  We introduce a framework for online changepoint detection and simultaneous
model learning which is applicable to highly parametrized models, such as deep
neural networks. It is based on detecting changepoints across time by
sequentially performing generalized likelihood ratio tests that require only
evaluations of simple prediction score functions. This procedure makes use of
checkpoints, consisting of early versions of the actual model parameters, that
allow to detect distributional changes by performing predictions on future
data. We define an algorithm that bounds the Type I error in the sequential
testing procedure. We demonstrate the efficiency of our method in challenging
continual learning applications with unknown task changepoints, and show
improved performance compared to online Bayesian changepoint detection.
