# Robust Sequential Change-Point Detection by Convex Optimization

arXiv: https://arxiv.org/abs/1701.06952 · HTML: https://mlsanity.com/paper/1701.06952/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2017.

## Built on by (most cited first, 5 of 5)

- [Distribution Grid Line Outage Identification with Unknown Pattern and Performance Guarantee](https://mlsanity.com/paper/2309.07157/) (2023)
- [Signal Processing Challenges and Examples for it in-situ Transmission Electron Microscopy](https://mlsanity.com/paper/2104.08688/) (2021)
- [Noise-contrastive Online Change Point Detection](https://mlsanity.com/paper/2206.10143/) (2022)
- [Predictive change point detection for heterogeneous data](https://mlsanity.com/paper/2305.06630/) (2023)
- [Score-based change point detection via tracking the best of infinitely many experts](https://mlsanity.com/paper/2408.14073/) (2024)

## Abstract (arXiv)

We address the computational challenge of finding the robust sequential change-point detection procedures when the pre- and post-change distributions are not completely specified. Earlier works [veeravalli 1994] and [Unnikrishnan 2011] establish the general conditions for robust procedures which include finding a pair of least favorable distributions (LFDs). However, in the multi-dimensional setting, it is hard to find such LFDs computationally. We present a method based on convex optimization that addresses this issue when the distributions are Gaussian with unknown parameters from pre-specified uncertainty sets. We also establish theoretical properties of our robust procedures, and numerical examples demonstrate their good performance.