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

III：小：RareXplain：可解释稀有类别分析的计算框架

• 批准号: 2117902
• 负责人: Jingrui He
• 金额: $ 50万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2117902&HistoricalAwards=false
• 关键词: III; Small; RareXplain; Computational; Framework

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.

尽管每天在各个领域产生大量数据，但通常是罕见的类别对我们的社会具有最重要和深远的影响。然而，用于分析这种罕见类别的现有工作主要解决了可分离的情况或同质和静态设置，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。受这两个用例的启发，该项目专注于弥合分析复杂稀有类别的迫切需要与最先进技术无法以有效，高效和可解释的方式解决此问题之间的差距。该项目开发了RareXplain，一个用于可解释的稀有类别分析的计算框架，它包括新的模型和算法来分析复杂的稀有类别，同时为模型输出提供解释。所开发的技术通过在检测和跟踪精度、运行时间以及模型可扩展性方面推进实践状态而使多个应用领域受益。该项目为各级学生，特别是代表性不足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到行业中来传播。本项目解决了稀有类别分析中的三种复杂性，包括数据复杂性、动力学复杂性和模型复杂性。更具体地说，它包括两个互补的研究重点，即（重点1）复杂稀有类别检测，和（重点2）复杂稀有类别跟踪。第一个推力以一种可解释的方式检测复杂的稀有类别，即，在存在数据复杂性的情况下，借助oracle从感兴趣的稀有类别中识别第一个示例。特别是，数据的复杂性是通过带通滤波器和跨域稀有类别检测在开集设置。第二次推力以一种可解释的方式随着时间的推移跟踪从第一次推力中检测到的罕见类别，即，分别关于所述底层图拓扑和所述顶点特征来跟踪所检测到的稀有类别。特别是，动态的复杂性是通过在线局部聚类的时间演化图和连续迁移学习的时间演化顶点功能。此外，模型的复杂性是通过逐点和成对模型解释来解决的。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

DISCO: Comprehensive and Explainable Disinformation Detection

• DOI: 10.1145/3511808.3557202
• 发表时间: 2022-03
• 期刊: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Comprehensive Fair Meta-learned Recommender System

• DOI: 10.1145/3534678.3539269
• 发表时间: 2022-06
• 期刊: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• 作者: Tianxin Wei;Jingrui He

NTK-approximating MLP Fusion for Efficient Language Model Fine-tuning

• DOI: 10.48550/arxiv.2307.08941
• 发表时间: 2023-07
• 期刊: ArXiv
• 作者: Tianxin Wei;Zeming Guo;Yifan Chen;Jingrui He

Fairness-aware Model-agnostic Positive and Unlabeled Learning

• DOI: 10.1145/3531146.3533225
• 发表时间: 2022-06
• 期刊: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency
• 作者: Ziwei Wu;Jingrui He

Contrastive Learning with Complex Heterogeneity

• DOI: 10.1145/3534678.3539311
• 发表时间: 2021-05
• 期刊: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• 作者: Lecheng Zheng;Jinjun Xiong;Yada Zhu;Jingrui He