# Noise-contrastive Online Change Point Detection

arXiv: https://arxiv.org/abs/2206.10143 · HTML: https://mlsanity.com/paper/2206.10143/

## 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.

## Builds on (most cited first, 23 of 23)

- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting](https://mlsanity.com/paper/1906.09531/) (2019)
- [An Evaluation of Change Point Detection Algorithms](https://mlsanity.com/paper/2003.06222/) (2020)
- [Kernel Change-point Detection with Auxiliary Deep Generative Models](https://mlsanity.com/paper/1901.06077/) (2019)
- [Scan B-Statistic for Kernel Change-Point Detection](https://mlsanity.com/paper/1507.01279/) (2015)
- [A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data](https://mlsanity.com/paper/1306.4933/) (2013)
- [High-dimensional, multiscale online changepoint detection](https://mlsanity.com/paper/2003.03668/) (2020)
- [Random Forests for Change Point Detection](https://mlsanity.com/paper/2205.04997/) (2022)
- [Univariate Mean Change Point Detection: Penalization, CUSUM and Optimality](https://mlsanity.com/paper/1810.09498/) (2018)
- [Rates of convergence for density estimation with generative adversarial networks](https://mlsanity.com/paper/2102.00199/) (2021)
- [Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio Estimation](https://mlsanity.com/paper/2001.06386/) (2020)
- [Optimal nonparametric multivariate change point detection and localization](https://mlsanity.com/paper/1910.13289/) (2019)
- [Real-Time Nonparametric Anomaly Detection in High-Dimensional Settings](https://mlsanity.com/paper/1809.05250/) (2018)
- [A likelihood ratio approach to sequential change point detection for a general class of parameters](https://mlsanity.com/paper/1802.07696/) (2018)
- [Localizing Changes in High-Dimensional Regression Models](https://mlsanity.com/paper/2010.10410/) (2020)
- [Online change-point detection with kernels](https://mlsanity.com/paper/2002.02704/) (2020)
- [Robust Sequential Change-Point Detection by Convex Optimization](https://mlsanity.com/paper/1701.06952/) (2017)
- [Heterogeneous Change Point Inference](https://mlsanity.com/paper/1505.04898/) (2015)
- [Sequential Change-point Detection for High-dimensional and non-Euclidean Data](https://mlsanity.com/paper/1810.05973/) (2018)
- [Sequential Changepoint Detection in Neural Networks with Checkpoints](https://mlsanity.com/paper/2010.03053/) (2020)
- [Quickest Change Detection with Non-Stationary Post-Change Observations](https://mlsanity.com/paper/2110.01581/) (2021)
- [Localising change points in piecewise polynomials of general degrees](https://mlsanity.com/paper/2007.09910/) (2020)
- [High dimensional change-point detection: a complete graph approach](https://mlsanity.com/paper/2203.08709/) (2022)

## 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.