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Jonathan N. Lee
Jonathan N. Lee
Andere namenJonathan Lee
Stanford University
Geverifieerd e-mailadres voor stanford.edu - Homepage
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DART: Noise Injection for Robust Imitation Learning
M Laskey, J Lee, R Fox, A Dragan, K Goldberg
Conference on Robot Learning, 143-156, 2017
3072017
Robot grasping in clutter: Using a hierarchy of supervisors for learning from demonstrations
M Laskey, J Lee, C Chuck, D Gealy, W Hsieh, FT Pokorny, AD Dragan, ...
2016 IEEE International Conference on Automation Science and Engineering …, 2016
1042016
Dueling RL: Reinforcement Learning with Trajectory Preferences
A Saha, A Pacchiano, J Lee
International Conference on Artificial Intelligence and Statistics, 6263-6289, 2023
90*2023
Comparing human-centric and robot-centric sampling for robot deep learning from demonstrations
M Laskey, C Chuck, J Lee, J Mahler, S Krishnan, K Jamieson, A Dragan, ...
2017 IEEE International Conference on Robotics and Automation (ICRA), 358-365, 2017
872017
Supervised Pretraining Can Learn In-Context Reinforcement Learning
JN Lee, A Xie, A Pacchiano, Y Chandak, C Finn, O Nachum, E Brunskill
arXiv preprint arXiv:2306.14892, 2023
742023
Online model selection for reinforcement learning with function approximation
J Lee, A Pacchiano, V Muthukumar, W Kong, E Brunskill
International Conference on Artificial Intelligence and Statistics, 3340-3348, 2021
462021
Design of experiments for stochastic contextual linear bandits
A Zanette, K Dong, JN Lee, E Brunskill
Advances in Neural Information Processing Systems 34, 22720-22731, 2021
322021
Learning in pomdps is sample-efficient with hindsight observability
J Lee, A Agarwal, C Dann, T Zhang
International Conference on Machine Learning, 18733-18773, 2023
262023
On-policy robot imitation learning from a converging supervisor
A Balakrishna, B Thananjeyan, J Lee, F Li, A Zahed, JE Gonzalez, ...
Conference on Robot Learning, 24-41, 2020
262020
Online learning with continuous variations: Dynamic regret and reductions
CA Cheng, J Lee, K Goldberg, B Boots
International Conference on Artificial Intelligence and Statistics, 2218-2228, 2020
212020
Dynamic regret convergence analysis and an adaptive regularization algorithm for on-policy robot imitation learning
JN Lee, M Laskey, AK Tanwani, A Aswani, K Goldberg
The International Journal of Robotics Research 40 (10-11), 1284-1305, 2021
16*2021
Oracle inequalities for model selection in offline reinforcement learning
JN Lee, G Tucker, O Nachum, B Dai, E Brunskill
Advances in Neural Information Processing Systems 35, 28194-28207, 2022
152022
Model selection in batch policy optimization
J Lee, G Tucker, O Nachum, B Dai
International Conference on Machine Learning, 12542-12569, 2022
152022
Sequential robot imitation learning from observations
AK Tanwani, A Yan, J Lee, S Calinon, K Goldberg
The International Journal of Robotics Research 40 (10-11), 1306-1325, 2021
152021
Generalizing Robot Imitation Learning with Invariant Hidden Semi-Markov Models
AK Tanwani, J Lee, B Thananjeyan, M Laskey, S Krishnan, R Fox, ...
arXiv preprint arXiv:1811.07489, 2018
142018
Near optimal policy optimization via REPS
A Pacchiano, JN Lee, P Bartlett, O Nachum
Advances in Neural Information Processing Systems 34, 1100-1110, 2021
82021
Improved Estimator Selection for Off-Policy Evaluation
G Tucker, J Lee
Workshop on Reinforcement Learning Theory at the 38th International …, 2021
82021
Simple Regret Minimization for Contextual Bandits Using Bayesian Optimal Experimental Design
M Jörke, J Lee, E Brunskill
6*
Experiment planning with function approximation
A Pacchiano, J Lee, E Brunskill
Advances in Neural Information Processing Systems 36, 2024
52024
Continuous online learning and new insights to online imitation learning
J Lee, CA Cheng, K Goldberg, B Boots
arXiv preprint arXiv:1912.01261, 2019
42019
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Artikelen 1–20