Score-Based Change Detection for Gradient-Based Learning Machines
Abstract
The widespread use of machine learning algorithms calls for automatic change detection algorithms to monitor their behavior over time. As a machine learning algorithm learns from a continuous, possibly evolving, stream of data, it is desirable and often critical to supplement it with a companion change detection algorithm to facilitate its monitoring and control. We present a generic score-based change detection method that can detect a change in any number of components of a machine learning model trained via empirical risk minimization. This proposed statistical hypothesis test can be readily implemented for such models designed within a differentiable programming framework. We establish the consistency of the hypothesis test and show how to calibrate it to achieve a prescribed false alarm rate. We illustrate the versatility of the approach on synthetic and real data.
Cite
@article{arxiv.2106.14122,
title = {Score-Based Change Detection for Gradient-Based Learning Machines},
author = {Lang Liu and Joseph Salmon and Zaid Harchaoui},
journal= {arXiv preprint arXiv:2106.14122},
year = {2021}
}