[2510.10324] On some practical challenges of conformal prediction

Statistics > Machine Learning arXiv:2510.10324 (stat) [Submitted on 11 Oct 2025 (v1), last revised 30 Mar 2026 (this version, v2)] Title:On some practical challenges of conformal prediction Authors:Liang Hong, Noura Raydan Nasreddine View a PDF of the paper titled On some practical challenges of conformal prediction, by Liang Hong and Noura Raydan Nasreddine View PDF HTML (experimental) Abstract:Conformal prediction is a model-free machine learning method for constructing prediction regions at a guaranteed coverage probability level. However, a data scientist often faces three challenges in practice: (i) the determination of a conformal prediction region is only approximate, jeopardizing the finite-sample validity of prediction, (ii) the computation required could be prohibitively expensive, and (iii) the shape of a conformal prediction region is hard to control. This article offers new insights into the relationship among the monotonicity of the non-conformity measure, the monotonicity of the plausibility function, and the exact determination of a conformal prediction region. Based on these new insights, we propose a quadratic-polynomial non-conformity measure that allows a data scientist to circumvent the three challenges simultaneously within the full conformal prediction framework. Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG) MSC classes: 62G99 ACM classes: I.1.2 Cite as: arXiv:2510.10324 [stat.ML]   (or arXiv:2510.10324v2 [stat.ML] for this version) ...