5 papers
Spectral Biclustering-Driven Scalability for Post-Hoc Explainability in Recommender Systems
Jose L. Salmeron, Irina Arévalo
Explainability in recommender systems is essential for ensuring transparency, accountability, and trust, yet existing post-hoc methods often encounter severe scalability challenges…
CAFP: A Post-Processing Framework for Group Fairness via Counterfactual Model Averaging
Irina Arévalo, Marcos Oliva
Ensuring fairness in machine learning predictions is a critical challenge, especially when models are deployed in sensitive domains such as credit scoring, healthcare, and criminal…
Model-agnostic post-hoc explainability for recommender systems
Irina Arévalo, Jose L Salmeron
Recommender systems often benefit from complex feature embeddings and deep learning algorithms, which deliver sophisticated recommendations that enhance user experience, engagement…
Concurrent vertical and horizontal federated learning with fuzzy cognitive maps
Jose L Salmeron, Irina Arévalo
Data privacy is a major concern in industries such as healthcare or finance. The requirement to safeguard privacy is essential to prevent data breaches and misuse, which can have s…
Blind Federated Learning without initial model
Jose L. Salmeron, Irina Arévalo
Federated learning is an emerging machine learning approach that allows the construction of a model between several participants who hold their own private data. This method is sec…