# Adaptive Bernstein Change Detector for High-Dimensional Data Streams

arXiv: https://arxiv.org/abs/2306.12974 · HTML: https://mlsanity.com/paper/2306.12974/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 60 references that are in the map.
- First posted on arXiv 22 Jun 2023.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Scikit-Multiflow: A Multi-output Streaming Framework](https://mlsanity.com/paper/1807.04662/) (2018)
- [A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data](https://mlsanity.com/paper/1306.4933/) (2013)
- [WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data](https://mlsanity.com/paper/2201.07125/) (2022)
- [Unsupervised Change Detection in Satellite Images Using Convolutional Neural Networks](https://mlsanity.com/paper/1812.05815/) (2018)

## Built on by (most cited first, 4 of 4)

- [CapyMOA: Efficient Machine Learning for Data Streams and Online Continual Learning in Python](https://mlsanity.com/paper/2502.07432/) (2025)
- [Detecting Domain Shifts in Myoelectric Activations: Challenges and Opportunities in Stream Learning](https://mlsanity.com/paper/2508.21278/) (2025)
- [A Framework for Evaluating and Benchmarking Concept Drift Detection Methods](https://mlsanity.com/paper/2606.07789/) (2026)
- [Detecting Interpretable Subgroup Drifts](https://mlsanity.com/paper/2408.14682/) (2024)

## Abstract (arXiv)

Change detection is of fundamental importance when analyzing data streams. Detecting changes both quickly and accurately enables monitoring and prediction systems to react, e.g., by issuing an alarm or by updating a learning algorithm. However, detecting changes is challenging when observations are high-dimensional. In high-dimensional data, change detectors should not only be able to identify when changes happen, but also in which subspace they occur. Ideally, one should also quantify how severe they are. Our approach, ABCD, has these properties. ABCD learns an encoder-decoder model and monitors its accuracy over a window of adaptive size. ABCD derives a change score based on Bernstein's inequality to detect deviations in terms of accuracy, which indicate changes. Our experiments demonstrate that ABCD outperforms its best competitor by up to 20% in F1-score on average. It can also accurately estimate changes' subspace, together with a severity measure that correlates with the ground truth.