| Minimax weight and q-function learning for off-policy evaluation M Uehara, J Huang, N Jiang International Conference on Machine Learning, 9659-9668, 2019 | 140 | 2019 |
| Weightnet: Revisiting the design space of weight networks N Ma, X Zhang, J Huang, J Sun Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 64 | 2020 |
| Minimax value interval for off-policy evaluation and policy optimization N Jiang, J Huang Advances in Neural Information Processing Systems 33, 2747-2758, 2020 | 55 | 2020 |
| A minimax learning approach to off-policy evaluation in confounded Partially Observable Markov Decision Processes C Shi, M Uehara, J Huang, N Jiang International Conference on Machine Learning, 2022 | 25* | 2022 |
| From Importance Sampling to Doubly Robust Policy Gradient J Huang, N Jiang International Conference on Machine Learning, 4434-4443, 2019 | 21 | 2019 |
| Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality J Huang, J Chen, L Zhao, T Qin, N Jiang, TY Liu International Conference on Learning Representations 2022, 2022 | 13 | 2022 |
| On the convergence rate of off-policy policy optimization methods with density-ratio correction J Huang, N Jiang International Conference on Artificial Intelligence and Statistics, 2658-2705, 2022 | 7* | 2022 |
| Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret J Huang, L Zhao, T Qin, W Chen, N Jiang, TY Liu Advances in Neural Information Processing Systems 35, 2022 | 1 | 2022 |
| On the Statistical Efficiency of Mean Field Reinforcement Learning with General Function Approximation J Huang, B Yardim, N He arXiv preprint arXiv:2305.11283, 2023 | | 2023 |
| Robust Knowledge Transfer in Tiered Reinforcement Learning J Huang, N He arXiv preprint arXiv:2302.05534, 2023 | | 2023 |
Nan JiangAssistant Professor of Computer Science, UIUCVerified email at illinois.edu
