Hello! My name is Gene Li. I’m a third year PhD student at the Toyota Technological Institute at Chicago, where I’m advised by Nathan Srebro. I also work with Cong Ma at UChicago Stats.

Previously, I graduated with a BSE from the Electrical and Computer Engineering department at Princeton University. There, I had the pleasure of working with Yuxin Chen and Emmanuel Abbe.

I’m broadly interested in theoretical machine learning and decision making. Recently, I have been thinking about function approximation for decision making settings like contextual bandits and reinforcement learning, with the goal of characterizing the fundamental limits for these problems and understanding new algorithmic paradigms.

If you want to get in touch, you can reach me at: [gene at ttic dot edu].

You can find my (perpetually outdated) CV here.

Recent News

• Spending Summer 2022 at Princeton University, working with Prof. Jason Lee.
• Co-organized the TTIC Student Workshop in August 2021.
• Spent Fall 2020 as a (virtual) visiting graduate student at the Theory of Reinforcement Learning program at the Simons Institute.

Publications

• Pessimism for Offline Linear Contextual Bandits using \(\ell_p\) Confidence Sets
  Gene Li, Cong Ma, Nathan Srebro.
  arxiv, 2022. [Poster]
• Understanding the Eluder Dimension
  Gene Li, Pritish Kamath, Dylan J. Foster, Nathan Srebro.
  arXiv, 2022.
• Exponential Family Model-Based Reinforcement Learning via Score Matching
  Gene Li, Junbo Li, Nathan Srebro, Zhaoran Wang, Zhuoran Yang.
  Deep Reinforcement Learning Workshop, NeurIPS, 2021. [Poster]

Notes

Some notes on learning theory. These notes are mostly for my own personal reference, but others may find them useful. Any mistakes are my own.

Website last updated July 2022.