I am currently a PhD student in the Computer Science Department at Stanford University advised by Emma Brunskill. Previously, I graduated with a B.S. in Electrical Engineering & Computer Science from UC Berkeley, advised by Ken Goldberg.

This summer (2021), I am interning at Google Brain, hosted by George Tucker, Ofir Nachum, and Bo Dai.

Preprints

• Estimating Optimal Policy Value in Linear Contextual Bandits beyond Gaussianity.
  Jonathan Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill
  (forthcoming)


• Design of Experiments for Stochastic Contextual Linear Bandits.
  Andrea Zanette*, Kefan Dong*, Jonathan Lee*, Emma Brunskill
  arxiv, 2021.


• Near Optimal Policy Optimization via REPS.
  Aldo Pacchiano, Jonathan Lee, Peter Bartlett, Ofir Nachum
  arxiv, 2021.

Papers

• Online Model Selection for Reinforcement Learning with Function Approximation.
  Jonathan Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.


• Dynamic Regret Convergence Analysis and an Adaptive Regularization Algorithm for On-Policy Robot Imitation Learning.
  Jonathan Lee, Michael Laskey, Ajay Kumar Tanwani, Anil Aswani, Ken Goldberg.
  International Journal of Robotics Research (IJRR), 2021.


• Accelerated Message Passing for Entropy-Regularized MAP Inference.
  Jonathan Lee, Aldo Pacchiano, Peter Bartlett, Michael I. Jordan.
  International Conference on Machine Learning (ICML), 2020.


• Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference.
  Jonathan Lee*, Aldo Pacchiano*, Michael I. Jordan.
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.


• Online Learning with Continuous Variations: Dynamic Regret and Reductions.
  Ching-An Cheng*, Jonathan Lee*, Ken Goldberg, Byron Boots.
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.


• On-Policy Robot Imitation Learning from a Converging Supervisor.
  Ashwin Balakrishna*, Brijen Thananjeyan*, Jonathan Lee, Arsh Zahed, Felix Li, Joseph E. Gonzalez, Ken Goldberg.
  Conference on Robot Learning (CoRL), 2019.
  Selected for Oral Presentation


• A Dynamic Regret Analysis and Adaptive Regularization Algorithm for On-Policy Robot Imitation Learning.
  Jonathan Lee, Michael Laskey, Ajay Kumar Tanwani, Anil Aswani, Ken Goldberg.
  Springer Proceedings in Advanced Robotics: Algorithmic Foundations of Robotics, 2019.
  International Workshop on the Algorithmic Foundations of Robotics (WAFR), 2018.
  Invited to special issue in International Journal of Robotics Research


• Generalizing Robot Imitation Learning with Invariant Hidden Semi-Markov Models.
  Ajay Kumar Tanwani, Jonathan Lee, Brijen Thananjeyan, Michael Laskey, Sanjay Krishnan, Roy Fox, Ken Goldberg, Sylvain Calinon
  Springer Proceedings in Advanced Robotics: Algorithmic Foundations of Robotics, 2019.
  International Workshop on the Algorithmic Foundations of Robotics (WAFR), 2018.
  Invited to special issue in International Journal of Robotics Research


• Constraint Estimation and Derivative-Free Recovery for Robot Learning from Demonstrations.
  Jonathan Lee, Michael Laskey, Roy Fox, Ken Goldberg.
  IEEE Conference on Automation Science and Engineering (CASE), 2018.


• DART: Noise Injection for Robust Imitation Learning.
  Michael Laskey, Jonathan Lee, Roy Fox, Anca Dragan, Ken Goldberg.
  Conference on Robot Learning (CoRL), 2017.
  [BAIR Blog]


• Comparing Human-Centric and Robot-Centric Sample Efficiency for Robot Deep Learning from Demonstrations.
  Michael Laskey, Caleb Chuck, Jonathan Lee, Jeffrey Mahler, Sanjay Krishnan, Kevin Jamieson, Anca Dragan, Ken Goldberg.
  IEEE Conference on Robotics and Automation (ICRA), 2017.


• Robot Grasping in Clutter: Using a Hierarchy of Supervisors for Learning from Demonstrations.
  Michael Laskey, Jonathan Lee, Caleb Chuck, David Gealy, Wesley Hsieh, Florian T. Pokorny, Anca D. Dragan, and Ken Goldberg.
  IEEE Conference on Automation Science and Engineering (CASE), 2016.

Short papers, workshop papers, etc.

• Continuous Online Learning and New Insights into Online Imitation Learning.
  Jonathan Lee*, Ching-An Cheng*, Ken Golberg, Byron Boots.
  NeurIPS Optimization Foundations for Reinforcement Learning Workshop, 2019.
  Best Paper Award


• Stability Analysis of On-Policy Imitation Learning Algorithms Using Dynamic Regret.
  Jonathan Lee, Michael Laskey, Ajay Kumar Tanwani, Ken Goldberg.
  RSS Workshop on Imitation and Causality, 2018.
  Selected for Spotlight Presentation


• Iterative Noise Injection for Scalable Imitation Learning.
  Michael Laskey, Jonathan Lee, Wesley Hsieh, Richard Liaw, Jeffrey Mahler, Roy Fox, Ken Goldberg.
  arXiv, 2017.