28 citations · 184 across the 34 of their papers we have counts for
5 papers · 1 filter
Randomized gradient-free methods in convex optimization
Alexander Gasnikov, Darina Dvinskikh, Pavel Dvurechensky +3
This review presents modern gradient-free methods to solve convex optimization problems. By gradient-free methods, we mean those that use only (noisy) realizations of the objective…
Gradient-Free Federated Learning Methods with and -Randomization for Non-Smooth Convex Stochastic Optimization Problems
Aleksandr Lobanov, Belal Alashqar, Darina Dvinskikh +1
This paper studies non-smooth problems of convex stochastic optimization. Using the smoothing technique based on the replacement of the function value at the considered point by th…
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…
Gradient-Free Optimization for Non-Smooth Saddle Point Problems under Adversarial Noise
Darina Dvinskikh, Vladislav Tominin, Yaroslav Tominin +1
We consider non-smooth saddle point optimization problems. To solve these problems, we propose a zeroth-order method under bounded or Lipschitz continuous noise, possible adversari…