Solving ridge regression using sketched preconditioned SVRG

Alon Gonen, Francesco Orabona, Shai Shalev-Shwartz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations


We develop a novel preconditioning method for ridge regression, based on recent linear sketching methods. By equipping Stochastic Variance Reduced Gradient (SVRG) with this preconditioning process, we obtain a significant speed-up relative to fast stochastic methods such as SVRG, SDCA and SAG.
Original languageEnglish (US)
Title of host publication33rd International Conference on Machine Learning, ICML 2016
PublisherInternational Machine Learning Society (IMLS)
Number of pages10
ISBN (Print)9781510829008
StatePublished - Jan 1 2016
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-25


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