9 papers
Closing the Alignment-Maturity Gap in Federated Prototype Learning
Mario Casado-Diez, Alejandro Dopico-Castro, Verónica Bolón-Canedo +1
Learning discriminative visual representations from distributed, heterogeneous data is a fundamental challenge in Federated Learning (FL). Prototype-based methods address statistic…
Robust gene prioritization for Dietary Restriction via Fast-mRMR Feature Selection techniques
Rubén Fernández-Farelo, Jorge Paz-Ruza, Bertha Guijarro-Berdiñas +2
Gene prioritization (identifying genes potentially associated with a biological process) is increasingly tackled with Artificial Intelligence. However, existing methods struggle wi…
A robust methodology for long-term sustainability evaluation of Machine Learning models
Jorge Paz-Ruza, João Gama, Amparo Alonso-Betanzos +1
Sustainability and efficiency have become essential considerations in the development and deployment of Artificial Intelligence systems, but existing regulatory practices for Green…
FedHENet: A Frugal Federated Learning Framework for Heterogeneous Environments
Alejandro Dopico-Castro, Oscar Fontenla-Romero, Bertha Guijarro-Berdiñas +2
Federated Learning (FL) enables collaborative training without centralizing data, essential for privacy compliance in real-world scenarios involving sensitive visual information. M…
CIFNet: An Analytic Neural Learning Framework for Efficient and Calibrated Class-Incremental Learning
Alejandro Dopico-Castro, Oscar Fontenla-Romero, Bertha Guijarro-Berdiñas +1
Class-Incremental Learning (CIL) in deep neural networks is conventionally framed as an iterative gradient-based optimization problem, incurring high computational cost, hyperparam…
Beyond RMSE and MAE: Introducing EAUC to unmask hidden bias and unfairness in dyadic regression models
Jorge Paz-Ruza, Amparo Alonso-Betanzos, Bertha Guijarro-Berdiñas +2
Dyadic regression models, which output real-valued predictions for pairs of entities, are fundamental in many domains (e.g. obtaining user-product ratings in Recommender Systems) a…