Multi-Fidelity Bayesian Optimization via Deep Neural Networks
Abstract
Bayesian optimization (BO) is a popular framework to optimize black-box functions. In many applications, the objective function can be evaluated at multiple fidelities to enable a trade-off between the cost and accuracy. To reduce the optimization cost, many multi-fidelity BO methods have been proposed. Despite their success, these methods either ignore or over-simplify the strong, complex correlations across the fidelities, and hence can be inefficient in estimating the objective function. To address this issue, we propose Deep Neural Network Multi-Fidelity Bayesian Optimization (DNN-MFBO) that can flexibly capture all kinds of complicated relationships between the fidelities to improve the objective function estimation and hence the optimization performance. We use sequential, fidelity-wise Gauss-Hermite quadrature and moment-matching to fulfill a mutual information-based acquisition function, which is computationally tractable and efficient. We show the advantages of our method in both synthetic benchmark datasets and real-world applications in engineering design.
Cite
@article{arxiv.2007.03117,
title = {Multi-Fidelity Bayesian Optimization via Deep Neural Networks},
author = {Shibo Li and Wei Xing and Mike Kirby and Shandian Zhe},
journal= {arXiv preprint arXiv:2007.03117},
year = {2020}
}