A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates

28 June 2018

Papers citing "A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates"

22 / 22 papers shown

Title
OptEx: Expediting First-Order Optimization with Approximately Parallelized Iterations Yao Shu Jiongfeng Fang Y. He Fei Richard Yu 72 0 0 18 Feb 2024
Composite federated learning with heterogeneous data Jiaojiao Zhang Jiang Hu Mikael Johansson FedML 78 4 0 04 Sep 2023
Accelerating Perturbed Stochastic Iterates in Asynchronous Lock-Free Optimization Kaiwen Zhou Anthony Man-Cho So James Cheng 76 1 0 30 Sep 2021
Asynchronous Stochastic Optimization Robust to Arbitrary Delays Alon Cohen Amit Daniely Yoel Drori Tomer Koren Mariano Schain 102 33 0 22 Jun 2021
Practical Schemes for Finding Near-Stationary Points of Convex Finite-Sums Kaiwen Zhou Lai Tian Anthony Man-Cho So James Cheng 70 10 0 25 May 2021
Distributed Learning Systems with First-order Methods Ji Liu Ce Zhang 36 44 0 12 Apr 2021
Variance Reduction via Primal-Dual Accelerated Dual Averaging for Nonsmooth Convex Finite-Sums Chaobing Song Stephen J. Wright Jelena Diakonikolas 142 17 0 26 Feb 2021
Lower Bounds and Optimal Algorithms for Personalized Federated Learning Filip Hanzely Slavomír Hanzely Samuel Horváth Peter Richtárik FedML 137 190 0 05 Oct 2020
Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization Chaobing Song Yong Jiang Yi-An Ma 171 23 0 18 Jun 2020
Stochastic batch size for adaptive regularization in deep network optimization Kensuke Nakamura Stefano Soatto Byung-Woo Hong ODL 54 6 0 14 Apr 2020
Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems Filip Hanzely D. Kovalev Peter Richtárik 84 17 0 11 Feb 2020
The Practicality of Stochastic Optimization in Imaging Inverse Problems Junqi Tang K. Egiazarian Mohammad Golbabaee Mike Davies 74 32 0 22 Oct 2019
Adaptive Weight Decay for Deep Neural Networks Kensuke Nakamura Byung-Woo Hong 63 43 0 21 Jul 2019
A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization Quoc Tran-Dinh Nhan H. Pham T. Dzung Lam M. Nguyen 80 51 0 08 Jul 2019
A Generic Acceleration Framework for Stochastic Composite Optimization A. Kulunchakov Julien Mairal 105 44 0 03 Jun 2019
Convergence of Distributed Stochastic Variance Reduced Methods without Sampling Extra Data Shicong Cen Huishuai Zhang Yuejie Chi Wei-neng Chen Tie-Yan Liu FedML 111 27 0 29 May 2019
One Method to Rule Them All: Variance Reduction for Data, Parameters and Many New Methods Filip Hanzely Peter Richtárik 101 27 0 27 May 2019
Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise A. Kulunchakov Julien Mairal 111 45 0 25 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 129 156 0 24 Jan 2019
Direct Acceleration of SAGA using Sampled Negative Momentum Kaiwen Zhou 106 45 0 28 Jun 2018
VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning Fanhua Shang Kaiwen Zhou Hongying Liu James Cheng Ivor W. Tsang Lijun Zhang Dacheng Tao L. Jiao 98 67 0 26 Feb 2018
Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods Nicolas Loizou Peter Richtárik 82 204 0 27 Dec 2017