A Review and Comparative Analysis of Univariate Conformal Regression Methods

Jie Bao; Nicolo Colombo; Valery Manokhin; Suqun Cao; Rui Luo

A Review and Comparative Analysis of Univariate Conformal Regression Methods

Jie Bao
, Nicolo Colombo
, Valery Manokhin
, Suqun Cao
, Rui Luo

Department of Systems Engineering

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

3 Citations (Scopus)

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. © 2025 J. Bao, N. Colombo, V. Manokhin, S. Cao & R. Luo.

Original language	English
Title of host publication	Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications
Editors	Khuong An Nguyen, Zhiyuan Luo, Harris Papadopoulos, Tuwe Löfström, Lars Carlsson, Henrik Boström
Publisher	ML Research Press
Pages	282-304
Number of pages	23
Publication status	Published - Sept 2025
Event	14th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2025) - Royal Holloway, London, United Kingdom Duration: 10 Sept 2025 → 12 Sept 2025 https://proceedings.mlr.press/v266/

Publication series

Name	Proceedings of Machine Learning Research
Volume	266
ISSN (Print)	2640-3498

Conference

Conference	14th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2025)
Place	United Kingdom
City	London
Period	10/09/25 → 12/09/25
Internet address	https://proceedings.mlr.press/v266/

Funding

The work described in this paper was partially supported by grants from City University of Hong Kong (9610639, 6000864) and Chengdu Municipal Office of Philosophy and Social Science (2024BS013).

Research Keywords

conformal prediction
conformal regression
exchangeable
uncertainty quantification

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https://proceedings.mlr.press/v266/bao25a.html

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Cite this

Bao, J., Colombo, N., Manokhin, V., Cao, S., & Luo, R. (2025). A Review and Comparative Analysis of Univariate Conformal Regression Methods. In K. A. Nguyen, Z. Luo, H. Papadopoulos, T. Löfström, L. Carlsson, & H. Boström (Eds.), Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications (pp. 282-304). (Proceedings of Machine Learning Research; Vol. 266). ML Research Press. https://proceedings.mlr.press/v266/bao25a.html

Bao, Jie ; Colombo, Nicolo ; Manokhin, Valery et al. / A Review and Comparative Analysis of Univariate Conformal Regression Methods. Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications. editor / Khuong An Nguyen ; Zhiyuan Luo ; Harris Papadopoulos ; Tuwe Löfström ; Lars Carlsson ; Henrik Boström. ML Research Press, 2025. pp. 282-304 (Proceedings of Machine Learning Research).

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title = "A Review and Comparative Analysis of Univariate Conformal Regression Methods",

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. {\textcopyright} 2025 J. Bao, N. Colombo, V. Manokhin, S. Cao \& R. Luo.",

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Bao, J, Colombo, N, Manokhin, V, Cao, S & Luo, R 2025, A Review and Comparative Analysis of Univariate Conformal Regression Methods. in KA Nguyen, Z Luo, H Papadopoulos, T Löfström, L Carlsson & H Boström (eds), Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications. Proceedings of Machine Learning Research, vol. 266, ML Research Press, pp. 282-304, 14th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2025), London, United Kingdom, 10/09/25. <https://proceedings.mlr.press/v266/bao25a.html>

A Review and Comparative Analysis of Univariate Conformal Regression Methods. / Bao, Jie; Colombo, Nicolo; Manokhin, Valery et al.
Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications. ed. / Khuong An Nguyen; Zhiyuan Luo; Harris Papadopoulos; Tuwe Löfström; Lars Carlsson; Henrik Boström. ML Research Press, 2025. p. 282-304 (Proceedings of Machine Learning Research; Vol. 266).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A Review and Comparative Analysis of Univariate Conformal Regression Methods

AU - Bao, Jie

AU - Colombo, Nicolo

AU - Manokhin, Valery

AU - Cao, Suqun

AU - Luo, Rui

PY - 2025/9

Y1 - 2025/9

N2 - 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. © 2025 J. Bao, N. Colombo, V. Manokhin, S. Cao & R. Luo.

AB - 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. © 2025 J. Bao, N. Colombo, V. Manokhin, S. Cao & R. Luo.

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KW - conformal regression

KW - exchangeable

KW - uncertainty quantification

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M3 - RGC 32 - Refereed conference paper (with host publication)

T3 - Proceedings of Machine Learning Research

SP - 282

EP - 304

BT - Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications

A2 - Nguyen, Khuong An

A2 - Luo, Zhiyuan

A2 - Papadopoulos, Harris

A2 - Löfström, Tuwe

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A2 - Boström, Henrik

PB - ML Research Press

T2 - 14th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2025)

Y2 - 10 September 2025 through 12 September 2025

ER -

Bao J, Colombo N, Manokhin V, Cao S, Luo R. A Review and Comparative Analysis of Univariate Conformal Regression Methods. In Nguyen KA, Luo Z, Papadopoulos H, Löfström T, Carlsson L, Boström H, editors, Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications. ML Research Press. 2025. p. 282-304. (Proceedings of Machine Learning Research).

A Review and Comparative Analysis of Univariate Conformal Regression Methods

Abstract

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Conference

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Research Keywords

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A Review and Comparative Analysis of Univariate Conformal Regression Methods

Abstract

Publication series

Conference

Funding

Research Keywords

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Projects

TSG(CityU): Peer-Based Learning in Engineering Education Through Integrating CityU GPT Chatbot and Surprisingly Popular Algorithm

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