Department of Statistics and Data Science, Yale University
I am a fourth year Ph.D. student in the Department of Statistics and Data Science at Yale University. I am very fortunate to be advised by Prof. Zhou Fan. I am broadly interested in various aspects of statistics and machine learning theory, with a recent focus on AMP algorithms and optimization in deep learning.
Prior to Yale, I obtained my Bachelor's degree in mathematics with a dual degree in computer science at University of Science and Technology of China.
Tianhao Wang, Xinyi Zhong, Zhou Fan. Universality of Approximate Message Passing algorithms and tensor networks. arXiv, 2022
Yifei Min, Tianhao Wang, Ruitu Xu, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang. Learn to match with no regret: Reinforcement Learning in Markov matching markets. arXiv, 2022.
Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu. Learning stochastic shortest path with linear function approximation. ICML, 2022.
Zhiyuan Li, Tianhao Wang, Sanjeev Arora. What happens after SGD reaches zero loss? -- A mathematical framework. ICLR, 2022 (Spotlight).
Xinyi Zhong*, Tianhao Wang*, Zhou Fan. Approximate Message Passing for orthogonally invariant ensembles: Multivariate non-linearities and spectral initialization. arXiv, 2021.
Yifei Min*, Tianhao Wang*, Dongruo Zhou, Quanquan Gu. Variance-aware off-policy evaluation with linear function approximation. NeurIPS, 2021.
Tianhao Wang*, Dongruo Zhou*, Quanquan Gu. Provably efficient reinforcement learning with linear function approximation under adaptivity constraints. NeurIPS, 2021.
Zhou Fan, Roy R. Lederman, Yi Sun, Tianhao Wang, Sheng Xu. Maximum likelihood for high-noise group orbit estimation and single-particle cryo-EM. arXiv, 2021.
Zhou Fan, Yi Sun, Tianhao Wang, Yihong Wu. Likelihood landscape and maximum likelihood estimation for the discrete orbit recovery model. Communications on Pure and Applied Mathematics, to appear.