A Review and Comparative Analysis of Univariate Conformal Regression Methods

Jie Bao, Nicolo Colombo, Valery Manokhin, Suqun Cao, Rui Luo
Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 266:282-304, 2025.

Abstract

As machine learning models continue to evolve and improve, quantifying their uncertainty has become increasingly crucial in high-stakes applications. Conformal prediction has emerged as a powerful tool and has been widely applied in univariate regression tasks. While numerous conformal regression methods and models have been developed, few studies have provided a unified summary and comparison of these approaches. In this paper, we address this gap by discussing, summarizing, and providing an overview of the majority of existing univariate conformal regression methods. Furthermore, we conduct a detailed examination and experimentation of eight major, popular, and advanced conformal regression methods, representing a significant contribution to the field by offering a comprehensive analysis and insights into their performance and applicability.

Cite this Paper

BibTeX
@InProceedings{pmlr-v266-bao25a, title = {A Review and Comparative Analysis of Univariate Conformal Regression Methods}, author = {Bao, Jie and Colombo, Nicolo and Manokhin, Valery and Cao, Suqun and Luo, Rui}, booktitle = {Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications}, pages = {282--304}, year = {2025}, editor = {Nguyen, Khuong An and Luo, Zhiyuan and Papadopoulos, Harris and Löfström, Tuwe and Carlsson, Lars and Boström, Henrik}, volume = {266}, series = {Proceedings of Machine Learning Research}, month = {10--12 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v266/main/assets/bao25a/bao25a.pdf}, url = {https://proceedings.mlr.press/v266/bao25a.html}, abstract = {As machine learning models continue to evolve and improve, quantifying their uncertainty has become increasingly crucial in high-stakes applications. Conformal prediction has emerged as a powerful tool and has been widely applied in univariate regression tasks. While numerous conformal regression methods and models have been developed, few studies have provided a unified summary and comparison of these approaches. In this paper, we address this gap by discussing, summarizing, and providing an overview of the majority of existing univariate conformal regression methods. Furthermore, we conduct a detailed examination and experimentation of eight major, popular, and advanced conformal regression methods, representing a significant contribution to the field by offering a comprehensive analysis and insights into their performance and applicability.} }
Endnote
%0 Conference Paper %T A Review and Comparative Analysis of Univariate Conformal Regression Methods %A Jie Bao %A Nicolo Colombo %A Valery Manokhin %A Suqun Cao %A Rui Luo %B Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications %C Proceedings of Machine Learning Research %D 2025 %E Khuong An Nguyen %E Zhiyuan Luo %E Harris Papadopoulos %E Tuwe Löfström %E Lars Carlsson %E Henrik Boström %F pmlr-v266-bao25a %I PMLR %P 282--304 %U https://proceedings.mlr.press/v266/bao25a.html %V 266 %X As machine learning models continue to evolve and improve, quantifying their uncertainty has become increasingly crucial in high-stakes applications. Conformal prediction has emerged as a powerful tool and has been widely applied in univariate regression tasks. While numerous conformal regression methods and models have been developed, few studies have provided a unified summary and comparison of these approaches. In this paper, we address this gap by discussing, summarizing, and providing an overview of the majority of existing univariate conformal regression methods. Furthermore, we conduct a detailed examination and experimentation of eight major, popular, and advanced conformal regression methods, representing a significant contribution to the field by offering a comprehensive analysis and insights into their performance and applicability.
APA
Bao, J., Colombo, N., Manokhin, V., Cao, S. & Luo, R.. (2025). A Review and Comparative Analysis of Univariate Conformal Regression Methods. Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications, in Proceedings of Machine Learning Research 266:282-304 Available from https://proceedings.mlr.press/v266/bao25a.html.

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