Bayesian Optimization (BO) is a common approach for hyperparameter
optimization (HPO) in automated machine learning. Although it is well-accepted
that HPO is crucial to obtain well-performing machine learning models, tuning
BO's own hyperparameters is often neglected. In this paper, we empirically
study the impact of optimizing BO's own hyperparameters and the transferability
of the found settings using a wide range of benchmarks, including artificial
functions, HPO and HPO combined with neural architecture search. In particular,
we show (i) that tuning can improve the any-time performance of different BO
approaches, that optimized BO settings also perform well (ii) on similar
problems and (iii) partially even on problems from other problem families, and
(iv) which BO hyperparameters are most important.

本文研究贝叶斯优化在超参数优化中的应用，发现优化 BO 的超参数可以提高 BO 方法在各种基准测试中的的表现，优化后的 BO 调参效果在其他相似或不同领域的问题上有良好的推广性，并指出了最重要的 BO 超参数。