Solving ridge regression using sketched preconditioned SVRG

Alon Gonen; Francesco Orabona; Shai Shalev-Shwartz

Solving ridge regression using sketched preconditioned SVRG

Alon Gonen, Francesco Orabona, Shai Shalev-Shwartz

The Rachel and Selim Benin School of Engineering and Computer Science

The Hebrew University of Jerusalem

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	33rd International Conference on Machine Learning, ICML 2016
Editors	Kilian Q. Weinberger, Maria Florina Balcan
Pages	2102-2111
Number of pages	10
ISBN (Electronic)	9781510829008
State	Published - 2016
Event	33rd International Conference on Machine Learning, ICML 2016 - New York City, United States Duration: 19 Jun 2016 → 24 Jun 2016

Publication series

Name	33rd International Conference on Machine Learning, ICML 2016
Volume	3

Conference

Conference	33rd International Conference on Machine Learning, ICML 2016
Country/Territory	United States
City	New York City
Period	19/06/16 → 24/06/16

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Software
Computer Networks and Communications

Cite this

@inproceedings{802537f7ea924e4791015d52f529a285,

title = "Solving ridge regression using sketched preconditioned SVRG",

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.",

author = "Alon Gonen and Francesco Orabona and Shai Shalev-Shwartz",

year = "2016",

language = "الإنجليزيّة",

series = "33rd International Conference on Machine Learning, ICML 2016",

pages = "2102--2111",

editor = "Weinberger, {Kilian Q.} and Balcan, {Maria Florina}",

booktitle = "33rd International Conference on Machine Learning, ICML 2016",

note = "33rd International Conference on Machine Learning, ICML 2016 ; Conference date: 19-06-2016 Through 24-06-2016",

}

Gonen, A, Orabona, F & Shalev-Shwartz, S 2016, Solving ridge regression using sketched preconditioned SVRG. in KQ Weinberger & MF Balcan (eds), 33rd International Conference on Machine Learning, ICML 2016. 33rd International Conference on Machine Learning, ICML 2016, vol. 3, pp. 2102-2111, 33rd International Conference on Machine Learning, ICML 2016, New York City, United States, 19/06/16.

Solving ridge regression using sketched preconditioned SVRG. / Gonen, Alon; Orabona, Francesco; Shalev-Shwartz, Shai.
33rd International Conference on Machine Learning, ICML 2016. ed. / Kilian Q. Weinberger; Maria Florina Balcan. 2016. p. 2102-2111 (33rd International Conference on Machine Learning, ICML 2016; Vol. 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Solving ridge regression using sketched preconditioned SVRG

AU - Gonen, Alon

AU - Orabona, Francesco

AU - Shalev-Shwartz, Shai

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84998673804&partnerID=8YFLogxK

M3 - منشور من مؤتمر

T3 - 33rd International Conference on Machine Learning, ICML 2016

SP - 2102

EP - 2111

BT - 33rd International Conference on Machine Learning, ICML 2016

A2 - Weinberger, Kilian Q.

A2 - Balcan, Maria Florina

T2 - 33rd International Conference on Machine Learning, ICML 2016

Y2 - 19 June 2016 through 24 June 2016

ER -

Solving ridge regression using sketched preconditioned SVRG

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

Other files and links

Cite this