Conformal Graph Prediction with Z-Gromov Wasserstein Distances

ArXi:2603.02460v4 Announce Type: replace-cross Supervised graph prediction addresses regression problems where the outputs are structured graphs. Although several approaches exist for graph-valued prediction, principled uncertainty quantification remains limited. We propose a conformal prediction framework for graph-valued outputs, providing distribution-free coverage guarantees in structured output spaces.