  We introduce a model that learns active learning algorithms via metalearning.
For a distribution of related tasks, our model jointly learns: a data
representation, an item selection heuristic, and a method for constructing
prediction functions from labeled training sets. Our model uses the item
selection heuristic to gather labeled training sets from which to construct
prediction functions. Using the Omniglot and MovieLens datasets, we test our
model in synthetic and practical settings.
