Author

Eva Milara

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#machine learning Preprint Aug 2026

VG-TIE: An interpretable tabular-to-image encoding method based on visibility graphs

Experiments show that VG-TIE is competitive with other tabular-to-image methods while providing interpretability on feature importance and ranking similar to intrinsic interpretable methods, and highlights the potential of the proposed image-based transformation to provide an effective framework that expands the use of...

David Chushig-Muzo, L. López-Ramos, Ángeles Rodríguez de Cara et al. · 0 citations

#graph neural networks Preprint Aug 2026

TabSOM: A tabular-to-image encoding method based on self-organizing maps

TabSOM is proposed, a tabular-to-image encoding built on the Self-Organizing Map, which provides a spatial layout in which every input feature occupies a fixed canvas position derived from its component plane via collision-free Hungarian assignment and a graph that captures pairwise feature relationships derived from t...

David Chushig-Muzo, M. D. de Cara, Eva Milara et al. · 1 citation

Preprint Aug 2026

Test-Time Augmentation for Tabular-to-Image Classifiers under Distribution Shifts

Results indicate that TTA improves OOD performance, with composite and photometric strategies providing the best trade-off between robustness and variance, in contrast to frequency-domain transformations that alter the encoder's feature-to-intensity mapping consistently degrade performance.

Malena Loza, Felipe Grijalva, Eva Milara et al. · 0 citations

Preprint Jul 2026

Empirical Evaluation of Out-Of-Distribution Performance of Tabular Foundation Models

Existing benchmarks for OOD in tabular data are extended, providing evidence to support their adoption in high-stakes domains characterized by structural distribution shifts and identifying a scalability gap.

Malena Loza, David Chushig-Muzo, Eva Milara et al. · 0 citations