| Doubly Robust Off-policy Value Evaluation for Reinforcement Learning N Jiang, L Li Proceedings of the 33rd International Conference on Machine Learning (ICML-16), 2015 | 550 | 2015 |
| Contextual Decision Processes with Low Bellman Rank are PAC-Learnable N Jiang, A Krishnamurthy, A Agarwal, J Langford, RE Schapire Proceedings of the 34th International Conference on Machine Learning (ICML-17), 2016 | 323 | 2016 |
| Information-Theoretic Considerations in Batch Reinforcement Learning J Chen, N Jiang Proceedings of the 36th International Conference on Machine Learning (ICML …, 2019 | 234 | 2019 |
| Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches W Sun, N Jiang, A Krishnamurthy, A Agarwal, J Langford Conference on Learning Theory, 2019 | 168* | 2019 |
| Provably efficient RL with Rich Observations via Latent State Decoding SS Du, A Krishnamurthy, N Jiang, A Agarwal, M Dudík, J Langford Proceedings of the 36th International Conference on Machine Learning (ICML …, 2019 | 165 | 2019 |
| Hierarchical Imitation and Reinforcement Learning HM Le, N Jiang, A Agarwal, M Dudík, Y Yue, H Daumé III Proceedings of the 35th International Conference on Machine Learning (ICML-18), 2018 | 160 | 2018 |
| Minimax Weight and Q-Function Learning for Off-Policy Evaluation M Uehara, J Huang, N Jiang arXiv preprint arXiv:1910.12809, 2019 | 133 | 2019 |
| The Dependence of Effective Planning Horizon on Model Accuracy N Jiang, A Kulesza, S Singh, R Lewis Proceedings of the 2015 International Conference on Autonomous Agents and …, 2015 | 129 | 2015 |
| Reinforcement Learning: Theory and Algorithms A Agarwal, N Jiang, SM Kakade | 120 | 2019 |
| Bellman-consistent pessimism for offline reinforcement learning T Xie, CA Cheng, N Jiang, P Mineiro, A Agarwal Advances in neural information processing systems 34, 6683-6694, 2021 | 100 | 2021 |
| Sample complexity of reinforcement learning using linearly combined model ensembles A Modi, N Jiang, A Tewari, S Singh International Conference on Artificial Intelligence and Statistics, 2010-2020, 2020 | 96 | 2020 |
| Empirical study of off-policy policy evaluation for reinforcement learning C Voloshin, HM Le, N Jiang, Y Yue arXiv preprint arXiv:1911.06854, 2019 | 89 | 2019 |
| On Oracle-Efficient PAC Reinforcement Learning with Rich Observations C Dann, N Jiang, A Krishnamurthy, A Agarwal, J Langford, RE Schapire Advances in Neural Information Processing Systems, 2018, 2018 | 89 | 2018 |
| Abstraction Selection in Model-based Reinforcement Learning N Jiang, A Kulesza, S Singh Proceedings of the 32nd International Conference on Machine Learning (ICML …, 2015 | 69 | 2015 |
| Batch value-function approximation with only realizability T Xie, N Jiang International Conference on Machine Learning, 11404-11413, 2021 | 65 | 2021 |
| Repeated Inverse Reinforcement Learning K Amin, N Jiang, S Singh Advances in Neural Information Processing Systems, 2017, 2017 | 61 | 2017 |
| Policy finetuning: Bridging sample-efficient offline and online reinforcement learning T Xie, N Jiang, H Wang, C Xiong, Y Bai Advances in neural information processing systems 34, 27395-27407, 2021 | 59 | 2021 |
| Provably efficient q-learning with low switching cost Y Bai, T Xie, N Jiang, YX Wang Advances in Neural Information Processing Systems, 8004-8013, 2019 | 58 | 2019 |
| Open Problem: The Dependence of Sample Complexity Lower Bounds on Planning Horizon N Jiang, A Agarwal Conference On Learning Theory, 3395-3398, 2018 | 58 | 2018 |
| Q* approximation schemes for batch reinforcement learning: A theoretical comparison T Xie, N Jiang Conference on Uncertainty in Artificial Intelligence, 550-559, 2020 | 50 | 2020 |
Alekh AgarwalGoogleVerified email at google.com
