3 papers
cs.AI2026
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…
cs.IR2025
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…
cs.LG2024
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…