Efficient Bayesian Optimization using Multiscale Graph Correlation

• URL: http://arxiv.org/abs/2103.09434v1
• Date: Wed, 17 Mar 2021 04:35:09 GMT
• Title: Efficient Bayesian Optimization using Multiscale Graph Correlation
• Authors: Takuya Kanazawa
• Abstract summary: We propose a new approach to Bayesian optimization called GP-MGC. We present our evaluation of GP-MGC in applications involving both synthetic benchmark functions and real-world datasets.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 even better than state-of-the-art methods such as max-value entropy search and GP-UCB.

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