# RoS-Guard: Robust and Scalable Online Change Detection with Delay-Optimal Guarantees

arXiv: https://arxiv.org/abs/2511.12846 · HTML: https://mlsanity.com/paper/2511.12846/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 17 Nov 2025.

## Builds on (most cited first, 4 of 4)

- [Bandit Quickest Changepoint Detection](https://mlsanity.com/paper/2107.10492/) (2021)
- [Algorithm Unrolling for Massive Access via Deep Neural Network with Theoretical Guarantee](https://mlsanity.com/paper/2106.10426/) (2021)
- [Minimax Robust Quickest Change Detection using Wasserstein Ambiguity Sets](https://mlsanity.com/paper/2204.13034/) (2022)
- [Cooperative Change Detection for Online Power Quality Monitoring](https://mlsanity.com/paper/1412.2773/) (2014)

## Abstract (arXiv)

Online change detection (OCD) aims to rapidly identify change points in streaming data and is critical in applications such as power system monitoring, wireless network sensing, and financial anomaly detection. Existing OCD methods typically assume precise system knowledge, which is unrealistic due to estimation errors and environmental variations. Moreover, existing OCD methods often struggle with efficiency in large-scale systems. To overcome these challenges, we propose RoS-Guard, a robust and optimal OCD algorithm tailored for linear systems with uncertainty. Through a tight relaxation and reformulation of the OCD optimization problem, RoS-Guard employs neural unrolling to enable efficient parallel computation via GPU acceleration. The algorithm provides theoretical guarantees on performance, including expected false alarm rate and worst-case average detection delay. Extensive experiments validate the effectiveness of RoS-Guard and demonstrate significant computational speedup in large-scale system scenarios.