  Learning to infer Bayesian posterior from a few-shot dataset is an important
step towards robust meta-learning due to the model uncertainty inherent in the
problem. In this paper, we propose a novel Bayesian model-agnostic
meta-learning method. The proposed method combines scalable gradient-based
meta-learning with nonparametric variational inference in a principled
probabilistic framework. During fast adaptation, the method is capable of
learning complex uncertainty structure beyond a point estimate or a simple
Gaussian approximation. In addition, a robust Bayesian meta-update mechanism
with a new meta-loss prevents overfitting during meta-update. Remaining an
efficient gradient-based meta-learner, the method is also model-agnostic and
simple to implement. Experiment results show the accuracy and robustness of the
proposed method in various tasks: sinusoidal regression, image classification,
active learning, and reinforcement learning.
