Publications (76)
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimizations
Pan Xu, Jian Ma, Quanquan Gu
We study the estimation of the latent variable Gaussian graphical model (LVGGM), where the precision matrix is the superposition of a sparse matrix and a low-rank matrix. In order…
MOBODY: Model Based Off-Dynamics Offline Reinforcement Learning
Yihong Guo, Yu Yang, Pan Xu +1
We study off-dynamics offline reinforcement learning, where the goal is to learn a policy from offline source and limited target datasets with mismatched dynamics. Existing methods…
Breaking the Total Variance Barrier: Sharp Sample Complexity for Linear Heteroscedastic Bandits with Fixed Action Set
Heyang Zhao, Tianyuan Jin, Weixin Wang +3
Recent years have witnessed increasing interests in tackling heteroscedastic noise in bandits and reinforcement learning. In these works, the cumulative variance of the noise $Î=…
More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling
Haque Ishfaq, Yixin Tan, Yu Yang +5
Thompson sampling (TS) is one of the most popular exploration techniques in reinforcement learning (RL). However, most TS algorithms with theoretical guarantees are difficult to im…
Trading the System Efficiency for the Income Equality of Drivers in Rideshare
Yifan Xu, Pan Xu
Several scientific studies have reported the existence of the income gap among rideshare drivers based on demographic factors such as gender, age, race, etc. In this paper, we stud…
Provable Anytime Ensemble Sampling Algorithms in Nonlinear Contextual Bandits
Jiazheng Sun, Weixin Wang, Pan Xu
We provide a unified algorithmic framework for ensemble sampling in nonlinear contextual bandits and develop corresponding regret bounds for two most common nonlinear contextual ba…