论文标题
Deltagrad：机器学习模型的快速再培训
DeltaGrad: Rapid retraining of machine learning models
论文作者
论文摘要
机器学习模型不是静态的，并且可能需要在略微更改的数据集上重新训练，例如，增加或删除了一组数据点。这有许多应用程序，包括隐私，鲁棒性，降低偏见和不确定性量化。但是，从头开始重新训练型号是昂贵的。为了解决这个问题，我们根据培训阶段缓存的信息提出了用于快速重新培训机器学习模型的Deltagrad算法。我们为Deltagrad的有效性提供了理论和经验支持，并表明它与最新的现状相比。
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.
