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EF-BV: A Unified Theory of Error Feedback and Variance Reduction
  Mechanisms for Biased and Unbiased Compression in Distributed Optimization

EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization

9 May 2022
Laurent Condat
Kai Yi
Peter Richtárik
ArXivPDFHTML

Papers citing "EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization"

5 / 5 papers shown
Title
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression
Laurent Condat
A. Maranjyan
Peter Richtárik
39
3
0
07 Mar 2024
Permutation Compressors for Provably Faster Distributed Nonconvex
  Optimization
Permutation Compressors for Provably Faster Distributed Nonconvex Optimization
Rafal Szlendak
A. Tyurin
Peter Richtárik
122
35
0
07 Oct 2021
EF21 with Bells & Whistles: Practical Algorithmic Extensions of Modern
  Error Feedback
EF21 with Bells & Whistles: Practical Algorithmic Extensions of Modern Error Feedback
Ilyas Fatkhullin
Igor Sokolov
Eduard A. Gorbunov
Zhize Li
Peter Richtárik
44
44
0
07 Oct 2021
Linearly Converging Error Compensated SGD
Linearly Converging Error Compensated SGD
Eduard A. Gorbunov
D. Kovalev
Dmitry Makarenko
Peter Richtárik
163
77
0
23 Oct 2020
Linear Convergence of Gradient and Proximal-Gradient Methods Under the
  Polyak-Łojasiewicz Condition
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition
Hamed Karimi
J. Nutini
Mark W. Schmidt
133
1,198
0
16 Aug 2016
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