@inproceedings{
guha2023conformal,
title={Conformal Prediction via Regression-as-Classification},
author={Etash Guha and Shlok Natarajan and Thomas M{\"o}llenhoff and Mohammad Emtiyaz Khan and Eugene Ndiaye},
booktitle={NeurIPS 2023 Workshop on Regulatable ML},
year={2023},
url={https://openreview.net/forum?id=eKrYMGpXVY}
}
Conformal Prediction (CP) is a method of estimating risk or uncertainty when using Machine Learning to help abide by common Risk Management regulations often seen in fields like healthcare and finance. CP for regression can be challenging, especially when the output distribution is heteroscedastic, multimodal, or skewed. Some of the issues can be addressed by estimating a distribution over the output, but in reality, such approaches can be sensitive to estimation error and yield unstable intervals.~Here, we circumvent the challenges by converting regression to a classification problem and then use CP for classification to obtain CP sets for regression.~To preserve the ordering of the continuous-output space, we design a new loss function and present necessary modifications to the CP classification techniques.~Empirical results on many benchmarks shows that this simple approach gives surprisingly good results on many practical problems.
Conformal Prediction via Regression-as-Classification
Etash Guha Shlok Natarajan Thomas Möllenhoff Mohammad Emtiyaz Khan Eugene Ndiaye