Variance-Reduced Methods for Machine Learning

2 October 2020

Papers citing "Variance-Reduced Methods for Machine Learning"

9 / 59 papers shown

Title
SVRG Meets AdaGrad: Painless Variance Reduction Benjamin Dubois-Taine Sharan Vaswani Reza Babanezhad Mark Schmidt Simon Lacoste-Julien 61 18 0 18 Feb 2021
IntSGD: Adaptive Floatless Compression of Stochastic Gradients Konstantin Mishchenko Bokun Wang D. Kovalev Peter Richtárik 100 15 0 16 Feb 2021
Stochastic Variance Reduction for Variational Inequality Methods Ahmet Alacaoglu Yura Malitsky 103 71 0 16 Feb 2021
Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization M. Safaryan Filip Hanzely Peter Richtárik 42 24 0 14 Feb 2021
Query Complexity of Least Absolute Deviation Regression via Robust Uniform Convergence Xue Chen Michal Derezinski 59 31 0 03 Feb 2021
Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications Xiang Li Zhihua Zhang 53 4 0 05 Jan 2021
First-Order Methods for Convex Optimization Pavel Dvurechensky Mathias Staudigl Shimrit Shtern ODL 83 26 0 04 Jan 2021
Improved SVRG for quadratic functions N. Kahalé 40 0 0 01 Jun 2020
Dualize, Split, Randomize: Toward Fast Nonsmooth Optimization Algorithms Adil Salim Laurent Condat Konstantin Mishchenko Peter Richtárik 64 23 0 03 Apr 2020