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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…
stat.ML2024
Learning Differentiable Surrogate Losses for Structured Prediction
Junjie Yang, Matthieu Labeau, Florence d'Alché-Buc
Structured prediction involves learning to predict complex structures rather than simple scalar values. The main challenge arises from the non-Euclidean nature of the output space,…
stat.ML2024
Deep Sketched Output Kernel Regression for Structured Prediction
Tamim El Ahmad, Junjie Yang, Pierre Laforgue +1
By leveraging the kernel trick in the output space, kernel-induced losses provide a principled way to define structured output prediction tasks for a wide variety of output modalit…