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

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 …

We study the classical problem of maximizing a monotone submodular function subject to a
cardinality constraint k, with two additional twists:(i) elements arrive in a streaming fashion …

Submodular maximization is a fundamental problem in many Internet of Things applications,
such as sensor placement, resource allocation, and mobile crowdsourcing. Despite being …

Submodular functions are at the core of many machine learning and data mining tasks. The
underlying submodular functions for many of these tasks are decomposable, ie, they are …

In this article, we investigate problem reduction techniques using stochastic sampling and
machine learning to tackle large-scale optimization problems. These techniques …

The problem of selecting a small-size representative summary of a large dataset is a
cornerstone of machine learning, optimization and data science. Motivated by applications …

We study the problem of extracting a small subset of representative items from a large data
stream. In many data mining and machine learning applications such as social network …

The predominant measure for the performance of an algorithm is its worst-case
approximation guarantee. While worst-case approximations give desirable robustness …

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