Starting Small -- Learning with Adaptive Sample Sizes

A stochastic approximation method

Robbins, Herbert and Monro, Sutton · 1951

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Acceleration of stochastic approximation by averaging

Polyak, Boris T and Juditsky, Anatoli B · 1992

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Statistical learning theory , volume 1

Vapnik, Vlamimir · 1998

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Concentration inequalities and empirical processes theory applied to the analysis of learning algorithms

Bousquet, Olivier · 2002

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Stochastic approximation and recursive algorithms and applications , volume 35

Kushner, Harold J and Yin, George · 2003

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Convex Optimization

Boyd, Stephen and Vandenberghe, Lieven · 2004

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Local rademacher complexities

Bartlett, Peter L, Bousquet, Olivier, and Mendelson, Shahar · 2005

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Theory of classification: A survey of some recent advances

Boucheron, Stéphane, Bousquet, Olivier, and Lugosi, Gábor · 2005

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Convexity, classification, and risk bounds

Bartlett, Peter L, Jordan, Michael I, and McAuliffe, Jon D · 2006

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The tradeoffs of large scale learning

Bousquet, Olivier and Bottou, Léon · 2008

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Large-scale machine learning with stochastic gradient descent

Bottou, Léon · 2010

Cited alongside, same era.

Non-asymptotic analysis of stochastic approximation algorithms for machine learning

Moulines, Eric and Bach, Francis R · 2011

Cited alongside, same era.

A stochastic gradient method with an exponential convergence rate for finite training sets

Roux, Nicolas L, Schmidt, Mark, and Bach, Francis R · 2012

Cited alongside, same era.

Computational and statistical tradeoffs via convex relaxation

Chandrasekaran, Venkat and Jordan, Michael I · 2013

Cited alongside, same era.

Accelerating stochastic gradient descent using predictive variance reduction

Johnson, Rie and Zhang, Tong · 2013

Cited alongside, same era.

Semi-stochastic gradient descent methods

Original

Linear convergence with condition number independent access of full gradients

Original

Zhang, Lijun, Mahdavi, Mehrdad, and Jin, Rong · 2013

Later among the works it cites.

Saga: A fast incremental gradient method with support for non-strongly convex composite objectives

Defazio, Aaron, Bach, Francis, and Lacoste-Julien, Simon · 2014

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Stop wasting my gradients: Practical svrg

Babanezhad, Reza, Ahmed, Mohamed Osama, Virani, Alim, Schmidt, Mark, Konečnỳ, Jakub, and Sallinen, Scott · 2015

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Finito: A faster, permutable incremental gradient method for big data problems

Defazio, Aaron J, Caetano, Tibério S, and Domke, Justin · 2015

Later among the works it cites.

Competing with the empirical risk minimizer in a single pass

Frostig, Roy, Ge, Rong, Kakade, Sham M., and Sidford, Aaron · 2015

Later among the works it cites.