| 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 | 861 | 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 | 501 | 2016 |
| Information-Theoretic Considerations in Batch Reinforcement Learning J Chen, N Jiang Proceedings of the 36th International Conference on Machine Learning (ICML …, 2019 | 412 | 2019 |
| Reinforcement Learning: Theory and Algorithms A Agarwal, N Jiang, SM Kakade | 317 | 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 | 291 | 2021 |
| 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 | 279 | 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 | 277 | 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 | 239 | 2018 |
| Minimax Weight and Q-Function Learning for Off-Policy Evaluation M Uehara, J Huang, N Jiang arXiv preprint arXiv:1910.12809, 2019 | 199 | 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 | 175 | 2015 |
| 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 | 173 | 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 | 166 | 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 | 159 | 2019 |
| Adversarially trained actor critic for offline reinforcement learning CA Cheng, T Xie, N Jiang, A Agarwal International Conference on Machine Learning, 3852-3878, 2022 | 140 | 2022 |
| 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 | 138 | 2018 |
| Batch value-function approximation with only realizability T Xie, N Jiang International Conference on Machine Learning, 11404-11413, 2021 | 128 | 2021 |
| Offline reinforcement learning with realizability and single-policy concentrability W Zhan, B Huang, A Huang, N Jiang, J Lee Conference on Learning Theory, 2730-2775, 2022 | 127 | 2022 |
| 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 | 116 | 2019 |
| Q* approximation schemes for batch reinforcement learning: A theoretical comparison T Xie, N Jiang Conference on Uncertainty in Artificial Intelligence, 550-559, 2020 | 107 | 2020 |
| Iterative preference learning from human feedback: Bridging theory and practice for rlhf under kl-constraint W Xiong, H Dong, C Ye, Z Wang, H Zhong, H Ji, N Jiang, T Zhang Forty-first International Conference on Machine Learning, 2024 | 101* | 2024 |
Alekh AgarwalGoogleVerified email at google.com
