Siu Lun Chau

According to our database, Siu Lun Chau authored at least 17 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Credal Two-Sample Tests of Epistemic Ignorance.

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

CoRR, 2024

Domain Generalisation via Imprecise Learning.

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

Proceedings of the Forty-first International Conference on Machine Learning, 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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Causal Strategic Learning with Competitive Selection.

Kiet Q. H. Vo, Muneeb Aadil, Siu Lun Chau, Krikamol Muandet

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

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

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

Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models.

Siu Lun Chau, Krikamol Muandet, Dino Sejdinovic

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Spectral Ranking with Covariates.

Siu Lun Chau, Mihai Cucuringu, Dino Sejdinovic

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

Giga-scale Kernel Matrix-Vector Multiplication on GPU.

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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Explaining Preferences with Shapley Values.

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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

RKHS-SHAP: Shapley Values for Kernel Methods.

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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Learning Inconsistent Preferences with Gaussian Processes.

Siu Lun Chau, Javier González, Dino Sejdinovic

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Kernel-Based Graph Learning From Smooth Signals: A Functional Viewpoint.

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

IEEE Trans. Signal Inf. Process. over Networks, 2021

RKHS-SHAP: Shapley Values for Kernel Methods.

Siu Lun Chau, Javier González, Dino Sejdinovic

CoRR, 2021

BayesIMP: Uncertainty Quantification for Causal Data Fusion.

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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Deconditional Downscaling with Gaussian Processes.

Siu Lun Chau, Shahine Bouabid, Dino Sejdinovic

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Learning Inconsistent Preferences with Kernel Methods.

Siu Lun Chau, Javier González, Dino Sejdinovic

CoRR, 2020