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
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…
cs.CL2025
SCoRE: Streamlined Corpus-based Relation Extraction using Multi-Label Contrastive Learning and Bayesian kNN
Luca Mariotti, Veronica Guidetti, Federica Mandreoli
The growing demand for efficient knowledge graph (KG) enrichment leveraging external corpora has intensified interest in relation extraction (RE), particularly under low-supervisio…