Practical Bayesian Optimization with Threshold-Guided Marginal Likelihood Maximization
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We propose a practical Bayesian optimization method, of which the surrogate function is Gaussian process regression with threshold-guided marginal likelihood maximization. Because Bayesian optimization consumes much time in finding optimal free parameters of Gaussian process regression, mitigating a time complexity of this step is critical to speed up Bayesian optimization. For this reason, we propose a simple, but straightforward Bayesian optimization method, assuming a reasonable condition, which is observed in many practical examples. Our experimental results confirm that our method is effective to reduce the execution time. All implementations are available in our repository.
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