提出一种自适应元贝叶斯优化方法，兼顾任务相关性与泛化能力。
BayMOTH: Bayesian optiMizatiOn with meTa-lookahead -- a simple approacH
- 融合元学习与前瞻搜索，在相关任务中利用历史信息，否则退回到前瞻策略。
- 在低相关性任务上仍保持优异性能，避免因任务错配导致查询质量下降。
- 适合多任务优化场景，尤其适用于测试任务与训练任务结构不一致的情况。
贝叶斯优化（BO）在昂贵黑箱函数的序列优化中展现了实用性和有效性。元贝叶斯优化（meta-BO）通过利用相关任务的信息，提升优化样本效率。然而，当元训练任务与测试任务结构不匹配时，可能导致在线优化中产生次优查询。为此，我们提出一种简单的元贝叶斯优化算法，在相关任务中利用历史信息，否则退回到前瞻搜索，统一框架内实现自适应切换。我们在函数优化任务上验证了该方法的竞争力，同时在测试任务与元训练集结构差异较大的低相关性场景下仍保持强性能。
原文摘要 · Abstract (English)
Bayesian optimization (BO) has for sequential optimization of expensive black-box functions demonstrated practicality and effectiveness in many real-world settings. Meta-Bayesian optimization (meta-BO) focuses on improving the sample efficiency of BO by making use of information from related tasks. Although meta-BO is sample-efficient when task structure transfers, poor alignment between meta-training and test tasks can cause suboptimal queries to be suggested during online optimization. To this end, we propose a simple meta-BO algorithm that utilizes related-task information when determined useful, falling back to lookahead otherwise, within a unified framework. We demonstrate competitiveness of our method with existing approaches on function optimization tasks, while retaining strong performance in low task-relatedness regimes where test tasks share limited structure with the meta-training set.
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