Modern minimax problems, such as generative adversarial network and adversarial training,
are often under a nonconvex-nonconcave setting, and develo** an efficient method for …

Gradient descent ascent (GDA), the simplest single-loop algorithm for nonconvex minimax
optimization, is widely used in practical applications such as generative adversarial …

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

The recent framework of performative prediction [Perdomo et al. 2020] is aimed at capturing
settings where predictions influence the outcome they want to predict. In this paper, we …

We study gradient descent-ascent learning dynamics with timescale separation ($\tau $-
GDA) in unconstrained continuous action zero-sum games where the minimizing player …

In this paper we study two-player bilinear zero-sum games with constrained strategy spaces.
An instance of natural occurrences of such constraints is when mixed strategies are used …

Many multi-agent systems with strategic interactions have their desired functionality
encoded as the Nash equilibrium of a game, eg machine learning architectures such as …

Understanding and analyzing markets is crucial, yet analytical equilibrium solutions remain
largely infeasible. Recent breakthroughs in equilibrium computation rely on zeroth-order …

Minimax optimization has become a central tool in machine learning with applications in
robust optimization, reinforcement learning, GANs, etc. These applications are often …

We propose a zero-order optimization method for sequential min-max problems based on
two populations of interacting particles. The systems are coupled so that one population …