Dmitry Kovalev

PhD Student

KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY

About

I’m a student at the King Abdullah University of Science and Technology, where I’m currently studying Computer Science and doing research in the Visual Computing Center under the supervision of Peter Richtárik. Prior to this I got my BS degree in Applied Mathematics and Physics at the Moscow Institute of Physics and Technology.

Interests

  • Optimization
  • Machine Learning

Education

  • PhD in Computer Science, in progress
    King Abdullah University of Science and Technology
  • MS in Applied Mathematics and Physics, 2021
    MOSCOW INSTITUTE OF PHYSICS AND TECHNOLOGY
  • MS in Computer Science, 2019
    King Abdullah University of Science and Technology
  • BS in Applied Mathematics and Physics, 2018
    Moscow Institute of Physics and Technology

Recent Papers

Optimal Decentralized Algorithms for Saddle Point Problems over Time-Varying Networks

Aleksandr Beznosikov , Alexander Rogozin, Dmitry Kovalev, Alexander Gasnikov
13 Jul 2021

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

Dmitry Kovalev, Elnur Gasanov, Peter Richtárik, Alexander Gasnikov
08 Jun 2021

An Optimal Algorithm for Strongly Convex Minimization under Affine Constraints

Adil Salim, Laurent Condat, Dmitry Kovalev, Peter Richtárik
22 Feb 2021

ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks

Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Alexander Rogozin, Alexander Gasnikov
18 Feb 2021

IntSGD: Floatless Compression of Stochastic Gradients

Konstantin Mishchenko, Bokun Wang, Dmitry Kovalev, Peter Richtárik
16 Feb 2021

Decentralized Distributed Optimization for Saddle Point Problems

Alexander Rogozin, Alexander Beznosikov, Darina Dvinskikh, Dmitry Kovalev, Pavel Dvurechensky, Alexander Gasnikov
15 Feb 2021

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

Dmitry Kovalev, Anastasia Koloskova, Martin Jaggi, Peter Richtárik, Sebastian U. Stich
03 Nov 2020

Linearly Converging Error Compensated SGD

Eduard Gorbunov, Dmitry Kovalev, Dmitry Makarenko , Peter Richtárik
23 Oct 2020

Towards Accelerated Rates for Distributed Optimization over Time-varying Networks

Alexander Rogozin, Vladislav Lukoshkin , Alexander Gasnikov, Dmitry Kovalev, Egor Shulgin
23 Sep 2020

Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization

Dmitry Kovalev, Adil Salim, Peter Richtárik
21 Jun 2020

From Local SGD to Local Fixed Point Methods for Federated Learning

Grigory Malinovsky, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, Peter Richtárik
03 Apr 2020

Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization

Zhize Li, Dmitry Kovalev, Xun Qian, Peter Richtárik
26 Feb 2020

Fast Linear Convergence of Randomized BFGS

Dmitry Kovalev, Robert M. Gower, Peter Richtárik, Alexander Rogozin
26 Feb 2020

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

Filip Hanzely, Dmitry Kovalev, Peter Richtárik
11 Feb 2020

Distributed Fixed Point Methods with Compressed Iterates

Sélim Chraibi, Ahmed Khaled, Dmitry Kovalev, Peter Richtárik, Adil Salim, Martin Takáč
20 Dec 2019

Accelerated methods for composite non-bilinear saddle point problem

Mohammad Alkousa, Darina Dvinskikh, Fedor Stonyakin, Alexander Gasnikov, Dmitry Kovalev
09 Dec 2019

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

Dmitry Kovalev, Konstantin Mishchenko, Peter Richtárik
03 Dec 2019

Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates

Adil Salim, Dmitry Kovalev, Peter Richtárik
28 May 2019

Revisiting Stochastic Extragradient

Konstantin Mishchenko, Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Yura Malitsky
27 May 2019

RSN: Randomized Subspace Newton

Robert M. Gower, Dmitry Kovalev, Felix Lieder, Peter Richtárik
26 May 2019

Stochastic Distributed Learning with Gradient Quantization and Variance Reduction

Samuel Horváth, Dmitry Kovalev, Konstantin Mishchenko, Peter Richtárik, Sebastian U. Stich
04 Apr 2019

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

Dmitry Kovalev, Samuel Horváth, Peter Richtárik
24 Jan 2019

A hypothesis about the rate of global convergence for optimal methods (Newton's type) in smooth convex optimization

Alexander Gasnikov, Dmitry Kovalev
28 Feb 2018

Stochastic Spectral and Conjugate Descent Methods

Dmitry Kovalev, Eduard Gorbunov, Elnur Gasanov, Peter Richtárik
11 Feb 2018

Recent Posts

ICCOPT 2019

I gave a talk "Revisiting Stochastic Extragradient Method" which was a part of "Variational Inequalities, Minimax Problems and GANs" session organised by Konstantin Mishchenko.

05 Aug 2019

Data Science Summer School 2019

I'm presenting poster at Data Science Summer School at École polytechnique, Paris, France.

26 Jun 2019

June 2019 plans

I'm staying in Moscow until June 16. I will attend "Control, Information, Optimization" summer school in Voronovo, Moscow region from June 17 to June 22. Then I will attend Data Science Summer School in Paris, France from June 24 to June 28.

11 Jun 2019

Talk: Stochastic Spectral and Conjugate Descent Methods

Today I gave a talk at the "Automatic control and Optimization Theory" seminar at the Institute for Control Problems, Moscow. The talk is based on my NeurIPS 2018 paper.

26 Mar 2019

Moscow visit

I’m staying in Moscow from 18 March to 2 April. I'm going to give 2 talks and have a rest from work.

18 Mar 2019

Contact

© 2021 Dmitry Kovalev