Jonathan Lee

According to our database, Jonathan Lee authored at least 31 papers between 2016 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Cost-Aware Near-Optimal Policy Learning.

Joy He-Yueya, Jonathan Lee, Matthew Jörke, Emma Brunskill

Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024

Estimating Optimal Policy Value in Linear Contextual Bandits Beyond Gaussianity.

Jonathan Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill

Trans. Mach. Learn. Res., 2024

EVOLvE: Evaluating and Optimizing LLMs For Exploration.

Allen Nie, Yi Su, Bo Chang, Jonathan N. Lee, Ed H. Chi, Quoc V. Le, Minmin Chen

CoRR, 2024

2023

Estimating Optimal Policy Value in General Linear Contextual Bandits.

Jonathan N. Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill

CoRR, 2023

Experiment Planning with Function Approximation.

Aldo Pacchiano, Jonathan Lee, Emma Brunskill

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Supervised Pretraining Can Learn In-Context Reinforcement Learning.

Jonathan Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning in POMDPs is Sample-Efficient with Hindsight Observability.

Jonathan Lee, Alekh Agarwal, Christoph Dann, Tong Zhang

Proceedings of the International Conference on Machine Learning, 2023

Dueling RL: Reinforcement Learning with Trajectory Preferences.

Aadirupa Saha, Aldo Pacchiano, Jonathan Lee

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Oracle Inequalities for Model Selection in Offline Reinforcement Learning.

Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai, Emma Brunskill

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Model Selection in Batch Policy Optimization.

Jonathan Lee, George Tucker, Ofir Nachum, Bo Dai

Proceedings of the International Conference on Machine Learning, 2022

2021

Sequential robot imitation learning from observations.

Ajay Kumar Tanwani, Andy Yan, Jonathan Lee, Sylvain Calinon, Ken Goldberg

Int. J. Robotics Res., 2021

Dynamic regret convergence analysis and an adaptive regularization algorithm for on-policy robot imitation learning.

Jonathan N. Lee, Michael Laskey, Ajay Kumar Tanwani, Anil Aswani, Ken Goldberg

Int. J. Robotics Res., 2021

Design of Experiments for Stochastic Contextual Linear Bandits.

Andrea Zanette, Kefan Dong, Jonathan N. Lee, Emma Brunskill

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Near Optimal Policy Optimization via REPS.

Aldo Pacchiano, Jonathan N. Lee, Peter L. Bartlett, Ofir Nachum

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Online Model Selection for Reinforcement Learning with Function Approximation.

Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Accelerated Message Passing for Entropy-Regularized MAP Inference.

Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan

Proceedings of the 37th International Conference on Machine Learning, 2020

Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference.

Jonathan N. Lee, Aldo Pacchiano, Michael I. Jordan

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Online Learning with Continuous Variations: Dynamic Regret and Reductions.

Ching-An Cheng, Jonathan Lee, Ken Goldberg, Byron Boots

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Continuous Online Learning and New Insights to Online Imitation Learning.

Jonathan Lee, Ching-An Cheng, Ken Goldberg, Byron Boots

CoRR, 2019

Approximate Sherali-Adams Relaxations for MAP Inference via Entropy Regularization.

Jonathan N. Lee, Aldo Pacchiano, Michael I. Jordan

CoRR, 2019

On-Policy Robot Imitation Learning from a Converging Supervisor.

Ashwin Balakrishna, Brijen Thananjeyan, Jonathan Lee, Felix Li, Arsh Zahed, Joseph E. Gonzalez, Ken Goldberg

Proceedings of the 3rd Annual Conference on Robot Learning, 2019

2018

Derivative-Free Failure Avoidance Control for Manipulation using Learned Support Constraints.

Jonathan Lee, Michael Laskey, Roy Fox, Ken Goldberg

CoRR, 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

Proceedings of the Algorithmic Foundations of Robotics XIII, 2018

A Dynamic Regret Analysis and Adaptive Regularization Algorithm for On-Policy Robot Imitation Learning.

Jonathan N. Lee, Michael Laskey, Ajay Kumar Tanwani, Anil Aswani, Ken Goldberg

Proceedings of the Algorithmic Foundations of Robotics XIII, 2018

Malasakit 2.0: A Participatory Online Platform with Feature Phone Integration and Voice Recognition for Crowdsourcing Disaster Risk Reduction Strategies in the Philippines.

Brandie Nonnecke, Shrestha Mohanty, Andrew Lee, Jonathan Lee, Sequoia Beckman, Justin Mi, Thanatcha Panpairoj, Jeffrey Rosario Ancheta, Hilary Martinez, Nathaniel Oco, Rachel Edita Roxas, Camille Crittenden, Ken Goldberg

Proceedings of the 2018 IEEE Global Humanitarian Technology Conference, 2018

Constraint Estimation and Derivative-Free Recovery for Robot Learning from Demonstrations.

Jonathan Lee, Michael Laskey, Roy Fox, Ken Goldberg

Proceedings of the 14th IEEE International Conference on Automation Science and Engineering, 2018

2017

Iterative Noise Injection for Scalable Imitation Learning.

Michael Laskey, Jonathan Lee, Wesley Yu-Shu Hsieh, Richard Liaw, Jeffrey Mahler, Roy Fox, Ken Goldberg

CoRR, 2017

Comparing human-centric and robot-centric sampling for robot deep learning from demonstrations.

Michael Laskey, Caleb Chuck, Jonathan Lee, Jeffrey Mahler, Sanjay Krishnan, Kevin G. Jamieson, Anca D. Dragan, Ken Goldberg

Proceedings of the 2017 IEEE International Conference on Robotics and Automation, 2017

Malasakit 1.0: A participatory online platform for crowdsourcing disaster risk reduction strategies in the philippines.

Brandie Nonnecke, Shrestha Mohanty, Andrew Lee, Jonathan Lee, Sequoia Beckman, Justin Mi, Sanjay Krishnan, Rachel Edita Roxas, Nathaniel Oco, Camille Crittenden, Ken Goldberg

Proceedings of the IEEE Global Humanitarian Technology Conference, 2017

DART: Noise Injection for Robust Imitation Learning.

Michael Laskey, Jonathan Lee, Roy Fox, Anca D. Dragan, Ken Goldberg

Proceedings of the 1st Annual Conference on Robot Learning, CoRL 2017, Mountain View, 2017

2016

Robot grasping in clutter: Using a hierarchy of supervisors for learning from demonstrations.

Michael Laskey, Jonathan Lee, Caleb Chuck, David V. Gealy, Wesley Yu-Shu Hsieh, Florian T. Pokorny, Anca D. Dragan, Ken Goldberg

Proceedings of the IEEE International Conference on Automation Science and Engineering, 2016