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 …

The problem of maximizing nonnegative monotone submodular functions under a certain
constraint has been intensively studied in the last decade, and a wide range of efficient …

Correlation clustering is a widely used technique in unsupervised machine learning.
Motivated by applications where individual privacy is a concern, we initiate the study of …

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 …

We study the problem of differentially private constrained maximization of decomposable
submodular functions. A submodular function is decomposable if it takes the form of a sum of …

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

In this work, we study the problem of privately maximizing a submodular function in the
streaming setting. Extensive work has been done on privately maximizing submodular …

Submodular functions, as well as the sub-class of decomposable submodular functions, and
their optimization appear in a wide range of applications in machine learning …

Many sequential decision making tasks can be viewed as combinatorial optimiza-tion
problems over a large number of actions. When the cost of evaluating an ac-tion is high …

A soft-max function has two main efficiency measures:(1) approximation-which corresponds
to how well it approximates the maximum function,(2) smoothness-which shows how …