Reviews: Probabilistic Model-Agnostic Meta-Learning

This paper presents an extension to the popular metalearning algorithm MAML, in which it is re-cast as inference in a graphical model. This framing allows samples to be drawn from a model posterior, enabling reasoning about uncertainty and capturing multiple modes of ambiguous data, while MAML can only make a single point estimate of model parameters at test time. This is shown in several experiments to better capture the characteristic of ambiguous, noisy data than MAML. Strengths: The paper makes a strong point that few shot learning is often too ambiguous to confine to a single-model metalearning paradigm. Especially with the high level of recent interest in topics such as safe learning, risk-aware learning, and active learning, this is a relevant area of work.