Efficient Bayesian Optimization using Multiscale Graph Correlation
This work addresses the challenge of efficient optimization for costly evaluations, which is incremental as it builds on existing Bayesian optimization techniques.
The paper tackles the problem of optimizing expensive black-box functions by proposing GP-MGC, a Bayesian optimization method that uses multiscale graph correlation to select query points, and demonstrates it performs as well as or better than state-of-the-art methods like max-value entropy search and GP-UCB on synthetic and real-world datasets.
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.