2026

Incentive Aware AI Regulations: A Credal Characterisation
Anurag Singh, Julian Rodemann, Rajeev Verma, Siu Lun Chau, Krikamol Muandet
ArXiV, 2026
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Verbalizing LLM’s Higher-order Uncertainty via Imprecise Probabilities
Anita Yang, Krikamol Muandet, Michele Caprio, Siu Lun Chau*, Masaki Adachi*
ArXiv, 2026
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Instrumental and Proximal Causal Inference with Gaussian Processes
Yuqi Zhang, Krikamol Muandet, Dino Sejdinovic, Edwin Fong, Siu Lun Chau
ArXiV, 2026
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Robust Predictive Uncertainty and Double Descent in Contaminated Bayesian Random Features
Michele Caprio, Katerina Papagiannouli, Siu Lun Chau, Sayan Mukherjee
ArXiV, 2026
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Quantifying Epistemic Predictive Uncertainty in Conformal Prediction
Siu Lun Chau, Soroush H Zargarbashi, Yusuf Sale, Michele Caprio
ArXiV, 2026
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Learning Credal Ensembles via Distributionally Robust Optimization
Kaizheng Wang, Ghifari Adam Faza, Fabio Cuzzolin, Siu Lun Chau, David Moens, Hans Hallez
ArXiV, 2026
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Exact Shapley Attributions in Quadratic-time for FANOVA Gaussian Processes
Majid Mohammadi, Krikamol Muandet, Ilaria Tiddi, Annette Ten Teije, Siu Lun Chau
The 40th Annual AAAI Conference on Artificial Intelligence (AAAI), 2026
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Explanation Design in Strategic Learning: Sufficient Explanations that Induce Non-harmful Responses
Kiet Q.H. Vo, Siu Lun Chau, Masahiro Kato, Yixin Wang, Krikamol Muandet
International Conference on Artificial Intelligence and Statistics (AISTATS), 2026
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2025

When Do Credal Sets Stabilize? Fixed Point Theorems for Credal Set Updates
Michele Caprio, Siu Lun Chau, Krikamol Muandet
ArXiV, 2025
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Integral Imprecise Probability Metrics
Siu Lun Chau, Michele Caprio, Krikamol Muandet
Conference on Neural Information Processing Systems (NeurIPS), 2025
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Computing Exact Shapley Values in Polynomial Time for Product-Kernel Methods
Majid Mohammadi, Siu Lun Chau, Krikamol Muandet
ArXiV, 2025
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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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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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Credal Two-sample Tests of Epistemic Ignorance
Siu Lun Chau, Antonin Schrab, Arthur Gretton, Dino Sejdinovic, Krikamol Muandet
International Conference on Artificial Intelligence and Statistics (AISTATS), 2025
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Kernel Quantile Embedding and Associated Probability Metrics
Masha Naslidnyk, Siu Lun Chau, Francois-Xavier Briol, Krikamol Muandet
International Conference on Machine Learning (ICML), 2025
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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2024

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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Collaborative and Explainable Bayesian Optimization
Masaki Adachi, Brady Planden, David A. Howey, Krikamol Muandet, Michael A. Osborne, Siu Lun Chau
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
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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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2023

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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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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Towards Trustworthy Machine Learning with Kernels
Siu Lun Chau
Ph.D. Thesis, University of Oxford, 2023
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2022

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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Explaining Preferences with Shapley Values
Robert Hu*, Siu Lun Chau*, Jaime Ferrando Huertas, Dino Sejdinovic
Conference on Neural Information Processing Systems (NeurIPS), 2022
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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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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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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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2021

Deconditional Downscaling with Gaussian Processes
Siu Lun Chau*, Shahine Bouabid*, Dino Sejdinovic
Conference on Neural Information Processing Systems (NeurIPS), 2021
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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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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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