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Efficient Bayesian Optimization using Multiscale Graph Correlation
[article]
2021
arXiv
pre-print
Bayesian optimization is a powerful tool to optimize a black-box function, the evaluation of which is time-consuming or costly. In this paper, we propose a new approach to Bayesian optimization called GP-MGC, which maximizes multiscale graph correlation with respect to the global maximum to determine the next query point. We present our evaluation of GP-MGC in applications involving both synthetic benchmark functions and real-world datasets and demonstrate that GP-MGC performs as well as or
arXiv:2103.09434v1
fatcat:6goamdjgwngwhczhp7gqjl7sju