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 consider fast algorithms for monotone submodular maximization with a general matroid
constraint. We present a randomized $(1-1/e-\epsilon) $-approximation algorithm that …

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

In dynamic submodular maximization, the goal is to maintain a high-value solution over a
sequence of element insertions and deletions with a fast update time. Motivated by large …

Maximizing monotone submodular functions under cardinality constraints is a classic
optimization task with several applications in data mining and machine learning. In this …

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

We initiate the study of the submodular cover problem in dynamic setting where the
elements of the ground set are inserted and deleted. In the classical submodular cover …

In this work, we study online submodular maximization, and how the requirement of
maintaining a stable solution impacts the approximation. In particular, we seek bounds on …

Submodular maximization is to maximize a submodular function under specific constraints,
which plays a crucial role in both theoretical combinatorial optimization and practical …