Hi there! I am a second-year PhD student in Computer Science department at UCLA, where I am fortunate to be advised by Prof. Quanquan Gu. Previously, I was an undergrad in Yao's class, Tsinghua University . My research interest lies in foundation of machine learning, especially sequential decision-making models, including bandits and reinforcement learning. I am also interested in the application of these models in real world problems.

Education

• Sep 2022 - present, University of California, Los Angeles,
  PhD in Computer Science

• Sep 2018 - Jun 2022, Tsinghua University,
  B.E in Computer Science and Technology

Publications

• Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency
  Heyang Zhao, Jiafan He, Dongruo Zhou, Tong Zhang and Quanquan Gu, in Proc. of the 36th Annual Conference on Learning Theory (COLT), Bangalore, India, 2023. [arXiv]

• Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes
  Jiafan He, Heyang Zhao, Dongruo Zhou and Quanquan Gu, in Proc. of the 40th International Conference on Machine Learning (ICML), Hawaii, USA, 2023. [arXiv]

• Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning
  Qiwei Di, Heyang Zhao, Jiafan He and Quanquan Gu, in Proc. of the 12th International Conference on Learning Representations (ICLR), Vienna, Austria, 2024. [arXiv]

• Variance-Aware Regret Bounds for Stochastic Contextual Dueling Bandits
  Qiwei Di, Tao Jin, Yue Wu, Heyang Zhao, Farzad Farnoud and Quanquan Gu, in Proc. of the 12th International Conference on Learning Representations (ICLR), Vienna, Austria, 2024. [arXiv]

• Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits
  Heyang Zhao, Dongruo Zhou, Jiafan He and Quanquan Gu, in Proc. of the 40th International Conference on Machine Learning (ICML), Hawaii, USA, 2023. [arXiv]