Nan Jiang

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Alekh AgarwalMicrosoft ResearchVerified email at microsoft.com
Akshay KrishnamurthyUniversity of Massachusetts AmherstVerified email at cs.umass.edu
John LangfordMicrosoft Research New YorkVerified email at hunch.net
Alex KuleszaResearch Scientist, GoogleVerified email at google.com
Tengyang XieUniversity of Illinois at Urbana-ChampaignVerified email at illinois.edu
Robert SchapireMicrosoft ResearchVerified email at microsoft.com
Yisong YueCalifornia Institute of TechnologyVerified email at caltech.edu
Wen SunAssistant Professor, Cornell UniversityVerified email at cornell.edu
Hoang M. LeCalifornia Institute of TechnologyVerified email at caltech.edu
Huang JiaweiUniversity of Illinois Urbana-ChampaignVerified email at illinois.edu
Miroslav DudikMicrosoft ResearchVerified email at microsoft.com
Lihong Li (李力鸿)AmazonVerified email at amazon.com
Richard L. LewisProfessor of Psychology, Linguistics and Cognitive Science, University of MichiganVerified email at umich.edu
Ambuj Tewari | अम्बुज तिवारीAssociate Professor, Department of Statistics and Department of EECS, University of MichiganVerified email at umich.edu
Aditya ModiPhD Student, University of MichiganVerified email at umich.edu
Masatoshi UeharaCornell UniversityVerified email at cornell.edu
Christoph DannResearch Scientist, GoogleVerified email at google.com
Kareem AminGoogleVerified email at google.com
Hal Daumé IIIAssociate Professor of Computer Science, University of MarylandVerified email at umiacs.umd.edu
Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonVerified email at cs.washington.edu

Nan Jiang

Assistant Professor of Computer Science, UIUC

Verified email at illinois.edu - Homepage

Reinforcement Learning Machine Learning Artificial Intelligence


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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	312	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	162	2016
Information-Theoretic Considerations in Batch Reinforcement Learning J Chen, N Jiang Proceedings of the 36th International Conference on Machine Learning (ICML …, 2019	88	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	88	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	81	2018
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	76*	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	69	2019
Minimax Weight and Q-Function Learning for Off-Policy Evaluation M Uehara, J Huang, N Jiang arXiv preprint arXiv:1910.12809, 2019	53	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	53*	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	46	2015
Repeated Inverse Reinforcement Learning K Amin, N Jiang, S Singh Advances in Neural Information Processing Systems, 2017, 2017	44	2017
Open Problem: The Dependence of Sample Complexity Lower Bounds on Planning Horizon N Jiang, A Agarwal Conference On Learning Theory, 3395-3398, 2018	35	2018
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	32	2020
Improving UCT planning via approximate homomorphisms N Jiang, S Singh, R Lewis Proceedings of the 2014 international conference on Autonomous agents and …, 2014	32	2014
Low-Rank Spectral Learning with Weighted Loss Functions A Kulesza, N Jiang, S Singh Proceedings of the 18th International Conference on Artificial Intelligence …, 2015	30	2015
The Caltech Off-Policy Policy Evaluation Benchmarking Suite C Voloshin, HM Le, N Jiang, Y Yue	29*	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	24	2019
Reinforcement Learning: Theory and Algorithms A Agarwal, N Jiang, SM Kakade	23	2019
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	21	2015
Markov decision processes with continuous side information A Modi, N Jiang, S Singh, A Tewari Algorithmic Learning Theory, 597-618, 2018	19	2018

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