# Optimal Sub-sampling to Boost Power of Kernel Sequential Change-point Detection

arXiv: https://arxiv.org/abs/2210.15060 · HTML: https://mlsanity.com/paper/2210.15060/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 26 Oct 2022.

## Builds on (most cited first, 3 of 3)

- [Kernel Thinning](https://mlsanity.com/paper/2105.05842/) (2021)
- [Support points](https://mlsanity.com/paper/1609.01811/) (2016)
- [Change Detection with the Kernel Cumulative Sum Algorithm](https://mlsanity.com/paper/1903.01661/) (2019)

## Abstract (arXiv)

We present a novel scheme to boost detection power for kernel maximum mean discrepancy based sequential change-point detection procedures. Our proposed scheme features an optimal sub-sampling of the history data before the detection procedure, in order to tackle the power loss incurred by the random sub-sample from the enormous history data. We apply our proposed scheme to both Scan B and Kernel Cumulative Sum (CUSUM) procedures, and improved performance is observed from extensive numerical experiments.