  Determinantal point processes (DPPs) are well-suited for modeling repulsion
and have proven useful in many applications where diversity is desired. While
DPPs have many appealing properties, such as efficient sampling, learning the
parameters of a DPP is still considered a difficult problem due to the
non-convex nature of the likelihood function. In this paper, we propose using
Bayesian methods to learn the DPP kernel parameters. These methods are
applicable in large-scale and continuous DPP settings even when the exact form
of the eigendecomposition is unknown. We demonstrate the utility of our DPP
learning methods in studying the progression of diabetic neuropathy based on
spatial distribution of nerve fibers, and in studying human perception of
diversity in images.
