On Optimal Probabilities in Stochastic Coordinate Descent Methods

13 October 2013

Papers citing "On Optimal Probabilities in Stochastic Coordinate Descent Methods"

38 / 38 papers shown

Title
A stochastic gradient descent algorithm with random search directions Eméric Gbaguidi ODL 104 0 0 25 Mar 2025
Importance Sampling for Stochastic Gradient Descent in Deep Neural Networks Thibault Lahire 31 2 0 29 Mar 2023
Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditions S. Choudhury Eduard A. Gorbunov Nicolas Loizou 103 13 0 27 Feb 2023
GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity Artavazd Maranjyan M. Safaryan Peter Richtárik 96 13 0 28 Oct 2022
L-SVRG and L-Katyusha with Adaptive Sampling Boxin Zhao Boxiang Lyu Mladen Kolar 75 3 0 31 Jan 2022
Stochastic Extragradient: General Analysis and Improved Rates Eduard A. Gorbunov Hugo Berard Gauthier Gidel Nicolas Loizou 70 42 0 16 Nov 2021
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity Nicolas Loizou Hugo Berard Gauthier Gidel Ioannis Mitliagkas Simon Lacoste-Julien 94 54 0 30 Jun 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
Adam with Bandit Sampling for Deep Learning Rui Liu Tianyi Wu Barzan Mozafari 84 24 0 24 Oct 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 87 0 0 26 Aug 2020
Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling Mojmír Mutný Michal Derezinski Andreas Krause 118 21 0 25 Oct 2019
One Method to Rule Them All: Variance Reduction for Data, Parameters and Many New Methods Filip Hanzely Peter Richtárik 90 27 0 27 May 2019
A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent Eduard A. Gorbunov Filip Hanzely Peter Richtárik 111 147 0 27 May 2019
SGD: General Analysis and Improved Rates Robert Mansel Gower Nicolas Loizou Xun Qian Alibek Sailanbayev Egor Shulgin Peter Richtárik 94 383 0 27 Jan 2019
SAGA with Arbitrary Sampling Xun Qian Zheng Qu Peter Richtárik 83 26 0 24 Jan 2019
SEGA: Variance Reduction via Gradient Sketching Filip Hanzely Konstantin Mishchenko Peter Richtárik 85 71 0 09 Sep 2018
Parallel and Distributed Successive Convex Approximation Methods for Big-Data Optimization G. Scutari Ying Sun 103 64 0 17 May 2018
Not All Samples Are Created Equal: Deep Learning with Importance Sampling Angelos Katharopoulos François Fleuret 120 525 0 02 Mar 2018
A Randomized Exchange Algorithm for Computing Optimal Approximate Designs of Experiments Radoslav Harman Lenka Filová Peter Richtárik 51 52 0 17 Jan 2018
Safe Adaptive Importance Sampling Sebastian U. Stich Anant Raj Martin Jaggi 94 55 0 07 Nov 2017
Stochastic, Distributed and Federated Optimization for Machine Learning Jakub Konecný FedML 70 38 0 04 Jul 2017
Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications A. Chambolle Matthias Joachim Ehrhardt Peter Richtárik Carola-Bibiane Schönlieb 104 187 0 15 Jun 2017
Biased Importance Sampling for Deep Neural Network Training Angelos Katharopoulos François Fleuret 73 68 0 31 May 2017
A Primer on Coordinate Descent Algorithms Hao-Jun Michael Shi Shenyinying Tu Yangyang Xu W. Yin 91 89 0 30 Sep 2016
Importance Sampling for Minibatches Dominik Csiba Peter Richtárik 111 117 0 06 Feb 2016
Distributed Optimization with Arbitrary Local Solvers Chenxin Ma Jakub Konecný Martin Jaggi Virginia Smith Michael I. Jordan Peter Richtárik Martin Takáč 128 197 0 13 Dec 2015
Primal Method for ERM with Flexible Mini-batching Schemes and Non-convex Losses Dominik Csiba Peter Richtárik 92 23 0 07 Jun 2015
Stochastic Dual Coordinate Ascent with Adaptive Probabilities Dominik Csiba Zheng Qu Peter Richtárik ODL 137 97 0 27 Feb 2015
Adding vs. Averaging in Distributed Primal-Dual Optimization Chenxin Ma Virginia Smith Martin Jaggi Michael I. Jordan Peter Richtárik Martin Takáč FedML 167 177 0 12 Feb 2015
SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization Zheng Qu Peter Richtárik Martin Takáč Olivier Fercoq ODL 126 99 0 08 Feb 2015
Coordinate Descent with Arbitrary Sampling II: Expected Separable Overapproximation Zheng Qu Peter Richtárik 195 83 0 27 Dec 2014
Coordinate Descent with Arbitrary Sampling I: Algorithms and Complexity Zheng Qu Peter Richtárik 83 130 0 27 Dec 2014
Randomized Dual Coordinate Ascent with Arbitrary Sampling Zheng Qu Peter Richtárik Tong Zhang 155 58 0 21 Nov 2014
Matrix Completion under Interval Uncertainty Jakub Mareˇcek Peter Richtárik Martin Takáč 176 19 0 11 Aug 2014
Hybrid Random/Deterministic Parallel Algorithms for Nonconvex Big Data Optimization Amir Daneshmand F. Facchinei Vyacheslav Kungurtsev G. Scutari 118 60 0 16 Jul 2014
Coordinate Descent with Online Adaptation of Coordinate Frequencies Tobias Glasmachers Ürün Dogan 66 4 0 15 Jan 2014
Accelerated, Parallel and Proximal Coordinate Descent Olivier Fercoq Peter Richtárik 119 372 0 20 Dec 2013
Parallel Coordinate Descent Methods for Big Data Optimization Peter Richtárik Martin Takáč 134 487 0 04 Dec 2012