5 citations · 5 across the 6 of their papers we have counts for
5 papers · 1 filter
Not all Jensen-Shannon Divergence Estimators are Equal
Alba Garrido, Alejandro Almodóvar, Mar Elizo +3
The Jensen-Shannon divergence is widely reported as a scalar measure of fidelity for synthetic tabular data. Yet, in practice, it is estimated from finite samples using protocols t…
Kolmogorov-Arnold causal generative models
Alejandro Almodóvar, Mar Elizo, Patricia A. Apellániz +2
Causal generative models provide a principled framework for answering observational, interventional, and counterfactual queries from observational data. However, many deep causal m…
Interpretable Clinical Classification with Kolmogorov-Arnold Networks
Alejandro Almodóvar, Patricia A. Apellániz, Alba Garrido +3
The increasing use of machine learning in clinical decision support has been limited by the lack of transparency of many high-performing models. In clinical settings, predictions m…
Deep Survival Analysis in Multimodal Medical Data: A Parametric and Probabilistic Approach with Competing Risks
Alba Garrido, Alejandro Almodóvar, Patricia A. Apellániz +2
Accurate survival prediction is critical in oncology for prognosis and treatment planning. Traditional approaches often rely on a single data modality, limiting their ability to ca…
Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce Scenarios
Patricia A. Apellániz, Ana Jiménez, Borja Arroyo Galende +2
While synthetic tabular data generation using Deep Generative Models (DGMs) offers a compelling solution to data scarcity and privacy concerns, their effectiveness relies on the av…