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Adversarially robust change point detection
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Li, Mengchu and Yu, Yi (2022) Adversarially robust change point detection. In: NeurIPS : 35th Conference on Neural Information Processing Systems, Virtual, 6-14 Dec 2022. Published in: Advances in Neural Information Processing Systems (NeurIPS 2021), 28 pp. 22955-22967. ISBN 9781713845393. ISSN 1049-5258.
An open access version can be found in:
Official URL: https://proceedings.neurips.cc/paper/2021/hash/c1e...
Abstract
Change point detection is becoming increasingly popular in many application areas. On one hand, most of the theoretically-justified methods are investigated in an ideal setting without model violations, or merely robust against identical heavy-tailed noise distribution across time and/or against isolate outliers; on the other hand, we are aware that there have been exponentially growing attacks from adversaries, who may pose systematic contamination on data to purposely create spurious change points or disguise true change points. In light of the timely need of a change point detection method that is robust against adversaries, we start with, arguably, the simplest univariate mean change point detection problem. The adversarial attacks are formulated through the Huber ε-contamination framework, which in particular allows the contamination distributions to be different at each time point. In this paper, we demonstrate a phase transition phenomenon in change point detection. This detection boundary is a function of the contamination proportion ε and is the first time shown in the literature. In addition, we derive the minimax-rate optimal localisation error rate, quantifying the cost of accuracy in terms of the contamination proportion. We propose a computationally-feasible method, matching the minimax lower bound under certain conditions, saving for logarithmic factors. Extensive numerical experiments are conducted with comparisons to existing robust change point detection methods.
| Item Type: | Conference Item (Paper) | ||||||
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||
| Series Name: | NeurIPS Proceedings | ||||||
| Journal or Publication Title: | Advances in Neural Information Processing Systems (NeurIPS 2021) | ||||||
| ISBN: | 9781713845393 | ||||||
| ISSN: | 1049-5258 | ||||||
| Official Date: | 2022 | ||||||
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| Volume: | 28 | ||||||
| Page Range: | pp. 22955-22967 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
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| Conference Paper Type: | Paper | ||||||
| Title of Event: | NeurIPS : 35th Conference on Neural Information Processing Systems | ||||||
| Type of Event: | Conference | ||||||
| Location of Event: | Virtual | ||||||
| Date(s) of Event: | 6-14 Dec 2022 | ||||||
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| Open Access Version: |
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