  Bayesian meta-learning enables robust and fast adaptation to new tasks with
uncertainty assessment. The key idea behind Bayesian meta-learning is empirical
Bayes inference of hierarchical model. In this work, we extend this framework
to include a variety of existing methods, before proposing our variant based on
gradient-EM algorithm. Our method improves computational efficiency by avoiding
back-propagation computation in the meta-update step, which is exhausting for
deep neural networks. Furthermore, it provides flexibility to the inner-update
optimization procedure by decoupling it from meta-update. Experiments on
sinusoidal regression, few-shot image classification, and policy-based
reinforcement learning show that our method not only achieves better accuracy
with less computation cost, but is also more robust to uncertainty.
