I4TSPS: a Visual-Interactive Web System for Industrial Time-Series Pre-processing. - Kevin Villalobos; Jon Vadillo; Borja Diez; Borja Calvo; Arantza Illarramendi (IEEE BigData, 2018)

Universal adversarial examples in speech command classification. - Jon Vadillo; Roberto Santana 001 (CoRR, 2019)

Exploring Gaps in DeepFool in Search of More Effective Adversarial Perturbations. - Jon Vadillo; Roberto Santana 001; José Antonio Lozano 001 (LOD (2), 2020)

On the human evaluation of audio adversarial examples. - Jon Vadillo; Roberto Santana 001 (CoRR, 2020)

Adversarial Perturbations for Evolutionary Optimization. - Unai Garciarena; Jon Vadillo; Alexander Mendiburu; Roberto Santana 001 (LOD, 2021)

When and How to Fool Explainable Models (and Humans) with Adversarial Examples. - Jon Vadillo; Roberto Santana 001; José Antonio Lozano 001 (CoRR, 2021)

On the human evaluation of universal audio adversarial perturbations. - Jon Vadillo; Roberto Santana 001 (Comput. Secur., 2022)

Analysis of dominant classes in universal adversarial perturbations. - Jon Vadillo; Roberto Santana 001; José Antonio Lozano 001 (Knowl. Based Syst., 2022)

Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions. - Jon Vadillo; Roberto Santana 001; José Antonio Lozano 001 (J. Mach. Learn. Res., 2023)

IKER-GAITU: Research on Language Technology for Basque and Other Low-Resource Languages. - Eneko Agirre; Itziar Aldabe; Xabier Arregi; Mikel Artetxe; Unai Atutxa; Ekhi Azurmendi; Iker de la Iglesia; Julen Etxaniz; Víctor García Romillo; Inma Hernáez Rioja; Asier Herranz; Mikel Iruskieta; et al. (SEPLN (Projects and Demonstrations), 2024)

Uncertainty-Aware Explanations Through Probabilistic Self-Explainable Neural Networks. - Jon Vadillo; Roberto Santana 001; José Antonio Lozano 001; Marta Kwiatkowska (CoRR, 2024)

Adversarial Attacks in Explainable Machine Learning: A Survey of Threats Against Models and Humans. - Jon Vadillo; Roberto Santana 001; José Antonio Lozano 001 (WIREs Data. Mining. Knowl. Discov., 2025)

Fixed Point Explainability. - Emanuele La Malfa; Jon Vadillo; Marco Molinari; Michael J. Wooldridge (CoRR, 2025)