  Meta-learning aims to learn a model that can handle multiple tasks generated
from an unknown but shared distribution. However, typical meta-learning
algorithms have assumed the tasks to be similar such that a single meta-learner
is sufficient to aggregate the variations in all aspects. In addition, there
has been less consideration on uncertainty when limited information is given as
context. In this paper, we devise a novel meta-learning framework, called
Meta-learning Amidst Heterogeneity and Ambiguity (MAHA), that outperforms
previous works in terms of prediction based on its ability on task
identification. By extensively conducting several experiments in regression and
classification, we demonstrate the validity of our model, which turns out to be
robust to both task heterogeneity and ambiguity.
