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stat.ME2026
Random mixtures in Bayes Hilbert spaces
Giulia Patanè, Sonja Greven, Alessandra Menafoglio
We present a framework for the analysis and unmixing of random density mixtures in the Bayes Hilbert space. General identifiability results for mixtures in Hilbert spaces are estab…
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…