# Conformal testing in a binary model situation

arXiv: https://arxiv.org/abs/2104.01885 · HTML: https://mlsanity.com/paper/2104.01885/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 5 Apr 2021.

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

- [Safe Testing](https://mlsanity.com/paper/1906.07801/) (2019)
- [Testing randomness](https://mlsanity.com/paper/1906.09256/) (2019)
- [Retrain or not retrain: Conformal test martingales for change-point detection](https://mlsanity.com/paper/2102.10439/) (2021)
- [Testing for concept shift online](https://mlsanity.com/paper/2012.14246/) (2020)

## Built on by (most cited first, 4 of 4)

- [Combining Evidence Across Filtrations](https://mlsanity.com/paper/2402.09698/) (2024)
- [Diagnostic Runtime Monitoring with Martingales](https://mlsanity.com/paper/2407.21748/) (2024)
- [Enhancement of prediction algorithms by betting](https://mlsanity.com/paper/2105.08669/) (2021)
- [Conformal testing: binary case with Markov alternatives](https://mlsanity.com/paper/2111.01885/) (2021)

## Abstract (arXiv)

Conformal testing is a way of testing the IID assumption based on conformal prediction. The topic of this note is computational evaluation of the performance of conformal testing in a model situation in which IID binary observations generated from a Bernoulli distribution are followed by IID binary observations generated from another Bernoulli distribution, with the parameters of the distributions and changepoint unknown. Existing conformal test martingales can be used for this task and work well in simple cases, but their efficiency can be improved greatly.