Summary
I am a PhD student in Statistics at Imperial College London, supervised by Professor Axel Gandy and Dr Andrew Duncan. I joined in the StatML CDT in Autumn 2020.
My current research includes developing fast, adaptive MCMC schemes and the applications of Stein's method in machine learning. I am generally interested in computational Bayesian statistics and approximate inference.
I am helping Dr James Martin with the courses Applicable Maths (Autumn term 2021) and Exploratory Data Analysis and Visualisation (Spring term 2022), both of which are part of the degree MSc Machine Learning and Data Science (Online).
More information is available on my personal website http://xinglliu.github.io/.
Publications
- Liu, X., Zhu, H., Ton, J-F., Wynne, G., Duncan, A. (2022). Grassmann Stein Variational Gradient Descent. AISTATS 2022. (Paper) (Slides).
- Zhu, H., Liu, X., Kang, R., Shen, Z., Flaxman, S., Briol, F-X. (2020). Bayesian probabilistic numerical integration with tree-based models. NeurIPS 2020. (Paper).
Selected Publications
Conference
Zhu H, Liu X, Kang R, et al., Bayesian Probabilistic Numerical Integration with Tree-Based Models, Conference on Neural Information Processing Systems