# Score-based change point detection via tracking the best of infinitely many experts

arXiv: https://arxiv.org/abs/2408.14073 · HTML: https://mlsanity.com/paper/2408.14073/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 69 references that are in the map.
- First posted on arXiv 26 Aug 2024.

## Builds on (most cited first, 21 of 21)

- [An Evaluation of Change Point Detection Algorithms](https://mlsanity.com/paper/2003.06222/) (2020)
- [Relative Loss Bounds for On-line Density Estimation with the Exponential Family of Distributions](https://mlsanity.com/paper/1301.6677/) (2013)
- [Improved Strongly Adaptive Online Learning using Coin Betting](https://mlsanity.com/paper/1610.04578/) (2016)
- [The Many Faces of Exponential Weights in Online Learning](https://mlsanity.com/paper/1802.07543/) (2018)
- [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)
- [MetaGrad: Adaptation using Multiple Learning Rates in Online Learning](https://mlsanity.com/paper/2102.06622/) (2021)
- [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)
- [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)
- [Online Convex Optimisation: The Optimal Switching Regret for all Segmentations Simultaneously](https://mlsanity.com/paper/2405.20824/) (2024)
- [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 Changepoint Detection in Neural Networks with Checkpoints](https://mlsanity.com/paper/2010.03053/) (2020)
- [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 propose an algorithm for nonparametric online change point detection based on sequential score function estimation and the tracking the best expert approach. The core of the procedure is a version of the fixed share forecaster tailored to the case of infinite number of experts and quadratic loss functions. The algorithm shows promising results in numerical experiments on artificial and real-world data sets. Its performance is supported by rigorous high-probability bounds describing behaviour of the test statistic in the pre-change and post-change regimes.