Fetching the paper…
Reading the bibliography…
Stochastic dual coordinate ascent (SDCA) is an effective technique for solving regularized loss minimization problems in machine learning.
Smooth minimization of non-smooth functions
Yurii Nesterov · 2005
Earlier work this paper cites.
Gradient methods for minimizing composite objective function, 2007
Yurii Nesterov · 2007
Earlier work this paper cites.
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
S. Shalev-Shwartz, Y. Singer, and N. Srebro · 2007
Earlier work this paper cites.
Distribution-calibrated hierarchical classification
Ofer Dekel · 2010
Earlier work this paper cites.
Distributed dual averaging in networks
John Duchi, Alekh Agarwal, and Martin J Wainwright · 2010
Earlier work this paper cites.
Graphlab: A new framework for parallel machine learning
Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny Bickson, Carlos Guestrin, and Joseph M Hellerstein · 2010
Earlier work this paper cites.
A reliable effective terascale linear learning system
Alekh Agarwal, Olivier Chapelle, Miroslav Dudík, and John Langford · 2011
Earlier work this paper cites.
Parallel coordinate descent for l1-regularized loss minimization
Joseph K Bradley, Aapo Kyrola, Danny Bickson, and Carlos Guestrin · 2011
Cited alongside, same era.
Better mini-batch algorithms via accelerated gradient methods
Andrew Cotter, Ohad Shamir, Nathan Srebro, and Karthik Sridharan · 2011
Cited alongside, same era.
Algorithms and hardness results for parallel large margin learning
Phil Long and Rocco Servedio · 2011
Cited alongside, same era.
Hogwild!: A lock-free approach to parallelizing stochastic gradient descent
Feng Niu, Benjamin Recht, Christopher Ré, and Stephen J Wright · 2011
Cited alongside, same era.
Distributed delayed stochastic optimization
Alekh Agarwal and John C Duchi · 2012
Cited alongside, same era.
Optimal distributed online prediction using mini-batches
Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, and Lin Xiao · 2012
Later among the works it cites.
Nicolas Le Roux, Mark Schmidt, and Francis Bach · 2012
Later among the works it cites.
Distributed graphlab: A framework for machine learning and data mining in the cloud
Yucheng Low, Danny Bickson, Joseph Gonzalez, Carlos Guestrin, Aapo Kyrola, and Joseph M Hellerstein · 2012
Later among the works it cites.
Parallel coordinate descent methods for big data optimization
Peter Richtárik and Martin Takáč · 2012
Later among the works it cites.
Stochastic dual coordinate ascent methods for regularized loss minimization
Shai Shalev-Shwartz and Tong Zhang · 2013
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Maria-Florina Balcan, Avrim Blum, Shai Fine, and Yishay Mansour · 2012
Cited alongside, same era.
Protocols for learning classifiers on distributed data
Hal Daume III, Jeff M Phillips, Avishek Saha, and Suresh Venkatasubramanian · 2012
Cited alongside, same era.
Mini-batch primal and dual methods for svms
Martin Takác, Avleen Bijral, Peter Richtárik, and Nathan Srebro · 2013
Closest in time.