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.
REF ) introduced a policy gradient based method which jointly learns data representation, selection heuristic as well as the model prediction function.
Learning Algorithms for Active Learning
ArXiv
ArXiv
Mathematics Computer Science