4 citations · 4 across the 3 of their papers we have counts for
3 papers · 1 filter
Close to Reality: Interpretable and Feasible Data Augmentation for Imbalanced Learning
Matheus Camilo da Silva, Gabriel Gustavo Costanzo, Andrea de Lorenzo +1
Many machine learning classification tasks involve imbalanced datasets, which are often subject to over-sampling techniques aimed at improving model performance. However, these tec…
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…
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…