This study aims to optimize the performance of the Convolutional Neural Network (CNN) in
the image classification task by applying data augmentation and fine-tuning techniques to a …

The computational study of equilibria involving constraints on players' strategies has been
largely neglected. However, in real-world applications, players are usually subject to …

In many applications, learning systems are required to process continuous non-stationary
data streams. We study this problem in an online learning framework and propose an …

Policy gradient methods enjoy strong practical performance in numerous tasks in
reinforcement learning. Their theoretical understanding in multiagent settings, however …

Constrained variational inequality problems are recognized for their broad applications
across various fields including machine learning and operations research. First-order …

This paper is concerned with the distributed generalized Nash equilibrium (GNE) tracking
problem of noncooperative games in dynamic environments, where the cost function and/or …

We consider the problem of solving stochastic monotone variational inequalities with a
separable structure using a stochastic first-order oracle. Building on standard extragradient …

A Fisher market is an economic model of buyer and seller interactions in which each buyer's
utility depends only on the bundle of goods she obtains. Many people's interests, however …

We consider the problem of computing stationary points in min-max optimization, with a
particular focus on the special case of computing Nash equilibria in (two-) team zero-sum …

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 same …