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Stochastic variance reduction has proven effective at accelerating first-order algorithms for solving convex finite-sum optimization tasks such as empirical risk minimization.
Advanced calculus
Gerald Folland · 2002
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Stephen Boyd and Lieven Vandenberghe · 2004
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Sampling algorithms for ℓ 2 \ell_{2} regression and applications
Petros Drineas, Michael W Mahoney, and S Muthukrishnan · 2006
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Improved approximation algorithms for large matrices via random projections
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A fast randomized algorithm for overdetermined linear least-squares regression
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The fast Johnson–Lindenstrauss transform and approximate nearest neighbors
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Haim Avron, Petar Maymounkov, and Sivan Toledo · 2010
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Nicolas Roux, Mark Schmidt, and Francis Bach · 2012
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User-friendly tail bounds for sums of random matrices
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Jelani Nelson and Huy L Nguyên · 2013
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Shai Shalev-Shwartz and Tong Zhang · 2013
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Aaron Defazio, Francis Bach, and Simon Lacoste-Julien · 2014
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Xiangrui Meng, Michael A Saunders, and Michael W Mahoney · 2014
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Sketching as a tool for numerical linear algebra
David P Woodruff · 2014
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Convergence rates of sub-sampled Newton methods
Murat A Erdogdu and Andrea Montanari · 2015
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Competing with the empirical risk minimizer in a single pass
Roy Frostig, Rong Ge, Sham M Kakade, and Aaron Sidford · 2015
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Mini-batch semi-stochastic gradient descent in the proximal setting
Jakub Konecný, Jie Liu, Peter Richtárik, and Martin Takác · 2015
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A universal catalyst for first-order optimization
Hongzhou Lin, Julien Mairal, and Zaid Harchaoui · 2015
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Improved svrg for non-strongly-convex or sum-of-non-convex objectives
Zeyuan Allen-Zhu and Yang Yuan · 2016
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RandNLA: Randomized numerical linear algebra
Petros Drineas and Michael W. Mahoney · 2016
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Alon Gonen, Francesco Orabona, and Shai Shalev-Shwartz · 2016
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Robert Gower, Donald Goldfarb, and Peter Richtárik · 2016
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Aryan Mokhtari, Mark Eisen, and Alejandro Ribeiro · 2018
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Stochastic cubic regularization for fast nonconvex optimization
Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, and Michael I Jordan · 2018
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Distributed estimation of the inverse hessian by determinantal averaging
Michał Dereziński and Michael W Mahoney · 2019
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Stochastic newton and cubic newton methods with simple local linear-quadratic rates
Dmitry Kovalev, Konstantin Mishchenko, and Peter Richtárik · 2019
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Acceleration of svrg and katyusha x by inexact preconditioning
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Anton Rodomanov and Dmitry Kropotov · 2016
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Katyusha: The first direct acceleration of stochastic gradient methods
Zeyuan Allen-Zhu · 2017
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Low-rank approximation and regression in input sparsity time
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Sub-sampled newton methods
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Sparse sketches with small inversion bias
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Determinantal point processes in randomized numerical linear algebra
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Localnewton: Reducing communication rounds for distributed learning
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Hessian averaging in stochastic newton methods achieves superlinear convergence
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