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A Novel Sequential Coreset Method for Gradient Descent Algorithms
[article]
2022
arXiv
pre-print
A wide range of optimization problems arising in machine learning can be solved by gradient descent algorithms, and a central question in this area is how to efficiently compress a large-scale dataset so as to reduce the computational complexity. Coreset is a popular data compression technique that has been extensively studied before. However, most of existing coreset methods are problem-dependent and cannot be used as a general tool for a broader range of applications. A key obstacle is that
arXiv:2112.02504v3
fatcat:46cf3nmhfrbelciindlsb6r7du