In this paper, we study the problem of learning in quantum games-and other classes of
semidefinite games-with scalar, payoff-based feedback. For concreteness, we focus on the …

Universal methods achieve optimal convergence rate guarantees in convex optimization
without any prior knowledge of the problem's regularity parameters or the attributes of the …

The high degree of variability present in current and emerging mobile wireless networks
calls for mathematical tools and techniques that transcend classical (convex) optimization …

Zeroth-order optimization (ZO) typically relies on two-point feedback to estimate the
unknown gradient of the objective function. Nevertheless, two-point feedback can not be …

Recent developments in domains such as non-local games, quantum interactive proofs, and
quantum generative adversarial networks have renewed interest in quantum game theory …

In this paper, we deal with a network of agents that want to cooperatively minimize the sum
of local cost functions depending on a common decision variable. We consider the …

A major challenge of applying zeroth-order (ZO) methods is the high query complexity,
especially when queries are costly. We propose a novel gradient estimation technique for …

Universal methods for optimization are designed to achieve theoretically optimal
convergence rates without any prior knowledge of the problem's regularity parameters or the …

Zeroth-order optimization (ZO) typically relies on two-point feedback to estimate the gradient
of the objective function. Nevertheless, two-point feedback cannot be used for online …

Several decision and control tasks involve networks of cyber-physical systems that need to
be coordinated and controlled according to a fully-distributed paradigm involving only local …