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 BSc degree in Applied Mathematics and Physics at the Moscow Institute of Physics and Technology.

Interests

  • Optimization
  • Machine Learning

Education

  • PhD in Computer Science
    King Abdullah University of Science 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

From Local SGD to Local Fixed Point Methods for Federated Learning

Grigory Malinovsky, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, Peter Richtárik

Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization

Zhize Li, Dmitry Kovalev, Xun Qian, Peter Richtárik

Fast Linear Convergence of Randomized BFGS

Dmitry Kovalev, Robert M. Gower, Peter Richtárik, Alexander Rogozin

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

Filip Hanzely, Dmitry Kovalev, Peter Richtárik

Distributed Fixed Point Methods with Compressed Iterates

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

Accelerated methods for composite non-bilinear saddle point problem

Mohammad Alkousa, Darina Dvinskikh, Fedor Stonyakin, Alexander Gasnikov, Dmitry Kovalev

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

Dmitry Kovalev, Konstantin Mishchenko, Peter Richtárik

Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates

Adil Salim, Dmitry Kovalev, Peter Richtárik

Revisiting Stochastic Extragradient

Konstantin Mishchenko, Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Yura Malitsky

RSN: Randomized Subspace Newton

Robert M. Gower, Dmitry Kovalev, Felix Lieder, Peter Richtárik

Stochastic Distributed Learning with Gradient Quantization and Variance Reduction

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

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

Dmitry Kovalev, Samuel Horváth, Peter Richtárik

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

Alexander Gasnikov, Dmitry Kovalev

Stochastic Spectral and Conjugate Descent Methods

Dmitry Kovalev, Eduard Gorbunov, Elnur Gasanov, Peter Richtárik

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, 12:49

Data Science Summer School 2019

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

26 Jun. 2019, 13:55

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, 17:56

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, 17:00

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, 20:00

Contact

© 2020 Dmitry Kovalev