Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop

24 January 2019

Papers citing "Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop"

32 / 32 papers shown

Title
Variance Reduction Methods Do Not Need to Compute Full Gradients: Improved Efficiency through Shuffling Daniil Medyakov Gleb Molodtsov S. Chezhegov Alexey Rebrikov Aleksandr Beznosikov 103 0 0 21 Feb 2025
SOREL: A Stochastic Algorithm for Spectral Risks Minimization Yuze Ge Rujun Jiang 38 0 0 19 Jul 2024
Decentralized Sum-of-Nonconvex Optimization Zhuanghua Liu K. H. Low 21 0 0 04 Feb 2024
Correlated Quantization for Faster Nonconvex Distributed Optimization Andrei Panferov Yury Demidovich Ahmad Rammal Peter Richtárik MQ 47 4 0 10 Jan 2024
Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features Aleksandr Beznosikov David Dobre Gauthier Gidel 25 5 0 23 Apr 2023
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis Dachao Lin Yuze Han Haishan Ye Zhihua Zhang 25 11 0 15 Apr 2023
Gradient Descent-Type Methods: Background and Simple Unified Convergence Analysis Quoc Tran-Dinh Marten van Dijk 34 0 0 19 Dec 2022
BALPA: A Balanced Primal-Dual Algorithm for Nonsmooth Optimization with Application to Distributed Optimization Luyao Guo Jinde Cao Xinli Shi Shaofu Yang 12 0 0 06 Dec 2022
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms Vincent Plassier Alain Durmus Eric Moulines FedML 21 6 0 31 Oct 2022
Smooth Monotone Stochastic Variational Inequalities and Saddle Point Problems: A Survey Aleksandr Beznosikov Boris Polyak Eduard A. Gorbunov D. Kovalev Alexander Gasnikov 42 31 0 29 Aug 2022
Federated Random Reshuffling with Compression and Variance Reduction Grigory Malinovsky Peter Richtárik FedML 27 10 0 08 May 2022
An Adaptive Incremental Gradient Method With Support for Non-Euclidean Norms Binghui Xie Chen Jin Kaiwen Zhou James Cheng Wei Meng 40 1 0 28 Apr 2022
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods Aleksandr Beznosikov Eduard A. Gorbunov Hugo Berard Nicolas Loizou 19 49 0 15 Feb 2022
Decentralized Stochastic Variance Reduced Extragradient Method Luo Luo Haishan Ye 27 7 0 01 Feb 2022
PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation Matilde Gargiani Andrea Zanelli Andrea Martinelli Tyler H. Summers John Lygeros 33 14 0 01 Feb 2022
L-SVRG and L-Katyusha with Adaptive Sampling Boxin Zhao Boxiang Lyu Mladen Kolar 21 3 0 31 Jan 2022
Decentralized Composite Optimization with Compression Yao Li Xiaorui Liu Jiliang Tang Ming Yan Kun Yuan 27 9 0 10 Aug 2021
ANITA: An Optimal Loopless Accelerated Variance-Reduced Gradient Method Zhize Li 43 14 0 21 Mar 2021
SVRG Meets AdaGrad: Painless Variance Reduction Benjamin Dubois-Taine Sharan Vaswani Reza Babanezhad Mark W. Schmidt Simon Lacoste-Julien 18 18 0 18 Feb 2021
IntSGD: Adaptive Floatless Compression of Stochastic Gradients Konstantin Mishchenko Bokun Wang D. Kovalev Peter Richtárik 75 14 0 16 Feb 2021
PMGT-VR: A decentralized proximal-gradient algorithmic framework with variance reduction Haishan Ye Wei Xiong Tong Zhang 16 16 0 30 Dec 2020
Local SGD: Unified Theory and New Efficient Methods Eduard A. Gorbunov Filip Hanzely Peter Richtárik FedML 35 109 0 03 Nov 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 34 0 0 26 Aug 2020
PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization Zhize Li Hongyan Bao Xiangliang Zhang Peter Richtárik ODL 31 126 0 25 Aug 2020
Stochastic Hamiltonian Gradient Methods for Smooth Games Nicolas Loizou Hugo Berard Alexia Jolicoeur-Martineau Pascal Vincent Simon Lacoste-Julien Ioannis Mitliagkas 39 50 0 08 Jul 2020
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization Zhize Li D. Kovalev Xun Qian Peter Richtárik FedML AI4CE 29 135 0 26 Feb 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 35 17 0 11 Feb 2020
Cocoercivity, Smoothness and Bias in Variance-Reduced Stochastic Gradient Methods Martin Morin Pontus Giselsson 20 2 0 21 Mar 2019
Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods Nicolas Loizou Peter Richtárik 19 200 0 27 Dec 2017
Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning Julien Mairal 79 317 0 18 Feb 2014