Rectifying Conformity Scores for Better Conditional Coverage
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908493796081664 |
|---|---|
| author | Plassier, Vincent Fishkov, Alexander Dheur, Victor Guizani, Mohsen Taieb, Souhaib Ben Panov, Maxim Moulines, Eric |
| author_facet | Plassier, Vincent Fishkov, Alexander Dheur, Victor Guizani, Mohsen Taieb, Souhaib Ben Panov, Maxim Moulines, Eric |
| contents | We present a new method for generating confidence sets within the split conformal prediction framework. Our method performs a trainable transformation of any given conformity score to improve conditional coverage while ensuring exact marginal coverage. The transformation is based on an estimate of the conditional quantile of conformity scores. The resulting method is particularly beneficial for constructing adaptive confidence sets in multi-output problems where standard conformal quantile regression approaches have limited applicability. We develop a theoretical bound that captures the influence of the accuracy of the quantile estimate on the approximate conditional validity, unlike classical bounds for conformal prediction methods that only offer marginal coverage. We experimentally show that our method is highly adaptive to the local data structure and outperforms existing methods in terms of conditional coverage, improving the reliability of statistical inference in various applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16336 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Rectifying Conformity Scores for Better Conditional Coverage Plassier, Vincent Fishkov, Alexander Dheur, Victor Guizani, Mohsen Taieb, Souhaib Ben Panov, Maxim Moulines, Eric Machine Learning Statistics Theory We present a new method for generating confidence sets within the split conformal prediction framework. Our method performs a trainable transformation of any given conformity score to improve conditional coverage while ensuring exact marginal coverage. The transformation is based on an estimate of the conditional quantile of conformity scores. The resulting method is particularly beneficial for constructing adaptive confidence sets in multi-output problems where standard conformal quantile regression approaches have limited applicability. We develop a theoretical bound that captures the influence of the accuracy of the quantile estimate on the approximate conditional validity, unlike classical bounds for conformal prediction methods that only offer marginal coverage. We experimentally show that our method is highly adaptive to the local data structure and outperforms existing methods in terms of conditional coverage, improving the reliability of statistical inference in various applications. |
| title | Rectifying Conformity Scores for Better Conditional Coverage |
| topic | Machine Learning Statistics Theory |
| url | https://arxiv.org/abs/2502.16336 |