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 the adversarially robust streaming model, a stream of elements is presented to an
algorithm and is allowed to depend on the output of the algorithm at earlier times during the …

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 consider the classical problem of maximizing a monotone submodular function subject
to a cardinality constraint, which, due to its numerous applications, has recently been …

Robustness against adversarial attacks has recently been at the forefront of algorithmic
design for machine learning tasks. In the adversarial streaming model, an adversary gives …

Submodular optimization generalizes many classic problems in combinatorial optimization
and has recently found a wide range of applications in machine learning (eg, feature …

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 …

Streaming submodular maximization is a natural model for the task of selecting a
representative subset from a large-scale dataset. If datapoints have sensitive attributes such …