In this manuscript, we offer a gentle review of submodularity and supermodularity and their
properties. We offer a plethora of submodular definitions; a full description of a number of …

Most sequencing data analyses start by aligning sequencing reads to a linear reference
genome, but failure to account for genetic variation leads to reference bias and confounding …

In this paper, we develop the first one-pass streaming algorithm for submodular
maximization that does not evaluate the entire stream even once. By carefully subsampling …

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

Submodular maximization under matroid and cardinality constraints are classical problems
with a wide range of applications in machine learning, auction theory, and combinatorial …

The ecological role of microorganisms is of utmost importance due to their multiple
interactions with the environment. However, assessing the contribution of individual …

Weak submodularity is a natural relaxation of the diminishing return property, which is
equivalent to submodularity. Weak submodularity has been used to show that many …

We study combinatorial, parallelizable algorithms for maximization of a submodular function,
not necessarily monotone, with respect to a cardinality constraint k. We improve the best …

We consider the problem of maximizing a non-negative monotone submodular function
subject to a knapsack constraint, which is also known as the Budgeted Submodular …

We propose subsampling as a unified algorithmic technique for submodular maximization in
centralized and online settings. The idea is simple: independently sample elements from the …