2 papers
cs.LG2021
Model Learning with Personalized Interpretability Estimation (ML-PIE)
Marco Virgolin, Andrea De Lorenzo, Francesca Randone +2
High-stakes applications require AI-generated models to be interpretable. Current algorithms for the synthesis of potentially interpretable models rely on objectives or regularizat…
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…