Meimei Liu

List of courses taught

• CMDA 2005: Integrated Quantitative Science I

List of research projects

• Deep learning: graph embedding, variational inference with application in neuroscience.
• Learning theory: stochastic gradient descent inference, variational inference.
• Big data analysis: domain adaptation, random projection, divide-and-conquer, active learning.
• Semi/Non-parametric inference

• Meimei Liu, Zuofeng Shang, Guang Cheng. Nonparametric distributed learning under general designs. Electronic Journal of Statistics 14.2 (2020): 3070-3102.
• Xin Xing, Meimei Liu, Wenxuan Zhong, Ping Ma. Minimax Nonparametric Parallelism Test. Journal of Machine Learning Research 21.94 (2020): 1-47.
• Meimei Liu, Zuofeng Shang, Guang Cheng. Sharp Theoretical Analysis for Nonparametric Testing under Random Projection. Conference on Computational Learning Theory (COLT) 2019, in Proceedings of Machine Learning Research 99:2175-2209.
• Meimei Liu, Guang Cheng. Early Stopping for Nonparametric Testing. Advances in Neural Information Processing Systems 31 (NeurIPS 2018), 3985–3994.
• Meimei Liu, Jean Honorio, Guang Cheng. Statistically and computationally efficient variance estimator for kernel ridge regression. 2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton), IEEE, 1005-1011.