Conditional Validity of Conformal Regression Predictors for Heteroskedastic Data

Conformal prediction offers a distribution-free approach to estimating prediction intervals with statistical guarantees. This paper investigates how conformal predictors can be constructed to adapt to heteroskedastic noise in the data, while maintaining conditional validity with respect to the level of heteroskedasticity.