3 papers
stat.ML2026
K-Models: a Flexible and Interpretable Method for Ordinal Clustering with Application to Antigen-Antibody Interaction Profiles
Giulia Patanè, Alessandra Menafoglio, Alexander Krauth +4
Existing clustering methods for functional data often prioritize partitioning accuracy over interpretability, making it challenging to extract meaningful insights when the data-gen…
stat.ME2025
Functional-Ordinal Canonical Correlation Analysis With Application to Data from Optical Sensors
Giulia Patanè, Federica Nicolussi, Alexander Krauth +4
We address the problem of predicting a target ordinal variable based on observable features consisting of functional profiles. This problem is crucial, especially in decision-makin…
stat.AP2024
An interpretable and transferable model for shallow landslides detachment combining spatial Poisson point processes and generalized additive models
Giulia Patanè, Teresa Bortolotti, Vasil Yordanov +4
Less than 10 meters deep, shallow landslides are rapidly moving and strongly dangerous slides. In the present work, the probabilistic distribution of the landslide detachment point…