# Aggregation in conformal e-classification

arXiv: https://arxiv.org/abs/2605.07963 · HTML: https://mlsanity.com/paper/2605.07963/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 8 May 2026.

## Builds on (most cited first, 4 of 4)

- [Predictive inference with the jackknife+](https://mlsanity.com/paper/1905.02928/) (2019)
- [Game-theoretic statistics and safe anytime-valid inference](https://mlsanity.com/paper/2210.01948/) (2022)
- [Safe Testing](https://mlsanity.com/paper/1906.07801/) (2019)
- [Conformal e-prediction](https://mlsanity.com/paper/2001.05989/) (2020)

## Abstract (arXiv)

Aggregating conformal predictors is a standard way of balancing their predictive and computational efficiency while retaining their validity, at least approximately. An important advantage of conformal e-predictors is that they are easier to aggregate without sacrificing their validity. This paper studies experimentally cross-conformal e-prediction, which is an existing method of aggregating conformal e-predictors, and its modifications that are conceptually simpler and more flexible.