Bayesian optimization (BO) is a powerful approach to sample-efficient optimization of black-box functions. However, in settings with very few function evaluations, a successful application of BO may require transferring information from historical experiments. These related experiments may not have exactly the same tunable parameters (search spaces), motivating the need for BO with transfer learning for heterogeneous search spaces. In this paper, we propose two methods for this setting. The first approach leverages a Gaussian process (GP) model with a conditional kernel to transfer information between different search spaces. Our second approach treats the missing parameters as hyperparameters of the GP model that can be inferred jointly with the other GP hyperparameters or set to fixed values. We show that these two methods perform well on several benchmark problems.

本研究解决了在极少函数评估下贝叶斯优化的应用问题，尤其是在异构搜索空间中信息转移的需求。提出的两种新方法通过高斯过程模型和条件核进行有效信息转移，并将缺失参数视为超参数进行联合推断。结果表明，这两种方法在多个基准问题上表现优异，具有重要的实用价值。

异构搜索空间下的样本效率贝叶斯优化与迁移学习