Distributed online optimization and online games have been increasingly researched in the
last decade, mostly motivated by their wide applications in sensor networks, robotics (eg …

The increasingly tight coupling between humans and system operations in domains ranging
from intelligent infrastructure to e-commerce has led to a challenging new class of problems …

Submodular continuous functions are a category of (generally) non-convex/non-concave
functions with a wide spectrum of applications. We characterize these functions and …

This paper considers stochastic optimization problems for a large class of objective
functions, including convex and continuous submodular. Stochastic proximal gradient …

In this paper, we study the problem of maximizing continuous submodular functions that
naturally arise in many learning applications such as those involving utility functions in …

We connect high-dimensional subset selection and submodular maximization. Our results
extend the work of Das and Kempe [In ICML (2011) 1057–1064] from the setting of linear …

DR-submodular continuous functions are important objectives with wide real-world
applications spanning MAP inference in determinantal point processes (DPPs), and mean …

In this paper, we consider an online optimization process, where the objective functions are
not convex (nor concave) but instead belong to a broad class of continuous submodular …

Online allocation problems with resource constraints have a rich history in computer science
and operations research. In this paper, we introduce the regularized online allocation …

In this paper, we study the problem of constrained and stochastic continuous submodular
maximization. Even though the objective function is not concave (nor convex) and is defined …