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Adaptive Second Order Coresets for Data-efficient Machine Learning
2022
International Conference on Machine Learning
Training machine learning models on massive datasets incurs substantial computational costs. To alleviate such costs, there has been a sustained effort to develop data-efficient training methods that can carefully select subsets of the training examples that generalize on par with the full training data. However, existing methods are limited in providing theoretical guarantees for the quality of the models trained on the extracted subsets, and may perform poorly in practice. We propose ADACORE,
dblp:conf/icml/PooladzandiDM22
fatcat:27h5y6pgwnfnzgmm4cujipl45y