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Huang Jiawei
Huang Jiawei
ETH Zurich
在 inf.ethz.ch 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
Minimax weight and q-function learning for off-policy evaluation
M Uehara, J Huang, N Jiang
International Conference on Machine Learning, 9659-9668, 2019
1832019
Weightnet: Revisiting the design space of weight networks
N Ma, X Zhang, J Huang, J Sun
European Conference on Computer Vision, 776-792, 2020
1092020
Minimax value interval for off-policy evaluation and policy optimization
N Jiang, J Huang
Advances in Neural Information Processing Systems 33, 2747-2758, 2020
802020
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
372022
From Importance Sampling to Doubly Robust Policy Gradient
J Huang, N Jiang
International Conference on Machine Learning, 4434-4443, 2019
302019
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
252022
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
10*2022
On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation
J Huang, B Yardim, N He
International Conference on Artificial Intelligence and Statistics, 289-297, 2024
72024
Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL
J Huang, N He, A Krause
arXiv preprint arXiv:2402.05724, 2024
22024
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
22022
Learning to Steer Markovian Agents under Model Uncertainty
J Huang, V Thoma, Z Shen, HH Nax, N He
arXiv preprint arXiv:2407.10207, 2024
2024
Robust Knowledge Transfer in Tiered Reinforcement Learning
J Huang, N He
Advances in Neural Information Processing Systems 36, 2023
2023
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