A Simple Practical Accelerated Method for Finite Sums

8 February 2016

Papers citing "A Simple Practical Accelerated Method for Finite Sums"

24 / 24 papers shown

Title
A Coefficient Makes SVRG Effective Yida Yin Zhiqiu Xu Zhiyuan Li Trevor Darrell Zhuang Liu 33 1 0 09 Nov 2023
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis Dachao Lin Yuze Han Haishan Ye Zhihua Zhang 22 11 0 15 Apr 2023
On the fast convergence of minibatch heavy ball momentum Raghu Bollapragada Tyler Chen Rachel A. Ward 26 17 0 15 Jun 2022
An Adaptive Incremental Gradient Method With Support for Non-Euclidean Norms Binghui Xie Chen Jin Kaiwen Zhou James Cheng Wei Meng 37 1 0 28 Apr 2022
Lower Bounds and Optimal Algorithms for Personalized Federated Learning Filip Hanzely Slavomír Hanzely Samuel Horváth Peter Richtárik FedML 47 186 0 05 Oct 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 32 0 0 26 Aug 2020
Stochastic Hamiltonian Gradient Methods for Smooth Games Nicolas Loizou Hugo Berard Alexia Jolicoeur-Martineau Pascal Vincent Simon Lacoste-Julien Ioannis Mitliagkas 39 50 0 08 Jul 2020
Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization Chaobing Song Yong Jiang Yi Ma 53 23 0 18 Jun 2020
Gradient tracking and variance reduction for decentralized optimization and machine learning Ran Xin S. Kar U. Khan 19 10 0 13 Feb 2020
Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems Filip Hanzely D. Kovalev Peter Richtárik 35 17 0 11 Feb 2020
The Practicality of Stochastic Optimization in Imaging Inverse Problems Junqi Tang K. Egiazarian Mohammad Golbabaee Mike Davies 25 30 0 22 Oct 2019
Asynchronous Accelerated Proximal Stochastic Gradient for Strongly Convex Distributed Finite Sums Hadrien Hendrikx Francis R. Bach Laurent Massoulié FedML 8 26 0 28 Jan 2019
99% of Distributed Optimization is a Waste of Time: The Issue and How to Fix it Konstantin Mishchenko Filip Hanzely Peter Richtárik 16 13 0 27 Jan 2019
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning Aaron Defazio Léon Bottou UQCV DRL 23 112 0 11 Dec 2018
A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates Kaiwen Zhou Fanhua Shang James Cheng 14 74 0 28 Jun 2018
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication Zebang Shen Aryan Mokhtari Tengfei Zhou P. Zhao Hui Qian 25 56 0 25 May 2018
On the insufficiency of existing momentum schemes for Stochastic Optimization Rahul Kidambi Praneeth Netrapalli Prateek Jain Sham Kakade ODL 22 117 0 15 Mar 2018
Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods Nicolas Loizou Peter Richtárik 19 199 0 27 Dec 2017
Stochastic Nonconvex Optimization with Large Minibatches Weiran Wang Nathan Srebro 36 26 0 25 Sep 2017
A Unified Analysis of Stochastic Optimization Methods Using Jump System Theory and Quadratic Constraints Bin Hu Peter M. Seiler Anders Rantzer 24 35 0 25 Jun 2017
Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch-Prox Jialei Wang Weiran Wang Nathan Srebro 16 54 0 21 Feb 2017
Federated Optimization: Distributed Machine Learning for On-Device Intelligence Jakub Konecný H. B. McMahan Daniel Ramage Peter Richtárik FedML 51 1,876 0 08 Oct 2016
AIDE: Fast and Communication Efficient Distributed Optimization Sashank J. Reddi Jakub Konecný Peter Richtárik Barnabás Póczós Alex Smola 16 150 0 24 Aug 2016
Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning Julien Mairal 79 317 0 18 Feb 2014