Conformal Prediction: a Unified Review of Theory and New Challenges
Keywords
Statistical learning
Code:
22/2020
Title:
Conformal Prediction: a Unified Review of Theory and New Challenges
Date:
Thursday 16th April 2020
Author(s):
Zeni, G.; Fontana, M.; Vantini, F.
Download link:
Abstract:
In this work we provide a review of basic ideas and novel developments about Conformal Prediction - an innovative distribution-free, non-parametric forecasting method, based on minimal assumptions - that is able to yield in a very straightforward way predictions sets that are valid in a statistical sense also in in the finite sample case.
The in-depth discussion provided in the paper covers the theoretical underpinnings of Conformal Prediction, and then proceeds to list the more advanced developments and adaptations of the original idea.