15 citations · 35 across the 5 of their papers we have counts for
5 papers
Top-Two Thompson Sampling for Contextual Top-mc Selection Problems
Xinbo Shi, Yijie Peng, Gongbo Zhang
We aim to efficiently allocate a fixed simulation budget to identify the top-mc designs for each context among a finite number of contexts. The performance of each design under a c…
Efficient Learning for Selecting Top-m Context-Dependent Designs
Gongbo Zhang, Sihua Chen, Kuihua Huang +1
We consider a simulation optimization problem for a context-dependent decision-making, which aims to determine the top-m designs for all contexts. Under a Bayesian framework, we fo…
An Efficient Dynamic Sampling Policy For Monte Carlo Tree Search
Gongbo Zhang, Yijie Peng, Yilong Xu
We consider the popular tree-based search strategy within the framework of reinforcement learning, the Monte Carlo Tree Search (MCTS), in the context of finite-horizon Markov decis…
Asymptotically Optimal Sampling Policy for Selecting Top-m Alternatives
Gongbo Zhang, Yijie Peng, Jianghua Zhang +1
We consider selecting the top- alternatives from a finite number of alternatives via Monte Carlo simulation. Under a Bayesian framework, we formulate the sampling decision as a…
Efficient Sampling Policy for Selecting a Good Enough Subset
Gongbo Zhang, Bin Chen, Qing-shan Jia +1
The note studies the problem of selecting a good enough subset out of a finite number of alternatives under a fixed simulation budget. Our work aims to maximize the posterior proba…