Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting

16 April 2015

Papers citing "Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting"

43 / 93 papers shown

Title
Accelerated Variance Reduced Stochastic ADMM Yuanyuan Liu Fanhua Shang James Cheng 35 40 0 11 Jul 2017
Stochastic, Distributed and Federated Optimization for Machine Learning Jakub Konecný FedML 26 38 0 04 Jul 2017
Improved Optimization of Finite Sums with Minibatch Stochastic Variance Reduced Proximal Iterations Jialei Wang Tong Zhang 19 12 0 21 Jun 2017
Gradient Diversity: a Key Ingredient for Scalable Distributed Learning Dong Yin A. Pananjady Max Lam Dimitris Papailiopoulos Kannan Ramchandran Peter L. Bartlett 14 11 0 18 Jun 2017
Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications A. Chambolle Matthias Joachim Ehrhardt Peter Richtárik Carola-Bibiane Schönlieb 38 184 0 15 Jun 2017
Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory Peter Richtárik Martin Takáč 22 92 0 04 Jun 2017
Stochastic Recursive Gradient Algorithm for Nonconvex Optimization Lam M. Nguyen Jie Liu K. Scheinberg Martin Takáč 11 94 0 20 May 2017
Larger is Better: The Effect of Learning Rates Enjoyed by Stochastic Optimization with Progressive Variance Reduction Fanhua Shang 17 1 0 17 Apr 2017
Fast Stochastic Variance Reduced Gradient Method with Momentum Acceleration for Machine Learning Fanhua Shang Yuanyuan Liu James Cheng Jiacheng Zhuo ODL 24 23 0 23 Mar 2017
Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization Tomoya Murata Taiji Suzuki OffRL 33 28 0 01 Mar 2017
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient Lam M. Nguyen Jie Liu K. Scheinberg Martin Takáč ODL 39 598 0 01 Mar 2017
A Universal Variance Reduction-Based Catalyst for Nonconvex Low-Rank Matrix Recovery Lingxiao Wang Xiao Zhang Quanquan Gu 35 11 0 09 Jan 2017
Stochastic Variance-reduced Gradient Descent for Low-rank Matrix Recovery from Linear Measurements Xiao Zhang Lingxiao Wang Quanquan Gu 28 6 0 02 Jan 2017
Projected Semi-Stochastic Gradient Descent Method with Mini-Batch Scheme under Weak Strong Convexity Assumption Jie Liu Martin Takáč ODL 20 4 0 16 Dec 2016
Federated Optimization: Distributed Machine Learning for On-Device Intelligence Jakub Konecný H. B. McMahan Daniel Ramage Peter Richtárik FedML 69 1,878 0 08 Oct 2016
Decoupled Asynchronous Proximal Stochastic Gradient Descent with Variance Reduction Zhouyuan Huo Bin Gu Heng-Chiao Huang 13 4 0 22 Sep 2016
Trading-off variance and complexity in stochastic gradient descent Vatsal Shah Megasthenis Asteris Anastasios Kyrillidis Sujay Sanghavi 25 13 0 22 Mar 2016
Stochastic Variance Reduction for Nonconvex Optimization Sashank J. Reddi Ahmed S. Hefny S. Sra Barnabás Póczós Alex Smola 50 597 0 19 Mar 2016
Katyusha: The First Direct Acceleration of Stochastic Gradient Methods Zeyuan Allen-Zhu ODL 17 577 0 18 Mar 2016
Distributed Inexact Damped Newton Method: Data Partitioning and Load-Balancing Chenxin Ma Martin Takáč 28 10 0 16 Mar 2016
Importance Sampling for Minibatches Dominik Csiba Peter Richtárik 32 113 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 27 197 0 13 Dec 2015
Efficient Distributed SGD with Variance Reduction Soham De Tom Goldstein 9 39 0 09 Dec 2015
Variance Reduction for Distributed Stochastic Gradient Descent Soham De Gavin Taylor Tom Goldstein 12 8 0 05 Dec 2015
Kalman-based Stochastic Gradient Method with Stop Condition and Insensitivity to Conditioning V. Patel 23 35 0 03 Dec 2015
Stop Wasting My Gradients: Practical SVRG Reza Babanezhad Mohamed Osama Ahmed Alim Virani Mark W. Schmidt Jakub Konecný Scott Sallinen 12 134 0 05 Nov 2015
Dual Free Adaptive Mini-batch SDCA for Empirical Risk Minimization Xi He Martin Takávc 29 1 0 22 Oct 2015
SGD with Variance Reduction beyond Empirical Risk Minimization M. Achab Agathe Guilloux Stéphane Gaïffas Emmanuel Bacry 27 5 0 16 Oct 2015
Doubly Stochastic Primal-Dual Coordinate Method for Bilinear Saddle-Point Problem Adams Wei Yu Qihang Lin Tianbao Yang 30 7 0 14 Aug 2015
Distributed Mini-Batch SDCA Martin Takáč Peter Richtárik Nathan Srebro 27 50 0 29 Jul 2015
On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants Sashank J. Reddi Ahmed S. Hefny S. Sra Barnabás Póczós Alex Smola 38 194 0 23 Jun 2015
Accelerated Stochastic Gradient Descent for Minimizing Finite Sums Atsushi Nitanda ODL 35 24 0 09 Jun 2015
Primal Method for ERM with Flexible Mini-batching Schemes and Non-convex Losses Dominik Csiba Peter Richtárik 33 23 0 07 Jun 2015
Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives Zeyuan Allen-Zhu Yang Yuan 29 195 0 05 Jun 2015
Stochastic Dual Coordinate Ascent with Adaptive Probabilities Dominik Csiba Zheng Qu Peter Richtárik ODL 61 97 0 27 Feb 2015
SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization Zheng Qu Peter Richtárik Martin Takáč Olivier Fercoq ODL 40 98 0 08 Feb 2015
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
Randomized Block Coordinate Descent for Online and Stochastic Optimization Huahua Wang A. Banerjee ODL 52 36 0 01 Jul 2014
A Proximal Stochastic Gradient Method with Progressive Variance Reduction Lin Xiao Tong Zhang ODL 93 737 0 19 Mar 2014
Semi-Stochastic Gradient Descent Methods Jakub Konecný Peter Richtárik ODL 62 237 0 05 Dec 2013
Non-Asymptotic Convergence Analysis of Inexact Gradient Methods for Machine Learning Without Strong Convexity Anthony Man-Cho So 55 40 0 31 Aug 2013
Optimal Distributed Online Prediction using Mini-Batches O. Dekel Ran Gilad-Bachrach Ohad Shamir Lin Xiao 182 683 0 07 Dec 2010