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

• URL: http://arxiv.org/abs/2311.02879v3
• Date: Wed, 24 Jul 2024 19:10:52 GMT
• Title: Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling
• Authors: Wonho Bae, Jing Wang, Danica J. Sutherland,
• Abstract summary: In some settings, it is feasible to actively select which points to label. We propose a natural algorithm based on fitting Gaussian mixtures for selecting which points to label. The proposed algorithm outperforms state-of-the-art active learning methods when used with various meta-learning algorithms across several benchmark datasets.
• Score: 17.563853245956455
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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