  Determinantal point processes (DPPs) offer an elegant tool for encoding
probabilities over subsets of a ground set. Discrete DPPs are parametrized by a
positive semidefinite matrix (called the DPP kernel), and estimating this
kernel is key to learning DPPs from observed data. We consider the task of
learning the DPP kernel, and develop for it a surprisingly simple yet effective
new algorithm. Our algorithm offers the following benefits over previous
approaches: (a) it is much simpler; (b) it yields equally good and sometimes
even better local maxima; and (c) it runs an order of magnitude faster on large
problems. We present experimental results on both real and simulated data to
illustrate the numerical performance of our technique.
