mlsanityData to 14 Oct 2026

Noise-contrastive Online Change Point Detection

arXiv 2206.10143 ↗First posted 21 Jun 2022stat.ML cs.LG math.STcited by 0

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Abstract (arXiv)

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 flexible algorithms 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 63 references that are in the map. First posted on arXiv 21 Jun 2022.

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23 of 63 references.

References · 23

  1. 2019Introduction to Online Convex Optimizationcited by 803
  2. 2019Bias Correction of Learned Generative Models using Likelihood-Free Importance Weightingcited by 70
  3. 2020An Evaluation of Change Point Detection Algorithmscited by 63
  4. 2019Kernel Change-point Detection with Auxiliary Deep Generative Modelscited by 33
  5. 2015Scan B-Statistic for Kernel Change-Point Detectioncited by 29
  6. 2013A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Datacited by 28
  7. 2020High-dimensional, multiscale online changepoint detectioncited by 19
  8. 2022Random Forests for Change Point Detectioncited by 14
  9. 2018Univariate Mean Change Point Detection: Penalization, CUSUM and Optimalitycited by 13
  10. 2021Rates of convergence for density estimation with generative adversarial networkscited by 13
  11. 2020Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio Estimationcited by 12
  12. 2019Optimal nonparametric multivariate change point detection and localizationcited by 11
11 more
  1. 2018Real-Time Nonparametric Anomaly Detection in High-Dimensional Settingscited by 10
  2. 2018A likelihood ratio approach to sequential change point detection for a general class of parameterscited by 8
  3. 2020Localizing Changes in High-Dimensional Regression Modelscited by 8
  4. 2020Online change-point detection with kernelscited by 7
  5. 2017Robust Sequential Change-Point Detection by Convex Optimizationcited by 5
  6. 2015Heterogeneous Change Point Inferencecited by 4
  7. 2018Sequential Change-point Detection for High-dimensional and non-Euclidean Datacited by 4
  8. 2020Sequential Changepoint Detection in Neural Networks with Checkpointscited by 4
  9. 2021Quickest Change Detection with Non-Stationary Post-Change Observationscited by 4
  10. 2020Localising change points in piecewise polynomials of general degreescited by 3
  11. 2022High dimensional change-point detection: a complete graph approachcited by 2

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