Chau, Siu Lun

14 publications

AISTATS 2025 Credal Two-Sample Tests of Epistemic Uncertainty Siu Lun Chau, Antonin Schrab, Arthur Gretton, Dino Sejdinovic, Krikamol Muandet

NeurIPS 2025 Integral Imprecise Probability Metrics Siu Lun Chau, Michele Caprio, Krikamol Muandet

ICML 2025 Kernel Quantile Embeddings and Associated Probability Metrics Masha Naslidnyk, Siu Lun Chau, Francois-Xavier Briol, Krikamol Muandet

UAI 2025 Truthful Elicitation of Imprecise Forecasts Anurag Singh, Siu Lun Chau, Krikamol Muandet

AAAI 2024 Causal Strategic Learning with Competitive Selection Kiet Q. H. Vo, Muneeb Aadil, Siu Lun Chau, Krikamol Muandet

ICML 2024 Domain Generalisation via Imprecise Learning Anurag Singh, Siu Lun Chau, Shahine Bouabid, Krikamol Muandet

NeurIPS 2023 Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models Siu Lun Chau, Krikamol Muandet, Dino Sejdinovic

TMLR 2023 Gated Domain Units for Multi-Source Domain Generalization Simon Föll, Alina Dubatovka, Eugen Ernst, Siu Lun Chau, Martin Maritsch, Patrik Okanovic, Gudrun Thaeter, Joachim M. Buhmann, Felix Wortmann, Krikamol Muandet

NeurIPS 2022 Explaining Preferences with Shapley Values Robert Hu, Siu Lun Chau, Jaime Ferrando Huertas, Dino Sejdinovic

NeurIPS 2022 Giga-Scale Kernel Matrix-Vector Multiplication on GPU Robert Hu, Siu Lun Chau, Dino Sejdinovic, Joan Glaunès

NeurIPS 2022 RKHS-SHAP: Shapley Values for Kernel Methods Siu Lun Chau, Robert Hu, Javier González, Dino Sejdinovic

ECML-PKDD 2022 Spectral Ranking with Covariates Siu Lun Chau, Mihai Cucuringu, Dino Sejdinovic

NeurIPS 2021 BayesIMP: Uncertainty Quantification for Causal Data Fusion Siu Lun Chau, Jean-Francois Ton, Javier González, Yee W. Teh, Dino Sejdinovic

NeurIPS 2021 Deconditional Downscaling with Gaussian Processes Siu Lun Chau, Shahine Bouabid, Dino Sejdinovic