论文标题
半监督的嵌入学习，以进行高维贝叶斯优化
Semi-supervised Embedding Learning for High-dimensional Bayesian Optimization
论文作者
论文摘要
贝叶斯优化是一种广泛应用的方法，可优化昂贵的黑盒功能。尽管取得了成功，但它仍然面临着高维搜索空间的挑战。为了减轻这个问题，我们提出了一个新型的贝叶斯优化框架（称为Silbo），该框架找到了一个低维空间，可以通过半监督的尺寸减小进行贝叶斯优化迭代。 Silbo合并了从采集功能获得的标记点和未标记的点，以指导嵌入式空间学习。为了加速学习过程，我们提出了一种生成投影矩阵的随机方法。此外，要从低维空间到高维原始空间，我们提出了两个映射策略：$ \ text {silbo} _ {fz} $和$ \ text {silbo} _ {fx} _ {fx} $根据目标函数的评估费用。合成功能和超参数优化任务的实验结果表明，Silbo优于现有的最新高维贝叶斯优化方法。
Bayesian optimization is a broadly applied methodology to optimize the expensive black-box function. Despite its success, it still faces the challenge from the high-dimensional search space. To alleviate this problem, we propose a novel Bayesian optimization framework (termed SILBO), which finds a low-dimensional space to perform Bayesian optimization iteratively through semi-supervised dimension reduction. SILBO incorporates both labeled points and unlabeled points acquired from the acquisition function to guide the embedding space learning. To accelerate the learning procedure, we present a randomized method for generating the projection matrix. Furthermore, to map from the low-dimensional space to the high-dimensional original space, we propose two mapping strategies: $\text{SILBO}_{FZ}$ and $\text{SILBO}_{FX}$ according to the evaluation overhead of the objective function. Experimental results on both synthetic function and hyperparameter optimization tasks demonstrate that SILBO outperforms the existing state-of-the-art high-dimensional Bayesian optimization methods.
