Integrating Uncertainty Awareness into Conformalized Quantile Regression

arXiv:2306.08693v2 Announce Type: replace-cross
Abstract: Conformalized Quantile Regression (CQR) is a recently proposed method for constructing prediction intervals for a response $Y$ given covariates $X$, without making distributional assumptions. However, existing constructions of CQR can be ineffective for problems where the quantile regressors perform better in certain parts of the feature space than others. The reason is that the prediction intervals of CQR do not distinguish between two forms of uncertainty: first, the variability of the conditional distribution of $Y$ …

abstract arxiv assumptions feature however making prediction quantile regression space stat.me stat.ml type uncertainty