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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.01957 |
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| _version_ | 1866908480055541760 |
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| author | Norcliffe, Alexander Lee, Changhee Imrie, Fergus van der Schaar, Mihaela Lio, Pietro |
| author_facet | Norcliffe, Alexander Lee, Changhee Imrie, Fergus van der Schaar, Mihaela Lio, Pietro |
| contents | Active Feature Acquisition is an instance-wise, sequential decision making problem. The aim is to dynamically select which feature to measure based on current observations, independently for each test instance. Common approaches either use Reinforcement Learning, which experiences training difficulties, or greedily maximize the conditional mutual information of the label and unobserved features, which makes myopic acquisitions. To address these shortcomings, we introduce a latent variable model, trained in a supervised manner. Acquisitions are made by reasoning about the features across many possible unobserved realizations in a stochastic latent space. Extensive evaluation on a large range of synthetic and real datasets demonstrates that our approach reliably outperforms a diverse set of baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01957 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stochastic Encodings for Active Feature Acquisition Norcliffe, Alexander Lee, Changhee Imrie, Fergus van der Schaar, Mihaela Lio, Pietro Machine Learning Active Feature Acquisition is an instance-wise, sequential decision making problem. The aim is to dynamically select which feature to measure based on current observations, independently for each test instance. Common approaches either use Reinforcement Learning, which experiences training difficulties, or greedily maximize the conditional mutual information of the label and unobserved features, which makes myopic acquisitions. To address these shortcomings, we introduce a latent variable model, trained in a supervised manner. Acquisitions are made by reasoning about the features across many possible unobserved realizations in a stochastic latent space. Extensive evaluation on a large range of synthetic and real datasets demonstrates that our approach reliably outperforms a diverse set of baselines. |
| title | Stochastic Encodings for Active Feature Acquisition |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.01957 |