My work on Conformal Predictors’ ensembles has been accepted by the Machine Learning journal.

The paper explores the idea of ensembling the predictions of several Conformal Predictors (CP) by taking a majority vote.

It shows two methods that maintain the same theoretical guarantees of the CPs (i.e., control on the empirical error), one for uncorrelated CPs and one for possibly correlated CPs.