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Stochastic Dual Coordinate Ascent is a popular method for solving regularized loss minimization for the case of convex losses.
A stochastic gradient method with an exponential convergence rate for finite training sets
Le Roux, Nicolas, Schmidt, Mark, and Bach, Francis · 2012
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Accelerating stochastic gradient descent using predictive variance reduction
Johnson, Rie and Zhang, Tong · 2013
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Semi-stochastic gradient descent methods
Konečnỳ, Jakub and Richtárik, Peter · 2013
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Stochastic dual coordinate ascent methods for regularized loss minimization
Shalev-Shwartz, Shai and Zhang, Tong · 2013
Earlier work this paper cites.
A lower bound for the optimization of finite sums
Agarwal, Alekh and Bottou, Leon · 2014
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New Optimisation Methods for Machine Learning
Defazio, Aaron · 2014
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Understanding Machine Learning: From Theory to Algorithms
Shalev-Shwartz, Shai and Ben-David, Shai · 2014
Cited alongside, same era.
A proximal stochastic gradient method with progressive variance reduction
Xiao, Lin and Zhang, Tong · 2014
Cited alongside, same era.
Univr: A universal variance reduction framework for proximal stochastic gradient method
Allen-Zhu, Zeyuan and Yuan, Yang · 2015
Cited alongside, same era.
On lower and upper bounds for smooth and strongly convex optimization problems
Arjevani, Yossi, Shalev-Shwartz, Shai, and Shamir, Ohad · 2015
Cited alongside, same era.
Primal method for erm with flexible mini-batching schemes and non-convex losses
Csiba, Dominik and Richtárik, Peter · 2015
Cited alongside, same era.
Jin, Chi, Kakade, Sham M, Musco, Cameron, Netrapalli, Praneeth, and Sidford, Aaron · 2015
Later among the works it cites.
A universal catalyst for first-order optimization
Lin, Hongzhou, Mairal, Julien, and Harchaoui, Zaid · 2015
Later among the works it cites.
Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization
Shalev-Shwartz, S. and Zhang, T · 2015
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Shalev-Shwartz, Shai · 2015
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A stochastic pca and svd algorithm with an exponential convergence rate
Shamir, Ohad · 2015
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Dual free sdca for empirical risk minimization with adaptive probabilities
He, Xi and Takáč, Martin · 2015
Cited alongside, same era.
Saga: A fast incremental gradient method with support for non-strongly convex composite objectives
Defazio, Aaron, Bach, Francis, and Lacoste-Julien, Simon
Cited in the paper.
Finito: A faster, permutable incremental gradient method for big data problems
Defazio, Aaron J, Caetano, Tibério S, and Domke, Justin
Cited in the paper.