Bayesian optimization (BO) is a powerful tool for seeking the global optimum of black-box functions. While evaluations of the black-box functions can be highly costly, it is desirable to reduce the use of expensive labeled data. For the first time, we introduce a teacher-student model to exploit semi-supervised learning that can make use of large amounts of unlabelled data under the context of BO. Importantly, we show that the selection of the validation and unlabeled data is key to the performance of BO. To optimize the sampling of unlabeled data, we employ a black-box parameterized sampling distribution optimized as part of the employed bi-level optimization framework. Taking one step further, we demonstrate that the performance of BO can be further improved by selecting unlabeled data from a dynamically fitted extreme value distribution. Our BO method operates in a learned latent space with reduced dimensionality, making it scalable to high-dimensional problems. The proposed approach outperforms significantly the existing BO methods on several synthetic and real-world optimization tasks.

本文提出一种基于半监督学习的师生模型的贝叶斯优化方法，利用未标记数据和黑盒函数，在学习到的潜在空间中降低维度，使其可应用于高维问题，优化 unlabeled data 的采样和选择，采用黑盒参数化采样分布和经过动态拟合的极值分布，该方法在多项真实世界和合成训练中优于其他已有BO方法。

基于半监督学习和优化无标签数据采样的高维贝叶斯优化