跨异构搜索空间的高效贝叶斯优化，实现少样本下知识迁移。
Sample-Efficient Bayesian Optimization with Transfer Learning for Heterogeneous Search Spaces
- 用条件核的高斯过程模型跨不同参数空间传递信息。
- 缺失参数作为超参数联合推断，提升少样本优化性能。
- 适用于参数空间不一致但相关的历史实验优化场景。
贝叶斯优化（BO）是黑箱函数样本高效优化的强大方法。但在函数评估次数极有限的情况下，成功应用BO可能需要从历史实验中转移信息。这些相关实验可能具有不同的可调参数（搜索空间），这促使了异构搜索空间下的贝叶斯优化与迁移学习结合的需求。本文提出两种方法：第一种利用带有条件核的高斯过程（GP）模型，在不同搜索空间间传递信息；第二种将缺失参数视为GP模型的超参数，可与其它超参数联合推断或固定取值。我们在多个基准问题上验证了这两种方法的有效性。
原文摘要 · Abstract (English)
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.
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