Improved conformalized quantile regression

Understand

Conformalized quantile regression is a procedure that inherits the advantages of conformal prediction and quantile regression.

• That is, we use quantile regression to estimate the true conditional quantile and then apply a conformal step on a calibration set to ensure marginal coverage.
• In this way, we get adaptive prediction intervals that account for heteroscedasticity.
• However, the aforementioned conformal step lacks adaptiveness as described in (Romano et al., 2019).