Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914885774868480 |
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| author | Bae, Wonho Wang, Jing Sutherland, Danica J. |
| author_facet | Bae, Wonho Wang, Jing Sutherland, Danica J. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2311_02879 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling Bae, Wonho Wang, Jing Sutherland, Danica J. Machine Learning 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. |
| title | Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2311.02879 |