81 citations · 159 across the 22 of their papers we have counts for
8 papers · 1 filter
Distributed Fixed Point Methods with Compressed Iterates
Sélim Chraibi, Ahmed Khaled, Dmitry Kovalev +3
We propose basic and natural assumptions under which iterative optimization methods with compressed iterates can be analyzed. This problem is motivated by the practice of federated…
Stochastic Newton and Cubic Newton Methods with Simple Local Linear-Quadratic Rates
Dmitry Kovalev, Konstantin Mishchenko, Peter Richtárik
We present two new remarkably simple stochastic second-order methods for minimizing the average of a very large number of sufficiently smooth and strongly convex functions. The fir…
Accelerated methods for composite non-bilinear saddle point problem
Mohammad Alkousa, Darina Dvinskikh, Fedor Stonyakin +2
Based on G. Lan's accelerated gradient sliding and general relation between the smoothness and strong convexity parameters of function under Legendre transformation we show that un…
Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates
Adil Salim, Dmitry Kovalev, Peter Richtárik
We propose a new algorithm---Stochastic Proximal Langevin Algorithm (SPLA)---for sampling from a log concave distribution. Our method is a generalization of the Langevin algorithm…
Revisiting Stochastic Extragradient
Konstantin Mishchenko, Dmitry Kovalev, Egor Shulgin +2
We fix a fundamental issue in the stochastic extragradient method by providing a new sampling strategy that is motivated by approximating implicit updates. Since the existing stoch…
RSN: Randomized Subspace Newton
Robert M. Gower, Dmitry Kovalev, Felix Lieder +1
We develop a randomized Newton method capable of solving learning problems with huge dimensional feature spaces, which is a common setting in applications such as medical imaging,…