arXiv：cs.LG· Satoshi Katayama, Shoyo Hunt, Shintaro Masuda, Masayuki Karasuyama·· 3 小时前AI 评分30
用贝叶斯优化预训练贝叶斯优化算法
Pre-training of Bayesian Optimization Algorithm through Bayesian Optimization
AI 导读
研究提出用贝叶斯优化（BO）自身来优化 BO 算法的参数配置：先从 BO 启动时可得信息推断高斯过程（GP），从该 GP 采样出样本路径，在其上运行 BO 算法以获得期望累积遗憾的经验估计，再通过外层 BO（outer BO）优化该估计。实验表明该框架能在候选参数配置中有效选出性能较强的配置。
正文
Abstract:Bayesian optimization (BO) is widely used as a standard approach for expensive black-box optimization. However, BO algorithms often involve parameters that must be specified in advance, and their performance can strongly depend on these choices. We propose a framework for optimizing such parameters using sample paths drawn from a Gaussian process (GP) inferred from the information available at the start of BO. We use cumulative regret as the performance metric for a BO algorithm. By running the BO algorithm on the generated sample paths, we obtain an empirical estimate of its expected cumulative regret for a given parameter configuration. Optimizing this estimate allows us to identify parameter configurations that, given the currently available information, are expected to achieve low cumulative regret. Since this parameter optimization is itself a black-box optimization problem, we employ another BO procedure to solve it, which we refer to as outer BO. Through experiments, we demonstrate that the proposed framework can effectively select parameter configurations that achieve strong performance among a range of candidate configurations.
| Subjects: | Machine Learning (cs.LG); Machine Learning (stat.ML) |
| Cite as: | arXiv:2610.10186 [cs.LG] |
| (or arXiv:2610.10186v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10186 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Masayuki Karasuyama [view email]
[v1]
Wed, 7 Oct 2026 14:53:25 UTC (32,873 KB)
来源：arXiv：cs.LG · arxiv.org