  Determinantal Point Processes (DPPs) are elegant probabilistic models of
repulsion and diversity over discrete sets of items. But their applicability to
large sets is hindered by expensive cubic-complexity matrix operations for
basic tasks such as sampling. In light of this, we propose a new method for
approximate sampling from discrete $k$-DPPs. Our method takes advantage of the
diversity property of subsets sampled from a DPP, and proceeds in two stages:
first it constructs coresets for the ground set of items; thereafter, it
efficiently samples subsets based on the constructed coresets. As opposed to
previous approaches, our algorithm aims to minimize the total variation
distance to the original distribution. Experiments on both synthetic and real
datasets indicate that our sampling algorithm works efficiently on large data
sets, and yields more accurate samples than previous approaches.
