Ziniu Li

Ph.D. student, School of Data Science, The Chinese University of Hong Kong, Shenzhen Email: ziniuli@link.cuhk.edu.cn [Twitter] [Zhihu]

About me

I am a Ph.D. student at The Chinese University of Hong Kong, Shenzhen (CUHKSZ), advised by Prof. Zhi-Quan (Tom) Luo.

My research interests include reinforcement learning theory, algorithms, and applications.

I have worked/interned at Tencent, Nanjing University, Cardinal Operations, etc.

My curriculum vitae can be downloaded from here.

Feel free to contact me if you want to discuss some ideas.

Selected Work

*: indicating equal contribution or alphabetic ordering.

• Theoretical Analysis of Offline Imitation With Supplementary Dataset
  Ziniu Li* , Tian Xu*, Yang Yu, Zhi-Quan Luo
  arXiv:2301.11687
  (This work explores the framework of transfer learning (or domain adaption) used in CV, NLP, and RL,
  in terms of data coverage, importance sampling (or re-weighting), and feature alignment,
  answering questions including what kind of source data is helpful? when can the importance sampling improve a lot?)

• Understanding Adversarial Imitation Learning in Small Sample Regime: A Stage-coupled Analysis
  Tian Xu*, Ziniu Li* , Yang Yu, Zhi-Quan Luo
  arXiv:2208.01899
  (This work presents the first horizon-free sample complexity bound of adversarial imitation learning (AIL),
  providing an answer to the open question –why does AIL outperform BC by a wide margin, particularly in the low data regime ? –
  raised in CoRL 2019 best paper by Ghasemipour et al.)

• Rethinking ValueDice: Does It Really Improve Performance?
  Ziniu Li* , Tian Xu*, Yang Yu, Zhi-Quan Luo
  In Proceedings of the 10th International Conference on Learning Representations (ICLR) (Blog Track), 2022
  (This work presents the first reduction of offline AIL to BC through a new DP-based analysis,
  proving that AIL is no better than BC in the offline setting and verifying this by experiments)

Service

Reviewer

NeurIPS’2022 (Top Reviewer), ICML’2022 (Outstanding Reviewer), ICLR’2022 (Highlighted Reviewer), DAI’2020.

Teaching Assistant

• CSC4120: Design and Analysis of Algorithms. 2022 Fall, CUHKSZ.

• DDA6111: Discrete Optimization. 2022 Spring, CUHKSZ.

• CSC4120: Design and Analysis of Algorithms. 2021 Fall, CUHKSZ.

• FTE4560: Basic Machine Learning. 2021 Spring, CUHKSZ.

• MAT3007: Introduction to Optimization. 2020 Fall, CUHKSZ.

Lecturer

• Machine Learning (Summer Course for Senior High School Students) @ X ACADEMY 2022 TechX.

Award

• [2021-04] Best oral presentation award at the first doctoral and postdoctoral forum of Shenzhen Research Institute of Big Data.