Matches in SemOpenAlex for { <https://semopenalex.org/work/W2945702962> ?p ?o ?g. }
- W2945702962 abstract "We propose a remarkably general variance-reduced method suitable for solving regularized empirical risk minimization problems with either a large number of training examples, or a large model dimension, or both. In special cases, our method reduces to several known and previously thought to be unrelated methods, such as {tt SAGA}, {tt LSVRG}, {tt JacSketch}, {tt SEGA} and {tt ISEGA}, and their arbitrary sampling and proximal generalizations. However, we also highlight a large number of new specific algorithms with interesting properties. We provide a single theorem establishing linear convergence of the method under smoothness and quasi strong convexity assumptions. With this theorem we recover best-known and sometimes improved rates for known methods arising in special cases. As a by-product, we provide the first unified method and theory for stochastic gradient and stochastic coordinate descent type methods." @default.
- W2945702962 created "2019-05-29" @default.
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- W2945702962 creator A5089882896 @default.
- W2945702962 date "2019-05-27" @default.
- W2945702962 modified "2023-09-27" @default.
- W2945702962 title "One Method to Rule Them All: Variance Reduction for Data, Parameters and Many New Methods" @default.
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