4 papers
Minimax Generalized Cross-Entropy
Kartheek Bondugula, Santiago Mazuelas, Aritz Pérez +1
Loss functions play a central role in supervised classification. Cross-entropy (CE) is widely used, whereas the mean absolute error (MAE) loss can offer robustness but is difficult…
Efficient Large-Scale Learning of Minimax Risk Classifiers
Kartheek Bondugula, Santiago Mazuelas, Aritz Pérez
Supervised learning with large-scale data usually leads to complex optimization problems, especially for classification tasks with multiple classes. Stochastic subgradient methods…
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…
PAC-Bayes-Chernoff bounds for unbounded losses
Ioar Casado, Luis A. Ortega, Aritz Pérez +1
We introduce a new PAC-Bayes oracle bound for unbounded losses that extends Cramér-Chernoff bounds to the PAC-Bayesian setting. The proof technique relies on controlling the tails…