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Nan Jiang
Nan Jiang
Assistant Professor of Computer Science, UIUC
Verified email at illinois.edu - Homepage
Title
Cited by
Cited by
Year
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
4332015
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
2462016
Information-Theoretic Considerations in Batch Reinforcement Learning
J Chen, N Jiang
Proceedings of the 36th International Conference on Machine Learning (ICML …, 2019
1602019
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
1372018
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
1222019
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
119*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
1082015
Minimax Weight and Q-Function Learning for Off-Policy Evaluation
M Uehara, J Huang, N Jiang
arXiv preprint arXiv:1910.12809, 2019
992019
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
782018
Empirical study of off-policy policy evaluation for reinforcement learning
C Voloshin, HM Le, N Jiang, Y Yue
arXiv preprint arXiv:1911.06854, 2019
732019
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
722020
Abstraction Selection in Model-based Reinforcement Learning
N Jiang, A Kulesza, S Singh
Proceedings of the 32nd International Conference on Machine Learning (ICML …, 2015
622015
Repeated Inverse Reinforcement Learning
K Amin, N Jiang, S Singh
Advances in Neural Information Processing Systems, 2017, 2017
542017
Reinforcement Learning: Theory and Algorithms
A Agarwal, N Jiang, SM Kakade
512019
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
512019
Open Problem: The Dependence of Sample Complexity Lower Bounds on Planning Horizon
N Jiang, A Agarwal
Conference On Learning Theory, 3395-3398, 2018
512018
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
432021
Batch value-function approximation with only realizability
T Xie, N Jiang
International Conference on Machine Learning, 11404-11413, 2021
412021
Improving UCT planning via approximate homomorphisms
N Jiang, S Singh, R Lewis
Proceedings of the 2014 international conference on Autonomous agents and …, 2014
372014
Minimax Value Interval for Off-Policy Evaluation and Policy Optimization
N Jiang, J Huang
arXiv preprint arXiv:2002.02081, 2020
342020
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