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We present an improved analysis of mini-batched stochastic dual coordinate ascent for regularized empirical loss minimization (i.e.
Solving large scale linear prediction using stochastic gradient descent algorithms
T. Zhang · 2004
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C-J. Hsieh, K-W. Chang, C-J. Lin, S.S. Keerthi, and S. Sundarajan · 2008
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Alekh Agarwal and John C Duchi · 2011
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Parallel coordinate descent for l1-regularized loss minimization
Joseph K. Bradley, Aapo Kyrola, Danny Bickson, and Carlos Guestrin · 2011
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Andrew Cotter, Ohad Shamir, Nati Srebro, and Karthik Sridharan · 2011
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Pegasos: Primal estimated sub-gradient solver for svm
S.S. Shalev-Shwartz, Y. Singer, N. Srebro, and A. Cotter · 2011
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Optimal distributed online prediction using mini-batches
Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, and Lin Xiao · 2012
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Parallel coordinate descent methods for big data optimization
Peter Richtárik and Martin Takáč · 2012
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Stochastic dual coordinate ascent methods for regularized loss minimization
S. Shalev-Shwartz and T. Zhang · 2012
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Accelerated, parallel and proximal coordinate descent
Olivier Fercoq and Peter Richtárik · 2013
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Rie Johnson and Tong Zhang · 2013
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Jakub Konečný and Peter Richtárik · 2013
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On the complexity analysis of randomized block-coordinate descent methods
Zhaosong Lu and Lin Xiao · 2013
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Distributed coordinate descent method for learning with big data
Peter Richtárik and Martin Takáč · 2013
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Fast distributed coordinate descent for non-strongly convex losses
Olivier Fercoq, Zheng Qu, Peter Richtárik, and Martin Takáč · 2014
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Communication-efficient distributed dual coordinate ascent
Martin Jaggi, Virginia Smith, Martin Takác, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, and Michael I Jordan · 2014
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mS2GD: Mini-batch semi-stochastic gradient descent in the proximal setting
Jakub Konečný, Jie Liu, Peter Richtárik, and Martin Takáč · 2014
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Incremental majorization-minimization optimization with application to large-scale machine learning
Julien Mairal · 2014
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Distributed block coordinate descent for minimizing partially separable functions
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Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function
Peter Richtárik and Martin Takáč · 2013
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Minimizing finite sums with the stochastic average gradient
Mark Schmidt, Nicolas Le Roux, and Francis Bach · 2013
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Mini-batch primal and dual methods for SVMs
Martin Takáč, Avleen Singh Bijral, Peter Richtárik, and Nathan Srebro · 2013
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Trading computation for communication: Distributed stochastic dual coordinate ascent
Tianbao Yang · 2013
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On theoretical analysis of distributed stochastic dual coordinate ascent
Tianbao Yang, Shenghuo Zhu, Rong Jin, and Yuanqing Lin · 2013
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Datasets
Libsvm
Cited in the paper.
Jakub Mareček, Peter Richtárik, and Martin Takáč · 2014
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Stochastic proximal gradient descent with acceleration techniques
Atsushi Nitanda · 2014
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Coordinate descent with arbitrary sampling I: Algorithms and complexity
Zheng Qu and Peter Richtárik · 2014
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Distributed stochastic optimization and learning
Ohad Shamir and Nathan Srebro · 2014
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Adding vs. averaging in distributed primal-dual optimization
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I Jordan, Peter Richtárik, and Martin Takáč · 2015
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