  In this paper, we introduce the online and streaming MAP inference and
learning problems for Non-symmetric Determinantal Point Processes (NDPPs) where
data points arrive in an arbitrary order and the algorithms are constrained to
use a single-pass over the data as well as sub-linear memory. The online
setting has an additional requirement of maintaining a valid solution at any
point in time. For solving these new problems, we propose algorithms with
theoretical guarantees, evaluate them on several real-world datasets, and show
that they give comparable performance to state-of-the-art offline algorithms
that store the entire data in memory and take multiple passes over it.
