Learning Algorithms for Active Learning

For many real-world tasks, labeled data is scarce while unlabeled data is abundant. In active learning, a model selects unlabeled instances for labeling so as to maximize a combination of task perfor- mance and data efficiency. Active learning is useful in many real-world scenarios. For example, in cold-start movie recommendation, a system aims to suggest movies to a new user; preference information for this user is initially unavailable, but may be obtained online by asking her to rate a selection of movies. Known ratings could inform the choice of future queries, to better estimate the user’s preferences with fewer queries overall. Or consider medical image classification, where labeling images is costly because it requires a specialist. Labeling costs could be reduced by clever strategies for selecting images to label. In contrast to most prior work on active learning, which relies on carefully designed heuristics for selecting instances to label, we propose learning active learning algorithms end-to-end via metalearning. I. e. , we propose a model which learns a selection heuristic, and how to use it, by interacting with data from many related tasks. Our model builds on methods developed for reinforcement and one-shot learning. Across a collection of prob- lems based on the Omniglot dataset, our model performs well relative to a set of strong baselines. We show that our model offers promising performance in a practical setting using the MovieLens dataset to simulate the cold-start problem faced by recommendation systems.

Authors

• Philip Bachman

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