Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems

11 February 2020

Papers citing "Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems"

7 / 7 papers shown

Title
An Enhanced Zeroth-Order Stochastic Frank-Wolfe Framework for Constrained Finite-Sum Optimization Haishan Ye Yinghui Huang Hao Di Xiangyu Chang 38 0 0 13 Jan 2025
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback Boxin Zhao Lingxiao Wang Mladen Kolar Ziqi Liu Zhiqiang Zhang Jun Zhou Chaochao Chen FedML 26 10 0 28 Dec 2021
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization Paul Mangold A. Bellet Joseph Salmon Marc Tommasi 25 14 0 22 Oct 2021
Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques Filip Hanzely Boxin Zhao Mladen Kolar FedML 21 52 0 19 Feb 2021
Lower Bounds and Optimal Algorithms for Personalized Federated Learning Filip Hanzely Slavomír Hanzely Samuel Horváth Peter Richtárik FedML 36 186 0 05 Oct 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 19 0 0 26 Aug 2020
Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning Julien Mairal 76 317 0 18 Feb 2014