March 14, 2024, 4:42 a.m. | Junwei Su, Difan Zou, Chuan Wu

cs.LG updates on

arXiv:2403.08585v1 Announce Type: new
Abstract: Stochastic gradient descent (SGD) exhibits strong algorithmic regularization effects in practice and plays an important role in the generalization of modern machine learning. However, prior research has revealed instances where the generalization performance of SGD is worse than ridge regression due to uneven optimization along different dimensions. Preconditioning offers a natural solution to this issue by rebalancing optimization across different directions. Yet, the extent to which preconditioning can enhance the generalization performance of SGD and …

abstract arxiv cs.lg effects gradient however instances least machine machine learning modern optimization performance practice prior regression regularization research ridge role square stochastic type

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