Hello! My name is Gene Li. I’m a fifth 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You can find my (perpetually outdated) CV here.

Publications

• Optimistic Rates for Learning with Label Proportions
  Gene Li, Lin Chen, Adel Javanmard, and Vahab Mirrokni.
  COLT 2024.
• Dueling Optimization with a Monotone Adversary
  Avrim Blum, Meghal Gupta, Gene Li, Naren Sarayu Manoj, Aadirupa Saha, and Yuanyuan Yang.
  ALT 2024. (Outstanding Paper)
  Short version appeared at OPT ML Workshop, NeurIPS 2023. (Oral Presentation)
• When is Agnostic Reinforcement Learning Statistically Tractable?
  Zeyu Jia, Gene Li, Alexander Rakhlin, Ayush Sekhari, Nathan Srebro.
  NeurIPS 2023.
• Pessimism for Offline Linear Contextual Bandits using \(\ell_p\) Confidence Sets
  Gene Li, Cong Ma, Nathan Srebro.
  NeurIPS 2022. [Poster]
• Understanding the Eluder Dimension
  Gene Li, Pritish Kamath, Dylan J. Foster, Nathan Srebro.
  NeurIPS 2022.
• Exponential Family Model-Based Reinforcement Learning via Score Matching
  Gene Li, Junbo Li, Anmol Kabra, Nathan Srebro, Zhaoran Wang, Zhuoran Yang.
  NeurIPS 2022. (Oral Presentation) [Poster]

Teaching

• Statistical and Computational Learning Theory - Winter 2023
  Instructor: Prof. Nathan Srebro, TTIC.

Misc

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 Oct 2023.