Jul 12, 2020
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.
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
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