  We develop a parallel variational inference (VI) procedure for use in
data-distributed settings, where each machine only has access to a subset of
data and runs VI independently, without communicating with other machines. This
type of "embarrassingly parallel" procedure has recently been developed for
MCMC inference algorithms; however, in many cases it is not possible to
directly extend this procedure to VI methods without requiring certain
restrictive exponential family conditions on the form of the model.
Furthermore, most existing (nonparallel) VI methods are restricted to use on
conditionally conjugate models, which limits their applicability. To combat
these issues, we make use of the recently proposed nonparametric VI to
facilitate an embarrassingly parallel VI procedure that can be applied to a
wider scope of models, including to nonconjugate models. We derive our
embarrassingly parallel VI algorithm, analyze our method theoretically, and
demonstrate our method empirically on a few nonconjugate models.
