Semi-supervised Embedding Learning for High-dimensional Bayesian Optimization

• URL: http://arxiv.org/abs/2005.14601v3
• Date: Mon, 19 Oct 2020 05:36:39 GMT
• Title: Semi-supervised Embedding Learning for High-dimensional Bayesian Optimization
• Authors: Jingfan Chen, Guanghui Zhu, Chunfeng Yuan, Yihua Huang
• Abstract summary: We propose a novel framework, 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. We show that SILBO outperforms the existing state-of-the-art high-dimensional Bayesian optimization methods.
• Score: 12.238019485880583
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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