37 citations · 179 across the 36 of their papers we have counts for
12 papers · 1 filter
Sample Complexity of Preference-Based Nonparametric Off-Policy Evaluation with Deep Networks
Zihao Li, Xiang Ji, Minshuo Chen +1
A recently popular approach to solving reinforcement learning is with data from human preferences. In fact, human preference data are now used with classic reinforcement learning a…
Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds
Zhenghao Xu, Xiang Ji, Minshuo Chen +2
Policy gradient methods equipped with deep neural networks have achieved great success in solving high-dimensional reinforcement learning (RL) problems. However, current analyses c…
Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement
Hui Yuan, Kaixuan Huang, Chengzhuo Ni +2
We explore the methodology and theory of reward-directed generation via conditional diffusion models. Directed generation aims to generate samples with desired properties as measur…
Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight
Jiacheng Guo, Minshuo Chen, Huan Wang +3
This paper studies the sample-efficiency of learning in Partially Observable Markov Decision Processes (POMDPs), a challenging problem in reinforcement learning that is known to be…
Provable Benefits of Policy Learning from Human Preferences in Contextual Bandit Problems
Xiang Ji, Huazheng Wang, Minshuo Chen +2
For a real-world decision-making problem, the reward function often needs to be engineered or learned. A popular approach is to utilize human feedback to learn a reward function fo…
Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks
Zixuan Zhang, Kaiqi Zhang, Minshuo Chen +4
Convolutional residual neural networks (ConvResNets), though overparameterized, can achieve remarkable prediction performance in practice, which cannot be well explained by convent…