# Protected probabilistic classification

arXiv: https://arxiv.org/abs/2107.01726 · HTML: https://mlsanity.com/paper/2107.01726/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 4 Jul 2021.

## Builds on (most cited first, 2 of 2)

- [Testing randomness](https://mlsanity.com/paper/1906.09256/) (2019)
- [The fundamental nature of the log loss function](https://mlsanity.com/paper/1502.06254/) (2015)

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

- [Protected Test-Time Adaptation via Online Entropy Matching: A Betting Approach](https://mlsanity.com/paper/2408.07511/) (2024)
- [Betting on Moments: Legendre Jumper Martingales for Online Exchangeability Testing](https://mlsanity.com/paper/2606.20859/) (2026)

## Abstract (arXiv)

This paper proposes a way of protecting probabilistic prediction models against changes in the data distribution, concentrating on the case of classification and paying particular attention to binary classification. This is important in applications of machine learning, where the quality of a trained prediction algorithm may drop significantly in the process of its exploitation. Our techniques are based on recent work on conformal test martingales and older work on prediction with expert advice, namely tracking the best expert.