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Stochastic Gradient Descent (SGD) is one of the simplest and most popular stochastic optimization methods.
Stochastic Approximation and Recursive Algorithms and Applications
Kushner, H. and Yin, G · 2003
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Online convex programming and generalized infinitesimal gradient ascent
Zinkevich, M · 2003
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Solving large scale linear prediction problems using stochastic gradient descent algorithms
Zhang, T · 2004
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Logarithmic regret algorithms for online convex optimization
Hazan, E., Agarwal, A., and Kale, S · 2007
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Stochastic convex optimization
Shalev-Shwartz, S., Shamir, O., Srebro, N., and Sridharan, K · 2009
Earlier work this paper cites.
Non-asymptotic analysis of stochastic approximation algorithms for machine learning
Bach, F. and Moulines, E · 2011
Cited alongside, same era.
Beyond the regret minimization barrier: An optimal algorithm for stochastic strongly-convex optimization
Hazan, E. and Kale, S · 2011
Cited alongside, same era.
Making gradient descent optimal for strongly convex stochastic optimization
Rakhlin, A., Shamir, O., and Sridharan, K · 2011
Cited alongside, same era.
Pegasos: primal estimated sub-gradient solver for svm
Shalev-Shwartz, S., Singer, Y., Srebro, N., and Cotter, A · 2011
Cited alongside, same era.
Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization
Agarwal, A., Bartlett, P., Ravikumar, P., and Wainwright, M · 2012
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Lacoste-Julien, S., Schmidt, M., and Bach, F · 2012
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Stochastic smoothing for nonsmooth minimizations: Accelerating sgd by exploiting structure
Ouyang, H. and Gray, A · 2012
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Is averaging needed for strongly convex stochastic gradient descent?
Shamir, O · 2012
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