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 …

Submodular maximization under various constraints is a fundamental problem studied
continuously, in both computer science and operations research, since the late 1970's. A …

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 …

Maximizing a monotone submodular function under cardinality constraint $ k $ is a core
problem in machine learning and database with many basic applications, including video …

We initiate a systematic study on {\em dynamic influence maximization}(DIM). In the DIM
problem, one maintains a seed set $ S $ of at most $ k $ nodes in a dynamically involving …

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

Submodular maximization has wide applications in machine learning and data mining,
where massive datasets have brought the great need for designing efficient and …

In this paper we describe a new parallel algorithm called Fast Adaptive Sequencing
Technique (FAST) for maximizing a monotone submodular function under a cardinality …

We study streaming submodular maximization subject to matching/b-matching constraints
(MSM/MSbM), and present improved upper and lower bounds for these problems. On the …

We study the problem of maximizing a nonmonotone submodular function subject to a
cardinality constraint in the streaming model. Our main contribution is a single-pass (semi) …