Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling

30 December 2015

Papers citing "Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling"

28 / 28 papers shown

Title
Randomized Pairwise Learning with Adaptive Sampling: A PAC-Bayes Analysis Sijia Zhou Yunwen Lei Ata Kabán 34 0 0 03 Apr 2025
A stochastic gradient descent algorithm with random search directions Eméric Gbaguidi ODL 80 0 0 25 Mar 2025
An Optimal Algorithm for Strongly Convex Min-min Optimization Alexander Gasnikov D. Kovalev Grigory Malinovsky 24 1 0 29 Dec 2022
Information FOMO: The unhealthy fear of missing out on information. A method for removing misleading data for healthier models Ethan Pickering T. Sapsis 24 6 0 27 Aug 2022
L-SVRG and L-Katyusha with Adaptive Sampling Boxin Zhao Boxiang Lyu Mladen Kolar 21 3 0 31 Jan 2022
A dual approach for federated learning Zhenan Fan Huang Fang M. Friedlander FedML 16 3 0 26 Jan 2022
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback Boxin Zhao Lingxiao Wang Mladen Kolar Ziqi Liu Qing Cui Jun Zhou Chaochao Chen FedML 34 10 0 28 Dec 2021
Cyclic Coordinate Dual Averaging with Extrapolation Chaobing Song Jelena Diakonikolas 27 6 0 26 Feb 2021
Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques Filip Hanzely Boxin Zhao Mladen Kolar FedML 27 52 0 19 Feb 2021
First-Order Methods for Convex Optimization Pavel Dvurechensky Mathias Staudigl Shimrit Shtern ODL 26 25 0 04 Jan 2021
Optimal Client Sampling for Federated Learning Wenlin Chen Samuel Horváth Peter Richtárik FedML 28 190 0 26 Oct 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 32 0 0 26 Aug 2020
Adaptive Task Sampling for Meta-Learning Chenghao Liu Zhihao Wang Doyen Sahoo Yuan Fang Anton van den Hengel S. Hoi 20 54 0 17 Jul 2020
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization Zhize Li D. Kovalev Xun Qian Peter Richtárik FedML AI4CE 21 133 0 26 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
Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling Mojmír Mutný Michal Derezinski Andreas Krause 30 20 0 25 Oct 2019
The Practicality of Stochastic Optimization in Imaging Inverse Problems Junqi Tang K. Egiazarian Mohammad Golbabaee Mike Davies 25 30 0 22 Oct 2019
Accelerated Decentralized Optimization with Local Updates for Smooth and Strongly Convex Objectives Hadrien Hendrikx Francis R. Bach Laurent Massoulié 13 42 0 05 Oct 2018
SEGA: Variance Reduction via Gradient Sketching Filip Hanzely Konstantin Mishchenko Peter Richtárik 25 71 0 09 Sep 2018
Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods Nicolas Loizou Peter Richtárik 19 199 0 27 Dec 2017
Leverage Score Sampling for Faster Accelerated Regression and ERM Naman Agarwal Sham Kakade Rahul Kidambi Y. Lee Praneeth Netrapalli Aaron Sidford 24 21 0 22 Nov 2017
Safe Adaptive Importance Sampling Sebastian U. Stich Anant Raj Martin Jaggi 27 53 0 07 Nov 2017
Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications A. Chambolle Matthias Joachim Ehrhardt Peter Richtárik Carola-Bibiane Schönlieb 29 184 0 15 Jun 2017
Faster Coordinate Descent via Adaptive Importance Sampling Dmytro Perekrestenko V. Cevher Martin Jaggi 19 42 0 07 Mar 2017
Faster Principal Component Regression and Stable Matrix Chebyshev Approximation Zeyuan Allen-Zhu Yuanzhi Li 19 20 0 16 Aug 2016
Katyusha: The First Direct Acceleration of Stochastic Gradient Methods Zeyuan Allen-Zhu ODL 15 575 0 18 Mar 2016
Variance Reduction for Faster Non-Convex Optimization Zeyuan Allen-Zhu Elad Hazan ODL 16 390 0 17 Mar 2016
A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights Weijie Su Stephen P. Boyd Emmanuel J. Candes 108 1,154 0 04 Mar 2015