1 citations · 1 across the 3 of their papers we have counts for
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Numerical Methods for Large-Scale Optimal Transport
Nazarii Tupitsa, Pavel Dvurechensky, Darina Dvinskikh +1
The optimal transport (OT) problem is a classical optimization problem having the form of linear programming. Machine learning applications put forward new computational challenges…
On the relations of stochastic convex optimization problems with empirical risk minimization problems on -norm balls
Darina Dvinskikh, Vitali Pirau, Alexander Gasnikov
In this paper, we consider convex stochastic optimization problems arising in machine learning applications (e.g., risk minimization) and mathematical statistics (e.g., maximum lik…
Decentralized Algorithms for Wasserstein Barycenters
Darina Dvinskikh
In this thesis, we consider the Wasserstein barycenter problem of discrete probability measures from computational and statistical sides. The statistical focus is estimating the sa…
Parallel and Distributed algorithms for ML problems
Darina Dvinskikh, Alexander Gasnikov, Alexander Rogozin +1
In this paper we make a survey of modern parallel and distributed approaches to solve sum-type convex minimization problems come from ML applications.
Improved Complexity Bounds in Wasserstein Barycenter Problem
Darina Dvinskikh, Daniil Tiapkin
In this paper, we focus on computational aspects of the Wasserstein barycenter problem. We propose two algorithms to compute Wasserstein barycenters of discrete measures of siz…
Accelerated and nonaccelerated stochastic gradient descent with inexact model
Darina Dvinskikh, Alexander Tyurin, Alexander Gasnikov +1
In this paper, we propose a new way to obtain optimal convergence rates for smooth stochastic (strong) convex optimization tasks. Our approach is based on results for optimization…