Given the availability of abundant data, deep learning models have been advanced and
become ubiquitous in the past decade. In practice, due to many different reasons (eg …

It is known that greedy methods perform well for maximizing\textitmonotone submodular
functions. At the same time, such methods perform poorly in the face of non-monotonicity. In …

In this paper, we introduce the public-private framework of data summarization motivated by
privacy concerns in personalized recommender systems and online social services. Such …

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 …

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

In this paper we analyze an adaptive sampling approach for submodular maximization.
Adaptive sampling is a technique that has recently been shown to achieve a constant factor …

In this paper, we provide the first deterministic algorithm that achieves $1/2$-approximation
for monotone submodular maximization subject to a knapsack constraint, while making a …

In this paper, we introduce a novel technique for constrained submodular maximization,
inspired by barrier functions in continuous optimization. This connection not only improves …

The concept of walkable urban development has gained increased attention due to its public
health, economic, and environmental sustainability benefits. Unfortunately, land zoning and …

Submodular maximization arises in many applications, and has attracted a lot of research
attentions from various areas such as artificial intelligence, finance and operations research …