Liu, X., Duncan, A. B., Gandy, A. (2023). Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy. ICML 2023.
Huang, K. H., Liu, X., Duncan, A. B., Gandy, A. (2023). A High-dimensional Convergence Theorem for U-statistics with Applications to Kernel-based Testing. COLT 2023.
Liu, X., Zhu, H., Ton, J-F., Wynne, G., Duncan, A. B. (2022). Grassmann Stein Variational Gradient Descent. AISTATS 2022.
Liu, X., Duncan, A. B., Gandy, A. (2022). Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy. NeurIPS 2022 Workshop on Score-Based Methods.
Zhu, H., Liu, X., Caron, A., Manolopoulou, I. Flaxman, S., Briol, F-X. (2020). Contributed Discussion of “Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects”. Bayesian Analysis, 15(3), 965-1056.
Talks
14th Dec 2022, Kernelized Stein Discrepancies and their applications in statistics and machine learning, Junior Stats Seminar at Imperial College London.
31st May 2022, Stein's Method in Statistics. Foundations and Landmark Reading Group at University of London (online)
19th Aug 2021, Bayesian probabilistic numerical integration with tree-based models. 13th International Conference on Monte Carlo Methods and Applications (MCM), Universität Mannheim, Germany (online)
31st Mar 2021, Stein Variational Gradient Descent. CSML Reading Group at Imperial College London (online)
Research Projects
Approximate Bayesian Computation with Optimal Transport. Part III Essay; supervised by Dr. Sergio Bacallado. 2020.
Predicting Sepsis at Triage. Fields Undergraduate Summer Research Program; supervised by Dr. Anna Goldenberg and Erik Drysdale. 2019
