DeltaGrad: Rapid retraining of machine learning models

TL;DR

DeltaGrad is a new algorithm that enables fast retraining of machine learning models by leveraging cached information, reducing computational costs in scenarios requiring model updates.

Contribution

The paper introduces DeltaGrad, a novel method for rapid model retraining that combines theoretical analysis with empirical validation, outperforming existing approaches.

Findings

DeltaGrad significantly reduces retraining time.

It maintains high accuracy during rapid updates.

Outperforms current state-of-the-art methods.

Abstract

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.