论文标题
Mercer功能，用于有效组合贝叶斯优化
Mercer Features for Efficient Combinatorial Bayesian Optimization
论文作者
论文摘要
贝叶斯优化（BO）是一个有效的框架，用于解决昂贵功能评估的黑盒优化问题。本文解决了在科学和工程应用中自然发生的组合空间（例如序列和图形）的BO问题设置。一个原型的例子是以昂贵的实验为指导的分子优化。关键挑战是平衡统计模型的复杂性和搜索的障碍，以选择用于评估的组合结构。在本文中，我们提出了一种有效的方法，称为组合贝叶斯优化（Mercbo）的Mercer特征。 Mercbo背后的关键思想是通过利用其组合图表示的结构来为离散对象提供显式特征图。这些Mercer功能与汤普森采样功能相结合，使我们可以使用可拖动的求解器找到下一个评估结构。对不同现实世界基准的实验表明，Mercbo的性能与先前的方法相似或更好。源代码可从https://github.com/aryandeshwal/mercbo获得。
Bayesian optimization (BO) is an efficient framework for solving black-box optimization problems with expensive function evaluations. This paper addresses the BO problem setting for combinatorial spaces (e.g., sequences and graphs) that occurs naturally in science and engineering applications. A prototypical example is molecular optimization guided by expensive experiments. The key challenge is to balance the complexity of statistical models and tractability of search to select combinatorial structures for evaluation. In this paper, we propose an efficient approach referred as Mercer Features for Combinatorial Bayesian Optimization (MerCBO). The key idea behind MerCBO is to provide explicit feature maps for diffusion kernels over discrete objects by exploiting the structure of their combinatorial graph representation. These Mercer features combined with Thompson sampling as the acquisition function allows us to employ tractable solvers to find next structures for evaluation. Experiments on diverse real-world benchmarks demonstrate that MerCBO performs similarly or better than prior methods. The source code is available at https://github.com/aryandeshwal/MerCBO .
