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

In many practical applications, such as poker, chess, drug interdiction, cybersecurity, and
national defense, players often have adversarial stances, ie, the selfish actions of each …

The hierarchical interaction between the actor and critic in actor-critic based reinforcement
learning algorithms naturally lends itself to a game-theoretic interpretation. We adopt this …

Multi-agent interactions are increasingly important in the context of reinforcement learning,
and the theoretical foundations of policy gradient methods have attracted surging research …

From optimal transport to robust dimensionality reduction, many machine learning
applicationscan be cast into the min-max optimization problems over Riemannian manifolds …

Numerous applications in machine learning and data analytics can be formulated as
equilibrium computation over Riemannian manifolds. Despite the extensive investigation of …

In this work, we consider constrained stochastic optimization problems under hidden
convexity, ie, those that admit a convex reformulation via non-linear (but invertible) map $ c …

A wide array of modern machine learning applications–from adversarial models to multi-
agent reinforcement learning–can be formulated as non-cooperative games whose Nash …

Game-theoretic formulations in machine learning have recently risen in prominence,
whereby entire modeling paradigms are best captured as zero-sum games. Despite their …

In this study, we consider a continuous min–max optimization problem min x∈ 𝕏 max y∈ 𝕐 f
(x, y) whose objective function is a black-box. We propose a novel approach to minimize the …