Learning Algorithms for Active Learning


Philip Bachman, Alessandro Sordoni, Adam Trischler ;
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:301-310, 2017.


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 prediction function. Our model uses the item selection heuristic to construct a labeled support set for training the prediction function. Using the Omniglot and MovieLens datasets, we test our model in synthetic and practical settings.

Related Material