[2603.02460] Conformal Graph Prediction with Z-Gromov Wasserstein Distances

Statistics > Machine Learning arXiv:2603.02460 (stat) [Submitted on 2 Mar 2026] Title:Conformal Graph Prediction with Z-Gromov Wasserstein Distances Authors:Gabriel Melo, Thibaut de Saivre, Anna Calissano, Florence d'Alché-Buc View a PDF of the paper titled Conformal Graph Prediction with Z-Gromov Wasserstein Distances, by Gabriel Melo and 3 other authors View PDF HTML (experimental) Abstract: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. Our method defines nonconformity via the Z--Gromov--Wasserstein distance, instantiated in practice through Fused Gromov--Wasserstein (FGW), enabling permutation invariant comparison between predicted and candidate this http URL obtain adaptive prediction sets, we introduce Score Conformalized Quantile Regression (SCQR), an extension of Conformalized Quantile Regression (CQR) to handle complex output spaces such as graph--valued outputs. We evaluate the proposed approach on a synthetic task and a real problem of molecule identification. Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG) Cite as: arXiv:2603.02460 [stat.ML]   (or arXiv:2603.02460v1 [stat.ML] for this version)   https://doi.org/10.48550/arXiv.2603.024...