Learning Inconsistent Preferences with Kernel Methods. - Siu Lun Chau; Javier González 002; Dino Sejdinovic (CoRR, 2020)

Deconditional Downscaling with Gaussian Processes. - Siu Lun Chau; Shahine Bouabid; Dino Sejdinovic (NeurIPS, 2021)

BayesIMP: Uncertainty Quantification for Causal Data Fusion. - Siu Lun Chau; Jean-Francois Ton; Javier González 002; Yee Whye Teh; Dino Sejdinovic (NeurIPS, 2021)

RKHS-SHAP: Shapley Values for Kernel Methods. - Siu Lun Chau; Javier González 002; Dino Sejdinovic (CoRR, 2021)

Kernel-Based Graph Learning From Smooth Signals: A Functional Viewpoint. - Xingyue Pu; Siu Lun Chau; Xiaowen Dong 001; Dino Sejdinovic (IEEE Trans. Signal Inf. Process. over Networks, 2021)

Learning Inconsistent Preferences with Gaussian Processes. - Siu Lun Chau; Javier González 002; Dino Sejdinovic (AISTATS, 2022)

RKHS-SHAP: Shapley Values for Kernel Methods. - Siu Lun Chau; Robert Hu; Javier González 002; Dino Sejdinovic (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)

Spectral Ranking with Covariates. - Siu Lun Chau; Mihai Cucuringu; Dino Sejdinovic (ECML/PKDD (5), 2022)

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

Looping in the Human: Collaborative and Explainable Bayesian Optimization. - Masaki Adachi; Brady Planden; David A. Howey; Krikamol Muandet; Michael A. Osborne; Siu Lun Chau (CoRR, 2023)

Gated Domain Units for Multi-source Domain Generalization. - Simon Föll; Alina Dubatovka; Eugen Ernst; Siu Lun Chau; Martin Maritsch; Patrik Okanovic; Gudrun Thäter; Joachim M. Buhmann; Felix Wortmann; Krikamol Muandet (Trans. Mach. Learn. Res., 2023)

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

Looping in the Human: Collaborative and Explainable Bayesian Optimization. - Masaki Adachi; Brady Planden; David A. Howey; Michael A. Osborne; Sebastian Orbell; Natalia Ares; Krikamol Muandet; Siu Lun Chau (AISTATS, 2024)

Domain Generalisation via Imprecise Learning. - Anurag Singh 006; Siu Lun Chau; Shahine Bouabid; Krikamol Muandet (ICML, 2024)

Credal Two-Sample Tests of Epistemic Ignorance. - Siu Lun Chau; Antonin Schrab; Arthur Gretton; Dino Sejdinovic; Krikamol Muandet (CoRR, 2024)

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

Truthful Elicitation of Imprecise Forecasts. - Anurag Singh 006; Siu Lun Chau; Krikamol Muandet (UAI, 2025)

Strategic Learning with Local Explanations as Feedback. - Kiet Q. H. Vo; Siu Lun Chau; Masahiro Kato; Yixin Wang; Krikamol Muandet (CoRR, 2025)

Bayesian Optimization for Building Social-Influence-Free Consensus. - Masaki Adachi; Siu Lun Chau; Wenjie Xu; Anurag Singh 006; Michael A. Osborne; Krikamol Muandet (CoRR, 2025)

Integral Imprecise Probability Metrics. - Siu Lun Chau; Michele Caprio; Krikamol Muandet (CoRR, 2025)

Computing Exact Shapley Values in Polynomial Time for Product-Kernel Methods. - Majid Mohammadi; Siu Lun Chau; Krikamol Muandet (CoRR, 2025)

Kernel Quantile Embeddings and Associated Probability Metrics. - Masha Naslidnyk; Siu Lun Chau; François-Xavier Briol; Krikamol Muandet (CoRR, 2025)