| DART: Noise Injection for Robust Imitation Learning M Laskey, J Lee, R Fox, A Dragan, K Goldberg Conference on Robot Learning, 143-156, 2017 | 205* | 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 | 84 | 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 | 72 | 2017 |
| Online Model Selection for Reinforcement Learning with Function Approximation JN Lee, A Pacchiano, V Muthukumar, W Kong, E Brunskill arXiv preprint arXiv:2011.09750, 2020 | 23 | 2020 |
| On-Policy Robot Imitation Learning from a Converging Supervisor A Balakrishna, B Thananjeyan, J Lee, A Zahed, F Li, JE Gonzalez, ... arXiv preprint arXiv:1907.03423, 2019 | 19 | 2019 |
| Online Learning with Continuous Variations: Dynamic Regret and Reductions CA Cheng, J Lee, K Goldberg, B Boots arXiv preprint arXiv:1902.07286, 2019 | 15 | 2019 |
| 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 | 11 | 2018 |
| A dynamic regret analysis and adaptive regularization algorithm for on-policy robot imitation learning J Lee, M Laskey, A Kumar Tanwani, A Aswani, K Goldberg Proceedings of the 13th Workshop on the Algorithmic Foundations of Robotics …, 2018 | 11 | 2018 |
| Design of Experiments for Stochastic Contextual Linear Bandits A Zanette, K Dong, J Lee, E Brunskill arXiv preprint arXiv:2107.09912, 2021 | 6 | 2021 |
| Model Selection in Batch Policy Optimization JN Lee, G Tucker, O Nachum, B Dai arXiv preprint arXiv:2112.12320, 2021 | 5 | 2021 |
| Dueling RL: Reinforcement Learning with Trajectory Preferences A Pacchiano, A Saha, J Lee arXiv preprint arXiv:2111.04850, 2021 | 4 | 2021 |
| Near Optimal Policy Optimization via REPS A Pacchiano, J Lee, P Bartlett, O Nachum arXiv preprint arXiv:2103.09756, 2021 | 4 | 2021 |
| Improved Estimator Selection for Off-Policy Evaluation G Tucker, J Lee Workshop on Reinforcement Learning Theory at the 38th International …, 2021 | 3 | 2021 |
| Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference J Lee, A Pacchiano, M Jordan International Conference on Artificial Intelligence and Statistics, 3003-3014, 2020 | 3 | 2020 |
| Stability analysis of on-policy imitation learning algorithms using dynamic regret J Lee, M Laskey, AK Tanwani, K Goldberg RSS Workshop on Imitation and Causality, 2018 | 2 | 2018 |
| Model-Free Error Detection and Recovery for Robot Learning from Demonstration J Lee, M Laskey, R Fox, K Goldberg | 2* | |
| Oracle Inequalities for Model Selection in Offline Reinforcement Learning JN Lee, G Tucker, O Nachum, B Dai, E Brunskill arXiv preprint arXiv:2211.02016, 2022 | 1 | 2022 |
| Learning in POMDPs is Sample-Efficient with Hindsight Observability JN Lee, A Agarwal, C Dann, T Zhang arXiv preprint arXiv:2301.13857, 2023 | | 2023 |
| Accelerated Message Passing for Entropy-Regularized MAP Inference JN Lee, A Pacchiano, P Bartlett, MI Jordan arXiv preprint arXiv:2007.00699, 2020 | | 2020 |
| Approximate Sherali-Adams Relaxations for MAP Inference via Entropy Regularization JN Lee, A Pacchiano, MI Jordan arXiv preprint arXiv:1907.01127, 2019 | | 2019 |
Ken GoldbergProfessor, UC Berkeley and UCSFVerified email at berkeley.edu
