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Stochastic Gradient Descent (SGD) is a popular optimization method which has been applied to many important machine learning tasks such as Support Vector Machines and Deep Neural Networks.
Stochastic approximation and recursive algorithms and applications
Harold J Kushner and George Yin · 2003
Earlier work this paper cites.
Solving large scale linear prediction problems using stochastic gradient descent algorithms
Tong Zhang · 2004
Earlier work this paper cites.
Convex analysis and nonlinear optimization: theory and examples
Jonathan Borwein and Adrian Lewis · 2006
Earlier work this paper cites.
Pegasos: Primal estimated sub-gradient solver for svm
Shai Shalev-Shwartz, Yoram Singer, and Nathan Srebro · 2007
Earlier work this paper cites.
Efficient online and batch learning using forward backward splitting
John Duchi and Yoram Singer · 2009
Cited alongside, same era.
Making gradient descent optimal for strongly convex stochastic optimization
Alexander Rakhlin, Ohad Shamir, and Karthik Sridharan · 2011
Cited alongside, same era.
Pegasos: primal estimated sub-gradient solver for svm
Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro, and Andrew Cotter · 2011
Cited alongside, same era.
Stochastic gradient descent with only one projection
Mehrdad Mahdavi, Tianbao Yang, Rong Jin, Shenghuo Zhu, and Jinfeng Yi · 2012
Cited alongside, same era.
Accelerating stochastic gradient descent using predictive variance reduction
Rie Johnson and Tong Zhang · 2013
Later among the works it cites.
Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes
Ohad Shamir and Tong Zhang · 2013
Later among the works it cites.
Variance reduction for stochastic gradient optimization
Chong Wang, Xi Chen, Alex J. Smola, and Eric P. Xing · 2013
Later among the works it cites.
Stochastic optimization with importance sampling
Peilin Zhao and Tong Zhang · 2014
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