Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling

Wonho Bae, Jing Wang, Danica J. Sutherland

Primary Area: transfer learning, meta learning, and lifelong learning

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Keywords: Active Learning, Meta Learning

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Abstract:

Most meta-learning methods assume that the (very small) context set used to establish a new task at test time is passively provided.
In some settings, however, it is feasible to actively select which points to label; the potential gain from a careful choice is substantial, but the setting requires major differences from typical active learning setups.
We clarify the ways in which active meta-learning can be used to label a context set, depending on which parts of the meta-learning process use active learning.
Within this framework, we propose a natural algorithm based on fitting Gaussian mixtures for selecting which points to label; though simple, the algorithm also has theoretical motivation.
The proposed algorithm outperforms state-of-the-art active learning methods when used with various meta-learning algorithms across several benchmark datasets.

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Submission Number: 9044