# Stacked conformal prediction

arXiv: https://arxiv.org/abs/2505.12578 · HTML: https://mlsanity.com/paper/2505.12578/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 18 May 2025.

## Builds on (most cited first, 5 of 5)

- [Distribution-Free Predictive Inference For Regression](https://mlsanity.com/paper/1604.04173/) (2016)
- [Conformalized Quantile Regression](https://mlsanity.com/paper/1905.03222/) (2019)
- [Predictive inference with the jackknife+](https://mlsanity.com/paper/1905.02928/) (2019)
- [Nested conformal prediction and quantile out-of-bag ensemble methods](https://mlsanity.com/paper/1910.10562/) (2019)
- [Universal distribution of the empirical coverage in split conformal prediction](https://mlsanity.com/paper/2303.02770/) (2023)

## Built on by (most cited first, 2 of 2)

- [Conformalized Super Learner](https://mlsanity.com/paper/2604.22391/) (2026)
- [Skew-adaptive conformal prediction](https://mlsanity.com/paper/2605.16145/) (2026)

## Abstract (arXiv)

We consider a method for conformalizing a stacked ensemble of predictive models, showing that the potentially simple form of the meta-learner at the top of the stack enables a procedure with manageable computational cost that achieves approximate marginal validity without requiring the use of a separate calibration sample. Empirical results indicate that the method compares favorably to a standard inductive alternative.