Publications

19. Truthful Elicitation of Imprecise Forecast

Anurag Singh, Siu Lun Chau, Krikamol Muandet

ArXiv, 2025

PDF Code

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

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

ArXiv, 2025

PDF

17. Strategic Learning with Local Explanations as Feedback

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

ArXiv, 2025

PDF Code

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

PDF Code Video

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

ChemRxiv, 2024

PDF Code

14. Domain Generalisation via Imprecise Learning

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

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

PDF Code

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

PDF Code

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

PDF Code

11. Towards Trustworthy Machine Learning with Kernels

Siu Lun Chau

Ph.D. Thesis, 2023

PDF

10. Stochastic Shapley Values for Gaussian Process Models

Siu Lun Chau, Krikamol Muandet*, Dino Sejdinovic*

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

PDF Code Video

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

PDF Code

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

PDF Code

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

PDF Code Video

6. Explaining Preferences with Shapley Values

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

Conference on Neural Information Processing Systems (NeurIPS), 2022

PDF Code Video

5. Learning Inconsistent Preferences with Gaussian Processes

Siu Lun Chau, Javier González, Dino Sejdinovic

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

PDF Video

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

PDF Code

3. 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

PDF

2. Deconditional Downscaling with Gaussian Processes

Siu Lun Chau*, Shahine Bouabid*, Dino Sejdinovic

Conference on Neural Information Processing Systems (NeurIPS), 2021

PDF Code Video

1. 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

PDF Video