  Determinantal point processes (DPPs) are elegant probabilistic models of
repulsion that arise in quantum physics and random matrix theory. In contrast
to traditional structured models like Markov random fields, which become
intractable and hard to approximate in the presence of negative correlations,
DPPs offer efficient and exact algorithms for sampling, marginalization,
conditioning, and other inference tasks. We provide a gentle introduction to
DPPs, focusing on the intuitions, algorithms, and extensions that are most
relevant to the machine learning community, and show how DPPs can be applied to
real-world applications like finding diverse sets of high-quality search
results, building informative summaries by selecting diverse sentences from
documents, modeling non-overlapping human poses in images or video, and
automatically building timelines of important news stories.
