Siu Lun Chau - RankMe

45 papers A* 10A 4Journal 30Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	Exact Shapley Attributions in Quadratic-time for FANOVA Gaussian Processes. AAAI Majid Mohammadi, Krikamol Muandet, Ilaria Tiddi, Annette ten Teije, Siu Lun Chau
2026	J	`jnl`	Incentive Aware AI Regulations: A Credal Characterisation. CoRR Anurag Singh, Julian Rodemann, Rajeev Verma, Siu Lun Chau, Krikamol Muandet
2026	J	`jnl`	Instrumental and Proximal Causal Inference with Gaussian Processes. CoRR Yuqi Zhang, Krikamol Muandet, Dino Sejdinovic, Edwin Fong, Siu Lun Chau
2026	J	`jnl`	Learning Credal Ensembles via Distributionally Robust Optimization. CoRR Kaizheng Wang, Ghifari Adam Faza, Fabio Cuzzolin, Siu Lun Chau, David Moens, Hans Hallez
2026	J	`jnl`	Quantification of Credal Uncertainty: A Distance-Based Approach. CoRR Xabier Gonzalez-Garcia, Siu Lun Chau, Julian Rodemann, Michele Caprio, Krikamol Muandet, Humberto Bustince, Sébastien Destercke, Eyke Hüllermeier, Yusuf Sale
2026	J	`jnl`	Quantifying Epistemic Predictive Uncertainty in Conformal Prediction. CoRR Siu Lun Chau, Soroush H. Zargarbashi, Yusuf Sale, Michele Caprio
2026	J	`jnl`	Robust Predictive Uncertainty and Double Descent in Contaminated Bayesian Random Features. CoRR Michele Caprio, Katerina Papagiannouli, Siu Lun Chau, Sayan Mukherjee
2026	J	`jnl`	Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study. CoRR Kaizheng Wang, Yunjia Wang, Fabio Cuzzolin, David Moens, Hans Hallez, Siu Lun Chau
2026	J	`jnl`	Verbalizing LLM's Higher-order Uncertainty via Imprecise Probabilities. CoRR Anita Yang, Krikamol Muandet, Michele Caprio, Siu Lun Chau, Masaki Adachi
2025	J	`jnl`	Bayesian Optimization for Building Social-Influence-Free Consensus. CoRR Masaki Adachi, Siu Lun Chau, Wenjie Xu, Anurag Singh, Michael A. Osborne, Krikamol Muandet
2025	J	`jnl`	Computing Exact Shapley Values in Polynomial Time for Product-Kernel Methods. CoRR Majid Mohammadi, Siu Lun Chau, Krikamol Muandet
2025	A	`conf`	Credal Two-Sample Tests of Epistemic Uncertainty. AISTATS Siu Lun Chau, Antonin Schrab, Arthur Gretton, Dino Sejdinovic, Krikamol Muandet
2025	J	`jnl`	Exact Shapley Attributions in Quadratic-time for FANOVA Gaussian Processes. CoRR Majid Mohammadi, Krikamol Muandet, Ilaria Tiddi, Annette ten Teije, Siu Lun Chau
2025	J	`jnl`	Integral Imprecise Probability Metrics. CoRR Siu Lun Chau, Michele Caprio, Krikamol Muandet
2025	A*	`conf`	Kernel Quantile Embeddings and Associated Probability Metrics. ICML Masha Naslidnyk, Siu Lun Chau, François-Xavier Briol, Krikamol Muandet
2025	J	`jnl`	Kernel Quantile Embeddings and Associated Probability Metrics. CoRR Masha Naslidnyk, Siu Lun Chau, François-Xavier Briol, Krikamol Muandet
2025	J	`jnl`	Strategic Learning with Local Explanations as Feedback. CoRR Kiet Q. H. Vo, Siu Lun Chau, Masahiro Kato, Yixin Wang, Krikamol Muandet
2025	A	`conf`	Truthful Elicitation of Imprecise Forecasts. UAI Anurag Singh, Siu Lun Chau, Krikamol Muandet
2025	J	`jnl`	Truthful Elicitation of Imprecise Forecasts. CoRR Anurag Singh, Siu Lun Chau, Krikamol Muandet
2025	J	`jnl`	When Do Credal Sets Stabilize? Fixed-Point Theorems for Credal Set Updates. CoRR Michele Caprio, Siu Lun Chau, Krikamol Muandet
2024	A*	`conf`	Causal Strategic Learning with Competitive Selection. AAAI Kiet Q. H. Vo, Muneeb Aadil, Siu Lun Chau, Krikamol Muandet
2024	J	`jnl`	Credal Two-Sample Tests of Epistemic Ignorance. CoRR Siu Lun Chau, Antonin Schrab, Arthur Gretton, Dino Sejdinovic, Krikamol Muandet
2024	A*	`conf`	Domain Generalisation via Imprecise Learning. ICML Anurag Singh, Siu Lun Chau, Shahine Bouabid, Krikamol Muandet
2024	J	`jnl`	Domain Generalisation via Imprecise Learning. CoRR Anurag Singh, Siu Lun Chau, Shahine Bouabid, Krikamol Muandet
2024	A	`conf`	Looping in the Human: Collaborative and Explainable Bayesian Optimization. AISTATS Masaki Adachi, Brady Planden, David A. Howey, Michael A. Osborne, Sebastian Orbell, Natalia Ares, Krikamol Muandet, Siu Lun Chau
2023	J	`jnl`	Causal Strategic Learning with Competitive Selection. CoRR Kiet Q. H. Vo, Muneeb Aadil, Siu Lun Chau, Krikamol Muandet
2023	A*	`conf`	Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models. NeurIPS Siu Lun Chau, Krikamol Muandet, Dino Sejdinovic
2023	J	`jnl`	Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models. CoRR Siu Lun Chau, Krikamol Muandet, Dino Sejdinovic
2023	J	`jnl`	Gated Domain Units for Multi-source Domain Generalization. Trans. Mach. Learn. Res. Simon Föll, Alina Dubatovka, Eugen Ernst, Siu Lun Chau, Martin Maritsch, Patrik Okanovic, Gudrun Thäter, Joachim M. Buhmann, Felix Wortmann, Krikamol Muandet
2023	J	`jnl`	Looping in the Human: Collaborative and Explainable Bayesian Optimization. CoRR Masaki Adachi, Brady Planden, David A. Howey, Krikamol Muandet, Michael A. Osborne, Siu Lun Chau
2022	A*	`conf`	Explaining Preferences with Shapley Values. NeurIPS Robert Hu, Siu Lun Chau, Jaime Ferrando Huertas, Dino Sejdinovic
2022	J	`jnl`	Explaining Preferences with Shapley Values. CoRR Robert Hu, Siu Lun Chau, Jaime Ferrando Huertas, Dino Sejdinovic
2022	A*	`conf`	Giga-scale Kernel Matrix-Vector Multiplication on GPU. NeurIPS Robert Hu, Siu Lun Chau, Dino Sejdinovic, Joan Glaunès
2022	A	`conf`	Learning Inconsistent Preferences with Gaussian Processes. AISTATS Siu Lun Chau, Javier González, Dino Sejdinovic
2022	A*	`conf`	RKHS-SHAP: Shapley Values for Kernel Methods. NeurIPS Siu Lun Chau, Robert Hu, Javier González, Dino Sejdinovic
2022	—	`conf`	Spectral Ranking with Covariates. ECML/PKDD (5) Siu Lun Chau, Mihai Cucuringu, Dino Sejdinovic
2021	A*	`conf`	BayesIMP: Uncertainty Quantification for Causal Data Fusion. NeurIPS Siu Lun Chau, Jean-Francois Ton, Javier González, Yee Whye Teh, Dino Sejdinovic
2021	J	`jnl`	BayesIMP: Uncertainty Quantification for Causal Data Fusion. CoRR Siu Lun Chau, Jean-François Ton, Javier González, Yee Whye Teh, Dino Sejdinovic
2021	A*	`conf`	Deconditional Downscaling with Gaussian Processes. NeurIPS Siu Lun Chau, Shahine Bouabid, Dino Sejdinovic
2021	J	`jnl`	Deconditional Downscaling with Gaussian Processes. CoRR Siu Lun Chau, Shahine Bouabid, Dino Sejdinovic
2021	J	`jnl`	Kernel-Based Graph Learning From Smooth Signals: A Functional Viewpoint. IEEE Trans. Signal Inf. Process. over Networks Xingyue Pu, Siu Lun Chau, Xiaowen Dong, Dino Sejdinovic
2021	J	`jnl`	RKHS-SHAP: Shapley Values for Kernel Methods. CoRR Siu Lun Chau, Javier González, Dino Sejdinovic
2020	J	`jnl`	Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint. CoRR Xingyue Pu, Siu Lun Chau, Xiaowen Dong, Dino Sejdinovic
2020	J	`jnl`	Learning Inconsistent Preferences with Kernel Methods. CoRR Siu Lun Chau, Javier González, Dino Sejdinovic
2020	J	`jnl`	Spectral Ranking with Covariates. CoRR Siu Lun Chau, Mihai Cucuringu, Dino Sejdinovic