  There are relatively few works dealing with conformal prediction for
multi-task learning issues, and this is particularly true for multi-target
regression. This paper focuses on the problem of providing valid (i.e.,
frequency calibrated) multi-variate predictions. To do so, we propose to use
copula functions applied to deep neural networks for inductive conformal
prediction. We show that the proposed method ensures efficiency and validity
for multi-target regression problems on various data sets.
