  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.
