Standard federated optimization methods successfully apply to stochastic problems with
single-level structure. However, many contemporary ML problems-including adversarial …

Optimal allocation of shared resources is key to deliver the promise of jointly operating radar
and communications systems. In this paper, unlike prior works which examine synergistic …

We consider distributed stochastic variational inequalities (VIs) on unbounded domains with
the problem data that is heterogeneous (non-IID) and distributed across many devices. We …

This paper focuses on the distributed optimization of stochastic saddle point problems. The
first part of the paper is devoted to lower bounds for the cenralized and decentralized …

We study the problem of finding approximate first-order stationary points in optimization
problems of the form $\min_ {x\in X}\max_ {y\in Y} f (x, y) $, where the sets $ X, Y $ are …

Adaptive methods are a crucial component widely used for training generative adversarial
networks (GANs). While there has been some work to pinpoint the" marginal value of …

Adaptive methods are widely used for training generative adversarial networks (GAN). While
there has been some work to pinpoint the marginal value of adaptive methods in …

Min-max saddle point games have recently been intensely studied, due to their wide range
of applications, including training Generative Adversarial Networks (GANs). However, most …

Lower-bound analyses for nonconvex strongly-concave minimax optimization problems
have shown that stochastic first-order algorithms require at least $\mathcal {O}(\varepsilon …

Compressed Stochastic Gradient Descent (SGD) algorithms have been proposed to address
the communication bottleneck in distributed and decentralized optimization problems such …