Sample-Efficient "Clustering and Conquer" Procedures for Parallel Large-Scale Ranking and Selection
The work improves sample efficiency for large-scale Ranking and Selection problems by using correlation information. It proposes a Parallel Correlation Clustering and Conquer (P3C) procedure that groups highly correlated alternatives onto single processors, reducing stochastic variance and accelerating the selection process.