Simone Antonelli

Conformal Prediction Sets for Graph Neural Networks

Despite the widespread use of graph neural networks (GNNs) we lack methods to reliably quantify their uncertainty. We propose a conformal procedure to equip GNNs with prediction sets that come with distribution-free guarantees -- the output set contains the true label with arbitrarily high probabili…