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"

28 / 28 papers shown

Title
SpiderBoost and Momentum: Faster Stochastic Variance Reduction Algorithms Zhe Wang Kaiyi Ji Yi Zhou Yingbin Liang Vahid Tarokh ODL 72 82 0 25 Oct 2018
The Convergence of Sparsified Gradient Methods Dan Alistarh Torsten Hoefler M. Johansson Sarit Khirirat Nikola Konstantinov Cédric Renggli 151 492 0 27 Sep 2018
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
A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates Kaiwen Zhou Fanhua Shang James Cheng 58 75 0 28 Jun 2018
Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs Bin Hu S. Wright Laurent Lessard 41 20 0 10 Jun 2018
D $^2$ : Decentralized Training over Decentralized Data Hanlin Tang Xiangru Lian Ming Yan Ce Zhang Ji Liu 31 350 0 19 Mar 2018
Proximal SCOPE for Distributed Sparse Learning: Better Data Partition Implies Faster Convergence Rate Shen-Yi Zhao Gong-Duo Zhang Ming-Wei Li Wu-Jun Li 26 8 0 15 Mar 2018
signSGD: Compressed Optimisation for Non-Convex Problems Jeremy Bernstein Yu Wang Kamyar Azizzadenesheli Anima Anandkumar FedML ODL 87 1,041 0 13 Feb 2018
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training Chengyue Wu Song Han Huizi Mao Yu Wang W. Dally 122 1,407 0 05 Dec 2017
Gradient Sparsification for Communication-Efficient Distributed Optimization Jianqiao Wangni Jialei Wang Ji Liu Tong Zhang 74 525 0 26 Oct 2017
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization Shusen Wang Farbod Roosta-Khorasani Peng Xu Michael W. Mahoney 65 129 0 11 Sep 2017
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent Xiangru Lian Ce Zhang Huan Zhang Cho-Jui Hsieh Wei Zhang Ji Liu 48 1,226 0 25 May 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
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient Lam M. Nguyen Jie Liu K. Scheinberg Martin Takáč ODL 157 603 0 01 Mar 2017
Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch-Prox Jialei Wang Weiran Wang Nathan Srebro 82 54 0 21 Feb 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
The Landscape of Empirical Risk for Non-convex Losses Song Mei Yu Bai Andrea Montanari 94 312 0 22 Jul 2016
Katyusha: The First Direct Acceleration of Stochastic Gradient Methods Zeyuan Allen-Zhu ODL 96 580 0 18 Mar 2016
SCOPE: Scalable Composite Optimization for Learning on Spark Shen-Yi Zhao Ru Xiang Yinghuan Shi Peng Gao Wu-Jun Li 74 16 0 30 Jan 2016
Efficient Distributed SGD with Variance Reduction Soham De Tom Goldstein 49 39 0 09 Dec 2015
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 131 1,823 0 01 Jul 2014
A Proximal Stochastic Gradient Method with Progressive Variance Reduction Lin Xiao Tong Zhang ODL 150 738 0 19 Mar 2014
Communication Efficient Distributed Optimization using an Approximate Newton-type Method Ohad Shamir Nathan Srebro Tong Zhang 86 556 0 30 Dec 2013
Minimizing Finite Sums with the Stochastic Average Gradient Mark Schmidt Nicolas Le Roux Francis R. Bach 302 1,246 0 10 Sep 2013
Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization Shai Shalev-Shwartz Tong Zhang 163 1,032 0 10 Sep 2012
HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent Feng Niu Benjamin Recht Christopher Ré Stephen J. Wright 177 2,273 0 28 Jun 2011