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Multi-Fidelity Bayesian Optimization via Deep Neural Networks
Shibo Li · Wei Xing · Robert Kirby · Shandian Zhe

Wed Dec 09 09:00 AM -- 11:00 AM (PST) @ Poster Session 3 #899
Bayesian optimization (BO) is a popular framework for optimizing 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. While the acquisition function is therefore easy and convenient to calculate,  these methods 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 compute a mutual information-based acquisition function in a tractable and highly efficient way. We show the advantages of our method in both synthetic benchmark datasets and real-world applications in engineering design.

Author Information

Shibo Li (University of Utah)
Wei Xing (University of Utah)
Robert Kirby (University of Utah)
Shandian Zhe (University of Utah)

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