一句话：提出用组合测试与主动学习构建代理模型，以近似并分析黑盒机器学习模型的行为。
机器学习模型常呈黑盒特性，难以理解其决策过程。代理模型用于近似目标模型行为，是分析黑盒模型的重要工具。本文关注代理模型的构建问题：通常需要选取数据点查询目标模型，并利用其响应推断目标模型的结构与参数。作者提出结合组合测试与主动学习来构造代理模型，通过组合测试指导输入空间中的查询选择，并借助主动学习以较少查询获取对目标模型行为的高近似度。摘要未报告具体实验或评估结果，重点在于给出该构造思路与动机。
Machine learning (ML)-based models are often black box, making it challenging to understand and interpret their decision-making processes. Surrogate models are constructed to approximate the behavior of a target model and are an essential tool for analyzing black-box models. The construction of a surrogate model typically includes querying the target model with carefully selected data points and using the responses from the target model to infer information about its structure and parameters.
@inproceedings{paperbot8301,
title = {Constructing Surrogate Models in Machine Learning Using Combinatorial Testing and Active Learning},
author = {Sunny Shree and Krishna Khadka and Yu Lei and Raghu N. Kacker and D. Richard Kuhn},
booktitle = {ASE},
year = {2024},
doi = {10.1145/3691620.3695532}
}