Publication and preprints
Federated Transfer Learning with Differential Privacy
M. Li, Y. Tian, Y. Feng, Y. Yu.
Preprint, 2024.
Robust mean change point testing in high-dimensional data with heavy tails
M. Li*, Y. Chen*, T. Wang, Y. Yu.
Preprint, 2023. (*equal contribution)
On robustness and local differential privacy
M. Li, T.B. Berrett, Y. Yu.
Annals of Statistics 51(2), 717-737, 2023.
Contributions to robustness, local differential privacy and change point analysis
M. Li
PhD Thesis, 2023. (Harrison Award for highly commended thesis)
Network change point localisation under local differential privacy
M. Li, T.B. Berrett, Y. Yu.
Advances in Neural Information Processing Systems 35 (NeurIPS), 2022.
Adversarially robust change point detection
M. Li, Y. Yu.
Advances in Neural Information Processing Systems 34 (NeurIPS), 2021.
Multisite educational trials: estimating the effect size and its confidence intervals
A. Singh, G. Uwimpuhwe, M. Li, J. Einbeck, S. Higgins, A. Kasim
International Journal of Research & Method in Education, 45(1), 18-38, 2021.
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Teaching
In 2024-2025, I am lecturing LM Foundations of Statistical Inference in Semester 1 and LM Bayesian Inference and Computation in Semester 2, both with Lukas Trottner.
Previously, I led tutorials/labs for the following modules at Warwick: Introduction to Mathematical Statistics, Linear Statistical Modelling with R, Statistical Laboratory.
Software
I contributed to the R package changepoints: A Collection of Change-Point Detection Methods.
Events
(Seminar talks) U of Sydney Business School, U of York, U of Leeds, U of Southampton
(Conferences and workshops) ICSDS 2024, Workshop on heterogeneous and distributed data 2024, CMStatistics 2023, Workshop on Statistical Analysis of Networks 2023, RSS Conference 2023, Workshop on Change Point Analysis 2023, NeurIPS 2022 (with Scholar award), ICORS 2022, IMS Annual Meeting 2022, ISNPS 2022, Structural Breaks and Shape Constraints workshop 2022, NeurIPS 2021, StatScale workshop 2021
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