  Meta-learning or learning to learn is a popular approach for learning new
tasks with limited data (i.e., few-shot learning) by leveraging the
commonalities among different tasks. However, meta-learned models can perform
poorly when context data is limited, or when data is drawn from an
out-of-distribution (OoD) task. Especially in safety-critical settings, this
necessitates an uncertainty-aware approach to meta-learning. In addition, the
often multimodal nature of task distributions can pose unique challenges to
meta-learning methods. In this work, we present UnLiMiTD (uncertainty-aware
meta-learning for multimodal task distributions), a novel method for
meta-learning that (1) makes probabilistic predictions on in-distribution tasks
efficiently, (2) is capable of detecting OoD context data at test time, and (3)
performs on heterogeneous, multimodal task distributions. To achieve this goal,
we take a probabilistic perspective and train a parametric, tuneable
distribution over tasks on the meta-dataset. We construct this distribution by
performing Bayesian inference on a linearized neural network, leveraging
Gaussian process theory. We demonstrate that UnLiMiTD's predictions compare
favorably to, and outperform in most cases, the standard baselines, especially
in the low-data regime. Furthermore, we show that UnLiMiTD is effective in
detecting data from OoD tasks. Finally, we confirm that both of these findings
continue to hold in the multimodal task-distribution setting.
