Design is an inseparable part of most scientific and engineering tasks, including real and simulation-based experimental design processes and parameter/hyperparameter tuning/optimization. Several model-based experimental design techniques have been developed for design in domains with partial available knowledge about the underlying process. This article focuses on a powerful class of model-based experimental design called the mean objective cost of uncertainty (MOCU). The MOCU-based techniques are objective-based, meaning that they take the main objective of the process into account during the experimental design process. However, the lack of scalability of MOCU-based techniques prevents their application to most practical problems, including large discrete or combinatorial spaces. To achieve a scalable objective-based experimental design, this article proposes a graph-based MOCU-based Bayesian optimization framework. The correlations among samples in the large design space are accounted for using a graph-based Gaussian process, and an efficient closed-form sequential selection is achieved through the well-known expected improvement policy. The proposed framework’s performance is assessed through the structural intervention in gene regulatory networks, aiming to make the network away from the states associated with cancer.

中文翻译：

---

用于大规模基于目标的实验设计的基于图的贝叶斯优化

设计是大多数科学和工程任务不可分割的一部分，包括真实和基于仿真的实验设计过程以及参数/超参数调整/优化。已经开发了几种基于模型的实验设计技术，用于在具有关于基础过程的部分可用知识的领域中进行设计。本文重点介绍一类功能强大的基于模型的实验设计，称为不确定性平均目标成本 (MOCU)。基于 MOCU 的技术是基于目标的，这意味着它们在实验设计过程中考虑了过程的主要目标。然而，基于 MOCU 的技术缺乏可扩展性，阻碍了它们应用于大多数实际问题，包括大型离散或组合空间。为了实现可扩展的基于目标的实验设计，本文提出了一种基于图的基于MOCU的贝叶斯优化框架。大型设计空间中样本之间的相关性使用基于图形的高斯过程进行说明，并通过众所周知的预期改进策略实现有效的封闭形式顺序选择。拟议框架的性能通过基因调控网络的结构干预进行评估，旨在使网络远离与癌症相关的状态。