Yingru LI

Yingru LI

Ph.D. Candidate

Chinese University of Hong Kong

Biography

Mr. Yingru Li is a Ph.D. Candidate in the Chinese University of Hong Kong (CUHK), Shenzhen, China. Fortunately, he is advised by Zhi-Quan (Tom) Luo. He received the bachelor degree in Computer Science (ACM Honors Program) from Huazhong University of Science and Technology with an advisory of Kun He. He was a research visiting student at Cornell University with John E. Hopcroft. His Ph.D. research is supported by SRIBD Scholarship, Presidential Fellowship and Tencent Ph.D. Fellowship.

He organized RL Seminar in CUHK-SZ from 2019 to 2022.

Now actively seeking posdoctoral and research positions! my resumé; cards.

NeurIPS 2023, New Orleans 🚀 My research in RL encompasses both theoretical aspects of high-dim probability and practical applications in Deep RL. I have developed a novel random projection tool for sequentially dependent data, which extends the Johnson–Lindenstrauss lemma in a non-trivial way and effectively addresses efficiency challenges in RL. 🚀

Interests
  • Sequential Decision-making and Reinforcement Learning.
  • Algorithms Design and Analysis. Probabilistic Analysis.
  • Random projection in sequential processes.
  • Human-centered AI.
Education
  • Ph.D. in Computer and Information Engineering., 2018 --

    The Chinese University of Hong Kong

  • B.Eng. in Computer Science (Honors Program). Outstanding Graduate, 2017

    Huazhong University of Science and Technology, China

Recent Publications

Quickly discover relevant content by filtering publications.
(2023). A value-targeted analysis of posterior sampling reinforcement learning with linear function approximation . Submitted to The 27th International Conference on Artificial Intelligence and Statistics (AISTATS).

Cite

(2023). Optimistic Thompson Sampling for No-Regret Learning in Unknown Games. Submitted to The 27th International Conference on Artificial Intelligence and Statistics (AISTATS).

Cite

(2022). HyperDQN: A Randomized Exploration Method for Deep Reinforcement Learning. International Conference on Learning Representations.

Cite

Contact

You only live once.

Gallery