Meta learning is a promising paradigm to enable skill transfer across tasks.

Standard contextual bandit problem assumes that all the relevant contexts are observed before the algorithm chooses an arm.

To meaningfully regularize the model's decision boundary, CODA introduces virtual unknown classes

To address these issues, we introduce a novel Bayesian meta-learning approach called meta-variational dropout (MetaVD).