Owing to their connection with generative adversarial networks (GANs), saddle-point
problems have recently attracted considerable interest in machine learning and beyond. By …

We introduce a general framework for competitive gradient-based learning that
encompasses a wide breadth of multiagent learning algorithms, and analyze the limiting …

Convergent sequences of real numbers play a fundamental role in many different problems
in system theory, eg, in Lyapunov stability analysis, as well as in optimization theory and …

Compared to minimization, the min-max optimization in machine learning applications is
considerably more convoluted because of the existence of cycles and similar phenomena …

We address scaling up equilibrium computation in Mean Field Games (MFGs) using Online
Mirror Descent (OMD). We show that continuous-time OMD provably converges to a Nash …

Learning problems commonly exhibit an interesting feedback mechanism wherein the
population data reacts to competing decision makers' actions. This paper formulates a new …

As predictive models are deployed into the real world, they must increasingly contend with
strategic behavior. A growing body of work on strategic classification treats this problem as a …

Multiagent decision making over networks has recently attracted an exponentially growing
number of researchers from the systems and control community. The area has gained …

The goal of this article is to promote the use of fixed point strategies in data science by
showing that they provide a simplifying and unifying framework to model, analyze, and solve …

We study the multi-agent game within the innovative framework of decision-dependent
games, which establishes a feedback mechanism that population data reacts to agents' …