Enhancing conformal prediction using E-test statistics

Balinsky, Alexander A. ORCID: https://orcid.org/0000-0002-8151-4462 and Balinsky, Alexander D. 2024. Enhancing conformal prediction using E-test statistics. Presented at: 13th Symposium on Conformal and Probabilistic Prediction with Applications, Milan, Italy, 9-11 September 2024. Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications. Proceedings of Machine Learning Research , vol.230 pp. 65-72.

PDF - Accepted Post-Print Version Download (352kB)

Abstract

Conformal Prediction (CP) serves as a robust framework that quantifies uncertainty in predictions made by Machine Learning (ML) models. Unlike traditional point predictors, CP generates statistically valid prediction regions, also known as prediction intervals, based on the assumption of data exchangeability. Typically, the construction of conformal predictions hinges on p-values. This paper, however, ventures down an alternative path, harnessing the power of e-test statistics to augment the efficacy of conformal predictions by introducing a BB-predictor (bounded from the below predictor). The BB-predictor can be constructed under even more lenient assumptions than exchangeability.

Item Type:	Conference or Workshop Item - published (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Schools > Mathematics
ISSN:	2640-3498
Date of First Compliant Deposit:	30 July 2024
Date of Acceptance:	24 May 2024
Last Modified:	16 Apr 2026 08:50
URI:	https://orca.cardiff.ac.uk/id/eprint/169594

Actions (repository staff only)

Edit Item