Variational Autoencoder based Bayesian Optimization (VAE-BO) has demonstrated its excellent performance in addressing high-dimensional structured optimization problems. However, current mainstream methods overlook the potential of utilizing a pool of unlabeled data to construct the latent space, while only concentrating on designing sophisticated models to leverage the labeled data. Despite their effective usage of labeled data, these methods often require extra network structures, additional procedure, resulting in computational inefficiency. To address this issue, we propose a novel method to effectively utilize unlabeled data with the guidance of labeled data. Specifically, we tailor the pseudo-labeling technique from semi-supervised learning to explicitly reveal the relative magnitudes of optimization objective values hidden within the unlabeled data. Based on this technique, we assign appropriate training weights to unlabeled data to enhance the construction of a discriminative latent space. Furthermore, we treat the VAE encoder and the Gaussian Process (GP) in Bayesian optimization as a unified deep kernel learning process, allowing the direct utilization of labeled data, which we term as Gaussian Process guidance. This directly and effectively integrates the goal of improving GP accuracy into the VAE training, thereby guiding the construction of the latent space. The extensive experiments demonstrate that our proposed method outperforms existing VAE-BO algorithms in various optimization scenarios. Our code will be published at https://github.com/TaicaiChen/PG-LBO.

翻译：基于变分自编码器的贝叶斯优化（VAE-BO）在解决高维结构化优化问题中展现了卓越性能。然而，当前主流方法仅专注于设计复杂模型以利用标注数据，忽视了利用未标注数据池构建潜在空间的潜力。尽管这些方法有效利用了标注数据，但通常需要额外的网络结构和额外步骤，导致计算效率低下。为应对该问题，我们提出了一种在标注数据引导下有效利用未标注数据的新方法。具体而言，我们将半监督学习中的伪标签技术定制化改进，以显式揭示未标注数据中隐藏的目标函数相对大小信息。基于该技术，我们为未标注数据分配适当的训练权重，从而增强判别性潜在空间的构建。此外，我们将贝叶斯优化中的VAE编码器与高斯过程视为统一的深度核学习过程，使得标注数据得以直接利用——我们称之为高斯过程引导。该方法直接将提升高斯过程精度的目标融入VAE训练，从而引导潜在空间的构建。大量实验表明，我们提出的方法在各种优化场景中均优于现有VAE-BO算法。相关代码将于https://github.com/TaicaiChen/PG-LBO公开发布。