Publications

24. When Do Credal Sets Stablize? Fixed Point Theorems for Credal Set Updates

Michele Caprio, Siu Lun Chau, Krikamol Muandet

Arxiv, 2025

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23. Exact Shapley Attributions in Quadratic-time for FANOVA Gaussian Processes

Majid Mohammadi, Krikamol Muandet, Ilaria Tiddi, Annette Ten Teije, Siu Lun Chau,

Arxiv, 2025

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22. Integral Imprecise Probability Metrics

Siu Lun Chau, Michele Caprio, Krikamol Muandet

Conference on Neural Information Processing Systems (NeurIPS), 2025

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21. Computing Exact Shapley Values in Polynomial Time for Product-Kernel Methods

Majid Mohammadi, Siu Lun Chau, Krikamol Muandet

Arxiv, 2025

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20. Truthful Elicitation of Imprecise Forecast

Anurag Singh, Siu Lun Chau, Krikamol Muandet

(Oral) Conference on Uncertainty in Artificial Intelligence (UAI), 2025

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19. Bayesian Optimization for Building Social-Influence-Free Consensus

Masaki Adachi, Siu Lun Chau, Wenjie Xu, Anurag Singh, Mike Osborne, Krikamol Muandet

ArXiv, 2025

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18. Strategic Learning with Local Explanations as Feedback

Kiet Q.H. Vo, Siu Lun Chau, Masahiro Kato, Yixin Wang, Krikamol Muandet

ArXiv, 2025

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17. Kernel Quantile Embedding and Associated Probability Metircs

Masha Naslidnyk, Siu Lun Chau, Francois-Xavier Briol, Krikamol Muandet

International Conference on Machine Learning (ICML), 2025

16. Credal Two-sample Tests of Epistemic Ignorance

Siu Lun Chau, Antonin Schrab, Arthur Gretton, Dino Sejdinovic, Krikamol Muandet

The International Conference on Artificial Intelligence and Statistics, 2025

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15. Highly Parallel Optimisation of Nickel-Catalysed Suzuki Reactions through Automation and Machine Intelligence

Joshua W. Sin, Siu Lun Chau, Ryan P.Burwood, Kurt Püntener, Raphael Bigler, Philippe Schwaller

Nature Communications, 2025

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14. Domain Generalisation via Imprecise Learning

Anurag Singh, Siu Lun Chau, Shahine Bouabid, Krikamol Muandet

(Spotlight) International Conference on Machine Learning (ICML), 2024

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13. Causal Strategic Learning with Competitive Selection

Kiet QH Vo, Muneeb Aadil, Siu Lun Chau, Krikamol Muandet

(Oral) The 38th Annual AAAI Conference on Artificial Intelligence (AAAI), 2024

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12. Collaborative and Explainable Bayesian Optimization

Masaki Adachi, Brady Planden, David A. Howey, Krikamol Maundet, Michael A. Osborne, Siu Lun Chau

The International Conference on Artificial Intelligence and Statistics (AISTATS), 2024

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11. Towards Trustworthy Machine Learning with Kernels

Siu Lun Chau

Ph.D. Thesis, 2023

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10. Stochastic Shapley Values for Gaussian Process Models

Siu Lun Chau, Krikamol Muandet*, Dino Sejdinovic*

(Spotlight) Conference on Neural Information Processing Systems (NeurIPS), 2023

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9. 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

Transactions on Machine Learning Research, 2023

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8. Spectral ranking with covariates

Siu Lun Chau, Mihai Cucuringu, Dino Sejdinovic

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2022

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7. RKHS-SHAP: Shapley Values for Kernel Methods

Siu Lun Chau, Robert Hu, Javier Gonzalez, Dino Sejdinovic

Conference on Neural Information Processing Systems (NeurIPS), 2022

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6. Explaining Preferences with Shapley Values

Hu Robert*, Siu Lun Chau*, Jaime Ferrando Huertas, Dino Sejdinovic

Conference on Neural Information Processing Systems (NeurIPS), 2022

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5. Learning Inconsistent Preferences with Gaussian Processes

Siu Lun Chau, Javier González, Dino Sejdinovic

International Conference on Artificial Intelligence and Statistics (AISTATS), 2022

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4. Giga-scale Kernel Matrix Vector Multiplication on GPU

Robert Hu, Siu Lun Chau, Dino Sejdinovic, Joan Alexis Glaunès

Conference on Neural Information Processing Systems (NeurIPS), 2022

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3. Deconditional Downscaling with Gaussian Processes

Siu Lun Chau*, Shahine Bouabid*, Dino Sejdinovic

Conference on Neural Information Processing Systems (NeurIPS), 2021

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2. Uncertainty Quantification for Causal Data Fusion

Siu Lun Chau*, Jean-François Ton*, Javier González, Yee Whye Teh, Dino Sejdinovic

Conference on Neural Information Processing Systems (NeurIPS), 2021

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1. Kernel-based graph learning from smooth signals: A functional viewpoint

Xingyue Pu, Siu Lun Chau, Xiaowen Dong, Dino Sejdinovic

IEEE Transactions on Signal and Information Processing over Networks, 2021

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