, Fengxue Zhang, Yuxin Chen:
No-Regret Learning of Nash Equilibrium for Black-Box Games via Gaussian Processes. 1541-1557 - Juha Harviainen, Mikko Koivisto:
Faster Perfect Sampling of Bayesian Network Structures. 1558-1568 - Leonard Henckel, Theo Würtzen, Sebastian Weichwald:
Adjustment Identification Distance: A gadjid for Causal Structure Learning. 1569-1598 - Christine Herlihy, Jennifer Neville, Tobias Schnabel, Adith Swaminathan:
On Overcoming Miscalibrated Conversational Priors in LLM-based ChatBots. 1599-1620 - Maxime Heuillet, Ola Ahmad, Audrey Durand:
Neural Active Learning Meets the Partial Monitoring Framework. 1621-1639 - Yasunari Hikima, Kazunori Murao, Sho Takemori, Yuhei Umeda:
Quantum Kernelized Bandits. 1640-1657 - Qi Heng Ho, Tyler J. Becker, Benjamin Kraske, Zakariya Laouar, Martin S. Feather, Federico Rossi, Morteza Lahijanian, Zachary Sunberg:
Recursively-Constrained Partially Observable Markov Decision Processes. 1658-1680 - Qi Heng Ho, Martin S. Feather, Federico Rossi, Zachary Sunberg, Morteza Lahijanian:
Sound Heuristic Search Value Iteration for Undiscounted POMDPs with Reachability Objectives. 1681-1697 - Tom Hochsprung, Jakob Runge, Andreas Gerhardus:
A Global Markov Property for Solutions of Stochastic Difference Equations and the corresponding Full Time Graphs. 1698-1726 - Yusu Hong, Junhong Lin:
Revisiting Convergence of AdaGrad with Relaxed Assumptions. 1727-1750 - Mohammad T. Irfan, Hau Chan, Jared Soundy:
Equilibrium Computation in Multidimensional Congestion Games: CSP and Learning Dynamics Approaches. 1751-1779 - Metod Jazbec, Patrick Forré, Stephan Mandt, Dan Zhang, Eric T. Nalisnick:
Early-Exit Neural Networks with Nested Prediction Sets. 1780-1796 - Xiaohan Jiang, Hongbin Zhu:
On the Convergence of Hierarchical Federated Learning with Partial Worker Participation. 1797-1824 - Rasul Kairgeldin, Magzhan Gabidolla, Miguel Á. Carreira-Perpiñán:
Adaptive Softmax Trees for Many-Class Classification. 1825-1841 - Peter Johannes Tejlgaard Kampen, Gustav Ragnar Stoettrup Als, Michael Riis Andersen:
Towards Scalable Bayesian Transformers: Investigating stochastic subset selection for NLP. 1842-1862 - Yue Kang, Cho-Jui Hsieh, Thomas Chun Man Lee:
Low-rank Matrix Bandits with Heavy-tailed Rewards. 1863-1889 - Moussa Kassem Sbeyti, Michelle Karg, Christian Wirth, Nadja Klein, Sahin Albayrak:
Cost-Sensitive Uncertainty-Based Failure Recognition for Object Detection. 1890-1900 - Yuta Kawakami, Manabu Kuroki, Jin Tian:
Probabilities of Causation for Continuous and Vector Variables. 1901-1921 - Yuta Kawakami, Manabu Kuroki, Jin Tian:
Identification and Estimation of Conditional Average Partial Causal Effects via Instrumental Variable. 1922-1952 - Armin Kekic, Bernhard Schölkopf, Michel Besserve:
Targeted Reduction of Causal Models. 1953-1980 - Tung Khong, Cong Tran, Cuong Pham:
Active Learning Framework for Incomplete Networks. 1981-1998 - Jonghwan Kim, Inwoo Hwang, Sanghack Lee:
Causal Discovery with Deductive Reasoning: One Less Problem. 1999-2017 - Shufeng Kong, Caihua Liu, Carla Gomes:
ILP-FORMER: Solving Integer Linear Programming with Sequence to Multi-Label Learning. 2018-2028 - Lucas Kook, Chris Kolb, Philipp Schiele, Daniel Dold, Marcel Arpogaus, Cornelius Fritz, Philipp F. M. Baumann, Philipp Kopper, Tobias Pielok, Emilio Dorigatti, David Rügamer:
How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression. 2029-2046 - Patrick K. Kuiper, Ali Hasan, Wenhao Yang, Yuting Ng, Hoda Bidkhori, Jose H. Blanchet, Vahid Tarokh:
Distributionally Robust Optimization as a Scalable Framework to Characterize Extreme Value Distributions. 2047-2063 - Manoj Kumar, Subhanu Halder, Archit Kane, Ruchir Gupta, Sandeep Kumar:
Optimization Framework for Semi-supervised Attributed Graph Coarsening. 2064-2075 - Yunhyeok Kwak, Inwoo Hwang, Dooyoung Kim, Sanghack Lee, Byoung-Tak Zhang:
Efficient Monte Carlo Tree Search via On-the-Fly State-Conditioned Action Abstraction. 2076-2093 - Alan A. Lahoud, Erik Schaffernicht, Johannes A. Stork:
DataSP: A Differential All-to-All Shortest Path Algorithm for Learning Costs and Predicting Paths with Context. 2094-2112 - Sven Lämmle, Can Bogoclu, Robert Vosshall, Anselm Haselhoff, Dirk Roos:
Quantifying Local Model Validity using Active Learning. 2113-2135 - Quinn Lanners, Qin Weng, Marie-Louise Meng, Matthew M. Engelhard:
Common Event Tethering to Improve Prediction of Rare Clinical Events. 2136-2162 - Hanbyul Lee, Qifan Song, Jean Honorio:
Support Recovery in Sparse PCA with General Missing Data. 2163-2187 - Jaron Jia Rong Lee, AmirEmad Ghassami, Ilya Shpitser:
A General Identification Algorithm For Data Fusion Problems Under Systematic Selection. 2188-2204 - Haoyu Lei, Amin Gohari, Farzan Farnia:
On the Inductive Biases of Demographic Parity-based Fair Learning Algorithms. 2205-2225 - Jiao Li, Liangxiao Jiang, Chaoqun Li, Wenjun Zhang:
Label Consistency-based Worker Filtering for Crowdsourcing. 2226-2237 - Jiao Li, Liangxiao Jiang, Xue Wu, Wenjun Zhang:
Learning from Crowds with Dual-View K-Nearest Neighbor. 2238-2249 - Victoria Lin, Eli Ben-Michael, Louis-Philippe Morency:
Optimizing Language Models for Human Preferences is a Causal Inference Problem. 2250-2270 - Ofir Lindenbaum, Yariv Aizenbud, Yuval Kluger:
Transductive and Inductive Outlier Detection with Robust Autoencoders. 2271-2293 - Jiashun Liu, Xiaotian Hao, Jianye Hao, Yan Zheng, Yujing Hu, Changjie Fan, Tangjie Lv, Zhipeng Hu:
Hybrid CtrlFormer: Learning Adaptive Search Space Partition for Hybrid Action Control via Transformer-based Monte Carlo Tree Search. 2294-2308 - Xinyang Liu, Dongsheng Wang, Bowei Fang, Miaoge Li, Yishi Xu, Zhibin Duan, Bo Chen, Mingyuan Zhou:
Patch-Prompt Aligned Bayesian Prompt Tuning for Vision-Language Models. 2309-2330 - Jorge Loría, Anindya Bhadra:
Posterior Inference on Shallow Infinitely Wide Bayesian Neural Networks under Weights with Unbounded Variance. 2331-2349 - Jacqueline R. M. A. Maasch, Weishen Pan, Shantanu Gupta, Volodymyr Kuleshov, Kyra Gan, Fei Wang:
Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs. 2350-2382 - Vineet Malik, Kevin Bello, Asish Ghoshal, Jean Honorio:
Identifying Causal Changes Between Linear Structural Equation Models. 2383-2398 - Emanuele Marconato, Samuele Bortolotti, Emile van Krieken, Antonio Vergari, Andrea Passerini, Stefano Teso:
BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts. 2399-2433 - Charles C. Margossian, David M. Blei:
Amortized Variational Inference: When and Why? 2434-2449 - Julien Martinelli, Ayush Bharti, Armi Tiihonen, S. T. John, Louis Filstroff, Sabina J. Sloman, Patrick Rinke, Samuel Kaski:
Learning relevant contextual variables within Bayesian optimization. 2450-2470 - Natalia Martinez Gil, Dhaval Patel, Chandra Reddy, Giridhar Ganapavarapu, Roman Vaculín, Jayant Kalagnanam:
Identifying Homogeneous and Interpretable Groups for Conformal Prediction. 2471-2485 - Riccardo Massidda, Sara Magliacane, Davide Bacciu:
Learning Causal Abstractions of Linear Structural Causal Models. 2486-2515 - Saurabh Mathur, Alessandro Antonucci, Sriraam Natarajan:
Knowledge Intensive Learning of Credal Networks. 2516-2526 - Daniil Merkulov, Daria Cherniuk, Alexander Rudikov, Ivan V. Oseledets, Ekaterina A. Muravleva, Aleksandr Mikhalev, Boris Kashin:
Quantization of Large Language Models with an Overdetermined Basis. 2527-2536 - Alexander Mey, Rui Manuel Castro:
Invariant Causal Prediction with Local Models. 2537-2559 - Manuj Mishra, James Fox, Michael J. Wooldridge:
Characterising Interventions in Causal Games. 2560-2572 - Shuwa Miura, Olivier Buffet, Shlomo Zilberstein:
Approximation Algorithms for Observer Aware MDPs. 2573-2586 - Devina Mohan, Anna M. M. Scaife:
Evaluating Bayesian deep learning for radio galaxy classification. 2587-2597 - Sang Bin Moon, Abolfazl Hashemi:
Optimistic Regret Bounds for Online Learning in Adversarial Markov Decision Processes. 2598-2622 - Ron Nafshi, Maggie Makar:
Partial identification of the maximum mean discrepancy with mismeasured data. 2623-2645 - Andrzej Nagórko, Marcin Waniek, Malgorzata Róg, Michal Tomasz Godziszewski, Barbara Rosiak, Tomasz Pawel Michalak:
General Markov Model for Solving Patrolling Games. 2646-2669 - Adhyyan Narang, Omid Sadeghi, Lillian J. Ratliff, Maryam Fazel, Jeff A. Bilmes:
Efficient Interactive Maximization of BP and Weakly Submodular Objectives. 2670-2699 - Utkarsh Nath, Yancheng Wang, Yingzhen Yang:
Neural Architecture Search Finds Robust Models by Knowledge Distillation. 2700-2715 - Achille Nazaret, David M. Blei:
Extremely Greedy Equivalence Search. 2716-2745 - Tin Lok James Ng:
A Generalized Bayesian Approach to Distribution-on-Distribution Regression. 2746-2765 - Hieu Trung Nguyen, Duy Nguyen, Khoa D. Doan, Viet Anh Nguyen:
Cold-start Recommendation by Personalized Embedding Region Elicitation. 2766-2786 - Prashansa Panda, Shalabh Bhatnagar:
Finite-Time Analysis of Three-Timescale Constrained Actor-Critic and Constrained Natural Actor-Critic Algorithms. 2787-2834 - Young-Jin Park, Hao Wang, Shervin Ardeshir, Navid Azizan:
Quantifying Representation Reliability in Self-Supervised Learning Models. 2835-2860 - Bobak Pezeshki, Kalev Kask, Alexander Ihler, Rina Dechter:
Value-Based Abstraction Functions for Abstraction Sampling. 2861-2901 - Thai-Hoang Pham, Xueru Zhang, Ping Zhang:
Non-stationary Domain Generalization: Theory and Algorithm. 2902-2927 - Trung Phung, Jaron J. R. Lee, Opeyemi Oladapo-Shittu, Eili Y. Klein, Ayse Pinar Gurses, Susan M. Hannum, Kimberly Weems, Jill A. Marsteller, Sara E. Cosgrove, Sara C. Keller, Ilya Shpitser:
Zero Inflation as a Missing Data Problem: a Proxy-based Approach. 2928-2955 - Matías P. Pizarro B., Dorothea Kolossa, Asja Fischer:
DistriBlock: Identifying adversarial audio samples by leveraging characteristics of the output distribution. 2956-2988 - Aram-Alexandre Pooladian, Carles Domingo-Enrich, Ricky T. Q. Chen, Brandon Amos:
Neural Optimal Transport with Lagrangian Costs. 2989-3003 - Harsh Poonia, Moritz Willig, Zhongjie Yu, Matej Zecevic, Kristian Kersting, Devendra Singh Dhami:
χSPN: Characteristic Interventional Sum-Product Networks for Causal Inference in Hybrid Domains. 3004-3020 - Han Qi, Guo Fei, Li Zhu:
Graph Feedback Bandits with Similar Arms. 3021-3040 - Ben Rank, Stelios Triantafyllou, Debmalya Mandal, Goran Radanovic:
Performative Reinforcement Learning in Gradually Shifting Environments. 3041-3075 - Kevin Ren, Yewon Byun, Bryan Wilder:
Decision-Focused Evaluation of Worst-Case Distribution Shift. 3076-3093 - Teodora Reu, Francisco Vargas, Anna Kerekes, Michael M. Bronstein:
To smooth a cloud or to pin it down: Expressiveness guarantees and insights on score matching in denoising diffusion models. 3094-3120 - Amit Rozner, Barak Battash, Henry Li, Lior Wolf, Ofir Lindenbaum:
Anomaly Detection with Variance Stabilized Density Estimation. 3121-3137 - Aadirupa Saha, Arun Rajkumar:
A Graph Theoretic Approach for Preference Learning with Feature Information. 3138-3158 - Yusuf Sale, Paul Hofman, Timo Löhr, Lisa Wimmer, Thomas Nagler, Eyke Hüllermeier:
Label-wise Aleatoric and Epistemic Uncertainty Quantification. 3159-3179 - Wangduk Seo, Jaesung Lee:
Unsupervised Feature Selection towards Pattern Discrimination Power. 3180-3197 - Jongyun Shin, Seungjin Han, Jangho Kim:
Cooperative Meta-Learning with Gradient Augmentation. 3198-3210 - Michael Shvartsman, Benjamin Letham, Eytan Bakshy, Stephen Keeley:
Response Time Improves Gaussian Process Models for Perception and Preferences. 3211-3226 - Abhishek Sinha:
BanditQ: Fair Bandits with Guaranteed Rewards. 3227-3244 - Sabina J. Sloman, Ayush Bharti, Julien Martinelli, Samuel Kaski:
Bayesian Active Learning in the Presence of Nuisance Parameters. 3245-3263 - Jy-yong Sohn, Dohyun Kwon, Seoyeon An, Kangwook Lee:
Memorization Capacity for Additive Fine-Tuning with Small ReLU Networks. 3264-3278 - Samuel Sokota, Dylan Sam, Christian Schröder de Witt, Spencer Compton, Jakob N. Foerster, J. Zico Kolter:
Computing Low-Entropy Couplings for Large-Support Distributions. 3279-3298 - Rustem Takhanov:
Multi-layer random features and the approximation power of neural networks. 3299-3322 - Jiyuan Tan, Chenyu Xue, Chuwen Zhang, Qi Deng, Dongdong Ge, Yinyu Ye:
A Homogenization Approach for Gradient-Dominated Stochastic Optimization. 3323-3344 - Kai Z. Teh, Kayvan Sadeghi, Terry Soo:
Localised Natural Causal Learning Algorithms for Weak Consistency Conditions. 3345-3355 - Karim Tit, Teddy Furon:
Fast Reliability Estimation for Neural Networks with Adversarial Attack-Driven Importance Sampling. 3356-3367 - Piyush Tiwary, Kumar Shubham, Vivek Kashyap, Prathosh A. P.:
Bayesian Pseudo-Coresets via Contrastive Divergence. 3368-3390 - Filippo Valdettaro, Aldo Faisal:
Offline Bayesian Aleatoric and Epistemic Uncertainty Quantification and Posterior Value Optimisation in Finite-State MDPs. 3391-3409 - Thijs van Ommen:
Efficiently Deciding Algebraic Equivalence of Bow-Free Acyclic Path Diagrams. 3410-3424 - Shyam Venkatasubramanian, Ahmed Aloui, Vahid Tarokh:
Random Linear Projections Loss for Hyperplane-Based Optimization in Neural Networks. 3425-3447 - Shresth Verma, Yunfan Zhao, Sanket Shah, Niclas Boehmer, Aparna Taneja, Milind Tambe:
Group Fairness in Predict-Then-Optimize Settings for Restless Bandits. 3448-3469 - Yudan Wang, Shaofeng Zou, Yue Wang:
Model-Free Robust Reinforcement Learning with Sample Complexity Analysis. 3470-3513 - Ziqiao Wang, Yongyi Mao:
Two Facets of SDE Under an Information-Theoretic Lens: Generalization of SGD via Training Trajectories and via Terminal States. 3514-3539 - Zichen Wang, Chuanhao Li, Chenyu Song, Lianghui Wang, Quanquan Gu, Huazheng Wang:
Pure Exploration in Asynchronous Federated Bandits. 3540-3570 - Zhi Wang, Geelon So, Ramya Korlakai Vinayak:
Metric Learning from Limited Pairwise Preference Comparisons. 3571-3602 - Jing Wang, Yunfei Teng, Anna Choromanska:
AutoDrop: Training Deep Learning Models with Automatic Learning Rate Drop. 3603-3629 - Xiao Wang, Jia Wang, Tong Zhao, Yijie Wang, Nan Zhang, Yong Zang, Sha Cao, Chi Zhang:
Bias-aware Boolean Matrix Factorization Using Disentangled Representation Learning. 3630-3642 - Hanjing Wang, Qiang Ji:
Beyond Dirichlet-based Models: When Bayesian Neural Networks Meet Evidential Deep Learning. 3643-3665 - Houston Warren, Rafael Oliveira, Fabio T. Ramos:
Stein Random Feature Regression. 3666-3688 - David S. Watson, Jordan Penn, Lee M. Gunderson, Gecia Bravo Hermsdorff, Afsaneh Mastouri, Ricardo Silva:
Bounding causal effects with leaky instruments. 3689-3710 - Dorina Weichert, Alexander Kister, Sebastian Houben, Patrick Link, Gunar Ernis:
Robust Entropy Search for Safe Efficient Bayesian Optimization. 3711-3729 - Justin Weltz, Eric Laber, Alexander Volfovsky:
Hidden Population Estimation with Indirect Inference and Auxiliary Information. 3730-3746 - Qianlong Wen, Zhongyu Ouyang, Chunhui Zhang, Yiyue Qian, Chuxu Zhang, Yanfang Ye:
GCVR: Reconstruction from Cross-View Enable Sufficient and Robust Graph Contrastive Learning. 3747-3764 - Eliot Wong-Toi, Alex Boyd, Vincent Fortuin, Stephan Mandt:
Understanding Pathologies of Deep Heteroskedastic Regression. 3765-3790 - Mengjing Wu, Junyu Xuan, Jie Lu:
Functional Wasserstein Bridge Inference for Bayesian Deep Learning. 3791-3815 - Songli Wu, Liang Du, Jiaqi Yang, Yuai Wang, De-Chuan Zhan, Shuang Zhao, Zixun Sun:
RE-SORT: Removing Spurious Correlation in Multilevel Interaction for CTR Prediction. 3816-3828 - Antonia Wüst, Wolfgang Stammer, Quentin Delfosse, Devendra Singh Dhami, Kristian Kersting:
Pix2Code: Learning to Compose Neural Visual Concepts as Programs. 3829-3852 - Xingzi Xu, Ali Hasan, Jie Ding, Vahid Tarokh:
Base Models for Parabolic Partial Differential Equations. 3853-3878 - Zhi Xu, Bin Sun, Yue Bai, Yun Fu:
α-Former: Local-Feature-Aware (L-FA) Transformer. 3879-3892 - Junyu Xuan, Mengjing Wu, Zihe Liu, Jie Lu:
Functional Wasserstein Variational Policy Optimization. 3893-3911 - Yihao Xue, Kyle Whitecross, Baharan Mirzasoleiman:
Investigating the Impact of Model Width and Density on Generalization in Presence of Label Noise. 3912-3935 - Wenhan Yang, Baharan Mirzasoleiman:
Graph Contrastive Learning under Heterophily via Graph Filters. 3936-3955 - Junhui Yang, Rohit Bhattacharya, Youjin Lee, Ted Westling:
Statistical and Causal Robustness for Causal Null Hypothesis Tests. 3956-3978 - Lingyun Yao, Martin Trapp, Jelin Leslin, Gaurav Singh, Peng Zhang, Karthekeyan Periasamy, Martin Andraud:
On Hardware-efficient Inference in Probabilistic Circuits. 3979-3996 - Xun Yao, Zijian Huang, Xinrong Hu, Jie Yang, Yi Guo:
Masking the Unknown: Leveraging Masked Samples for Enhanced Data Augmentation. 3997-4010 - Wenzhe Yin, Shujian Yu, Yicong Lin, Jie Liu, Jan-Jakob Sonke, Efstratios Gavves:
Domain Adaptation with Cauchy-Schwarz Divergence. 4011-4040 - Zishun Yu, Siteng Kang, Xinhua Zhang:
Offline Reward Perturbation Boosts Distributional Shift in Online RL. 4041-4055 - Yaolong Yu, Haipeng Chen:
Decentralized Online Learning in General-Sum Stackelberg Games. 4056-4077 - Lorenzo Zambon, Dario Azzimonti, Nicolò Rubattu, Giorgio Corani:
Probabilistic reconciliation of mixed-type hierarchical time series. 4078-4095 - Min Zeng, Haiqin Yang, Wei Xue, Qifeng Liu, Yike Guo:
Dirichlet Continual Learning: Tackling Catastrophic Forgetting in NLP. 4096-4108 - Fabio Massimo Zennaro, Nicholas Bishop, Joel Dyer, Yorgos Felekis, Anisoara Calinescu, Michael J. Wooldridge, Theodoros Damoulas:
Causally Abstracted Multi-armed Bandits. 4109-4139 - Zhiheng Zhang, Xinyan Su:
Partial Identification with Proxy of Latent Confoundings via Sum-of-ratios Fractional Programming. 4140-4172 - Yirui Zhang, Zhixuan Fang:
Decentralized Two-Sided Bandit Learning in Matching Market. 4173-4191 - Cong Zhang, Zhiguang Cao, Yaoxin Wu, Wen Song, Jing Sun:
Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problem. 4192-4208 - Zhiqiang Zhang, Yongqiang Jiang, Qian Gao, Zhipeng Wang:
Neighbor Similarity and Multimodal Alignment based Product Recommendation Study. 4209-4218 - Aidong Zhao, Xuyang Zhao, Tianchen Gu, Zhaori Bi, Xinwei Sun, Changhao Yan, Fan Yang, Dian Zhou, Xuan Zeng:
Exploring High-dimensional Search Space via Voronoi Graph Traversing. 4219-4236 - Zhuoran Zheng, Chen Wu, Yeying Jin, Xiuyi Jia:
Trusted re-weighting for label distribution learning. 4237-4249 - Yi Zhou, Yanhao Wang, Long Teng, Qiang Huang, Cen Chen:
Approximate Kernel Density Estimation under Metric-based Local Differential Privacy. 4250-4270
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