Variance Reduced Stochastic Gradient Descent with Neighbors

11 June 2015

Papers citing "Variance Reduced Stochastic Gradient Descent with Neighbors"

35 / 35 papers shown

Title
SOREL: A Stochastic Algorithm for Spectral Risks Minimization Yuze Ge Rujun Jiang 47 0 0 19 Jul 2024
AdaSelection: Accelerating Deep Learning Training through Data Subsampling Minghe Zhang Chaosheng Dong Jinmiao Fu Tianchen Zhou Jia Liang ... Bo Liu Michinari Momma Bryan Wang Yan Gao Yi Sun 40 3 0 19 Jun 2023
Stochastic Optimization for Spectral Risk Measures Ronak R. Mehta Vincent Roulet Krishna Pillutla Lang Liu Zaïd Harchaoui 42 6 0 10 Dec 2022
Statistical and Computational Guarantees for Influence Diagnostics Jillian R. Fisher Lang Liu Krishna Pillutla Y. Choi Zaïd Harchaoui TDI 29 0 0 08 Dec 2022
BALPA: A Balanced Primal-Dual Algorithm for Nonsmooth Optimization with Application to Distributed Optimization Luyao Guo Jinde Cao Xinli Shi Shaofu Yang 20 0 0 06 Dec 2022
Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Design Minsu Kim Walid Saad Mohammad Mozaffari Merouane Debbah FedML MQ 36 28 0 19 Jul 2022
Tackling Data Heterogeneity: A New Unified Framework for Decentralized SGD with Sample-induced Topology Yan Huang Ying Sun Zehan Zhu Changzhi Yan Jinming Xu FedML 38 15 0 08 Jul 2022
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
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods Aleksandr Beznosikov Eduard A. Gorbunov Hugo Berard Nicolas Loizou 24 49 0 15 Feb 2022
MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks R. Waleffe J. Mohoney Theodoros Rekatsinas Shivaram Venkataraman GNN 39 25 0 04 Feb 2022
Decentralized Stochastic Variance Reduced Extragradient Method Luo Luo Haishan Ye 27 7 0 01 Feb 2022
Accelerating Perturbed Stochastic Iterates in Asynchronous Lock-Free Optimization Kaiwen Zhou Anthony Man-Cho So James Cheng 29 1 0 30 Sep 2021
Stochastic Polyak Stepsize with a Moving Target Robert Mansel Gower Aaron Defazio Michael G. Rabbat 32 17 0 22 Jun 2021
SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning Jianhong Wang Yuan Zhang Yunjie Gu Tae-Kyun Kim OffRL FAtt 22 19 0 31 May 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
Local SGD: Unified Theory and New Efficient Methods Eduard A. Gorbunov Filip Hanzely Peter Richtárik FedML 37 109 0 03 Nov 2020
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
Stochastic Hamiltonian Gradient Methods for Smooth Games Nicolas Loizou Hugo Berard Alexia Jolicoeur-Martineau Pascal Vincent Simon Lacoste-Julien Ioannis Mitliagkas 41 50 0 08 Jul 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
Reducing the variance in online optimization by transporting past gradients Sébastien M. R. Arnold Pierre-Antoine Manzagol Reza Babanezhad Ioannis Mitliagkas Nicolas Le Roux 26 28 0 08 Jun 2019
Reducing Noise in GAN Training with Variance Reduced Extragradient Tatjana Chavdarova Gauthier Gidel François Fleuret Simon Lacoste-Julien 25 135 0 18 Apr 2019
Cocoercivity, Smoothness and Bias in Variance-Reduced Stochastic Gradient Methods Martin Morin Pontus Giselsson 20 2 0 21 Mar 2019
Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise A. Kulunchakov Julien Mairal 34 44 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 36 155 0 24 Jan 2019
Improved asynchronous parallel optimization analysis for stochastic incremental methods Rémi Leblond Fabian Pedregosa Simon Lacoste-Julien 18 70 0 11 Jan 2018
Sub-sampled Cubic Regularization for Non-convex Optimization Jonas Köhler Aurelien Lucchi 19 165 0 16 May 2017
Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite-Sum Structure A. Bietti Julien Mairal 47 36 0 04 Oct 2016
Less than a Single Pass: Stochastically Controlled Stochastic Gradient Method Lihua Lei Michael I. Jordan 29 96 0 12 Sep 2016
ASAGA: Asynchronous Parallel SAGA Rémi Leblond Fabian Pedregosa Simon Lacoste-Julien AI4TS 31 101 0 15 Jun 2016
Variance-Reduced Proximal Stochastic Gradient Descent for Non-convex Composite optimization Xiyu Yu Dacheng Tao 32 5 0 02 Jun 2016
A Simple Practical Accelerated Method for Finite Sums Aaron Defazio 30 121 0 08 Feb 2016
Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters Zeyuan Allen-Zhu Yang Yuan Karthik Sridharan 20 27 0 05 Feb 2016