| DART: Noise Injection for Robust Imitation Learning M Laskey, J Lee, R Fox, A Dragan, K Goldberg Conference on Robot Learning, 143-156, 2017 | 302 | 2017 |
| 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 | 100 | 2016 |
| 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 | 87 | 2017 |
| Dueling RL: Reinforcement Learning with Trajectory Preferences A Saha, A Pacchiano, J Lee International Conference on Artificial Intelligence and Statistics, 6263-6289, 2023 | 82* | 2023 |
| 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 | 62 | 2023 |
| 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 | 41 | 2021 |
| 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 | 29 | 2021 |
| 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 | 26 | 2020 |
| 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 | 21 | 2023 |
| 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 | 20 | 2020 |
| 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 | 15 | 2022 |
| Model selection in batch policy optimization J Lee, G Tucker, O Nachum, B Dai International Conference on Machine Learning, 12542-12569, 2022 | 14 | 2022 |
| 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 | 13 | 2021 |
| 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 | 13 | 2018 |
| Near optimal policy optimization via REPS A Pacchiano, JN Lee, P Bartlett, O Nachum Advances in Neural Information Processing Systems 34, 1100-1110, 2021 | 8 | 2021 |
| Improved Estimator Selection for Off-Policy Evaluation G Tucker, J Lee Workshop on Reinforcement Learning Theory at the 38th International …, 2021 | 8 | 2021 |
| Experiment planning with function approximation A Pacchiano, J Lee, E Brunskill Advances in Neural Information Processing Systems 36, 2024 | 5 | 2024 |
| Simple Regret Minimization for Contextual Bandits Using Bayesian Optimal Experimental Design M Jörke, J Lee, E Brunskill | 5* | |
| Continuous online learning and new insights to online imitation learning J Lee, CA Cheng, K Goldberg, B Boots arXiv preprint arXiv:1912.01261, 2019 | 4 | 2019 |
Ken GoldbergProfessor, UC Berkeley and UCSFVerified email at berkeley.edu
