Publications

A full list of my publications is available on my Google Scholar profile.

Papers and Book Chapters

• Manipulating hidden-Markov-model inferences by corrupting batch data (2023). Caballero, W., Camacho, J. M., Ekin, T., and Naveiro, R. Computers \& Operations Research. https://doi.org/10.1016/j.cor.2023.106478

• Adversarial Machine Learning: Bayesian Perspectives (2023). Ríos Insua, D., Naveiro, R., Gallego, V. and Poulos, J. Journal of the American Statistical Association 1-22. https://doi.org/10.1080/01621459.2023.2183129
  

• Statistical Challenges in Automated Driving Systems (2023). Caballero, W., Ríos Insua, D. and Naveiro, R.. Applied Stochastic Models in Business and Industry. https://doi.org/10.1002/asmb.2765
  

• Design of new dispersants using machine learning and visual analytics (2023). Jimena, M., Naveiro, R. et. al. Polymers, 15(5), 1324. https://doi.org/10.3390/polym15051324
  

• Augmented Probability Simulation Methods for Sequential Games (2023). Ekin, T., Naveiro, R., Ríos Insua, D., and Torres-Barrán, A. European Journal of Operational Research, 49(3), 100768. https://doi.org/10.1016/j.ejor.2022.06.042
  

• Augmented probability simulation for adversarial risk analysis in general security games (2022). Naveiro, R., Ríos Insua, D., and Camacho, J. M. Proceedings of the International Defense and Homeland Security Simulation Workshop. I3M. https://www.cal-tek.eu/proceedings/i3m/2022/dhss/002/pdf.pdf. Awarded with Best Paper Award.
  

• Evaluation of female sexual health in routine gynaecological practice (2022). Fuentes, M. N., Villena, R. B., Naveiro, R., Sánchez, M. H., Roca, L. C., and Parra, J. F. Clínica e Investigación en Ginecología y Obstetricia, 49(3), 100768. https://doi.org/10.1016/j.gine.2022.100768
  
  

• Artificial Intelligence in Tribology: Design of new dispersants using artificial intelligence tools (2022). Campillo, N., Talavante, P., Ponzoni, I., Soto, A., Martínez, M. J., Naveiro, R., et. al. In 23rd International Colloquium Tribology: Industrial and Automotive Lubrication, p. 423. expert verlag.
  
  

• Managing Driving Modes in Automated Driving Systems (2022). Ríos Insua, D., Caballero, W., and Naveiro, R. Transportation Science, 0(0). https://doi.org/10.1287/trsc.2021.1110
  

• Modeling Ethical and Operational Preferences in Automated Driving Systems (2022). Caballero, W., Naveiro, R. and Ríos Insua, D. Decision Analysis, 19(1):21–43. https://doi.org/10.1287/deca.2021.0441
  

• Towards Acceptance of Automated Driving Systems (2021). Jamson, S. L., Risvas, K., Naveiro, R., et. al. Proceedings of the 5th International Conference on Computer-Human Interaction Research and Applications (CHIRA 2021). 232–239. https://www.scitepress.org/Papers/2021/107213/107213.pdf
  

• Challenge 8: Smart Cybersecurity (2021). Arroyo Guardeño, D., Brox Jiménez, P., Godoy, J. A., Villagra, J., Mueller, H., Gallego, V., Kosgodagan, A., Naveiro, R., et. al. White Papers. CSIC Scientific Challenges: Towards 2030, vol. 11. http://libros.csic.es/product_info.php?products_id=1493
  
  

• Adversarial attacks against Bayesian forecasting dynamic models (2021). Naveiro, R. Proceedings of the 22nd European Young Statisticians Meetings (EYSM 2021). https://www.eysm2021.panteion.gr/publications.html
  

• AI in drug development: a multidisciplinary perspective (2021). Gallego, V., Naveiro, R., Roca, C. et al. Molecular Diversity, 25(3):1461–1479. https://doi.org/10.1007/s11030-021-10266-8
  

• Perspectives on Adversarial Classification (2020). Ríos Insua, D., Naveiro, R., and Gallego, V. Mathematics, 8(11):1957. https://doi.org/10.3390/math8111957
  

• Adversarial Risk Analysis (Overview) (2020). Banks, D., Gallego, V., Naveiro, R., and Ríos Insua, D. WIREs Comput. Stat., e1530, 1–16. https://doi.org/10.1002/wics.1530
  

• Hydroxicloroquine for pre-exposure prophylaxis for SARS-CoV-2 (2020). Lopez de la Iglesia, J., Cubelos, N., Naveiro, R., et. al. Current Trends in Medicine, 2(1):9–18. https://doi.org/10.47726/ctm.1003
  

• Adversarial Classification: An adversarial Risk Analysis approach (2019). Naveiro, R., Redondo, A., Insua, D. R., and Ruggeri, F. International Journal of Approximate Reasoning, 113: 133–148. https://doi.org/10.1016/j.ijar.2019.07.003
  

• Large Scale Automated Forecasting for Monitoring Network Safety and Security (2019). Naveiro, R., Rodríguez, S., and Ríos Insua, D. Applied Stochastic Models in Business and Industry, 35: 431–447. https://doi.org/10.1002/asmb.2436
  

• Gradient Methods for Solving Stackelberg Games (2019). Naveiro, R., and Ríos Insua, D. In: Pekeč S., Venable K.B. (eds) Algorithmic Decision Theory. ADT 2019. Lecture Notes in Computer Science, 11834: 126–140. Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-030-31489-7_9
  

• Reinforcement Learning under Threats. (2019). Gallego, V., Naveiro, R., and Ríos Insua, D. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01): 9939-9940. https://doi.org/10.1609/aaai.v33i01.33019939
  

Books

• ¿Qué sabemos de Análisis de Riesgos? (2022). Ríos Insua D. and Naveiro R.. CSIC-La Catarata. ISBN: 978-84-1352-458-0 .

Submitted papers

• Adversarial Risk Analysis for Heterogeneous Traffic Management (2022). Caballero, W., Naveiro, R. and Ríos Insua, D.
  

• Statistical Challenges in Automated Driving Systems (2022). Caballero, W., Ríos Insua, D. and Naveiro, R.
  

• Poisoning Hidden-Markov-Model Inferences on Batch Data (2022). Camacho, J. M., Caballero, W., Ekin, T., and Naveiro, R.
  

• Deep learning for novel drug development (2022). Naveiro, R., Jimena, M., Soto, A., Ponzoni, I., Ríos Insua, D., and Campillo, N.
  

• Protecting Classifiers from Attacks. A Bayesian Approach (2021). Gallego, V., Naveiro, R., Redondo, A., Ríos Insua, D., and Ruggeri, F. arXiv preprint arXiv:2004.08705.
  

• Data Sharing Games (2020). Gallego, V., Naveiro, R., Ríos Insua, D., and Rozas W. arXiv preprint arXiv:2101.10721.
  

• Opponent Aware Reinforcenment Learning (2019). Gallego, V., Naveiro, R., and Ríos Insua, D. arXiv preprint arXiv:1809.01560.