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Marcos Matabuena

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#machine learning Preprint Oct 2026

Deep Learning for Random Objects in Metric Spaces: Beyond Manifold Assumptions

This work proposes a deep learning framework for regression with random objects in metric spaces that operates directly on the metric structure of the response space, avoiding restrictive manifold or Euclidean embedding assumptions imposed by many existing methods.

Marcos Matabuena, Xiao-Kai Luo, Oscar Hernan Madrid Padilla · 1 citation