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 …

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 …

In this paper, we study the tradeoff between the approximation guarantee and adaptivity for
the problem of maximizing a monotone submodular function subject to a cardinality …

Balkanski and Singer [4] recently initiated the study of adaptivity (or parallelism) for
constrained submodular function maximization, and studied the setting of a cardinality …

Constrained submodular maximization problems encompass a wide variety of applications,
including personalized recommendation, team formation, and revenue maximization via …

In this paper we consider parallelization for applications whose objective can be expressed
as maximizing a non-monotone submodular function under a cardinality constraint. Our …

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 …

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

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