Random Reshuffling with Variance Reduction: New Analysis and Better Rates

19 April 2021

Papers citing "Random Reshuffling with Variance Reduction: New Analysis and Better Rates"

20 / 20 papers shown

Title
Improved Analysis and Rates for Variance Reduction under Without-replacement Sampling Orders Xinmeng Huang Kun Yuan Xianghui Mao W. Yin 53 13 0 25 Apr 2021
Variance-Reduced Methods for Machine Learning Robert Mansel Gower Mark Schmidt Francis R. Bach Peter Richtárik 68 117 0 02 Oct 2020
Random Reshuffling: Simple Analysis with Vast Improvements Konstantin Mishchenko Ahmed Khaled Peter Richtárik 89 134 0 10 Jun 2020
Closing the convergence gap of SGD without replacement Shashank Rajput Anant Gupta Dimitris Papailiopoulos 53 62 0 24 Feb 2020
A Unified Convergence Analysis for Shuffling-Type Gradient Methods Lam M. Nguyen Quoc Tran-Dinh Dzung Phan Phuong Ha Nguyen Marten van Dijk 90 79 0 19 Feb 2020
The Complexity of Finding Stationary Points with Stochastic Gradient Descent Yoel Drori Shigehito Shimizu 56 64 0 04 Oct 2019
How Good is SGD with Random Shuffling? Itay Safran Ohad Shamir 61 82 0 31 Jul 2019
A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent Eduard A. Gorbunov Filip Hanzely Peter Richtárik 103 146 0 27 May 2019
SGD without Replacement: Sharper Rates for General Smooth Convex Functions Prateek Jain Dheeraj M. Nagaraj Praneeth Netrapalli 58 87 0 04 Mar 2019
SGD: General Analysis and Improved Rates Robert Mansel Gower Nicolas Loizou Xun Qian Alibek Sailanbayev Egor Shulgin Peter Richtárik 82 379 0 27 Jan 2019
Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop D. Kovalev Samuel Horváth Peter Richtárik 98 156 0 24 Jan 2019
Random Shuffling Beats SGD after Finite Epochs Jeff Z. HaoChen S. Sra 54 99 0 26 Jun 2018
Stochastic Learning under Random Reshuffling with Constant Step-sizes Bicheng Ying Kun Yuan Stefan Vlaski Ali H. Sayed 57 36 0 21 Mar 2018
Surpassing Gradient Descent Provably: A Cyclic Incremental Method with Linear Convergence Rate Aryan Mokhtari Mert Gurbuzbalaban Alejandro Ribeiro 112 37 0 01 Nov 2016
Optimization Methods for Large-Scale Machine Learning Léon Bottou Frank E. Curtis J. Nocedal 246 3,216 0 15 Jun 2016
Train faster, generalize better: Stability of stochastic gradient descent Moritz Hardt Benjamin Recht Y. Singer 116 1,241 0 03 Sep 2015
Finito: A Faster, Permutable Incremental Gradient Method for Big Data Problems Aaron Defazio T. Caetano Justin Domke 105 169 0 10 Jul 2014
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives Aaron Defazio Francis R. Bach Simon Lacoste-Julien ODL 133 1,826 0 01 Jul 2014
Practical recommendations for gradient-based training of deep architectures Yoshua Bengio 3DH ODL 193 2,200 0 24 Jun 2012
Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization Alexander Rakhlin Ohad Shamir Karthik Sridharan 169 768 0 26 Sep 2011