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1. Distributionally Robust Quickest Change Detection using Wasserstein Uncertainty Sets

   Xie, Liyan; Liang, Yuchen; Veeravalli, Venugopal V (May 2024, PMLR)

   Dasgupta, Sanjoy; Mandt, Stephan; Li, Yingzhen (Ed.)

   The problem of quickest detection of a change in the distribution of streaming data is considered. It is assumed that the pre-change distribution is known, while the only information about the post-change is through a (small) set of labeled data. This post-change data is used in a data-driven minimax robust framework, where an uncertainty set for the post-change distribution is constructed. The robust change detection problem is studied in an asymptotic setting where the mean time to false alarm goes to infinity. It is shown that the least favorable distribution (LFD) is an exponentially tilted version of the pre-change density and can be obtained efficiently. A Cumulative Sum (CuSum) test based on the LFD, which is referred to as the distributionally robust (DR) CuSum test, is then shown to be asymptotically robust. The results are extended to the case with multiple post-change uncertainty sets and validated using synthetic and real data examples. 

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2. Optimal Quickest Change Detection in Sensor Networks Using Ordered Transmissions

   https://doi.org/10.1109/SPAWC48557.2020.9154270  

   Chen, Yicheng; Blum, Rick S.; Sadler, Brian M. (May 2020, 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC))

   null (Ed.)

   Quickest change detection in a sensor network is considered where each sensor observes a sequence of random variables and transmits its local information on the observations to a fusion center. At an unknown point in time, the distribution of the observations at all sensors changes. The objective is to detect the change in distribution as soon as possible, subject to a false alarm constraint. We consider minimax formulations for this problem and propose a new approach where transmissions are ordered and halted when sufficient information is accumulated at the fusion center. We show that the proposed approach can achieve the optimal performance equivalent to the centralized cumulative sum (CUSUM) algorithm while requiring fewer sensor transmissions. Numerical results for a shift in mean of independent and identically distributed Gaussian observations show significant communication savings for the case where the change seldom occurs which is frequently true in many important applications. 

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3. Quantifying input data drift in medical machine learning models by detecting change-points in time-series data

   https://doi.org/10.1117/12.3008771  

   Prathapan, Smriti; Samala, Ravi K; Hadjiyski, Nathan; D’Haese, Pierre-François; Maldonado, Fabien; Nguyen, Phuong; Yesha, Yelena; Sahiner, Berkman (April 2024, SPIE)

   Astley, Susan M; Chen, Weijie (Ed.)

   Devices enabled by artificial intelligence (AI) and machine learning (ML) are being introduced for clinical use at an accelerating pace. In a dynamic clinical environment, these devices may encounter conditions different from those they were developed for. The statistical data mismatch between training/initial testing and production is often referred to as data drift. Detecting and quantifying data drift is significant for ensuring that AI model performs as expected in clinical environments. A drift detector signals when a corrective action is needed if the performance changes. In this study, we investigate how a change in the performance of an AI model due to data drift can be detected and quantified using a cumulative sum (CUSUM) control chart. To study the properties of CUSUM, we first simulate different scenarios that change the performance of an AI model. We simulate a sudden change in the mean of the performance metric at a change-point (change day) in time. The task is to quickly detect the change while providing few false-alarms before the change-point, which may be caused by the statistical variation of the performance metric over time. Subsequently, we simulate data drift by denoising the Emory Breast Imaging Dataset (EMBED) after a pre-defined change-point. We detect the change-point by studying the pre- and post-change specificity of a mammographic CAD algorithm. Our results indicate that with the appropriate choice of parameters, CUSUM is able to quickly detect relatively small drifts with a small number of false-positive alarms. 

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4. Correlation-aware Online Change Point Detection

   Deng, Chengyuan; Chen, Zhengzhangn; Zhao, Xujiang; Wang, Haoyu; Wang, Junxiang; Chen, Haifeng; Gao, Jie (November 2025, Proceedings of the 34th ACM Conference on Information and Knowledge Management (CIKM’25))

   Change point detection aims to identify abrupt shifts occurring at multiple points within a data sequence. This task becomes particularly challenging in the online setting, where different types of changes can occur, including shifts in both the marginal and joint distributions of the data. In this paper, we address these challenges by tracking the Riemannian geometry of correlation matrices, allowing Riemannian metrics to compute the geodesic distance as an accurate measure of correlation dynamics. We introduce Rio-CPD, a non-parametric, correlation-aware online change point detection framework that integrates the Riemannian geometry of the manifold of symmetric positive definite matrices with the cumulative sum (CUSUM) statistic for detecting change points. Rio-CPD employs a novel CUSUM design by computing the geodesic distance between current observations and the Fréchet mean of prior observations. With appropriate choices of Riemannian metrics, Rio-CPD offers a simple yet effective and computationally efficient algorithm. Experimental results on both synthetic and real-world datasets demonstrate that Rio-CPD outperforms existing methods on detection accuracy, average detection delay and efficiency. 

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5. CuSum for sequential change diagnosis

   https://doi.org/10.1109/ISIT50566.2022.9834755  

   Warner, Austin; Fellouris, Georgios (January 2022, 2022 IEEE International Symposium on Information Theory (ISIT))

   The problem of sequential change diagnosis is considered, where a sequence of independent random elements is accessed sequentially, there is an abrupt change in its distribution at some unknown time, and there are two main operational goals: to quickly detect the change and to accurately identify the post-change distribution among a finite set of alternatives. A standard algorithm is considered, which does not explicitly address the isolation task and raises an alarm as soon as the CuSum statistic that corresponds to one of the post-change alternatives exceeds a certain threshold. It is shown that in certain cases, such as the so-called multichannel problem, this algorithm controls the worst-case conditional probability of false isolation and minimizes Lorden’s criterion, for every possible post-change distribution, to a first-order asymptotic approximation as the false alarm rate goes to zero sufficiently faster than the worst-case conditional probability of false isolation. These theoretical results are also illustrated with a numerical study. 

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