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cs.LG2025
Decentralized Federated Learning of Probabilistic Generative Classifiers
Aritz Pérez, Carlos Echegoyen, Guzmán Santafé
Federated learning is a paradigm of increasing relevance in real world applications, aimed at building a global model across a network of heterogeneous users without requiring the…
cs.LG2024
Risk-based Calibration for Generative Classifiers
Aritz Pérez, Carlos Echegoyen, Guzmán Santafé
Generative classifiers are constructed on the basis of a joint probability distribution and are typically learned using closed-form procedures that rely on data statistics and maxi…