Jonathan N. Lee
jnl AT stanford DOT edu

I am a PhD student in the Computer Science Department at Stanford University advised by Emma Brunskill. I am broadly interested in machine learning and decision-making. My work focuses on theoretical foundations for bandits and reinforcement learning. I have been supported by an NSF Graduate Research Fellowship.

Previously, I graduated with a B.S. in Electrical Engineering & Computer Science from UC Berkeley advised by Ken Goldberg.

I am thankful to have spent two wonderful summers at Google with

CV  /  Google Scholar  /  Github

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Papers
2023
Learning in POMDPs is Sample-Efficient with Hindsight Observability
Jonathan Lee, Alekh Agarwal, Christoph Dann, Tong Zhang
arXiv, 2023
Dueling RL: Reinforcement Learning with Trajectory Preferences
Aldo Pacchiano, Aadirupa Saha, Jonathan Lee
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
2022
Oracle Inequalities for Model Selection in Offline Reinforcement Learning
Jonathan Lee, George Tucker, Ofir Nachum, Bo Dai, Emma Brunskill
Neural Information Processing Systems (NeurIPS), 2022
Model Selection in Batch Policy Optimization
Jonathan Lee, George Tucker, Ofir Nachum, Bo Dai
International Conference on Machine Learning (ICML), 2022
2021
Design of Experiments for Stochastic Contextual Linear Bandits
Andrea Zanette*, Kefan Dong*, Jonathan Lee*, Emma Brunskill
Neural Information Processing Systems (NeurIPS), 2021
Near Optimal Policy Optimization via REPS
Aldo Pacchiano, Jonathan Lee, Peter Bartlett, Ofir Nachum
Neural Information Processing Systems (NeurIPS), 2021
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
2020
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
2019
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
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
2018 and earlier
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
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

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