  With ever-increasing dataset sizes, subset selection techniques are becoming
increasingly important for a plethora of tasks. It is often necessary to guide
the subset selection to achieve certain desiderata, which includes focusing or
targeting certain data points, while avoiding others. Examples of such problems
include: i)targeted learning, where the goal is to find subsets with rare
classes or rare attributes on which the model is underperforming, and ii)guided
summarization, where data (e.g., image collection, text, document or video) is
summarized for quicker human consumption with specific additional user intent.
Motivated by such applications, we present PRISM, a rich class of PaRameterIzed
Submodular information Measures. Through novel functions and their
parameterizations, PRISM offers a variety of modeling capabilities that enable
a trade-off between desired qualities of a subset like diversity or
representation and similarity/dissimilarity with a set of data points. We
demonstrate how PRISM can be applied to the two real-world problems mentioned
above, which require guided subset selection. In doing so, we show that PRISM
interestingly generalizes some past work, therein reinforcing its broad
utility. Through extensive experiments on diverse datasets, we demonstrate the
superiority of PRISM over the state-of-the-art in targeted learning and in
guided image-collection summarization
