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04/23/2023
Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features
The Frank-Wolfe (FW) method is a popular approach for solving optimizati...
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06/01/2022
Stochastic Gradient Methods with Preconditioned Updates
This work considers non-convex finite sum minimization. There are a numb...
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02/15/2022
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods
Stochastic Gradient Descent-Ascent (SGDA) is one of the most prominent a...
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11/26/2021
Random-reshuffled SARAH does not need a full gradient computations
The StochAstic Recursive grAdient algoritHm (SARAH) algorithm is a varia...
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06/15/2021
Decentralized Local Stochastic Extra-Gradient for Variational Inequalities
We consider decentralized stochastic variational inequalities where the ...
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06/14/2021
Decentralized Personalized Federated Min-Max Problems
Personalized Federated Learning has recently seen tremendous progress, a...
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10/25/2020
Local SGD for Saddle-Point Problems
GAN is one of the most popular and commonly used neural network models. ...
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09/21/2020
Zeroth-Order Algorithms for Smooth Saddle-Point Problems
In recent years, the importance of saddle-point problems in machine lear...
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05/12/2020
Gradient-Free Methods for Saddle-Point Problem
In the paper, we generalize the approach Gasnikov et. al, 2017, which al...
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02/27/2020