9 papers
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…
No Free Lunch for Synthetic Images under Data Scarcity Conditions
Borja Arroyo Galende, Alejandro Almodóvar, Patricia A. Apellániz +3
This study investigates the trade-offs between fidelity, privacy, and utility in synthetic data generation under conditions of data scarcity and privacy sensitivity. We propose an…
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…
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…
CausalKANs: interpretable treatment effect estimation with Kolmogorov-Arnold networks
Alejandro Almodóvar, Patricia A. Apellániz, Santiago Zazo +1
Deep neural networks achieve state-of-the-art performance in estimating heterogeneous treatment effects, but their opacity limits trust and adoption in sensitive domains such as me…
DeCaFlow: A deconfounding causal generative model
Alejandro Almodóvar, Adrián Javaloy, Juan Parras +2
We introduce DeCaFlow, a deconfounding causal generative model. Training once per dataset using just observational data and the underlying causal graph, DeCaFlow enables accurate c…