Publications

My research mainly spans across the topics of Uncertainty-aware ML (UAI), Explanation in ML(XAI), and Preference Modelling(PM).

17. Strategic Learning with Local Explanations as Feedback XAI

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

ArXiv, 2025

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16. Credal Two-sample Tests of Epistemic Ignorance UAI

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 UAI

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

ChemRxiv, 2024

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14. Domain Generalisation via Imprecise Learning UAI

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 XAI

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 UAI XAI PM

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 UAI XAI PM

Siu Lun Chau

Ph.D. Thesis, 2023

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10. Stochastic Shapley Values for Gaussian Process Models UAI XAI

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 UAI

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 PM

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 XAI

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 XAI PM

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 UAI PM

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 UAI

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

Conference on Neural Information Processing Systems (NeurIPS), 2022

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

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

IEEE Transactions on Signal and Information Processing over Networks, 2021

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2. Deconditional Downscaling with Gaussian Processes UAI

Siu Lun Chau*, Shahine Bouabid*, Dino Sejdinovic

Conference on Neural Information Processing Systems (NeurIPS), 2021

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1. Uncertainty Quantification for Causal Data Fusion UAI

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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