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A Contrastive Approach to Online Change Point Detection
P. 5686–5713.
Puchkin N., Shcherbakova V.
We suggest a novel procedure for online change point detection. Our approach expands an idea of maximizing a discrepancy measure between points from pre-change and post-change distributions. This leads to a flexible procedure suitable for both parametric and nonparametric scenarios. We prove non-asymptotic bounds on the average running length of the procedure and its expected detection delay. The efficiency of the algorithm is illustrated with numerical experiments on synthetic and real-world data sets.
In book
Vol. 206. , Valencia: PMLR, 2023.
Dmitry Pronin, Evgeny Kazartsev, Digital Scholarship in the Humanities 2026 Vol. 41 No. 3 P. 1616–1630
This article repositions Burrows’s Delta as a flexible family of distance measures for exploratory and unsupervised stylometry, where interpretability and stability are as important as predictive accuracy. We introduce two probabilistic extensions, Rank-Turbulence Delta and Jensen–Shannon Delta, by reinterpreting uncentred standardized word-frequency vectors as non-negative representations that can be normalized into probability distributions and compared ...
Added: June 4, 2026
Hushchyn M., Arzymatov K., Derkach D., Machine Learning 2026 Vol. 115 Article 56
Moments when a time series changes its behavior are called change points. Occurrence of change point implies that the state of the system is altered and its timely detection might help to prevent unwanted consequences. In this paper, we present two change-point detection approaches based on neural networks and online learning. These algorithms demonstrate linear ...
Added: March 6, 2026
Ryzhikov A., Hushchyn M., Derkach D., IEEE Access 2023 Vol. 11 P. 104700–104711
Automated analysis of complex systems based on multiple readouts remains a challenge. Change point detection algorithms are aimed to locating abrupt changes in the time series behaviour of a process. In this paper, we present a novel change point detection algorithm based on Latent Neural Stochastic Differential Equations (SDE). Our method learns a non-linear deep ...
Added: October 5, 2023
Popkov Y., Popkov A. Y., Dubnov Y. A., Mathematical Models and Computer Simulations 2021 Vol. 13 No. 3 P. 382–394
© 2021, Pleiades Publishing, Ltd.Abstract: We develop/propose the method reducing the dimension of a data matrix, based on its direct and inverse projection, and the calculation of projectors that minimize the cross-entropy functional, remove. We introduce the concept of information capacity of a matrix, which is used as a constraint in the optimal reduction problem, ...
Added: October 28, 2022
Dubnov Y. A., Scientific and Technical Information Processing 2021 Vol. 48 No. 6 P. 430–435
This paper considers the problem of feature selection in the classification problem. A method for selecting informative features based on a probabilistic approach and cross-entropy metrics is proposed. Several variants of the information criterion for selecting features for a binary classification problem are considered, as well as its generalization to the case of a multiclass ...
Added: October 28, 2022
Chernobay E., Koreshnikova Y., Отечественная и зарубежная педагогика 2021 Т. 1 № 5 С. 177–190
One of the key characteristics of the modern world is high volatility. Accelerating changes cannot but affect the education system, requiring it to constantly improve itself, including the development of new solutions, proposals and approaches. As a result, in recent years, in foreign educational science there is a reassessment of the importance of such an approach as ...
Added: October 31, 2021
Popkov Y., Popkov A., Dubnov Y. A., Информатика и ее применения 2020 Т. 14 № 4 С. 47–54
The work is devoted to development of methods for deterministic and randomized projection aimed at dimensionality reduction problems. In the deterministic case, the authors develop the parallel reduction procedure minimizing Kullback-Leibler cross-entropy target to condition on information capacity based on the gradient projection method. In the randomized case, the authors solve the problem of reduction ...
Added: January 26, 2021
Popkov Y., Popkov A., Dubnov Y. A., Математическое моделирование 2020 Т. 32 № 9 С. 35–52
We develop a new method of dimensionality reduction based on direct and inverse projection of data matrix and calculation of projectors minimizing cross-entropy functional. Concept of information capacity of matrix which is used as a restriction in a problem of optimal reduction is introduced. We conduct a comparison of proposed method with known ones based ...
Added: October 31, 2020
Dubnov Y. A., Искусственный интеллект и принятие решений 2020 № 2 С. 78–85
The paper considers the problem of feature selection in the classification problem. A method for selecting informative features based on a probabilistic approach and cross-entropy metrics is proposed. Several variants of the information criterion for selecting features for a binary classification problem are considered, as well as its generalization to the case of a multiclass ...
Added: October 31, 2020
[б.и.], 2019.
Volume 99: Conference on Learning Theory, 25-28 June 2019, Phoenix, USA ...
Added: October 31, 2020
Zhivotovskiy N., Proceedings of Machine Learning Research 2017 Vol. 65 P. 2023–2065
Under margin assumptions, we prove several risk bounds, represented via the distribution dependent local entropies of the classes or the sizes of specific sample compression schemes. In some cases, our guarantees are optimal up to constant factors for families of classes. We discuss limitations of our approach and give several applications. In particular, we provide ...
Added: December 6, 2018
Zhivotovskiy N., Hanneke S., Theoretical Computer Science 2018 Vol. 9 No. 742 P. 27–49
In statistical learning the excess risk of empirical risk minimization (ERM) is controlled by (COMPn(F)n)α, where n is a size of a learning sample, COMPn(F) is a complexity term associated with a given class F and α∈[12,1] interpolates between slow and fast learning rates. In this paper we introduce an alternative localization approach for binary classificationthat leads to a novel complexity measure: fixed points of the local empirical entropy. We show that this ...
Added: December 6, 2018
Shiryaev A. N., Zhitlukhin M., Ziemba W. T., Quantitative Finance 2015 Vol. 15 No. 9 P. 1449–1469
We study the land and stock markets in Japan circa 1990 and in 2013. While the Nikkei stock average in the late 1980s and its (Formula presented.) % crash in 1990 is generally recognized as a financial market bubble, a bigger bubble and crash was in the land market. The crash in the Nikkei which ...
Added: September 3, 2015
Дранев Юрий Яковлевич, Корпоративные финансы 2012 № 1 С. 33–36
The variance and semivariance are traditional measures of asset returns volatility since Markowitz proposed the market portfolio theory. Well known models for expected asset returns were developed under assumptions of mean-variance or mean-semivariance investor’s behavior. But numerous papers provided arguments against these models because of unrealistic assumptions and controversial empiric evidence. More complicated models with ...
Added: November 16, 2012