24 citations · 30 across the 10 of their papers we have counts for
11 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…
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…
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…
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…
Synthetic Tabular Data Validation: A Divergence-Based Approach
Patricia A. Apellániz, Ana Jiménez, Borja Arroyo Galende +2
The ever-increasing use of generative models in various fields where tabular data is used highlights the need for robust and standardized validation metrics to assess the similarit…
An improved tabular data generator with VAE-GMM integration
Patricia A. Apellániz, Juan Parras, Santiago Zazo
The rising use of machine learning in various fields requires robust methods to create synthetic tabular data. Data should preserve key characteristics while addressing data scarci…
SAVAE: Leveraging the variational Bayes autoencoder for survival analysis
Patricia A. Apellániz, Juan Parras, Santiago Zazo
As in many fields of medical research, survival analysis has witnessed a growing interest in the application of deep learning techniques to model complex, high-dimensional, heterog…