Streaming algorithms are generally judged by the quality of their solution, memory footprint,
and computational complexity. In this paper, we study the problem of maximizing a …

Maximizing monotone submodular functions under a matroid constraint is a classic
algorithmic problem with multiple applications in data mining and machine learning. We …

Submodular maximization has become established as the method of choice for the task of
selecting representative and diverse summaries of data. However, if datapoints have …

We consider the classical problem of maximizing a monotone submodular function subject
to a cardinality constraint, which, due to its numerous applications, has recently been …

Maximizing submodular functions has been increasingly used in many applications of
machine learning, such as data summarization, recommendation systems, and feature …

In this paper, we propose scalable methods for maximizing a regularized submodular
function $ f\triangleq g-\ell $ expressed as the difference between a monotone submodular …

Streaming submodular maximization is a natural model for the task of selecting a
representative subset from a large-scale dataset. If datapoints have sensitive attributes such …

In this paper, we propose a novel framework that converts streaming algorithms for
monotone submodular maximization into streaming algorithms for non-monotone …

We consider parallel, or low adaptivity, algorithms for submodular function maximization.
This line of work was recently initiated by Balkanski and Singer and has already led to …

Submodular maximization is a general optimization problem with a wide range of
applications in machine learning (eg, active learning, clustering, and feature selection). In …