Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay

Reda Alami, Odalric Maillard, Raphael Feraud
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:211-221, 2020.

Abstract

we consider the problem of sequential change-point detection where both the change-points and the distributions before and after the change are assumed to be unknown. For this problem of primary importance in statistical and sequential learning theory, we derive a variant of the Bayesian Online Change Point Detector proposed by \cite{fearnhead2007line} which is easier to analyze than the original version while keeping its powerful message-passing algorithm. We provide a non-asymptotic analysis of the false-alarm rate and the detection delay that matches the existing lower-bound. We further provide the first explicit high-probability control of the detection delay for such approach. Experiments on synthetic and real-world data show that this proposal outperforms the state-of-art change-point detection strategy, namely the Improved Generalized Likelihood Ratio (Improved GLR) while compares favorably with the original Bayesian Online Change Point Detection strategy.

Cite this Paper


BibTeX
@InProceedings{pmlr-v119-alami20a, title = {Restarted {B}ayesian Online Change-point Detector achieves Optimal Detection Delay}, author = {Alami, Reda and Maillard, Odalric and Feraud, Raphael}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {211--221}, year = {2020}, editor = {Hal Daumé III and Aarti Singh}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/alami20a/alami20a.pdf}, url = { http://proceedings.mlr.press/v119/alami20a.html }, abstract = {we consider the problem of sequential change-point detection where both the change-points and the distributions before and after the change are assumed to be unknown. For this problem of primary importance in statistical and sequential learning theory, we derive a variant of the Bayesian Online Change Point Detector proposed by \cite{fearnhead2007line} which is easier to analyze than the original version while keeping its powerful message-passing algorithm. We provide a non-asymptotic analysis of the false-alarm rate and the detection delay that matches the existing lower-bound. We further provide the first explicit high-probability control of the detection delay for such approach. Experiments on synthetic and real-world data show that this proposal outperforms the state-of-art change-point detection strategy, namely the Improved Generalized Likelihood Ratio (Improved GLR) while compares favorably with the original Bayesian Online Change Point Detection strategy.} }
Endnote
%0 Conference Paper %T Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay %A Reda Alami %A Odalric Maillard %A Raphael Feraud %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-alami20a %I PMLR %P 211--221 %U http://proceedings.mlr.press/v119/alami20a.html %V 119 %X we consider the problem of sequential change-point detection where both the change-points and the distributions before and after the change are assumed to be unknown. For this problem of primary importance in statistical and sequential learning theory, we derive a variant of the Bayesian Online Change Point Detector proposed by \cite{fearnhead2007line} which is easier to analyze than the original version while keeping its powerful message-passing algorithm. We provide a non-asymptotic analysis of the false-alarm rate and the detection delay that matches the existing lower-bound. We further provide the first explicit high-probability control of the detection delay for such approach. Experiments on synthetic and real-world data show that this proposal outperforms the state-of-art change-point detection strategy, namely the Improved Generalized Likelihood Ratio (Improved GLR) while compares favorably with the original Bayesian Online Change Point Detection strategy.
APA
Alami, R., Maillard, O. & Feraud, R.. (2020). Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:211-221 Available from http://proceedings.mlr.press/v119/alami20a.html .

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