Sequential Changepoint Detection in Neural Networks with Checkpoints

• URL: http://arxiv.org/abs/2010.03053v1
• Date: Tue, 6 Oct 2020 21:49:54 GMT
• Title: Sequential Changepoint Detection in Neural Networks with Checkpoints
• Authors: Michalis K. Titsias, Jakub Sygnowski, Yutian Chen
• Abstract summary: We introduce a framework for online changepoint detection and simultaneous model learning. It is based on detecting changepoints across time by sequentially performing generalized likelihood ratio tests. We show improved performance compared to online Bayesian changepoint detection.
• Score: 11.763229353978321
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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