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Our goal is to improve variance reducing stochastic methods through better control variates.
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Herbert Robbins and Sutton Monro · 1951
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Jorge Nocedal · 1980
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Andrea Walther · 2008
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“A stochastic gradient method with an exponential convergence rate for finite training sets”
Nicolas Le Roux, Mark Schmidt and Francis Bach · 2012
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“Accelerating stochastic gradient descent using predictive variance reduction”
Rie Johnson and Tong Zhang · 2013
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“Linear convergence with condition number independent access of full gradients”
Lijun Zhang, Mehrdad Mahdavi and Rong Jin · 2015
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“Stochastic Block BFGS: Squeezing More Curvature out of Data”
Robert. Gower, Donald Goldfarb and Peter Richt“’arik · 2016
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“Randomized Quasi-Newton Updates are Linearly Convergent Matrix Inversion Algorithms”
Robert Gower and Peter Richt“’arik · 2016
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“SVRG++ with non-uniform sampling”
T Kern and A Gyorgy · 2016
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“Simulation and the Monte Carlo method”
Reuven Rubinstein and Dirk Kroese · 2016
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Robert. Gower and Jacek Gondzio · 2014
Cited alongside, same era.
“Convergence rate of stochastic gradient with constant step size”, 2014
Mark Schmidt · 2014
Cited alongside, same era.
“Convex optimization: Algorithms and complexity”
S“’ebastien Bubeck · 2015
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“Curvature-aided incremental aggregated gradient method”
Hoi-To Wai, Wei Shi, Angelia Nedic and Anna Scaglione · 2017
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