  This paper explores conformal prediction in the learning under privileged
information (LUPI) paradigm. We use the SVM+ realization of LUPI in an
inductive conformal predictor, and apply it to the MNIST benchmark dataset and
three datasets in drug discovery. The results show that using privileged
information produces valid models and improves efficiency compared to standard
SVM, however the improvement varies between the tested datasets and is not
substantial in the drug discovery applications. More importantly, using SVM+ in
a conformal prediction framework enables valid prediction intervals at
specified significance levels.
