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2002.07378
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Distributed Adaptive Newton Methods with Global Superlinear Convergence

v1v2v3 (latest)

Distributed Adaptive Newton Methods with Global Superlinear Convergence

18 February 2020

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Papers citing "Distributed Adaptive Newton Methods with Global Superlinear Convergence"

6 / 6 papers shown

Distributed Adaptive Greedy Quasi-Newton Methods with Explicit
Non-asymptotic Convergence Bounds

Distributed Adaptive Greedy Quasi-Newton Methods with Explicit Non-asymptotic Convergence Bounds

276

7

0

30 Nov 2023

Decentralized Riemannian natural gradient methods with Kronecker-product
approximations

Decentralized Riemannian natural gradient methods with Kronecker-product approximations

Na Li

204

10

0

16 Mar 2023

SHED: A Newton-type algorithm for federated learning based on
incremental Hessian eigenvector sharing

SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing

Nicolò Dal Fabbro

455

19

0

11 Feb 2022

Variance-Reduced Stochastic Quasi-Newton Methods for Decentralized
Learning: Part I

Variance-Reduced Stochastic Quasi-Newton Methods for Decentralized Learning: Part IIEEE Transactions on Signal Processing (IEEE Trans. Signal Process.), 2022

Anthony Man-Cho So

282

20

0

19 Jan 2022

On Second-order Optimization Methods for Federated Learning

On Second-order Optimization Methods for Federated Learning

Sebastian Bischoff

Stephan Günnemann

Sebastian U. Stich

209

14

0

06 Sep 2021

Newton Method over Networks is Fast up to the Statistical Precision

Newton Method over Networks is Fast up to the Statistical PrecisionInternational Conference on Machine Learning (ICML), 2021

Amir Daneshmand

Pavel Dvurechensky

Alexander Gasnikov

204

22

0

12 Feb 2021

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