论文标题
贝叶斯筛查：用于硅材料筛选中的多测试贝叶斯优化
Bayesian Screening: Multi-test Bayesian Optimization Applied to in silico Material Screening
论文作者
论文摘要
我们提出了新的多测试贝叶斯优化模型和用于大规模材料筛选应用的算法。我们的筛选问题是在两个测试中设计的，一项昂贵，一项便宜。本文通过使用灵活的模型，可以在廉价和昂贵的测试分数之间建立复杂的，非线性的关系，这与其他有关多测试贝叶斯优化的其他工作有所不同。在我们描述的材料筛选应用中，这种额外的建模灵活性至关重要。我们证明了新算法在一个合成玩具问题家族以及两个大规模筛选研究中的真实数据上的力量。
We present new multi-test Bayesian optimization models and algorithms for use in large scale material screening applications. Our screening problems are designed around two tests, one expensive and one cheap. This paper differs from other recent work on multi-test Bayesian optimization through use of a flexible model that allows for complex, non-linear relationships between the cheap and expensive test scores. This additional modeling flexibility is essential in the material screening applications which we describe. We demonstrate the power of our new algorithms on a family of synthetic toy problems as well as on real data from two large scale screening studies.
