Variational inequalities have recently attracted considerable interest in machine learning as
a flexible paradigm for models that go beyond ordinary loss function minimization (such as …

This paper considers an n-player stochastic Nash equilibrium problem (NEP) in which the i
th player minimizes a composite objective f_i(∙,x_-i)+r_i(∙), where f_i is an expectation-valued …

Motivated by the training of generative adversarial networks (GANs), we study methods for
solving minimax problems with additional nonsmooth regularizers. We do so by employing …

In this paper, we study the minimax optimization problem in the smooth and strongly convex-
strongly concave setting when we have access to noisy estimates of gradients. In particular …

We propose a variance reduced algorithm for solving monotone variational inequalities.
Without assuming strong monotonicity, cocoercivity, or boundedness of the domain, we …

We consider the stochastic generalized Nash equilibrium problem (SGNEP) with expected-
value cost functions. Inspired by Yi and Pavel (2019), we propose a distributed generalized …

We study the stochastic bilinear minimax optimization problem, presenting an analysis of the
same-sample Stochastic ExtraGradient (SEG) method with constant step size, and …

We solve the stochastic generalized Nash equilibrium (SGNE) problem in merely monotone
games with expected value cost functions. Specifically, we present the first distributed SGNE …

We develop two novel stochastic variance-reduction methods to approximate a solution of
root-finding problems applicable to both equations and inclusions. Our algorithms leverage …

Generative adversarial networks (GANs) are a class of generative models with two
antagonistic neural networks: a generator and a discriminator. These two neural networks …