Welcome
Hello! My name is Siu Lun Chau (兆麟 周). I am a postdoctoral researcher at the Rational Intelligence Lab of CISPA Helmholtz Center for Information Security working with Dr. Krikamol Muandet on Trustworthy Machine Learning in Saarbrücken, Germany. Prior to joining CISPA, I pursued my DPhil within the Oxford Computational Statisics and Machine Learning Group at the University of Oxford, under the supervision of Prof. Dino Sejdinovic, Prof. Mihai Cucuringu and Prof. Xiaowen Dong. During my DPhil, I interned at Amazon Transportation Service group as an Applied Scientist working on coherent forecasting problems for the EU logistics network. I have also interned at the Max Planck Institute for Intelligent Systems where I worked on improving econometric models with modern machine learning methods. I previously obtained a masters and bachelor degree with first class honours in mathematics and statistics at the University of Oxford. During my masters, I worked with Prof. Mihaela Van Der Schaar on disease trajectory modelling with Bayesian nonparametric models.
You can read more about my research interests here. Please do not hesitate to reach out if you would like to collaborate, I am always excited to hear from you :)
Recent Updates 🔔
- May-2024
Our paper Domain Generalisation via Imprecise Learning has been accepted as a spotlight paper for publication at the International Conference on Machine Learning (ICML) 2024! 🎉
- Feb-2024
I presented “Stochastic Shapley values for Gaussian process models with application to Explainable Bayesian Optimisation” at the Institute for Informatics, LMU.
- Feb-2024
I was invited to participate in the Dagstuhl Seminar on ‘AI for Social Good’ this year to learn about and tackle challenges faced by NGOs when implementing AI solutions.
- Jan-2024
Our paper ‘Looping in the Human: Collaborative and Explainable Bayesian Optimization’ has been accepted for in AISTATS for poster presentation!
- Dec-2023
Our paper ‘Causal Strategic Learning with Competitive Selection’ has been accepted for in AAAI as an oral paper!
- Dec-2023
I’m excited to present a poster spotlight on ‘Explaining Gaussian Process Models with Stochastic Shapley Values’ at the Australian Data Science Network conference.
- Nov-2023
I am at the Australian Institute for Machine Learning 🇦🇺 as a visiting researcher from November onwards!
- Sep-2023
Our paper ‘Explaining the Uncertain: Stochastic Shapley values for Gaussian process models’ has been accepted for in Neural Information Processing Systems (NeurIPS) as a spotlight paper!
- Sep-2023
Our paper “Gated Domain Units for Multi-source Domain Generalization” has been accepted for publication in Transactions on Machine Learning Research (TMLR).
- Sep-2023
I have officially started my postdoc at CISPA! 👨🏻
- Jul-2023
I have passed my Ph.D. viva!
Upcoming/Recent Talks 🗣️
- Mar-2024
- Stochastic Shapley Values for Gaussian Processes and application to Explainable Bayesian Optimisation
- Institute for Informatics, LMU - Dec-2023
- Stochastic Shapley Values for Gaussian Processes
- Australian Data Science Network - Nov-2023
- Collaborative and Explainable Bayesian Optimisation
- Data 61 Melbourne - Nov-2023
- Stochastic Shapley Values for Gaussian Processes
- School of Computing, Australian National University - Nov-2023
- Collaborative and Explainable Bayesian Optimisation
- School of Computing and Information Systems, The University of Melbourne - Nov-2023
- Stochastic Shapley Values for Gaussian Processes
- Australian Institute for Machine Learning (AIML) - Sep-2023
- Stochastic Shapley Values for Gaussian Processes
- Oxford Man Institute, University of Oxford - Sep-2023
- Stochastic Shapley Values for Gaussian Processes
- ETH AI Center - Sep-2023
- Stochastic Shapley Values for Gaussian Processes
- Department of Management, Technology, and Economics (D-MTEC) at ETH Zürich - Feb-2023
- Introduction to Explainable ML
- Oxford Strategy Group Digital