Dmitry Kovalev | DeepAI

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Peter Richtarik
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Martin Takáč
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Sebastian U. Stich
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Alexey Zaytsev
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Zhize Li
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Konstantin Mishchenko
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Alexander Gasnikov
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Robert M. Gower
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Samuel Horvath
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∙ 07/08/2022

Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with Inexact Prox

Inspired by a recent breakthrough of Mishchenko et al (2022), who for th...

8 Abdurakhmon Sadiev, et al. ∙

∙ 02/11/2022

Similarity learning for wells based on logging data

One of the first steps during the investigation of geological objects is...

12 Evgenia Romanenkova, et al. ∙

∙ 06/08/2021

Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks

We consider the task of minimizing the sum of smooth and strongly convex...

0 Dmitry Kovalev, et al. ∙

∙ 02/18/2021

ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks

We propose ADOM - an accelerated method for smooth and strongly convex d...

10 Dmitry Kovalev, et al. ∙

∙ 02/16/2021

IntSGD: Floatless Compression of Stochastic Gradients

We propose a family of lossy integer compressions for Stochastic Gradien...

17 Konstantin Mishchenko, et al. ∙

∙ 02/15/2021

Decentralized Distributed Optimization for Saddle Point Problems

We consider distributed convex-concave saddle point problems over arbitr...

0 Alexander Rogozin, et al. ∙

∙ 11/03/2020

A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free!

Decentralized optimization methods enable on-device training of machine ...

22 Dmitry Kovalev, et al. ∙

∙ 10/23/2020

Linearly Converging Error Compensated SGD

In this paper, we propose a unified analysis of variants of distributed ...

1 Eduard Gorbunov, et al. ∙

∙ 06/21/2020

Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization

We consider the task of decentralized minimization of the sum of smooth ...

19 Dmitry Kovalev, et al. ∙

∙ 04/03/2020

From Local SGD to Local Fixed Point Methods for Federated Learning

Most algorithms for solving optimization problems or finding saddle poin...

0 Grigory Malinovsky, et al. ∙

∙ 02/26/2020

Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization

Due to the high communication cost in distributed and federated learning...

0 Zhize Li, et al. ∙

∙ 02/26/2020

Fast Linear Convergence of Randomized BFGS

Since the late 1950's when quasi-Newton methods first appeared, they hav...

0 Dmitry Kovalev, et al. ∙

∙ 02/11/2020

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

We propose an accelerated version of stochastic variance reduced coordin...

0 Filip Hanzely, et al. ∙

∙ 12/20/2019

Distributed Fixed Point Methods with Compressed Iterates

We propose basic and natural assumptions under which iterative optimizat...

0 Sélim Chraibi, et al. ∙

∙ 12/03/2019

Stochastic Newton and Cubic Newton Methods with Simple Local Linear-Quadratic Rates

We present two new remarkably simple stochastic second-order methods for...

13 Dmitry Kovalev, et al. ∙

∙ 05/28/2019

Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates

We propose a new algorithm---Stochastic Proximal Langevin Algorithm (SPL...

0 Adil Salim, et al. ∙

∙ 05/27/2019

Revisiting Stochastic Extragradient

We consider a new extension of the extragradient method that is motivate...

0 Konstantin Mishchenko, et al. ∙

∙ 01/24/2019

Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop

The stochastic variance-reduced gradient method (SVRG) and its accelerat...

0 Dmitry Kovalev, et al. ∙

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