III: Small: RareXplain: A Computational Framework for Explainable Rare Category Analysis

尽管每天在各个领域产生大量数据，但通常是最重要和对我们社会产生深远影响的罕见类别。然而，现有的分析此类罕见类别的工作主要针对可分离病例或均匀和静态设置，因此不适合分析复杂的罕见类别，例如患者中的罕见并发症和半导体制造中的缺陷。在这两个用例的激励下，该项目专注于弥合分析复杂稀有类别的迫切需求与当前技术无法以有效、高效和可解释的方式解决该问题之间的差距。本项目开发了rareexplain，这是一个用于可解释稀有类别分析的计算框架，它由新的模型和算法组成，用于分析复杂的稀有类别，同时为模型输出提供解释。所开发的技术通过提高检测和跟踪精度、运行时间以及模型可解释性等方面的实践水平，使多个应用领域受益。该项目为不同层次的学生，特别是来自弱势群体的学生提供培训和研究机会。研究成果将通过出版物、课程整合、教程、研讨会和潜在的技术转移向行业传播。本课题研究稀有类别分析中的三种复杂性，包括数据复杂性、动态复杂性和模型复杂性。更具体地说，它由两个互补的研究方向组成，即（Thrust 1）复稀有类别检测和（Thrust 2）复稀有类别跟踪。第一个任务是以一种可解释的方式检测复杂的稀有类别，即，在数据复杂性存在的情况下，借助oracle从感兴趣的稀有类别中识别出第一个示例。特别地，在开放集设置下，通过带通滤波器和跨域稀有类别检测来解决数据复杂性问题。第二次推力以一种可解释的方式跟踪从第一次推力中检测到的稀有类别，即分别根据底层图拓扑和顶点特征跟踪检测到的稀有类别。特别是，通过对时间演化图的在线局部聚类和对时间演化顶点特征的连续迁移学习来解决动态复杂性问题。此外，通过点向和对向模型解释来解决模型复杂性问题。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Despite the large amount of data being generated across various domains every day, it is usually the rare categories that are of the greatest importance and profound impacts on our society. However, existing work for analyzing such rare categories mainly addresses the separable cases or the homogeneous and static settings, and thus is not suitable for analyzing complex rare categories such as rare complications among patients and defects in semiconductor manufacturing. Motivated by these two use cases, this project focuses on bridging the gap between the imminent need to analyze complex rare categories and the inability of state-of-the-art techniques to address this problem in an effective, efficient, and explainable way. This project develops RareXplain, a computational framework for explainable rare category analysis, which consists of new models and algorithms to analyze complex rare categories while providing explanation for the model outputs. The developed techniques benefit multiple application domains by advancing state-of-the-practice in terms of the detection and tracking accuracy, running time, as well as model explanability. This project provides training and research opportunities for students at various levels, especially those from under-represented groups. The research results will be disseminated via publications, course integration, tutorials, workshops, and potential tech transfer to industry.This project addresses three types of complexity in rare category analysis, including the data complexity, the dynamics complexity, and the model complexity. More specifically, it consists of two complementary research thrusts, namely (Thrust 1) complex rare category detection, and (Thrust 2) complex rare category tracking. The first thrust detects complex rare categories in an explainable way, i.e., identifying the first examples from the rare categories of interest with the help of an oracle in the presence of data complexity. In particular, the data complexity is addressed via band-pass filters and cross-domain rare category detection in the open set setting. The second thrust tracks over time the rare categories detected from the first thrust in an explainable way, i.e., tracking the detected rare categories with respect to the underlying graph topology and the vertex features respectively. In particular, the dynamics complexity is addressed via online local clustering on time-evolving graphs and continuous transfer learning on time-evolving vertex features. Furthermore, the model complexity is addressed via both point-wise and pair-wise model explanation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.