Distributed Coordinate Descent Method for Learning with Big Data

8 October 2013

Papers citing "Distributed Coordinate Descent Method for Learning with Big Data"

50 / 104 papers shown

Title
Federated Optimization in Heterogeneous Networks Tian Li Anit Kumar Sahu Manzil Zaheer Maziar Sanjabi Ameet Talwalkar Virginia Smith FedML 36 5,020 0 14 Dec 2018
Distributed Learning with Sparse Communications by Identification Dmitry Grishchenko F. Iutzeler J. Malick Massih-Reza Amini 16 19 0 10 Dec 2018
LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data Li Huang Yifeng Yin Z. Fu Shifa Zhang Hao Deng Dianbo Liu FedML 29 70 0 30 Nov 2018
Asynchronous Stochastic Composition Optimization with Variance Reduction Shuheng Shen Linli Xu Jingchang Liu Junliang Guo Qing Ling 8 2 0 15 Nov 2018
A Distributed Second-Order Algorithm You Can Trust Celestine Mendler-Dünner Aurelien Lucchi Matilde Gargiani An Bian Thomas Hofmann Martin Jaggi 34 32 0 20 Jun 2018
A Distributed Quasi-Newton Algorithm for Empirical Risk Minimization with Nonsmooth Regularization Ching-pei Lee Cong Han Lim Stephen J. Wright 13 29 0 04 Mar 2018
A Randomized Exchange Algorithm for Computing Optimal Approximate Designs of Experiments Radoslav Harman Lenka Filová Peter Richtárik 26 51 0 17 Jan 2018
Avoiding Synchronization in First-Order Methods for Sparse Convex Optimization Aditya Devarakonda K. Fountoulakis J. Demmel Michael W. Mahoney 19 5 0 17 Dec 2017
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization Shusen Wang Farbod Roosta-Khorasani Peng Xu Michael W. Mahoney 33 127 0 11 Sep 2017
Stochastic, Distributed and Federated Optimization for Machine Learning Jakub Konecný FedML 26 38 0 04 Jul 2017
Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory Peter Richtárik Martin Takáč 17 92 0 04 Jun 2017
Projected Semi-Stochastic Gradient Descent Method with Mini-Batch Scheme under Weak Strong Convexity Assumption Jie Liu Martin Takáč ODL 12 4 0 16 Dec 2016
Understanding and Optimizing the Performance of Distributed Machine Learning Applications on Apache Spark Celestine Mendler-Dünner Thomas Parnell Kubilay Atasu Manolis Sifalakis H. Pozidis 19 17 0 05 Dec 2016
Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features Zihao Chen Luo Luo Zhihua Zhang 32 3 0 02 Dec 2016
Randomized Distributed Mean Estimation: Accuracy vs Communication Jakub Konecný Peter Richtárik FedML 27 101 0 22 Nov 2016
CoCoA: A General Framework for Communication-Efficient Distributed Optimization Virginia Smith Simone Forte Chenxin Ma Martin Takáč Michael I. Jordan Martin Jaggi 16 272 0 07 Nov 2016
Optimization for Large-Scale Machine Learning with Distributed Features and Observations A. Nathan Diego Klabjan 30 13 0 31 Oct 2016
Hybrid-DCA: A Double Asynchronous Approach for Stochastic Dual Coordinate Ascent Soumitra Pal Tingyang Xu Tianbao Yang Sanguthevar Rajasekaran J. Bi 10 2 0 23 Oct 2016
Federated Optimization: Distributed Machine Learning for On-Device Intelligence Jakub Konecný H. B. McMahan Daniel Ramage Peter Richtárik FedML 60 1,878 0 08 Oct 2016
On Randomized Distributed Coordinate Descent with Quantized Updates M. Gamal Lifeng Lai 13 5 0 18 Sep 2016
A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning Aryan Mokhtari Alec Koppel Alejandro Ribeiro 21 14 0 15 Jun 2016
Learning Natural Language Inference using Bidirectional LSTM model and Inner-Attention Yang Liu Chengjie Sun Mehdi Alizadeh Xiaolong Wang 27 273 0 30 May 2016
Distributed Inexact Damped Newton Method: Data Partitioning and Load-Balancing Chenxin Ma Martin Takáč 28 10 0 16 Mar 2016
Large Scale Kernel Learning using Block Coordinate Descent Stephen Tu Rebecca Roelofs Shivaram Venkataraman Benjamin Recht 19 41 0 17 Feb 2016
Importance Sampling for Minibatches Dominik Csiba Peter Richtárik 26 113 0 06 Feb 2016
Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling Zeyuan Allen-Zhu Zheng Qu Peter Richtárik Yang Yuan 44 172 0 30 Dec 2015
Distributed Optimization with Arbitrary Local Solvers Chenxin Ma Jakub Konecný Martin Jaggi Virginia Smith Michael I. Jordan Peter Richtárik Martin Takáč 27 197 0 13 Dec 2015
Federated Optimization:Distributed Optimization Beyond the Datacenter Jakub Konecný H. B. McMahan Daniel Ramage FedML 17 725 0 11 Nov 2015
Partitioning Data on Features or Samples in Communication-Efficient Distributed Optimization? Chenxin Ma Martin Takáč 15 11 0 22 Oct 2015
Distributed Mini-Batch SDCA Martin Takáč Peter Richtárik Nathan Srebro 27 50 0 29 Jul 2015
Primal Method for ERM with Flexible Mini-batching Schemes and Non-convex Losses Dominik Csiba Peter Richtárik 33 23 0 07 Jun 2015
Communication Complexity of Distributed Convex Learning and Optimization Yossi Arjevani Ohad Shamir 34 207 0 05 Jun 2015
Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting Jakub Konecný Jie Liu Peter Richtárik Martin Takáč ODL 28 273 0 16 Apr 2015
Stochastic Dual Coordinate Ascent with Adaptive Probabilities Dominik Csiba Zheng Qu Peter Richtárik ODL 61 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 29 176 0 12 Feb 2015
SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization Zheng Qu Peter Richtárik Martin Takáč Olivier Fercoq ODL 40 97 0 08 Feb 2015
Coordinate Descent with Arbitrary Sampling II: Expected Separable Overapproximation Zheng Qu Peter Richtárik 41 83 0 27 Dec 2014
Coordinate Descent with Arbitrary Sampling I: Algorithms and Complexity Zheng Qu Peter Richtárik 24 131 0 27 Dec 2014
Randomized Dual Coordinate Ascent with Arbitrary Sampling Zheng Qu Peter Richtárik Tong Zhang 38 58 0 21 Nov 2014
Learning Theory for Distribution Regression Z. Szabó Bharath K. Sriperumbudur Barnabás Póczós Arthur Gretton OOD 18 135 0 08 Nov 2014
mS2GD: Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting Jakub Konecný Jie Liu Peter Richtárik Martin Takáč ODL 37 22 0 17 Oct 2014
Large-scale randomized-coordinate descent methods with non-separable linear constraints Sashank J. Reddi Ahmed S. Hefny Carlton Downey Kumar Avinava Dubey S. Sra 21 19 0 09 Sep 2014
Communication-Efficient Distributed Dual Coordinate Ascent Martin Jaggi Virginia Smith Martin Takáč Jonathan Terhorst S. Krishnan Thomas Hofmann Michael I. Jordan 35 353 0 04 Sep 2014
Matrix Completion under Interval Uncertainty Jakub Mareˇcek Peter Richtárik Martin Takáč 45 19 0 11 Aug 2014
Hybrid Random/Deterministic Parallel Algorithms for Nonconvex Big Data Optimization Amir Daneshmand F. Facchinei Vyacheslav Kungurtsev G. Scutari 32 60 0 16 Jul 2014
LOCO: Distributing Ridge Regression with Random Projections C. Heinze Brian McWilliams N. Meinshausen Gabriel Krummenacher 52 34 0 13 Jun 2014
Fast Distributed Coordinate Descent for Non-Strongly Convex Losses Olivier Fercoq Zheng Qu Peter Richtárik Martin Takáč 32 59 0 21 May 2014
A distributed block coordinate descent method for training $l_1$ regularized linear classifiers D. Mahajan S. Keerthi S. Sundararajan 32 36 0 18 May 2014
Communication Efficient Distributed Optimization using an Approximate Newton-type Method Ohad Shamir Nathan Srebro Tong Zhang 35 551 0 30 Dec 2013
Accelerated, Parallel and Proximal Coordinate Descent Olivier Fercoq Peter Richtárik 36 373 0 20 Dec 2013