3 papers
cs.LG2026
Coefficient Calibration as Selection Pressure in Symbolic Regression
Mattia Billa, Veronica Guidetti, Federica Mandreoli
In memetic symbolic regression, candidate structures are compared after coefficient calibration, so the calibration protocol itself contributes to evolutionary selection. Standard…
cs.LG2026
A Comparative Study of Model Selection Criteria for Symbolic Regression
Ali Soltani, Gabriel Kronberger, Fabricio Olivetti de Franca +2
Effective model selection is critical in symbolic regression (SR) to identify mathematical expressions that balance accuracy and complexity, and have low expected error on unseen d…
cs.LG2026
Interpretable ML Under the Microscope: Performance, Meta-Features, and the Regression-Classification Predictability Gap
Mattia Billa, Giovanni Orlandi, Veronica Guidetti +1
As machine learning models are increasingly deployed in high-stakes domains, the need for interpretability has grown to meet strict regulatory and accountability constraints. Despi…