Solving Ridge Regression using Sketched Preconditioned SVRG

@InProceedings{pmlr-v48-gonen16,
  title = 	 {Solving Ridge Regression using Sketched Preconditioned SVRG},
  author = 	 {Gonen, Alon and Orabona, Francesco and Shalev-Shwartz, Shai},
  booktitle = 	 {Proceedings of The 33rd International Conference on Machine Learning},
  pages = 	 {1397--1405},
  year = 	 {2016},
  editor = 	 {Balcan, Maria Florina and Weinberger, Kilian Q.},
  volume = 	 {48},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {New York, New York, USA},
  month = 	 {20--22 Jun},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v48/gonen16.pdf},
  url = 	 {https://proceedings.mlr.press/v48/gonen16.html},
  abstract = 	 {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.}
}