Convergence of Distributed Stochastic Variance Reduced Methods without
Sampling Extra Data

Convergence of Distributed Stochastic Variance Reduced Methods without Sampling Extra Data

29 May 2019

Papers citing "Convergence of Distributed Stochastic Variance Reduced Methods without Sampling Extra Data"

11 / 11 papers shown

Title
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator Cong Fang C. J. Li Zhouchen Lin Tong Zhang 85 577 0 04 Jul 2018
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training Chengyue Wu Song Han Huizi Mao Yu Wang W. Dally 120 1,407 0 05 Dec 2017
Gradient Sparsification for Communication-Efficient Distributed Optimization Jianqiao Wangni Jialei Wang Ji Liu Tong Zhang 72 525 0 26 Oct 2017
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning W. Wen Cong Xu Feng Yan Chunpeng Wu Yandan Wang Yiran Chen Hai Helen Li 137 987 0 22 May 2017
CoCoA: A General Framework for Communication-Efficient Distributed Optimization Virginia Smith Simone Forte Chenxin Ma Martin Takáč Michael I. Jordan Martin Jaggi 66 273 0 07 Nov 2016
AIDE: Fast and Communication Efficient Distributed Optimization Sashank J. Reddi Jakub Konecný Peter Richtárik Barnabás Póczós Alex Smola 53 151 0 24 Aug 2016
Federated Optimization:Distributed Optimization Beyond the Datacenter Jakub Konecný H. B. McMahan Daniel Ramage FedML 113 735 0 11 Nov 2015
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives Aaron Defazio Francis R. Bach Simon Lacoste-Julien ODL 128 1,823 0 01 Jul 2014
A Proximal Stochastic Gradient Method with Progressive Variance Reduction Lin Xiao Tong Zhang ODL 147 738 0 19 Mar 2014
Minimizing Finite Sums with the Stochastic Average Gradient Mark Schmidt Nicolas Le Roux Francis R. Bach 291 1,246 0 10 Sep 2013
Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization Shai Shalev-Shwartz Tong Zhang 155 1,032 0 10 Sep 2012