Can we summarize multi-category data based on user preferences in a scalable manner?
Many utility functions used for data summarization satisfy submodularity, a natural …

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 …

Many tasks in machine learning and data mining, such as data diversification, non-
parametric learning, kernel machines, clustering etc., require extracting a small but …

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

In many machine learning applications, it is important to explain the predictions of a black-
box classifier. For example, why does a deep neural network assign an image to a particular …

Submodular maximization has become established as the method of choice for the task of
selecting representative and diverse summaries of data. However, if datapoints have …

Randomization is a fundamental tool used in many theoretical and practical areas of
computer science. We study here the role of randomization in the area of submodular …

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

We introduce a novel framework of Prophet Inequalities for combinatorial valuation
functions. For a (n on-monotone) submodular objective function over an arbitrary matroid …

Maximizing a monotone k-submodular function subject to cardinality constraints is a general
model for several applications ranging from influence maximization with multiple products to …