Valid Selection among Conformal Sets
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913911904665600 |
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| author | Hegazy, Mahmoud Aolaritei, Liviu Jordan, Michael I. Dieuleveut, Aymeric |
| author_facet | Hegazy, Mahmoud Aolaritei, Liviu Jordan, Michael I. Dieuleveut, Aymeric |
| contents | Conformal prediction offers a distribution-free framework for constructing prediction sets with coverage guarantees. In practice, multiple valid conformal prediction sets may be available, arising from different models or methodologies. However, selecting the most desirable set, such as the smallest, can invalidate the coverage guarantees. To address this challenge, we propose a stability-based approach that ensures coverage for the selected prediction set. We extend our results to the online conformal setting, propose several refinements in settings where additional structure is available, and demonstrate its effectiveness through experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20173 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Valid Selection among Conformal Sets Hegazy, Mahmoud Aolaritei, Liviu Jordan, Michael I. Dieuleveut, Aymeric Machine Learning Artificial Intelligence Methodology Other Statistics Conformal prediction offers a distribution-free framework for constructing prediction sets with coverage guarantees. In practice, multiple valid conformal prediction sets may be available, arising from different models or methodologies. However, selecting the most desirable set, such as the smallest, can invalidate the coverage guarantees. To address this challenge, we propose a stability-based approach that ensures coverage for the selected prediction set. We extend our results to the online conformal setting, propose several refinements in settings where additional structure is available, and demonstrate its effectiveness through experiments. |
| title | Valid Selection among Conformal Sets |
| topic | Machine Learning Artificial Intelligence Methodology Other Statistics |
| url | https://arxiv.org/abs/2506.20173 |