Fetching the paper…
Reading the bibliography…
In recent years, variance-reducing stochastic methods have shown great practical performance, exhibiting linear convergence rate when other stochastic methods offered a sub-linear rate.
“A stochastic approximation method”
Herbert Robbins and Sutton Monro · 1951
Earlier work this paper cites.
“A limit theorem for the norm of random matrices”
Stuart Geman · 1980
Earlier work this paper cites.
“Some limit theorems for the eigenvalues of a sample covariance matrix”
Dag Jonsson · 1982
Earlier work this paper cites.
“Numerical Optimization”
Jorge Nocedal and Stephen. Wright · 2006
Earlier work this paper cites.
“Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent”
Benjamin Recht, Christopher Re, Stephen Wright and Feng Niu · 2011
Earlier work this paper cites.
“Large scale distributed deep networks”
Jeffrey Dean et al · 2012
Cited alongside, same era.
“A stochastic gradient method with an exponential convergence _rate for finite training sets”
Nicolas Le Roux, Mark Schmidt and Francis Bach · 2012
Cited alongside, same era.
“Accelerating stochastic gradient descent using predictive variance reduction”
Rie Johnson and Tong Zhang · 2013
Cited alongside, same era.
“Optimization with First-Order Surrogate Functions”
Julien Mairal · 2013
Cited alongside, same era.
“Stochastic Dual Coordinate Ascent Methods for Regularized Loss”
Shai Shalev-Shwartz and Tong Zhang · 2013
Cited alongside, same era.
“Méthode générale pour la résolution des systèmes d’équations simultanées”
Augustin Cauchy
Cited in the paper.
“A parallel SGD method with strong convergence”
Dhruv Mahajan, S Keerthi, S Sundararajan and L“’eon Bottou
Cited in the paper.
“An efficient distributed learning algorithm based on effective local functional approximations”
Dhruv Mahajan et al
Cited in the paper.
“A Reliable Effective Terascale Linear Learning System”
Alekh Agarwal, Olivier Chapelle, Miroslav Dud“’ik and John Langford · 2014
Later among the works it cites.
“SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives”
Aaron Defazio, Francis Bach and Simon Lacoste-Julien · 2014
Later among the works it cites.
“Stop Wasting My Gradients: Practical SVRG”
Reza Babanezhad et al · 2015
Later among the works it cites.
“A universal catalyst for first-order optimization”
Hongzhou Lin, Julien Mairal and Zaid Harchaoui · 2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…