Bayesian optimization (BO) is a popular method to optimize costly black-box functions. While traditional BO optimizes each new target task from scratch, meta-learning has emerged as a way to leverage knowledge from related tasks to optimize new tasks faster. However, existing meta-learning BO methods rely on surrogate models that suffer from scalability issues and are sensitive to observations with different scales and noise types across tasks. Moreover, they often overlook the uncertainty associated with task similarity. This leads to unreliable task adaptation when only limited observations are obtained or when the new tasks differ significantly from the related tasks. To address these limitations, we propose a novel meta-learning BO approach that bypasses the surrogate model and directly learns the utility of queries across tasks. Our method explicitly models task uncertainty and includes an auxiliary model to enable robust adaptation to new tasks. Extensive experiments show that our method demonstrates strong anytime performance and outperforms state-of-the-art meta-learning BO methods in various benchmarks.

翻译：贝叶斯优化（BO）是一种优化昂贵黑箱函数的常用方法。传统BO从零开始优化每个新的目标任务，而元学习已成为一种利用相关任务知识以更快优化新任务的方法。然而，现有元学习BO方法依赖的代理模型存在可扩展性问题，且对不同任务的观测值尺度差异和噪声类型高度敏感。此外，这些方法常忽视任务相似性中的不确定性，导致在仅获得有限观测值或新任务与相关任务差异显著时，任务适配的可靠性降低。为解决上述局限，我们提出一种新型元学习BO方法：该方法绕过代理模型，直接学习跨任务查询的效用函数。我们的方法显式建模任务不确定性，并引入辅助模型以增强对新任务的鲁棒适应。大量实验表明，该方法在多种基准测试中展现出强大的全程性能，并优于现有最先进的元学习贝叶斯优化方法。