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Jonathan Lee 0002
Person information
- affiliation: University of California, Berkeley, Department of Industrial Engineering and Operations Research, USA
Other persons with the same name
- Jonathan Lee — disambiguation page
- Jonathan Lee 0001 — National Central University, Chung Li, Taiwan
- Jonathan Lee 0003 — Microsoft Research
- Jonathan Lee 0004
— National Taiwan University, Taipei, Taiwan
- Jonathan Lee 0005 (aka: Jonathan Wayne Lee) — Stanford University, CA, USA (and 1 more)
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2020 – today
- 2024
- [j3]Jonathan Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill:
Estimating Optimal Policy Value in Linear Contextual Bandits Beyond Gaussianity. Trans. Mach. Learn. Res. 2024 (2024) - [i20]Aldo Pacchiano, Jonathan N. Lee, Emma Brunskill:
Experiment Planning with Function Approximation. CoRR abs/2401.05193 (2024) - 2023
- [c21]Aadirupa Saha, Aldo Pacchiano, Jonathan Lee:
Dueling RL: Reinforcement Learning with Trajectory Preferences. AISTATS 2023: 6263-6289 - [c20]Jonathan Lee, Alekh Agarwal, Christoph Dann, Tong Zhang:
Learning in POMDPs is Sample-Efficient with Hindsight Observability. ICML 2023: 18733-18773 - [c19]Jonathan Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill:
Supervised Pretraining Can Learn In-Context Reinforcement Learning. NeurIPS 2023 - [c18]Aldo Pacchiano, Jonathan Lee, Emma Brunskill:
Experiment Planning with Function Approximation. NeurIPS 2023 - [i19]Jonathan N. Lee, Alekh Agarwal, Christoph Dann, Tong Zhang:
Learning in POMDPs is Sample-Efficient with Hindsight Observability. CoRR abs/2301.13857 (2023) - [i18]Jonathan N. Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill:
Estimating Optimal Policy Value in General Linear Contextual Bandits. CoRR abs/2302.09451 (2023) - [i17]Jonathan N. Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill:
Supervised Pretraining Can Learn In-Context Reinforcement Learning. CoRR abs/2306.14892 (2023) - 2022
- [c17]Jonathan Lee, George Tucker, Ofir Nachum, Bo Dai:
Model Selection in Batch Policy Optimization. ICML 2022: 12542-12569 - [c16]Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai, Emma Brunskill:
Oracle Inequalities for Model Selection in Offline Reinforcement Learning. NeurIPS 2022 - [i16]Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai, Emma Brunskill:
Oracle Inequalities for Model Selection in Offline Reinforcement Learning. CoRR abs/2211.02016 (2022) - 2021
- [j2]Jonathan N. Lee
Dynamic regret convergence analysis and an adaptive regularization algorithm for on-policy robot imitation learning. Int. J. Robotics Res. 40(10-11) (2021) - [j1]Ajay Kumar Tanwani
Sequential robot imitation learning from observations. Int. J. Robotics Res. 40(10-11) (2021) - [c15]Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill:
Online Model Selection for Reinforcement Learning with Function Approximation. AISTATS 2021: 3340-3348 - [c14]Aldo Pacchiano, Jonathan N. Lee, Peter L. Bartlett, Ofir Nachum:
Near Optimal Policy Optimization via REPS. NeurIPS 2021: 1100-1110 - [c13]Andrea Zanette, Kefan Dong, Jonathan N. Lee, Emma Brunskill:
Design of Experiments for Stochastic Contextual Linear Bandits. NeurIPS 2021: 22720-22731 - [i15]Aldo Pacchiano, Jonathan N. Lee, Peter L. Bartlett, Ofir Nachum:
Near Optimal Policy Optimization via REPS. CoRR abs/2103.09756 (2021) - [i14]Andrea Zanette, Kefan Dong, Jonathan N. Lee, Emma Brunskill:
Design of Experiments for Stochastic Contextual Linear Bandits. CoRR abs/2107.09912 (2021) - [i13]Aldo Pacchiano, Aadirupa Saha, Jonathan Lee:
Dueling RL: Reinforcement Learning with Trajectory Preferences. CoRR abs/2111.04850 (2021) - [i12]Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai:
Model Selection in Batch Policy Optimization. CoRR abs/2112.12320 (2021) - 2020
- [c12]Ching-An Cheng, Jonathan Lee, Ken Goldberg, Byron Boots:
Online Learning with Continuous Variations: Dynamic Regret and Reductions. AISTATS 2020: 2218-2228 - [c11]Jonathan N. Lee, Aldo Pacchiano, Michael I. Jordan:
Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference. AISTATS 2020: 3003-3014 - [c10]Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
Accelerated Message Passing for Entropy-Regularized MAP Inference. ICML 2020: 5736-5746 - [i11]Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
Accelerated Message Passing for Entropy-Regularized MAP Inference. CoRR abs/2007.00699 (2020) - [i10]Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill:
Online Model Selection for Reinforcement Learning with Function Approximation. CoRR abs/2011.09750 (2020)
2010 – 2019
- 2019
- [c9]Ashwin Balakrishna, Brijen Thananjeyan, Jonathan Lee, Felix Li, Arsh Zahed, Joseph E. Gonzalez, Ken Goldberg:
On-Policy Robot Imitation Learning from a Converging Supervisor. CoRL 2019: 24-41 - [i9]Ching-An Cheng, Jonathan Lee, Ken Goldberg, Byron Boots:
Online Learning with Continuous Variations: Dynamic Regret and Reductions. CoRR abs/1902.07286 (2019) - [i8]Jonathan N. Lee, Aldo Pacchiano, Michael I. Jordan:
Approximate Sherali-Adams Relaxations for MAP Inference via Entropy Regularization. CoRR abs/1907.01127 (2019) - [i7]Ashwin Balakrishna, Brijen Thananjeyan, Jonathan Lee, Arsh Zahed, Felix Li, Joseph E. Gonzalez, Ken Goldberg:
On-Policy Robot Imitation Learning from a Converging Supervisor. CoRR abs/1907.03423 (2019) - [i6]Jonathan Lee, Ching-An Cheng, Ken Goldberg, Byron Boots:
Continuous Online Learning and New Insights to Online Imitation Learning. CoRR abs/1912.01261 (2019) - 2018
- [c8]Jonathan Lee, Michael Laskey, Roy Fox, Ken Goldberg
Constraint Estimation and Derivative-Free Recovery for Robot Learning from Demonstrations. CASE 2018: 270-277 - [c7]Brandie Nonnecke, Shrestha Mohanty, Andrew Lee, Jonathan Lee, Sequoia Beckman, Justin Mi, Thanatcha Panpairoj, Jeffrey Rosario Ancheta
Malasakit 2.0: A Participatory Online Platform with Feature Phone Integration and Voice Recognition for Crowdsourcing Disaster Risk Reduction Strategies in the Philippines. GHTC 2018: 1-6 - [c6]Ajay Kumar Tanwani, Jonathan Lee, Brijen Thananjeyan, Michael Laskey, Sanjay Krishnan, Roy Fox, Ken Goldberg
Generalizing Robot Imitation Learning with Invariant Hidden Semi-Markov Models. WAFR 2018: 196-211 - [c5]Jonathan N. Lee, Michael Laskey, Ajay Kumar Tanwani, Anil Aswani
A Dynamic Regret Analysis and Adaptive Regularization Algorithm for On-Policy Robot Imitation Learning. WAFR 2018: 212-227 - [i5]Jonathan Lee, Michael Laskey, Roy Fox, Ken Goldberg:
Derivative-Free Failure Avoidance Control for Manipulation using Learned Support Constraints. CoRR abs/1801.10321 (2018) - [i4]Jonathan Lee, Michael Laskey, Ajay Kumar Tanwani, Anil Aswani, Ken Goldberg:
A Dynamic Regret Analysis and Adaptive Regularization Algorithm for On-Policy Robot Imitation Learning. CoRR abs/1811.02184 (2018) - [i3]Ajay Kumar Tanwani, Jonathan Lee, Brijen Thananjeyan, Michael Laskey, Sanjay Krishnan, Roy Fox, Ken Goldberg, Sylvain Calinon:
Generalizing Robot Imitation Learning with Invariant Hidden Semi-Markov Models. CoRR abs/1811.07489 (2018) - 2017
- [c4]Michael Laskey, Jonathan Lee, Roy Fox, Anca D. Dragan, Ken Goldberg:
DART: Noise Injection for Robust Imitation Learning. CoRL 2017: 143-156 - [c3]Brandie Nonnecke, Shrestha Mohanty, Andrew Lee, Jonathan Lee, Sequoia Beckman, Justin Mi, Sanjay Krishnan, Rachel Edita Roxas
Malasakit 1.0: A participatory online platform for crowdsourcing disaster risk reduction strategies in the philippines. GHTC 2017: 1-6 - [c2]Michael Laskey, Caleb Chuck, Jonathan Lee, Jeffrey Mahler, Sanjay Krishnan, Kevin G. Jamieson, Anca D. Dragan, Ken Goldberg:
Comparing human-centric and robot-centric sampling for robot deep learning from demonstrations. ICRA 2017: 358-365 - [i2]Michael Laskey, Jonathan Lee, Wesley Yu-Shu Hsieh, Richard Liaw, Jeffrey Mahler, Roy Fox, Ken Goldberg:
Iterative Noise Injection for Scalable Imitation Learning. CoRR abs/1703.09327 (2017) - 2016
- [c1]Michael Laskey, Jonathan Lee, Caleb Chuck, David V. Gealy, Wesley Yu-Shu Hsieh, Florian T. Pokorny, Anca D. Dragan, Ken Goldberg:
Robot grasping in clutter: Using a hierarchy of supervisors for learning from demonstrations. CASE 2016: 827-834 - [i1]Michael Laskey, Caleb Chuck, Jonathan Lee, Jeffrey Mahler, Sanjay Krishnan, Kevin G. Jamieson, Anca D. Dragan, Kenneth Y. Goldberg:
Comparing Human-Centric and Robot-Centric Sampling for Robot Deep Learning from Demonstrations. CoRR abs/1610.00850 (2016)
Coauthor Index
aka: Ken Goldberg
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