# On some practical challenges of conformal prediction

arXiv: https://arxiv.org/abs/2510.10324 · HTML: https://mlsanity.com/paper/2510.10324/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 35 references that are in the map.
- First posted on arXiv 11 Oct 2025.

## Builds on (most cited first, 8 of 8)

- [Distribution-Free Predictive Inference For Regression](https://mlsanity.com/paper/1604.04173/) (2016)
- [Conformalized Quantile Regression](https://mlsanity.com/paper/1905.03222/) (2019)
- [Testing for Outliers with Conformal p-values](https://mlsanity.com/paper/2104.08279/) (2021)
- [Fast Exact Conformalization of Lasso using Piecewise Linear Homotopy](https://mlsanity.com/paper/1708.00427/) (2017)
- [Efficiency of conformalized ridge regression](https://mlsanity.com/paper/1404.2083/) (2014)
- [On overfitting and post-selection uncertainty assessments](https://mlsanity.com/paper/1712.02379/) (2017)
- [Conformal prediction of future insurance claims in the regression problem](https://mlsanity.com/paper/2503.03659/) (2025)
- [Prior-free probabilistic prediction of future observations](https://mlsanity.com/paper/1403.7589/) (2014)

## Built on by (most cited first, 1 of 1)

- [A new strategy for finite-sample valid prediction of future insurance claims in the regression setting](https://mlsanity.com/paper/2601.21153/) (2026)

## Abstract (arXiv)

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.