Collaborative Research:CIF:Small:Fisher-Inspired Approach to Quickest Change Detection for Score-Based Models

合作研究：CIF：Small：Fisher 启发的基于评分模型的最快变化检测方法

• 批准号: 2334898
• 负责人: Vahid Tarokh
• 金额: $ 29.9万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2334898&HistoricalAwards=false
• 关键词: Collaborative; Research; CIF; Small; Fisher

Detecting abrupt changes in the underlying statistical characteristics of online data streams is an important problem commonly encountered in many science and engineering applications. Examples include anomaly detection using video streams, line-outage detection in power grids, onset detection of a pandemic, and detection of cyber-attacks. While traditional techniques assume that probability distributions for both before and after the change are known or can be found, this assumption is unrealistic in most scenarios of practical interest. As an alternative to such traditional change-detection approaches, this project considers the use of deep neural networks to effectuate change detection. However, rather than attempting to learn probability distributions directly, the project leverages the recently-demonstrated ability of deep neural networks to learn the "score" (i.e., the gradient of logarithm of the probability density) from the data and aims to develop score-based algorithms for change detection. These scores can be learned for a large class of high-dimensional data models using modern tools of artificial intelligence and rendering the developed algorithms applicable to a broad class of change-detection problems. Fundamental mathematical theories will be developed in the project to establish the efficacy and efficiency of the proposed methods, and the developed algorithms will be validated on several publicly available machine-learning and anomaly-detection datasets. Broader-impact aspects of the project include providing algorithms to the wider community for solving change- and anomaly-detection problems across multiple, disparate fields as well as activities centered on integrating research into graduate coursework and providing opportunities for underrepresented students to participate in the project. The algorithms developed in the project will be based on the score of the data; this score can be explicitly derived for known unnormalized models or can be learned using score matching using an artificial neural network, and developed algorithms will be optimized to detect the changes with the minimum possible delay while avoiding false alarms. The project is divided into four technical thrusts. The first thrust will develop the fundamental theory for score-based quickest change detection for independent and identically distributed single-stream data under Bayesian, generalized Bayesian, and minimax problem formulations. While the performance of classical change-detection methods depends on the Kullback-Leibler distance between the distributions before and after the change, it will be established that the performance of the score-based methods depends on the Fisher distance between distributions. The second thrust will develop robust methods for detecting changes under modeling uncertainty, using the Fisher distance between the elements of the uncertainty classes. The third thrust will define the notion of scores for dependent data sequences and obtain optimal algorithms for detecting changes, with the scores in this case being based on the gradient of the logarithm of the conditional densities. The fourth and final thrust will develop algorithms for distributed change detection wherein multiple agents may have partial knowledge of the distributions and may only communicate with their neighbors in a geographical area. 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.

检测在线数据流的基本统计特征的突然变化是许多科学和工程应用中通常遇到的重要问题。示例包括使用视频流的异常检测，功率网中的线路爆炸检测，大流行的发作检测以及网络攻击的检测。尽管传统技术假设已知或可以找到变更之前和之后的概率分布，但在大多数实践意义的情况下，这种假设都是不现实的。作为这种传统变更检测方法的替代方法，该项目认为使用深神经网络在有效的变化检测中使用。但是，该项目并没有试图直接学习概率分布，而是利用了深度神经元网络最近确定的能力来学习数据的“得分”（即概率密度对数的梯度），并旨在从基于得分的算法中开发基于得分的算法来进行变化检测。可以使用现代人工智能工具，并渲染适用于广泛的变更检测问题的已经开发的算法的大量高维数据模型，可以学习这些分数。项目将在项目中开发基本的数学理论，以确定所提出方法的效率和效率，并且将在几种公开可用的机器学习和异常检测数据集上验证开发的算法。该项目的更广泛影响的方面包括向更广泛的社区提供算法，以解决多个不同领域的变更和异常检测问题，以及将研究纳入研究生课程的活动，并为学生提供不足的学生参与该项目的机会。项目中开发的算法将基于数据的分数；该分数可以明确地用于已知的非标准模型，也可以使用人工中性网络进行分数匹配来学习，并且将优化开发的算法以在避免使用错误警报的同时，以最小可能的延迟来检测更改。该项目分为四个技术推力。第一个推力将开发基于分数的最快变化检测的基本理论，用于在贝叶斯，广义贝叶斯和最小值问题公式下独立和相同分布的单流数据。虽然经典变更检测方法的性能取决于变更之前和之后的分布之间的kullback-leibler距离，但将确定基于得分的方法的性能取决于分布之间的Fisher距离。第二个推力将开发出可靠的方法，用于使用不确定性类别元素之间的Fisher距离在建模不确定性下检测变化。第三个推力将定义相关数据序列的分数概念，并获得用于检测变化的最佳算法，在这种情况下，得分基于条件密度对数的梯度。第四和最终力量将开发用于分布式变更检测的算法，其中多个代理可能对分布有部分知识，并且只能在地理区域与其邻居进行交流。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识和更广泛影响的评估来审查Criteria来通过评估来通过评估来获得支持的。