Sample-Efficient Clustering and Conquer Procedures for Parallel Large-Scale Ranking and Selection
CoRR(2024)
摘要
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. In
parallel computing environments, correlation-based clustering can achieve an
𝒪(p) sample complexity reduction rate, which is the optimal
reduction rate theoretically attainable. Our proposed framework is versatile,
allowing for seamless integration of various prevalent R S methods under both
fixed-budget and fixed-precision paradigms. It can achieve improvements without
the necessity of highly accurate correlation estimation and precise clustering.
In large-scale AI applications such as neural architecture search, a
screening-free version of our procedure surprisingly surpasses fully-sequential
benchmarks in terms of sample efficiency. This suggests that leveraging
valuable structural information, such as correlation, is a viable path to
bypassing the traditional need for screening via pairwise comparison–a step
previously deemed essential for high sample efficiency but problematic for
parallelization. Additionally, we propose a parallel few-shot clustering
algorithm tailored for large-scale problems.
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