Practical Bayesian Optimization with Threshold-Guided Marginal Likelihood Maximization
This is an incremental improvement for practitioners using Bayesian optimization in time-sensitive applications.
The paper tackles the high execution time of Bayesian optimization by introducing a threshold-guided method to limit Gaussian process model selection steps, resulting in reduced runtime with minimal loss in optimization quality.
We propose a practical Bayesian optimization method using Gaussian process regression, of which the marginal likelihood is maximized where the number of model selection steps is guided by a pre-defined threshold. Since Bayesian optimization consumes a large portion of its execution time in finding the optimal free parameters for Gaussian process regression, our simple, but straightforward method is able to mitigate the time complexity and speed up the overall Bayesian optimization procedure. Finally, the experimental results show that our method is effective to reduce the execution time in most of cases, with less loss of optimization quality.