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

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

11 February 2020
Filip Hanzely
D. Kovalev
Peter Richtárik
ArXivPDFHTML

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

4 / 4 papers shown
Title
An Enhanced Zeroth-Order Stochastic Frank-Wolfe Framework for Constrained Finite-Sum Optimization
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
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
24
9
0
28 Dec 2021
Personalized Federated Learning: A Unified Framework and Universal
  Optimization Techniques
Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques
Filip Hanzely
Boxin Zhao
Mladen Kolar
FedML
16
52
0
19 Feb 2021
Incremental Majorization-Minimization Optimization with Application to
  Large-Scale Machine Learning
Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning
Julien Mairal
60
317
0
18 Feb 2014
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