| 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 | 263 | 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 | 124 | 2016 |
| 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 | 75 | 2015 |
| 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 | 72 | 2018 |
| Information-Theoretic Considerations in Batch Reinforcement Learning J Chen, N Jiang Proceedings of the 36th International Conference on Machine Learning (ICML …, 2019 | 67 | 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 | 57* | 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 | 51 | 2019 |
| Abstraction Selection in Model-based Reinforcement Learning N Jiang, A Kulesza, S Singh Proceedings of the 32nd International Conference on Machine Learning (ICML …, 2015 | 42 | 2015 |
| 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 | 41* | 2018 |
| Repeated Inverse Reinforcement Learning K Amin, N Jiang, S Singh Advances in Neural Information Processing Systems, 2017, 2017 | 36 | 2017 |
| Minimax Weight and Q-Function Learning for Off-Policy Evaluation M Uehara, J Huang, N Jiang arXiv preprint arXiv:1910.12809, 2019 | 34 | 2019 |
| Improving UCT planning via approximate homomorphisms N Jiang, S Singh, R Lewis Proceedings of the 2014 international conference on Autonomous agents and …, 2014 | 31 | 2014 |
| Open Problem: The Dependence of Sample Complexity Lower Bounds on Planning Horizon N Jiang, A Agarwal Conference On Learning Theory, 3395-3398, 2018 | 28 | 2018 |
| Low-Rank Spectral Learning with Weighted Loss Functions A Kulesza, N Jiang, S Singh Proceedings of the 18th International Conference on Artificial Intelligence …, 2015 | 27 | 2015 |
| 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 | 19 | 2020 |
| Spectral learning of predictive state representations with insufficient statistics A Kulesza, N Jiang, S Singh Proceedings of the 29th AAAI Conference on Artificial Intelligence, 2015 | 19 | 2015 |
| Markov decision processes with continuous side information A Modi, N Jiang, S Singh, A Tewari Algorithmic Learning Theory, 597-618, 2018 | 18 | 2018 |
| Reinforcement Learning: Theory and Algorithms A Agarwal, N Jiang, SM Kakade | 17 | 2019 |
| 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 | 16 | 2019 |
| Improving predictive state representations via gradient descent N Jiang, A Kulesza, S Singh 30th AAAI Conference on Artificial Intelligence, 2016 | 13 | 2016 |
Alekh AgarwalMicrosoft ResearchVerified email at microsoft.com
