Semi-Stochastic Gradient Descent Methods

5 December 2013

Papers citing "Semi-Stochastic Gradient Descent Methods"

50 / 50 papers shown

Title
Second-order Information Promotes Mini-Batch Robustness in Variance-Reduced Gradients Sachin Garg A. Berahas Michal Dereziñski 46 1 0 23 Apr 2024
Stochastic Gradient Methods with Preconditioned Updates Abdurakhmon Sadiev Aleksandr Beznosikov Abdulla Jasem Almansoori Dmitry Kamzolov R. Tappenden Martin Takáč ODL 39 9 0 01 Jun 2022
Accelerating Perturbed Stochastic Iterates in Asynchronous Lock-Free Optimization Kaiwen Zhou Anthony Man-Cho So James Cheng 27 1 0 30 Sep 2021
Physics-informed Dyna-Style Model-Based Deep Reinforcement Learning for Dynamic Control Xin-Yang Liu Jian-Xun Wang AI4CE 31 38 0 31 Jul 2021
Stochastic Polyak Stepsize with a Moving Target Robert Mansel Gower Aaron Defazio Michael G. Rabbat 32 17 0 22 Jun 2021
SVRG Meets AdaGrad: Painless Variance Reduction Benjamin Dubois-Taine Sharan Vaswani Reza Babanezhad Mark Schmidt Simon Lacoste-Julien 23 18 0 18 Feb 2021
Variance-Reduced Methods for Machine Learning Robert Mansel Gower Mark Schmidt Francis R. Bach Peter Richtárik 24 112 0 02 Oct 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 42 0 0 26 Aug 2020
Sampling and Update Frequencies in Proximal Variance-Reduced Stochastic Gradient Methods Martin Morin Pontus Giselsson 27 4 0 13 Feb 2020
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization Samuel Horváth Lihua Lei Peter Richtárik Michael I. Jordan 57 30 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 40 17 0 11 Feb 2020
A Unifying Framework for Variance Reduction Algorithms for Finding Zeroes of Monotone Operators Xun Zhang W. Haskell Z. Ye 25 3 0 22 Jun 2019
Why gradient clipping accelerates training: A theoretical justification for adaptivity Jiaming Zhang Tianxing He S. Sra Ali Jadbabaie 30 446 0 28 May 2019
Cocoercivity, Smoothness and Bias in Variance-Reduced Stochastic Gradient Methods Martin Morin Pontus Giselsson 20 2 0 21 Mar 2019
Sparse Regression and Adaptive Feature Generation for the Discovery of Dynamical Systems C. S. Kulkarni 23 10 0 07 Feb 2019
SAGA with Arbitrary Sampling Xun Qian Zheng Qu Peter Richtárik 37 25 0 24 Jan 2019
R-SPIDER: A Fast Riemannian Stochastic Optimization Algorithm with Curvature Independent Rate Jiaming Zhang Hongyi Zhang S. Sra 26 39 0 10 Nov 2018
Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy Majid Jahani Xi He Chenxin Ma Aryan Mokhtari Dheevatsa Mudigere Alejandro Ribeiro Martin Takáč 24 18 0 26 Oct 2018
A fast quasi-Newton-type method for large-scale stochastic optimisation A. Wills Carl Jidling Thomas B. Schon ODL 36 7 0 29 Sep 2018
An Improvement of Data Classification Using Random Multimodel Deep Learning (RMDL) Mojtaba Heidarysafa Kamran Kowsari Donald E. Brown K. Meimandi Laura E. Barnes 27 36 0 23 Aug 2018
Improved asynchronous parallel optimization analysis for stochastic incremental methods Rémi Leblond Fabian Pedregosa Simon Lacoste-Julien 16 70 0 11 Jan 2018
Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods Nicolas Loizou Peter Richtárik 24 200 0 27 Dec 2017
Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method Mark Eisen Aryan Mokhtari Alejandro Ribeiro 35 16 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 42 598 0 01 Mar 2017
Surpassing Gradient Descent Provably: A Cyclic Incremental Method with Linear Convergence Rate Aryan Mokhtari Mert Gurbuzbalaban Alejandro Ribeiro 37 36 0 01 Nov 2016
Federated Optimization: Distributed Machine Learning for On-Device Intelligence Jakub Konecný H. B. McMahan Daniel Ramage Peter Richtárik FedML 71 1,877 0 08 Oct 2016
AIDE: Fast and Communication Efficient Distributed Optimization Sashank J. Reddi Jakub Konecný Peter Richtárik Barnabás Póczós Alex Smola 19 150 0 24 Aug 2016
ASAGA: Asynchronous Parallel SAGA Rémi Leblond Fabian Pedregosa Simon Lacoste-Julien AI4TS 31 101 0 15 Jun 2016
Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy Aryan Mokhtari Alejandro Ribeiro ODL 25 32 0 24 May 2016
Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds Hongyi Zhang Sashank J. Reddi S. Sra 38 240 0 23 May 2016
Barzilai-Borwein Step Size for Stochastic Gradient Descent Conghui Tan Shiqian Ma Yuhong Dai Yuqiu Qian 40 182 0 13 May 2016
Trading-off variance and complexity in stochastic gradient descent Vatsal Shah Megasthenis Asteris Anastasios Kyrillidis Sujay Sanghavi 25 13 0 22 Mar 2016
A Simple Practical Accelerated Method for Finite Sums Aaron Defazio 30 121 0 08 Feb 2016
Importance Sampling for Minibatches Dominik Csiba Peter Richtárik 32 113 0 06 Feb 2016
Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters Zeyuan Allen-Zhu Yang Yuan Karthik Sridharan 20 27 0 05 Feb 2016
Kalman-based Stochastic Gradient Method with Stop Condition and Insensitivity to Conditioning V. Patel 31 35 0 03 Dec 2015
Federated Optimization:Distributed Optimization Beyond the Datacenter Jakub Konecný H. B. McMahan Daniel Ramage FedML 28 728 0 11 Nov 2015
Stop Wasting My Gradients: Practical SVRG Reza Babanezhad Mohamed Osama Ahmed Alim Virani Mark Schmidt Jakub Konecný Scott Sallinen 15 134 0 05 Nov 2015
New Optimisation Methods for Machine Learning Aaron Defazio 46 6 0 09 Oct 2015
Scalable Computation of Regularized Precision Matrices via Stochastic Optimization Yves F. Atchadé Rahul Mazumder Jie-bin Chen 7 8 0 01 Sep 2015
Distributed Stochastic Variance Reduced Gradient Methods and A Lower Bound for Communication Complexity Jason D. Lee Qihang Lin Tengyu Ma Tianbao Yang FedML 29 16 0 27 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 40 194 0 23 Jun 2015
Variance Reduced Stochastic Gradient Descent with Neighbors Thomas Hofmann Aurelien Lucchi Simon Lacoste-Julien Brian McWilliams ODL 33 153 0 11 Jun 2015
Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting Jakub Konecný Jie Liu Peter Richtárik Martin Takáč ODL 36 273 0 16 Apr 2015
A Variance Reduced Stochastic Newton Method Aurelien Lucchi Brian McWilliams Thomas Hofmann ODL 33 50 0 28 Mar 2015
Stochastic Dual Coordinate Ascent with Adaptive Probabilities Dominik Csiba Zheng Qu Peter Richtárik ODL 61 97 0 27 Feb 2015
SDCA without Duality Shai Shalev-Shwartz 27 47 0 22 Feb 2015
Global Convergence of Online Limited Memory BFGS Aryan Mokhtari Alejandro Ribeiro 32 164 0 06 Sep 2014
A Proximal Stochastic Gradient Method with Progressive Variance Reduction Lin Xiao Tong Zhang ODL 93 737 0 19 Mar 2014
Minimizing Finite Sums with the Stochastic Average Gradient Mark Schmidt Nicolas Le Roux Francis R. Bach 114 1,244 0 10 Sep 2013