2026
Quantification of Credal Uncertainty: A Distance-Based Approach
arXiv preprint arXiv:2603.27270, 2026
@article{gonzalezgarcia2026quantification,
title = {Quantification of Credal Uncertainty: A Distance-Based Approach},
author = {Gonzalez-Garcia, Xabier and Chau, Siu Lun and Rodemann, Julian and Caprio, Michele and Muandet, Krikamol and Bustince, Humberto and Destercke, S{\'e}bastien and H{\"u}llermeier, Eyke and Sale, Yusuf},
journal = {arXiv preprint arXiv:2603.27270},
year = {2026}
}
Verbalizing LLM's Higher-order Uncertainty via Imprecise Probabilities
arXiv preprint arXiv:2603.10396, 2026
@article{yang2026verbalizing,
title = {Verbalizing LLM's Higher-order Uncertainty via Imprecise Probabilities},
author = {Yang, Anita and Muandet, Krikamol and Caprio, Michele and Chau, Siu Lun and Adachi, Masaki},
journal = {arXiv preprint arXiv:2603.10396},
year = {2026}
}
Incentive Aware AI Regulations: A Credal Characterisation
arXiv preprint arXiv:2603.05175, 2026
@article{singh2026incentive,
title = {Incentive Aware AI Regulations: A Credal Characterisation},
author = {Singh, Anurag and Rodemann, Julian and Verma, Rajeev and Chau, Siu Lun and Muandet, Krikamol},
journal = {arXiv preprint arXiv:2603.05175},
year = {2026}
}
Instrumental and Proximal Causal Inference with Gaussian Processes
arXiv preprint arXiv:2603.02159, 2026
@article{zhang2026instrumental,
title = {Instrumental and Proximal Causal Inference with Gaussian Processes},
author = {Zhang, Yuqi and Muandet, Krikamol and Sejdinovic, Dino and Fong, Edwin and Chau, Siu Lun},
journal = {arXiv preprint arXiv:2603.02159},
year = {2026}
}
Set-based vs Distribution-based Representations of Epistemic Uncertainty: A Comparative Study
arXiv preprint arXiv:2602.22747, 2026
@article{wang2026set,
title = {Set-based vs Distribution-based Representations of Epistemic Uncertainty: A Comparative Study},
author = {Wang, Kaizheng and Wang, Yunjia and Cuzzolin, Fabio and Moens, David and Hallez, Hans and Chau, Siu Lun},
journal = {arXiv preprint arXiv:2602.22747},
year = {2026}
}
Robust Predictive Uncertainty and Double Descent in Contaminated Bayesian Random Features
arXiv preprint arXiv:2602.19126, 2026
@article{caprio2026robust,
title = {Robust Predictive Uncertainty and Double Descent in Contaminated Bayesian Random Features},
author = {Caprio, Michele and Papagiannouli, Katerina and Chau, Siu Lun and Mukherjee, Sayan},
journal = {arXiv preprint arXiv:2602.19126},
year = {2026}
}
Learning Credal Ensembles via Distributionally Robust Optimization
arXiv preprint arXiv:2602.08470, 2026
@article{wang2026learning,
title = {Learning Credal Ensembles via Distributionally Robust Optimization},
author = {Wang, Kaizheng and Faza, Ghifari Adam and Cuzzolin, Fabio and Chau, Siu Lun and Moens, David and Hallez, Hans},
journal = {arXiv preprint arXiv:2602.08470},
year = {2026}
}
Quantifying Epistemic Predictive Uncertainty in Conformal Prediction
arXiv preprint arXiv:2602.01667, 2026
@article{chau2026quantifying,
title = {Quantifying Epistemic Predictive Uncertainty in Conformal Prediction},
author = {Chau, Siu Lun and Zargarbashi, Soroush H and Sale, Yusuf and Caprio, Michele},
journal = {arXiv preprint arXiv:2602.01667},
year = {2026}
}
Exact shapley attributions in quadratic-time for fanova gaussian processes
Proceedings of the AAAI Conference on Artificial Intelligence, 2026
@inproceedings{mohammadi2026exact,
title = {Exact shapley attributions in quadratic-time for fanova gaussian processes},
author = {Mohammadi, Majid and Muandet, Krikamol and Tiddi, Ilaria and Ten Teije, Annette and Chau, Siu Lun},
booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
volume = {40},
number = {29},
pages = {24414--24421},
year = {2026}
}
Explanation Design in Strategic Learning: Sufficient Explanations that Induce Non-harmful Responses
The 29th International Conference on Artificial Intelligence and Statistics, 2026
@inproceedings{voexplanation,
title = {Explanation Design in Strategic Learning: Sufficient Explanations that Induce Non-harmful Responses},
author = {Vo, Kiet QH and Chau, Siu Lun and Kato, Masahiro and Wang, Yixin and Muandet, Krikamol},
booktitle = {The 29th International Conference on Artificial Intelligence and Statistics},
year = {2026}
}
2025
When do credal sets stabilize? fixed-point theorems for credal set updates
arXiv preprint arXiv:2510.04769, 2025
@article{caprio2025credal,
title = {When do credal sets stabilize? fixed-point theorems for credal set updates},
author = {Caprio, Michele and Chau, Siu Lun and Muandet, Krikamol},
journal = {arXiv preprint arXiv:2510.04769},
year = {2025}
}
Computing exact shapley values in polynomial time for product-kernel methods
arXiv preprint arXiv:2505.16516, 2025
@article{mohammadi2025computing,
title = {Computing exact shapley values in polynomial time for product-kernel methods},
author = {Mohammadi, Majid and Chau, Siu Lun and Muandet, Krikamol},
journal = {arXiv preprint arXiv:2505.16516},
year = {2025}
}
Highly parallel optimisation of chemical reactions through automation and machine intelligence
Nature Communications, 2025
@article{sin2025highly,
title = {Highly parallel optimisation of chemical reactions through automation and machine intelligence},
author = {Sin, Joshua W and Chau, Siu Lun and Burwood, Ryan P and P{\"u}ntener, Kurt and Bigler, Raphael and Schwaller, Philippe},
journal = {Nature Communications},
volume = {16},
number = {1},
pages = {6464},
year = {2025},
publisher = {Nature Publishing Group UK London}
}
Kernel Quantile Embeddings and Associated Probability Metrics
International Conference on Machine Learning, 2025
@inproceedings{naslidnyk2025kernel,
title = {Kernel Quantile Embeddings and Associated Probability Metrics},
author = {Naslidnyk, Masha and Chau, Siu Lun and Briol, Francois-Xavier and Muandet, Krikamol},
booktitle = {International Conference on Machine Learning},
pages = {45770--45800},
year = {2025},
organization = {PMLR}
}
Truthful Elicitation of Imprecise Forecasts
(Oral) Conference on Uncertainty in Artificial Intelligence, 2025
@inproceedings{singh2025truthful,
title = {Truthful Elicitation of Imprecise Forecasts},
author = {Singh, Anurag and Chau, Siu Lun and Muandet, Krikamol},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
pages = {3898--3919},
year = {2025},
organization = {PMLR}
}
Bayesian optimization for building social-influence-free consensus
arXiv preprint arXiv:2502.07166, 2025
@article{adachi2025bayesian,
title = {Bayesian optimization for building social-influence-free consensus},
author = {Adachi, Masaki and Chau, Siu Lun and Xu, Wenjie and Singh, Anurag and Osborne, Michael A and Muandet, Krikamol},
journal = {arXiv preprint arXiv:2502.07166},
year = {2025}
}
Integral Imprecise Probability Metrics
The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025
@inproceedings{chauintegral,
title = {Integral Imprecise Probability Metrics},
author = {Chau, Siu Lun and Caprio, Michele and Muandet, Krikamol},
booktitle = {The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year = {2025}
}
Credal Two-Sample Tests of Epistemic Uncertainty
International Conference on Artificial Intelligence and Statistics, 2025
@inproceedings{chau2025credal,
title = {Credal Two-Sample Tests of Epistemic Uncertainty},
author = {Chau, Siu Lun and Schrab, Antonin and Gretton, Arthur and Sejdinovic, Dino and Muandet, Krikamol},
booktitle = {International Conference on Artificial Intelligence and Statistics},
pages = {127--135},
year = {2025},
organization = {PMLR}
}
2024
Domain generalisation via imprecise learning
(Spotlight) Proceedings of the 41st International Conference on Machine Learning, 2024
@inproceedings{singh2024domain,
title = {Domain generalisation via imprecise learning},
author = {Singh, Anurag and Chau, Siu Lun and Bouabid, Shahine and Muandet, Krikamol},
booktitle = {Proceedings of the 41st International Conference on Machine Learning},
pages = {45544--45570},
year = {2024}
}
Looping in the Human: Collaborative and Explainable Bayesian Optimization
International Conference on Artificial Intelligence and Statistics, 2024
@inproceedings{adachi2024looping,
title = {Looping in the Human: Collaborative and Explainable Bayesian Optimization},
author = {Adachi, Masaki and Planden, Brady and Howey, David and Osborne, Michael A and Orbell, Sebastian and Ares, Natalia and Muandet, Krikamol and Chau, Siu Lun},
booktitle = {International Conference on Artificial Intelligence and Statistics},
pages = {505--513},
year = {2024},
organization = {PMLR}
}
Causal strategic learning with competitive selection
(Oral) Proceedings of the AAAI Conference on Artificial Intelligence, 2024
@inproceedings{vo2024causal,
title = {Causal strategic learning with competitive selection},
author = {Vo, Kiet QH and Aadil, Muneeb and Chau, Siu Lun and Muandet, Krikamol},
booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
volume = {38},
number = {14},
pages = {15411--15419},
year = {2024}
}
2023
Explaining the uncertain: Stochastic shapley values for gaussian process models
(Spotlight) Advances in Neural Information Processing Systems, 2023
@article{chau2023explaining,
title = {Explaining the uncertain: Stochastic shapley values for gaussian process models},
author = {Chau, Siu Lun and Muandet, Krikamol and Sejdinovic, Dino},
journal = {Advances in Neural Information Processing Systems},
volume = {36},
pages = {50769--50795},
year = {2023}
}
@phdthesis{chau2023towards,
title = {Towards trustworthy machine learning with kernels},
year = {2023},
school = {University of Oxford}
}
Gated Domain Units for Multi-source Domain Generalization
Transactions on Machine Learning Research, 2023
@article{follgated,
title = {Gated Domain Units for Multi-source Domain Generalization},
author = {F{\"o}ll, Simon and Dubatovka, Alina and Ernst, Eugen and Chau, Siu Lun and Maritsch, Martin and Okanovic, Patrik and Thaeter, Gudrun and Buhmann, Joachim M and Wortmann, Felix and Muandet, Krikamol},
journal = {Transactions on Machine Learning Research},
year = {2023}
}
2022
Explaining preferences with shapley values
Advances in Neural Information Processing Systems, 2022
@article{hu2022explaining,
title = {Explaining preferences with shapley values},
author = {Hu, Robert and Chau, Siu Lun and Ferrando Huertas, Jaime and Sejdinovic, Dino},
journal = {Advances in Neural Information Processing Systems},
volume = {35},
pages = {27664--27677},
year = {2022}
}
Giga-scale kernel matrix-vector multiplication on GPU
Advances in Neural Information Processing Systems, 2022
@article{hu2022giga,
title = {Giga-scale kernel matrix-vector multiplication on GPU},
author = {Hu, Robert and Chau, Siu Lun and Sejdinovic, Dino and Glaun{\`e}s, Joan},
journal = {Advances in Neural Information Processing Systems},
volume = {35},
pages = {9045--9057},
year = {2022}
}
RKHS-SHAP: Shapley values for kernel methods
Advances in neural information processing systems, 2022
@article{chau2022rkhs,
title = {RKHS-SHAP: Shapley values for kernel methods},
author = {Chau, Siu Lun and Hu, Robert and Gonzalez, Javier and Sejdinovic, Dino},
journal = {Advances in neural information processing systems},
volume = {35},
pages = {13050--13063},
year = {2022}
}
Learning inconsistent preferences with gaussian processes
International Conference on Artificial Intelligence and Statistics, 2022
@inproceedings{chau2022learning,
title = {Learning inconsistent preferences with gaussian processes},
author = {Chau, Siu Lun and Gonzalez, Javier and Sejdinovic, Dino},
booktitle = {International Conference on Artificial Intelligence and Statistics},
pages = {2266--2281},
year = {2022},
organization = {PMLR}
}
Spectral ranking with covariates
Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2022
@inproceedings{chau2022spectral,
title = {Spectral ranking with covariates},
author = {Chau, Siu Lun and Cucuringu, Mihai and Sejdinovic, Dino},
booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
pages = {70--86},
year = {2022},
organization = {Springer}
}
2021
Bayesimp: Uncertainty quantification for causal data fusion
Advances in Neural Information Processing Systems, 2021
@article{chau2021bayesimp,
title = {Bayesimp: Uncertainty quantification for causal data fusion},
author = {Chau, Siu Lun and Ton, Jean-Francois and Gonz{\'a}lez, Javier and Teh, Yee and Sejdinovic, Dino},
journal = {Advances in Neural Information Processing Systems},
volume = {34},
pages = {3466--3477},
year = {2021}
}
Deconditional downscaling with gaussian processes
Advances in Neural Information Processing Systems, 2021
@article{chau2021deconditional,
title = {Deconditional downscaling with gaussian processes},
author = {Chau, Siu Lun and Bouabid, Shahine and Sejdinovic, Dino},
journal = {Advances in Neural Information Processing Systems},
volume = {34},
pages = {17813--17825},
year = {2021}
}
Kernel-based graph learning from smooth signals: A functional viewpoint
IEEE Transactions on Signal and Information Processing over Networks, 2021
@article{pu2021kernel,
title = {Kernel-based graph learning from smooth signals: A functional viewpoint},
author = {Pu, Xingyue and Chau, Siu Lun and Dong, Xiaowen and Sejdinovic, Dino},
journal = {IEEE Transactions on Signal and Information Processing over Networks},
volume = {7},
pages = {192--207},
year = {2021},
publisher = {IEEE}
}