I am a third-year PhD student in the H. Milton Stewart School of Industrial and Systems Engineering (ISyE) at Georgia Tech, advised by Prof. Tuo Zhao. My research focuses on deep learning theory. I am interested in function approximation and optimization theory of neural networks, as well as their statistical guarantees in learning applications. I am also interested in deep learning applications including large language models and diffusion models.

Before joining Georgia Tech, I earned a B.S. in Statistics from University of Science and Technology of China (USTC).

Publications

• Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks
  Zixuan Zhang*, Kaiqi Zhang*, Minshuo Chen, Yuma Takeda, Mengdi Wang, Tuo Zhao, Yu-Xiang Wang
  Annual Conference on Neural Information Processing Systems (NeurIPS), 2024

• Robust Reinforcement Learning from Corrupted Human Feedback
  Alexander Bukharin, Ilgee Hong, Haoming Jiang, Zichong Li, Qingru Zhang, Zixuan Zhang, Tuo Zhao
  Annual Conference on Neural Information Processing Systems (NeurIPS), 2024

• Effective Minkowski dimension of deep nonparametric regression: function approximation and statistical theories
  Zixuan Zhang, Minshuo Chen, Mengdi Wang, Wenjing Liao, Tuo Zhao
  International Conference on Machine Learning (ICML), 2023

• Sequential information design: Markov persuasion process and its efficient reinforcement learning
  Jibang Wu, Zixuan Zhang, Zhe Feng, Zhaoran Wang, Zhuoran Yang, Michael I Jordan, Haifeng Xu
  ACM Conference on Economics and Computation, 2022

Talks

• Sample Complexity of Diffusion Models for Learning Distributions on Low Dimensional Manifolds
  INFORMS Annual 2024, Seattle