# Practical Bayesian Optimization with Threshold-Guided Marginal Likelihood Maximization

## TL;DR

This paper introduces a practical Bayesian optimization approach that accelerates Gaussian process regression by guiding marginal likelihood maximization with a threshold, reducing execution time while maintaining optimization quality.

## Contribution

It presents a threshold-guided method for marginal likelihood maximization that improves the efficiency of Bayesian optimization processes.

## Key findings

- Significant reduction in execution time across various experiments.
- Maintains comparable optimization quality to traditional methods.
- Effective in most tested scenarios.

## Abstract

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.