The dataminer's guide to scalable mixed-membership and nonparametric bayesian models

Abstract

Large amounts of data arise in a multitude of situations, ranging from bioinformatics to astronomy, manufacturing, and medical applications. For concreteness our tutorial focuses on data obtained in the context of the internet, such as user generated content (microblogs, e-mails, messages), behavioral data (locations, interactions, clicks, queries), and graphs. Due to its magnitude, much of the challenges are to extract structure and interpretable models without the need for additional labels, i.e. to design effective unsupervised techniques. We present design patterns for hierarchical nonparametric Bayesian models, efficient inference algorithms, and modeling tools to describe salient aspects of the data.