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title booktitle year volume series month publisher pdf 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
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
given family
Simone
Vantini
given family
Matteo
Fontana
given family
Aldo
Solari
given family
Henrik
Boström
given family
Lars
Carlsson
Jonkers, Jef and Van Wallendael, Glenn and Duchateau, Luc and Van Hoecke, Sofie
given family
Jef
Jonkers
given family
Glenn
Van Wallendael
given family
Luc
Duchateau
given family
Sofie
Van Hoecke
2024-09-10
Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications
inproceedings
date-parts
2024
9
10