Adjusting Regression Models for Conditional Uncertainty Calibration
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866929516089180160 |
|---|---|
| author | Gao, Ruijiang Yin, Mingzhang McInerney, James Kallus, Nathan |
| author_facet | Gao, Ruijiang Yin, Mingzhang McInerney, James Kallus, Nathan |
| contents | Conformal Prediction methods have finite-sample distribution-free marginal coverage guarantees. However, they generally do not offer conditional coverage guarantees, which can be important for high-stakes decisions. In this paper, we propose a novel algorithm to train a regression function to improve the conditional coverage after applying the split conformal prediction procedure. We establish an upper bound for the miscoverage gap between the conditional coverage and the nominal coverage rate and propose an end-to-end algorithm to control this upper bound. We demonstrate the efficacy of our method empirically on synthetic and real-world datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_17466 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adjusting Regression Models for Conditional Uncertainty Calibration Gao, Ruijiang Yin, Mingzhang McInerney, James Kallus, Nathan Machine Learning Artificial Intelligence Conformal Prediction methods have finite-sample distribution-free marginal coverage guarantees. However, they generally do not offer conditional coverage guarantees, which can be important for high-stakes decisions. In this paper, we propose a novel algorithm to train a regression function to improve the conditional coverage after applying the split conformal prediction procedure. We establish an upper bound for the miscoverage gap between the conditional coverage and the nominal coverage rate and propose an end-to-end algorithm to control this upper bound. We demonstrate the efficacy of our method empirically on synthetic and real-world datasets. |
| title | Adjusting Regression Models for Conditional Uncertainty Calibration |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2409.17466 |