title | booktitle | year | volume | series | month | publisher | url | abstract | layout | issn | id | tex_title | firstpage | lastpage | page | order | cycles | bibtex_editor | editor | bibtex_author | author | date | address | container-title | genre | issued | extras | |||||||||||||||||||||||||||||||||||||||||||||||||||
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Conformal Predictive Systems Under Covariate Shift |
Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications |
2024 |
230 |
Proceedings of Machine Learning Research |
0 |
PMLR |
Conformal predictive systems (CPS) offer a versatile framework for constructing predictive distributions, allowing for calibrated inference and informative decision-making. However, their applicability has been limited to scenarios adhering to the independent and identically distributed (IID) model assumption. This paper extends CPS to accommodate scenarios characterized by covariate shifts. We therefore propose weighted CPS (WCPS), akin to weighted conformal prediction (WCP), leveraging likelihood ratios between training and testing covariate distributions. This extension enables the construction of nonparametric predictive distributions capable of handling covariate shifts. We present theoretical underpinnings and conjectures regarding the validity and efficacy of WCPS and demonstrate its utility through empirical evaluations on both synthetic and real-world datasets. Our simulation experiments indicate that WCPS are probabilistically calibrated under covariate shift. |
inproceedings |
2640-3498 |
jonkers24a |
Conformal Predictive Systems Under Covariate Shift |
406 |
423 |
406-423 |
406 |
false |
Vantini, Simone and Fontana, Matteo and Solari, Aldo and Bostr\"{o}m, Henrik and Carlsson, Lars |
|
Jonkers, Jef and Van Wallendael, Glenn and Duchateau, Luc and Van Hoecke, Sofie |
|
2024-09-10 |
Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications |
inproceedings |
|