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ORCIDSiu Lun Chau
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2020 – today
- 2024
[c11]Kiet Q. H. Vo, Muneeb Aadil, Siu Lun Chau, Krikamol Muandet
:
Causal Strategic Learning with Competitive Selection. AAAI 2024: 15411-15419- [c10]Masaki Adachi, Brady Planden, David A. Howey, Michael A. Osborne, Sebastian Orbell, Natalia Ares, Krikamol Muandet, Siu Lun Chau:
Looping in the Human: Collaborative and Explainable Bayesian Optimization. AISTATS 2024: 505-513 - [c9]Anurag Singh, Siu Lun Chau, Shahine Bouabid, Krikamol Muandet:
Domain Generalisation via Imprecise Learning. ICML 2024 - [i11]Anurag Singh, Siu Lun Chau, Shahine Bouabid, Krikamol Muandet:
Domain Generalisation via Imprecise Learning. CoRR abs/2404.04669 (2024) - 2023
- [j2]Simon Föll, Alina Dubatovka, Eugen Ernst, Siu Lun Chau, Martin Maritsch, Patrik Okanovic, Gudrun Thäter, Joachim M. Buhmann, Felix Wortmann, Krikamol Muandet:
Gated Domain Units for Multi-source Domain Generalization. Trans. Mach. Learn. Res. 2023 (2023) - [c8]Siu Lun Chau, Krikamol Muandet, Dino Sejdinovic:
Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models. NeurIPS 2023 - [i10]Siu Lun Chau, Krikamol Muandet, Dino Sejdinovic:
Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models. CoRR abs/2305.15167 (2023) - [i9]Kiet Q. H. Vo, Muneeb Aadil, Siu Lun Chau, Krikamol Muandet:
Causal Strategic Learning with Competitive Selection. CoRR abs/2308.16262 (2023) - [i8]Masaki Adachi, Brady Planden, David A. Howey, Krikamol Muandet, Michael A. Osborne, Siu Lun Chau:
Looping in the Human: Collaborative and Explainable Bayesian Optimization. CoRR abs/2310.17273 (2023) - 2022
- [c7]Siu Lun Chau, Javier González, Dino Sejdinovic:
Learning Inconsistent Preferences with Gaussian Processes. AISTATS 2022: 2266-2281 - [c6]Siu Lun Chau, Robert Hu, Javier González, Dino Sejdinovic:
RKHS-SHAP: Shapley Values for Kernel Methods. NeurIPS 2022 - [c5]Robert Hu, Siu Lun Chau, Jaime Ferrando Huertas, Dino Sejdinovic:
Explaining Preferences with Shapley Values. NeurIPS 2022 - [c4]Robert Hu, Siu Lun Chau, Dino Sejdinovic, Joan Glaunès:
Giga-scale Kernel Matrix-Vector Multiplication on GPU. NeurIPS 2022 - [c3]Siu Lun Chau, Mihai Cucuringu, Dino Sejdinovic:
Spectral Ranking with Covariates. ECML/PKDD (5) 2022: 70-86 - [i7]Robert Hu, Siu Lun Chau, Jaime Ferrando Huertas, Dino Sejdinovic:
Explaining Preferences with Shapley Values. CoRR abs/2205.13662 (2022) - 2021
- [j1]Xingyue Pu, Siu Lun Chau, Xiaowen Dong, Dino Sejdinovic:
Kernel-Based Graph Learning From Smooth Signals: A Functional Viewpoint. IEEE Trans. Signal Inf. Process. over Networks 7: 192-207 (2021) - [c2]Siu Lun Chau, Jean-Francois Ton, Javier González, Yee Whye Teh, Dino Sejdinovic:
BayesIMP: Uncertainty Quantification for Causal Data Fusion. NeurIPS 2021: 3466-3477 - [c1]Siu Lun Chau, Shahine Bouabid, Dino Sejdinovic:
Deconditional Downscaling with Gaussian Processes. NeurIPS 2021: 17813-17825 - [i6]Siu Lun Chau, Shahine Bouabid, Dino Sejdinovic:
Deconditional Downscaling with Gaussian Processes. CoRR abs/2105.12909 (2021) - [i5]Siu Lun Chau, Jean-François Ton, Javier González, Yee Whye Teh, Dino Sejdinovic:
BayesIMP: Uncertainty Quantification for Causal Data Fusion. CoRR abs/2106.03477 (2021) - [i4]Siu Lun Chau, Javier González, Dino Sejdinovic:
RKHS-SHAP: Shapley Values for Kernel Methods. CoRR abs/2110.09167 (2021) - 2020
- [i3]Siu Lun Chau, Mihai Cucuringu, Dino Sejdinovic:
Spectral Ranking with Covariates. CoRR abs/2005.04035 (2020) - [i2]Siu Lun Chau, Javier González, Dino Sejdinovic:
Learning Inconsistent Preferences with Kernel Methods. CoRR abs/2006.03847 (2020) - [i1]Xingyue Pu, Siu Lun Chau, Xiaowen Dong, Dino Sejdinovic:
Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint. CoRR abs/2008.10065 (2020)
Coauthor Index
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