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Nan Jiang
Nan Jiang
Associate Professor of Computer Science, UIUC
Bestätigte E-Mail-Adresse bei illinois.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
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
8802015
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
5092016
Information-Theoretic Considerations in Batch Reinforcement Learning
J Chen, N Jiang
Proceedings of the 36th International Conference on Machine Learning (ICML …, 2019
4222019
Reinforcement Learning: Theory and Algorithms
A Agarwal, N Jiang, SM Kakade
3302019
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
3032021
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
2822019
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
2802019
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
2422018
Minimax Weight and Q-Function Learning for Off-Policy Evaluation
M Uehara, J Huang, N Jiang
arXiv preprint arXiv:1910.12809, 2019
2022019
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
1832021
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
1792015
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
1682020
Empirical study of off-policy policy evaluation for reinforcement learning
C Voloshin, HM Le, N Jiang, Y Yue
arXiv preprint arXiv:1911.06854, 2019
1632019
Adversarially trained actor critic for offline reinforcement learning
CA Cheng, T Xie, N Jiang, A Agarwal
International Conference on Machine Learning, 3852-3878, 2022
1492022
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
1382018
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
1292022
Batch value-function approximation with only realizability
T Xie, N Jiang
International Conference on Machine Learning, 11404-11413, 2021
1282021
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
119*2024
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
1182019
Q* approximation schemes for batch reinforcement learning: A theoretical comparison
T Xie, N Jiang
Conference on Uncertainty in Artificial Intelligence, 550-559, 2020
1092020
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