Multi-fidelity Bayesian Optimization with Max-value Entropy Search
Multi-fidelity Bayesian Optimization with Max-value Entropy Search
复制标题
具有最大值熵搜索的多保真贝叶斯优化
DOI：
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复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
Masayuki Karasuyama
中科院分区：
文献类型：
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作者：
Shion Takeno;H. Fukuoka;Yuhki Tsukada;T. Koyama;M. Shiga;I. Takeuchi;Masayuki Karasuyama
贝叶斯优化（BO）是一种有效的工具，黑箱优化中的目标函数的评估通常是相当昂贵的。在实践中，目标函数的较低保真度近似通常是可用的。最近，多保真度贝叶斯优化（MMBO）引起了相当大的关注，因为它可以显着加快优化过程中使用这些廉价的观察。我们提出了一种新的信息理论的方法MFBO。基于信息的方法在BO中是流行的，并且在经验上是成功的，但是现有的基于信息的MBO研究受到难以准确估计信息增益的困扰。我们的方法是基于一个变种的信息为基础的BO称为最大值熵搜索（MES），这大大有利于评估的信息增益MMBO。事实上，除了一维积分和采样之外，我们的采集函数的计算都是解析地编写的，可以高效准确地计算。我们通过使用合成和基准数据集来证明我们的方法的有效性，并进一步展示了材料科学数据的实际应用。
Bayesian optimization (BO) is an effective tool for black-box optimization in which objective function evaluation is usually quite expensive. In practice, lower fidelity approximations of the objective function are often available. Recently, multi-fidelity Bayesian optimization (MFBO) has attracted considerable attention because it can dramatically accelerate the optimization process by using those cheaper observations. We propose a novel information theoretic approach to MFBO. Information-based approaches are popular and empirically successful in BO, but existing studies for information-based MFBO are plagued by difficulty for accurately estimating the information gain. Our approach is based on a variant of information-based BO called max-value entropy search (MES), which greatly facilitates evaluation of the information gain in MFBO. In fact, computations of our acquisition function is written analytically except for one dimensional integral and sampling, which can be calculated efficiently and accurately. We demonstrate effectiveness of our approach by using synthetic and benchmark datasets, and further we show a real-world application to materials science data.
DOI：
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发表时间：
2018-11
期刊：
ArXiv
影响因子：
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作者：
Jialin Song;Yuxin Chen;Yisong Yue
通讯作者：
Jialin Song;Yuxin Chen;Yisong Yue