Patricia A. Apellániz

According to our database, Patricia A. Apellániz authored at least 14 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2025

CausalKANs: interpretable treatment effect estimation with Kolmogorov-Arnold networks.

Alejandro Almodóvar, Patricia A. Apellániz, Santiago Zazo, Juan Parras

CoRR, September, 2025

Interpretable Clinical Classification with Kolgomorov-Arnold Networks.

Alejandro Almodóvar, Patricia A. Apellániz, Alba Garrido, Fernando Fernández-Salvador, Santiago Zazo, Juan Parras

CoRR, September, 2025

Deep Survival Analysis in Multimodal Medical Data: A Parametric and Probabilistic Approach with Competing Risks.

Alba Garrido, Alejandro Almodóvar, Patricia A. Apellániz, Juan Parras, Santiago Zazo

CoRR, July, 2025

Membership Inference Attacks and Differential Privacy: A Study Within the Context of Generative Models.

Borja Arroyo Galende, Patricia A. Apellániz, Juan Parras, Santiago Zazo, Silvia Uribe

IEEE Open J. Comput. Soc., 2025

Artificial inductive bias for synthetic tabular data generation in data-scarce scenarios.

Patricia A. Apellániz, Ana Jiménez, Borja Arroyo Galende, Juan Parras, Santiago Zazo

Neurocomputing, 2025

Improving Synthetic Data Generation Through Federated Learning in Scarce and Heterogeneous Data Scenarios.

Patricia A. Apellániz, Juan Parras, Santiago Zazo

Big Data Cogn. Comput., 2025

2024

Synthetic Tabular Data Validation: A Divergence-Based Approach.

Patricia A. Apellániz, Ana Jiménez, Borja Arroyo Galende, Juan Parras, Santiago Zazo

IEEE Access, 2024

An Improved Tabular Data Generator with VAE-GMM Integration.

Patricia A. Apellániz, Juan Parras, Santiago Zazo

Proceedings of the 32nd European Signal Processing Conference, 2024

CR-SAVAE: A Parametric Method for Survival Analysis with Competing Risks.

Patricia A. Apellániz, Juan Parras, Santiago Zazo

Proceedings of the 32nd European Signal Processing Conference, 2024

Survival Model Optimization via Federated Learning: A Study Combining Simulations and Experiments.

Francesco Casadei, Luciana Carota, Gianluca Asti, Saverio D'Amico, Davide Piscia, Santiago Zazo, Patricia A. Apellániz, Juan Parras, Claudia Sala, Cesare Rollo, Nono S. C. Merleau, Piero Fariselli, Matteo Giovanni Della Porta, Tiziana Sanavia, Federico Álvarez García, Gastone C. Castellani, Enrico Giampieri

Proceedings of the IEEE International Conference on Big Data, 2024

2023

SAVAE: Leveraging the variational Bayes autoencoder for survival analysis.

Patricia A. Apellániz, Juan Parras, Santiago Zazo

CoRR, 2023

2022

Inverse Reinforcement Learning: A New Framework to Mitigate an Intelligent Backoff Attack.

Juan Parras, Alejandro Almodóvar, Patricia A. Apellániz, Santiago Zazo

IEEE Internet Things J., 2022

An online learning algorithm to play discounted repeated games in wireless networks.

Juan Parras, Patricia A. Apellániz, Santiago Zazo

Eng. Appl. Artif. Intell., 2022

2021

Deep Learning for Efficient and Optimal Motion Planning for AUVs with Disturbances.

Juan Parras, Patricia A. Apellániz, Santiago Zazo

Sensors, 2021