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With ever-increasing dataset sizes, subset selection techniques are becomingincreasingly important for a plethora of tasks. It is often necessary to guidethe subset selection to achieve certain desiderata, which includes focusing ortargeting certain data points, while avoiding others. Examples of such problemsinclude: i)targeted learning, where the goal is to find subsets with rareclasses or rare attributes on which the model is underperforming, and ii)guidedsummarization, where data (e.g., image collection, text, document or video) issummarized for quicker human consumption with specific additional user intent.Motivated by such applications, we present PRISM, a rich class of PaRameterIzedSubmodular information Measures. Through novel functions and theirparameterizations, PRISM offers a variety of modeling capabilities that enablea trade-off between desired qualities of a subset like diversity orrepresentation and similarity/dissimilarity with a set of data points. Wedemonstrate how PRISM can be applied to the two real-world problems mentionedabove, which require guided subset selection. In doing so, we show that PRISMinterestingly generalizes some past work, therein reinforcing its broadutility. Through extensive experiments on diverse datasets, we demonstrate thesuperiority of PRISM over the state-of-the-art in targeted learning and inguided image-collection summarization

PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Subset Selection