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…
Decision-Aware Proximal Bridge Learning for Optimal Treatment Selection
Tomàs Garriga, Alejandro Almodóvar, Axel Brando +3
Individualized treatment selection with continuous actions requires accurate causal response estimation in decision-relevant regions, rather than uniformly over the entire action s…
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…
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…
Proliferating active disks with game dynamical interaction
Alejandro Almodóvar, Tobias Galla, Cristóbal López
We study a system of self-propelled, proliferating finite-size disks with game-theoretical interactions, where growth rates depend on local population composition. We analyze how t…
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…