BOOST: A Data-Driven Framework for the Automated Joint Selection of Kernel and Acquisition Functions in Bayesian Optimization

TL;DR

BOOST is a data-driven framework that automates the joint selection of kernel and acquisition functions in Bayesian optimization, improving performance and reducing manual tuning.

Contribution

It introduces an offline evaluation method to predict and select the best kernel-acquisition pair before optimization, enhancing robustness and efficiency.

Findings

BOOST outperforms fixed-hyperparameter BO in experiments.

It is competitive with state-of-the-art adaptive methods.

Demonstrates robustness across synthetic and hyperparameter tuning tasks.

Abstract

The performance of Bayesian optimization (BO), a highly sample-efficient method for expensive black-box problems, is critically governed by the selection of its hyperparameters, including the kernel and acquisition functions. This presents a significant practical challenge: an inappropriate combination of these can lead to poor performance and wasted evaluations. While individual improvements to kernel functions and acquisition functions have been actively explored, the joint and autonomous selection of the best pair of these fundamental hyperparameters has been largely overlooked. This forced practitioners to rely on heuristics or costly manual training. In this work, we propose a framework, BOOST (Bayesian Optimization with Optimal Kernel and Acquisition Function Selection Technique), that automates this selection. BOOST utilizes a simple offline evaluation stage to predict the performance of various kernel-acquisition function pairs and identify the most promising pair before committing to the expensive evaluation process. BOOST is a data-driven strategy selection procedure that evaluates kernel-acquisition pairs based on their empirical performance on the data-in-hand. At each iteration, previously observed points are partitioned into a reference set and a query set. These subsets play roles analogous to training and validation sets in machine learning: the reference set is used for model construction, while the query set represents unseen regions to retrospectively evaluate how effectively each candidate strategy progresses toward the target value. Experiments on synthetic benchmarks and machine learning hyperparameter optimization tasks demonstrate that BOOST consistently improves over fixed-hyperparameter BO and remains competitive with state-of-the-art adaptive methods, highlighting its robustness across diverse landscapes.