It is of great importance to design streaming algorithms for submodular maximization, as
many applications (eg, crowdsourcing) have large volume of data satisfying the well …

A lot of applications in web economics need to maximize the revenue under a budget for
payments and also guarantee the truthfulness of users, so Budget-Feasible Mechanism …

Submodular function maximization is a fundamental combinatorial optimization problem with
plenty of applications--including data summarization, influence maximization, and …

Although the knapsack-constrained and k-system-constrained non-monotone adaptive
submodular maximization have been well studied in the literature, it has only been settled …

Submodular optimization has been identified as a powerful tool for many data mining
applications, where a representative subset of moderate size needs to be extracted from a …

Submodular maximization algorithms have found wide applications in various fields such as
data summarization, recommendation systems, and active learning. In recent years, deletion …

The problem of constrained subset selection from a large data stream for profit maximization
has many applications in web data mining and machine learning, such as social advertising …

Recent progress in maximizing submodular functions with a cardinality constraint through
centralized and streaming modes has demonstrated a wide range of applications and also …

In this work, we study a k-Cardinality Constrained Regularized Submodular Maximization (k-
CCRSM) problem, in which the objective utility is expressed as the difference between a non …

Submodular maximization has found extensive applications in various domains within the
field of artificial intelligence, including but not limited to machine learning, computer vision …