2023
AISTATS
AISTATS 2023
A Contrastive Approach to Online Change Point Detection
Abstract
We suggest a novel procedure for online change point detection. Our approach expands an idea of maximizing a discrepancy measure between points from pre-change and post-change distributions. This leads to a flexible procedure suitable for both parametric and nonparametric scenarios. We prove non-asymptotic bounds on the average running length of the procedure and its expected detection delay. The efficiency of the algorithm is illustrated with numerical experiments on synthetic and real-world data sets.
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Interdisciplinary Bridge
— Data Science & Analytics and Machine Learning
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Keyword Pioneer
— statistical bound
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Learning Types > Contrastive Learning
Machine Learning > Learning Types > Unsupervised Learning
Machine Learning > Optimization & Theory > Statistical Learning
Data Science & Analytics > Methods > Time Series
Deep Learning > Techniques > Contrastive Learning