Bayesian optimization (BO) is a popular black-box function optimization method, which makes sequential decisions based on a Bayesian model, typically a Gaussian process (GP), of the function. To ensure the quality of the model, transfer learning approaches have been developed to automatically design GP priors by learning from observations on "training" functions. These training functions are typically required to have the same domain as the "test" function (black-box function to be optimized). In this paper, we introduce MPHD, a model pre-training method on heterogeneous domains, which uses a neural net mapping from domain-specific contexts to specifications of hierarchical GPs. MPHD can be seamlessly integrated with BO to transfer knowledge across heterogeneous search spaces. Our theoretical and empirical results demonstrate the validity of MPHD and its superior performance on challenging black-box function optimization tasks.

本论文介绍了一种在异构领域中进行模型预训练的方法MPHD，该方法使用神经网络映射将领域特定上下文映射到层级高斯过程的规范，以便与BO集成以在异构搜索空间中进行知识转移，并通过理论和实证结果证明了MPHD的有效性和优越的黑盒函数优化性能。

异构搜索空间上的贝叶斯优化的迁移学习