Embarrassingly Parallel Variational Inference in Nonconjugate Models
This work addresses scalability issues for variational inference in distributed data scenarios, though it appears incremental as it builds on existing nonparametric VI methods.
The authors tackled the challenge of performing variational inference in data-distributed settings without communication between machines, developing an embarrassingly parallel algorithm that extends applicability to nonconjugate models, with empirical demonstrations on such models.
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.