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Poster
DeltaGrad: Rapid retraining of machine learning models
Yinjun Wu · Edgar Dobriban · Susan B Davidson
Machine learning models are not static and may need to be retrained on slightly changed datasets, for instance, with the addition or deletion of a set of data points. This has many applications, including privacy, robustness, bias reduction, and uncertainty quantifcation. However, it is expensive to retrain models from scratch. To address this problem, we propose the DeltaGrad algorithm for rapid 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.
Author Information
Yinjun Wu (university of pennsylvania)
Edgar Dobriban (University of Pennsylvania)
Susan B Davidson (University of Pennsylvania)
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