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We develop a novel preconditioning method for ridge regression, based on recent linear sketching methods.
Angenäherte auflösung von systemen linearer gleichungen
Stefan Kaczmarz · 1937
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Methods of conjugate gradients for solving linear systems
Magnus Rudolph Hestenes and Eduard Stiefel · 1952
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A method of solving a convex programming problem with convergence rate O ( 1 / k 2 ) O(1/k^{2})
Yurii Nesterov · 1983
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Numerical linear algebra
Lloyd N Trefethen and David Bau III · 1997
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Introductory lectures on convex optimization
Yurii Nesterov · 2004
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Improved approximation algorithms for large matrices via random projections
Tamas Sarlos · 2006
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A stochastic gradient method with an exponential convergence rate for finite training sets
Nicolas L Roux, Mark Schmidt, and Francis R Bach · 2012
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Laplacian solvers and their algorithmic applications
Nisheeth K Vishnoi · 2012
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Matrix analysis
Rajendra Bhatia · 2013
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Low rank approximation and regression in input sparsity time
Kenneth L Clarkson and David P Woodruff · 2013
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Accelerating stochastic gradient descent using predictive variance reduction
Rie Johnson and Tong Zhang · 2013
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Stochastic dual coordinate ascent methods for regularized loss
Shai Shalev-Shwartz and Tong Zhang · 2013
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Randomized algorithms for low-rank matrix factorizations: sharp performance bounds
Rafi Witten and Emmanuel Candès · 2013
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Understanding Machine Learning: From Theory to Algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
Cited alongside, same era.
Sketching as a tool for numerical linear algebra
David P Woodruff · 2014
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A proximal stochastic gradient method with progressive variance reduction
Lin Xiao and Tong Zhang · 2014
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On data preconditioning for regularized loss minimization
Tianbao Yang, Rong Jin, Shenghuo Zhu, and Qihang Lin · 2014
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Roy Frostig, Rong Ge, Sham M Kakade, and Aaron Sidford · 2015
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A universal catalyst for first-order optimization
Hongzhou Lin, Julien Mairal, and Zaid Harchaoui · 2015
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Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization
Shai Shalev-Shwartz and Tong Zhang · 2014
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Randomized block krylov methods for stronger and faster approximate singular value decomposition
Cameron Musco and Christopher Musco · 2015
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Sdca without duality, regularization, and individual convexity
Shai Shalev-Shwartz · 2016
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