2 citations · 2 across the 2 of their papers we have counts for
6 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…
Non-monotonic causal discovery with Kolmogorov-Arnold Fuzzy Cognitive Maps
Jose L. Salmeron
Fuzzy Cognitive Maps constitute a neuro-symbolic paradigm for modeling complex dynamic systems, widely adopted for their inherent interpretability and recurrent inference capabilit…
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…
A chaotic maps-based privacy-preserving distributed deep learning for incomplete and Non-IID datasets
Irina Arévalo, Jose L. Salmeron
Federated Learning is a machine learning approach that enables the training of a deep learning model among several participants with sensitive data that wish to share their own kno…