Influence maximization is a widely used model for information dissemination in social
networks. Recent work has employed such interventions across a wide range of social …

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

Real-world applications often combine learning and optimization problems on graphs. For
instance, our objective may be to cluster the graph in order to detect meaningful …

One of the beauties of the projected gradient descent method lies in its rather simple
mechanism and yet stable behavior with inexact, stochastic gradients, which has led to its …

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 …

In reinforcement learning (RL), rewards of states are typically considered additive, and
following the Markov assumption, they are $\textit {independent} $ of states visited …

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 …

Antenna selection is capable of reducing the hardware complexity of massive multiple-input
multiple-output (MIMO) networks at the cost of certain performance degradation …

In bipartite matching problems, vertices on one side of a bipartite graph are paired with
those on the other. In its online variant, one side of the graph is available offline, while the …

Submodular functions have applications throughout machine learning, but in many settings,
we do not have direct access to the underlying function f. We focus on stochastic functions …