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cs.LG2022
Generating Realistic Synthetic Relational Data through Graph Variational Autoencoders
Ciro Antonio Mami, Andrea Coser, Eric Medvet +7
Synthetic data generation has recently gained widespread attention as a more reliable alternative to traditional data anonymization. The involved methods are originally developed f…
cs.LG2020
Learning a Formula of Interpretability to Learn Interpretable Formulas
Marco Virgolin, Andrea De Lorenzo, Eric Medvet +1
Many risk-sensitive applications require Machine Learning (ML) models to be interpretable. Attempts to obtain interpretable models typically rely on tuning, by trial-and-error, hyp…