Many large-scale machine learning problems (such as clustering, non-parametric learning,
kernel machines, etc.) require selecting, out of a massive data set, a manageable …

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 …

There is a rapidly growing need for scalable algorithms that solve classical graph problems,
such as maximum matching and minimum vertex cover, on massive graphs. For massive …

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

In this paper we study the adaptive complexity of submodular optimization. Informally, the
adaptive complexity of a problem is the minimal number of sequential rounds required to …

We connect high-dimensional subset selection and submodular maximization. Our results
extend the work of Das and Kempe [In ICML (2011) 1057–1064] from the setting of linear …

In this paper we study the adaptivity of submodular maximization. Adaptivity quantifies the
number of sequential rounds that an algorithm makes when function evaluations can be …

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 …

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 …

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