DeltaGrad: Rapid retraining of machine learning models

Part of Proceedings of the International Conference on Machine Learning 1 pre-proceedings (ICML 2020)

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Yinjun Wu, Edgar Dobriban, Susan Davidson


<p>Machine learning models are not static and may need to be retrained on slightly different datasets, for instance, with the addition or deletion of a set of datapoints. This has many applications, including privacy, robustness, bias reduction, and uncertainty quantification. However, it is expensive to retrain models from scratch. To address this problem, we propose the DeltaGrad algorithm for rapidly retraining machine learning models based on information cached during the training phase. We provide both theoretical and empirical support for the effectiveness of DeltaGrad, and show that it compares favorably to the state of the art.</p>