Active Sequential Change-Point Analysis of Multi-Stream Data

多流数据的主动顺序变点分析

• 批准号: 2015405
• 负责人: Yajun Mei
• 金额: $ 21万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2015405&HistoricalAwards=false
• 关键词: Active; Sequential; Change; Point; Analysis

This project aims to develop efficient methodologies and algorithms for actively learning from high-dimensional streaming data under the sampling or resource constraints. In many real-world applications, a system consists of many processes that can generate many data streams. At some unknown time, an unusual event could occur to the system, for example, a disease outbreak, a manufacturing defect, or a fraud signal, yielding a set of anomalous processes. Most systems however are operated under resource constraints that prevent the simultaneous use of all resources all the time. Thus, the decision maker must be responsible for actively choosing which processes are prioritized for observation. This will enhance their existing knowledge about the occurring event or anomalous processes while exploring new information and accounting for the penalty of the wrong declaration. The research would have broader impacts in a wide range of real-world applications such as biosurveillance, epidemiology, engineering, homeland security, and finance. The project will integrate research and education by infusing research findings into the curriculum and by training graduate students.This project seeks to make comprehensive progress on methodology, theory, and application of active sequential change-point analysis of multi-stream data under the sampling or resource constraints. The specific research aims are: (1) design efficient active change-point detection algorithms with false alarm guarantees, (2) develop an asymptotic theory to characterize statistical performances of the developed methods, (3) post-hoc analysis to apply false discovery rate methods to identify anomalous processes; and (4) applications in sepsis screening with online monitoring data from medical sensors in intensive care units to identify sepsis patients as quickly as possible while avoiding alarm fatigue. Results of the project are expected to significantly advance the state of the art in sequential analysis, change-point detection, multi-armed bandit problems, streaming data analysis, and large-scale inference.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.

本项目旨在开发有效的方法和算法，用于在采样或资源约束下从高维流数据中主动学习。在许多现实世界的应用程序中，系统由可以生成许多数据流的许多进程组成。在某个未知的时间，系统可能发生不寻常的事件，例如，疾病爆发、制造缺陷或欺诈信号，从而产生一组异常过程。然而，大多数系统是在资源限制下运行的，这阻止了所有资源的同时使用。因此，决策者必须负责主动选择哪些过程是优先观察的。这将增强他们对正在发生的事件或异常过程的现有知识，同时探索新的信息并考虑错误申报的处罚。这项研究将在生物监测、流行病学、工程、国土安全和金融等广泛的现实应用中产生更广泛的影响。该项目将通过将研究成果融入课程和培养研究生来整合研究和教育。本项目寻求在采样或资源限制下多流数据的主动顺序变化点分析的方法、理论和应用方面取得全面进展。具体的研究目标是：(1)设计具有虚警保证的高效主动变点检测算法；(2)发展渐近理论来表征所开发方法的统计性能；(3)事后分析，应用错误发现率方法来识别异常过程；(4)重症监护病房医疗传感器在线监测数据在脓毒症筛查中的应用，以尽快识别脓毒症患者，同时避免报警疲劳。该项目的结果预计将显著推进序列分析、变化点检测、多臂强盗问题、流数据分析和大规模推理方面的最新技术。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。

项目成果

Implicit Regularization Properties of Variance Reduced Stochastic Mirror Descent

• DOI: 10.1109/isit50566.2022.9834827
• 发表时间: 2022-04
• 期刊: 2022 IEEE International Symposium on Information Theory (ISIT)
• 作者: Yiling Luo;X. Huo;Y. Mei

Asymptotic Theory of \(\boldsymbol \ell _1\) -Regularized PDE Identification from a Single Noisy Trajectory

(oldsymbol ell _1) 的渐近理论 - 来自单个噪声轨迹的正则化偏微分方程辨识

• DOI: 10.1137/21m1398884
• 发表时间: 2022
• 期刊: SIAM/ASA Journal on Uncertainty Quantification
• 作者: He, Yuchen;Suh, Namjoon;Huo, Xiaoming;Kang, Sung Ha;Mei, Yajun
• 通讯作者: Mei, Yajun

Active learning-based multistage sequential decision-making model with application on common bile duct stone evaluation

• DOI: 10.1080/02664763.2023.2164885
• 发表时间: 2022-01
• 期刊: Journal of Applied Statistics
• 影响因子: 1.5
• 作者: Hongzhen Tian;R. Cohen;Chuck Zhang;Yajun Mei

Optimum Multi-Stream Sequential Change-Point Detection With Sampling Control

带采样控制的最佳多流顺序变化点检测

• DOI: 10.1109/tit.2021.3074961
• 发表时间: 2021
• 期刊: IEEE Transactions on Information Theory
• 影响因子: 2.5
• 作者: Xu, Qunzhi;Mei, Yajun;Moustakides, George V.
• 通讯作者: Moustakides, George V.

Active quickest detection when monitoring multi-streams with two affected streams

监控具有两个受影响流的多流时主动最快检测

• 发表时间: 2022
• 期刊: Proceeding of 2022 IEEE International Symposium on Information Theory (ISIT
• 作者: Xu, Qunzhi;Mei, Yajun
• 通讯作者: Mei, Yajun