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Sample-Efficient Clustering and Conquer Procedures for Parallel Large-Scale Ranking and Selection
We propose novel "clustering and conquer" procedures for the parallel large-scale ranking and selection (R&S) problem, which leverage correlation information for clustering to break the bottleneck of sample efficiency.
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