# Online Centralized Non-parametric Change-point Detection via Graph-based Likelihood-ratio Estimation

arXiv: https://arxiv.org/abs/2301.03011 · HTML: https://mlsanity.com/paper/2301.03011/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 37 references that are in the map.
- First posted on arXiv 8 Jan 2023.

## Builds on (most cited first, 2 of 2)

- [Online change-point detection with kernels](https://mlsanity.com/paper/2002.02704/) (2020)
- [Collaborative likelihood-ratio estimation over graphs](https://mlsanity.com/paper/2205.14461/) (2022)

## Built on by (most cited first, 1 of 1)

- [Post Hoc Inference for Component Attribution in Multivariate Change-Point Detection](https://mlsanity.com/paper/2607.14814/) (2026)

## Abstract (arXiv)

Consider each node of a graph to be generating a data stream that is synchronized and observed at near real-time. At a change-point τ, a change occurs at a subset of nodes C, which affects the probability distribution of their associated node streams. In this paper, we propose a novel kernel-based method to both detect τ and localize C, based on the direct estimation of the likelihood-ratio between the post-change and the pre-change distributions of the node streams. Our main working hypothesis is the smoothness of the likelihood-ratio estimates over the graph, i.e connected nodes are expected to have similar likelihood-ratios. The quality of the proposed method is demonstrated on extensive experiments on synthetic scenarios.