# Triadic-OCD: Asynchronous Online Change Detection with Provable Robustness, Optimality, and Convergence

arXiv: https://arxiv.org/abs/2405.02372 · HTML: https://mlsanity.com/paper/2405.02372/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 3 May 2024.

## Builds on (most cited first, 10 of 10)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Bilevel Optimization: Convergence Analysis and Enhanced Design](https://mlsanity.com/paper/2010.07962/) (2020)
- [On Differentiating Parameterized Argmin and Argmax Problems with Application to Bi-level Optimization](https://mlsanity.com/paper/1607.05447/) (2016)
- [Provably Faster Algorithms for Bilevel Optimization](https://mlsanity.com/paper/2106.04692/) (2021)
- [Towards Gradient-based Bilevel Optimization with Non-convex Followers and Beyond](https://mlsanity.com/paper/2110.00455/) (2021)
- [Robust Optimization over Multiple Domains](https://mlsanity.com/paper/1805.07588/) (2018)
- [Asynchronous Stochastic Optimization Robust to Arbitrary Delays](https://mlsanity.com/paper/2106.11879/) (2021)
- [Asynchronous Distributed Bilevel Optimization](https://mlsanity.com/paper/2212.10048/) (2022)
- [Real-Time Detection of Hybrid and Stealthy Cyber-Attacks in Smart Grid](https://mlsanity.com/paper/1803.00128/) (2018)
- [Minimax Robust Quickest Change Detection using Wasserstein Ambiguity Sets](https://mlsanity.com/paper/2204.13034/) (2022)

## Built on by (most cited first, 2 of 2)

- [Stable Preference Optimization: A Bilevel Approach to Catastrophic Preference Shift](https://mlsanity.com/paper/2507.07723/) (2025)
- [Synergistic Localization and Sensing in MIMO-OFDM Systems via Mixed-Integer Bilevel Learning](https://mlsanity.com/paper/2507.07118/) (2025)

## Abstract (arXiv)

The primary goal of online change detection (OCD) is to promptly identify changes in the data stream. OCD problem find a wide variety of applications in diverse areas, e.g., security detection in smart grids and intrusion detection in communication networks. Prior research usually assumes precise knowledge of the system parameters. Nevertheless, this presumption often proves unattainable in practical scenarios due to factors such as estimation errors, system updates, etc. This paper aims to take the first attempt to develop a triadic-OCD framework with certifiable robustness, provable optimality, and guaranteed convergence. In addition, the proposed triadic-OCD algorithm can be realized in a fully asynchronous distributed manner, easing the necessity of transmitting the data to a single server. This asynchronous mechanism could also mitigate the straggler issue that faced by traditional synchronous algorithm. Moreover, the non-asymptotic convergence property of Triadic-OCD is theoretically analyzed, and its iteration complexity to achieve an ε-optimal point is derived. Extensive experiments have been conducted to elucidate the effectiveness of the proposed method.