NeurIPS 2021poster0 citations
A nonparametric method for gradual change problems with statistical guarantees
Abstract
We consider the detection and localization of gradual changes in the distribution of a sequence of time-ordered observations. Existing literature focuses mostly on the simpler abrupt setting which assumes a discontinuity jump in distribution, and is unrealistic for some applied settings. We propose a general method for detecting and localizing gradual changes that does not require any specific data generating model, any particular data type, or any prior knowledge about which features of the distribution are subject to change. Despite relaxed assumptions, the proposed method possesses proven theoretical guarantees for both detection and localization.
Change point detectiongradual changenonparametric
BibTeX
@inproceedings{
nie2021a,
title={A nonparametric method for gradual change problems with statistical guarantees},
author={Lizhen Nie and Dan L Nicolae},
booktitle={Advances in Neural Information Processing Systems},
editor={A. Beygelzimer and Y. Dauphin and P. Liang and J. Wortman Vaughan},
year={2021},
url={https://openreview.net/forum?id=bo75bBsQzeZ}
}