B. Alashqar

activity

20222026

most citedGradient-Free Federated Learning Methods with

l_{1}

and

l_{2}

-Randomization for Non-Smooth Convex Stochastic Optimization Problems

3 citations · 3 across the 3 of their papers we have counts for

collaborators

3 papers

cs.LG2026

Mirror Descent-Type Algorithms for the Variational Inequality Problem with Functional Constraints

Mohammad S. Alkousa, Fedor S. Stonyakin, Belal A. Alashqar +1

Variational inequalities play a key role in machine learning research, such as generative adversarial networks, reinforcement learning, adversarial training, and generative models.…

math.OC2025

Mirror Descent Methods with Weighting Scheme for Outputs for Constrained Variational Inequality Problems

Mohammad S. Alkousa, Belal A. Alashqar, Fedor S. Stonyakin +2

This paper is devoted to the variational inequality problems. We consider two classes of problems, the first is classical constrained variational inequality and the second is the s…

math.OC2022★ 3 cited

Gradient-Free Federated Learning Methods with $l_{1}$ and $l_{2}$ -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…

Mirror Descent-Type Algorithms for the Variational Inequality Problem with Functional Constraints

Mirror Descent Methods with Weighting Scheme for Outputs for Constrained Variational Inequality Problems

Gradient-Free Federated Learning Methods with l1​ and l2​-Randomization for Non-Smooth Convex Stochastic Optimization Problems

Gradient-Free Federated Learning Methods with $l_{1}$ and $l_{2}$ -Randomization for Non-Smooth Convex Stochastic Optimization Problems