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 …

Greedy algorithms are practitioners' best friends—they are intuitive, are simple to implement,
and often lead to very good solutions. However, implementing greedy algorithms in a …

In this paper we consider efficient construction of" composable core-sets" for basic diversity
and coverage maximization problems. A core-set for a point-set in a metric space is a subset …

An effective technique for solving optimization problems over massive data sets is to
partition the data into smaller pieces, solve the problem on each piece and compute a …

Many large-scale machine learning problems–clustering, non-parametric learning, kernel
machines, etc.–require selecting a small yet representative subset from a large dataset …

Most of the existing reachability indices perform well on small-to medium-size graphs, but
reach a scalability bottleneck around one million vertices/edges. As graphs become …

In this paper, we address the problem of efficiently managing the relative power demands of
a high-performance GPU and its memory subsystem. We develop a management approach …

We study the classic NP-Hard problem of finding the maximum k-set coverage in the data
stream model: given a set system of m sets that are subsets of a universe 1,…, n {1,...,n\}, find …

The minimum weight dominating set (MWDS) problem is an important generalization of the
minimum dominating set problem with various applications. In this work, we develop an …

Maximizing submodular functions under cardinality constraints lies at the core of numerous
data mining and machine learning applications, including data diversification, data …