A Tractable Probabilistic Model for Subset Selection

Abstract

Subset selection tasks, such as top-k ranking, induce datasets where examples have cardinalities that are known a priori. In this paper, we propose a tractable probabilistic model for subset selection and show how it can be learned from data. Our proposed model is interpretable and subsumes a previously introduced model based on logistic regression. We show how the parameters of our model can be estimated in closed form given complete data, and propose an algorithm for learning its structure in an interpretable space. We highlight the intuitive structures that we learn via case studies. We finally show how our proposed model can be viewed as an instance of the recently proposed Probabilistic Sentential Decision Diagram.