发表机构
Harvard University; Georgia Institute of Technology（哈佛大学; 佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对含定性与定量因素的昂贵黑箱模型，扩展最大单因素一次（MOFAT）设计以适配多类型因素，提出高效构建算法，经数值实验与机器学习超参数调优应用验证了设计的实用性。
AI 中文摘要
计算成本高昂的黑箱模型通常包含大量输入因素，这些因素存在复杂交互且重要性各异。实验设计技术可用于快速识别重要因素，从而提升复杂计算机模型优化或昂贵机器学习模型训练的效率。现有针对黑箱模型的筛选设计主要聚焦于连续因素，近期的一个典型例子是最大单因素一次（MOFAT）设计。本研究将该设计扩展为可纳入多种类型因素，包括名义型、有序型及离散数值型因素。我们首先确定实现最优筛选所需的特性，即需区别对待定性与定量因素；基于这些特性，提出实用算法以高效构建适用于所有类型因素的MOFAT设计。数值实验及在机器学习模型超参数调优中的应用均验证了该设计的实用性。
英文摘要
Computationally expensive black-box models often involve a large number of input factors with complex interactions and varying importance. Experimental design techniques can be used for quickly identifying the important factors, which can make the optimization of a complex computer model or the training of an expensive machine learning model more efficient. Existing screening designs for black-box models focus mainly on continuous factors, with the maximum one-factor-at-a-time (MOFAT) design being a recent example. In this work, we extend the design to incorporate multiple types of factors, including nominal, ordinal, and discrete-numeric. We first identify the properties leading to optimal screening, where qualitative and quantitative factors should be treated differently. Based on these properties, we propose practical algorithms to efficiently construct MOFAT designs for all types of factors. The usefulness of the design is demonstrated by both numerical experiments and an application to hyperparameter tuning in machine learning models.
Comments34 pages, 8 figures
