2000 character limit reached
Enhancing Conformal Prediction Using E-Test Statistics (2403.19082v1)
Published 28 Mar 2024 in cs.LG, cs.AI, math.ST, and stat.TH
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).
- A.N. Angelopoulos and S. Bates. Conformal Prediction: A Gentle Introduction. Foundations and Trends in Machine Learning Series. Now Publishers, 2023.
- Conformal prediction: A unified review of theory and new challenges. Bernoulli, 29(1):1 – 23, 2023.
- Safe testing. ArXiv. 1906.07801, 2019.
- Pascal Massart. Concentration Inequalities and Model Selection. Lecture Notes in Mathematics. Springer Berlin, Heidelberg, 2007.
Collections
Sign up for free to add this paper to one or more collections.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.