2 papers
stat.ML2026
Graph Matching Relaxations and Amortization for Supervised Graph Prediction
Federico Méndez, Paul Krzakala, Gabriel Melo +3
End-to-end Supervised Graph Prediction (SGP) requires a permutation-invariant loss to compare predicted and target graphs with arbitrary node orderings. Such losses typically invol…
stat.ML2026
Conformal Graph Prediction with Z-Gromov-Wasserstein Distances
Gabriel Melo, Thibaut de Saivre, Anna Calissano +1
Supervised graph prediction addresses regression problems where the outputs are structured graphs. Although several approaches exist for graph-valued prediction, principled uncerta…