Sample-Efficient "Clustering and Conquer" Procedures for Parallel Large-Scale Ranking and Selection

Zishi Zhang, Yijie Peng

This work aims to improve the sample efficiency of parallel large-scale ranking and selection (R&S) problems by leveraging correlation information. We modify the commonly used "div…