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.
Also, REF use metalearning to jointly learn a data selection heuristic, data representation and prediction function for a distribution of related tasks.
Learning Algorithms for Active Learning
ArXiv
ArXiv
Mathematics Computer Science