  Variational inference (VI) plays an essential role in approximate Bayesian
inference due to its computational efficiency and broad applicability. Crucial
to the performance of VI is the selection of the associated divergence measure,
as VI approximates the intractable distribution by minimizing this divergence.
In this paper we propose a meta-learning algorithm to learn the divergence
metric suited for the task of interest, automating the design of VI methods. In
addition, we learn the initialization of the variational parameters without
additional cost when our method is deployed in the few-shot learning scenarios.
We demonstrate our approach outperforms standard VI on Gaussian mixture
distribution approximation, Bayesian neural network regression, image
generation with variational autoencoders and recommender systems with a partial
variational autoencoder.
