Marco Benjumeda



Phone: +34-910673093

E-mail: marco.benjumeda.barquita (at)

Regular mail:

Departamento de Inteligencia Artificial

Escuela Técnica Superior de Ingenieros Informáticos

Campus de Montegancedo

Universidad Politécnica de Madrid

28660 Boadilla del Monte, Madrid, España


PHD student supported by a predoctoral contract for the formation of doctors (call 2014) from the Spanish Ministry of Economy and Competitiveness.

Short Bio

Marco recieved his M.Sc. degree in Computer Science from the Autonomous University of Madrid in 2012. He obtained a M.Sc. degree in Artificial Intelligence from the Technical University of Madrid (UPM) in 2014 and is currently a Ph.D. student at UPM's Artificial Intelligence Department.

Research interests

Probabilistic graphical models, Bayesian networks, thin models, multidimensional Bayesian classifiers, deep models.


Benjumeda, M., C. Bielza, and P. Larrañaga, "Tractability of Most Probable Explanations in Multidimensional Bayesian Network Classifiers", International Journal of Approximate Reasoning, vol. 93, pp. 74-87, 2018.
Benjumeda, M., C. Bielza, and P. Larrañaga, "Learning Bayesian networks with low inference complexity", Progress in Artificial Intelligence (no JCR), vol. 5, issue 1, pp. 15-26, 2016.


Benjumeda, M., C. Bielza, and P. Larrañaga, "Learning tractable multidimensional Bayesian network classifiers", Proceedings of the Eighth International Conference on Probabilistic Graphical Models, vol. 52, pp. 13-24, 2016.
Benjumeda, M., P. Larrañaga, and C. Bielza, "Learning low inference complexity Bayesian networks", Proc. of the Conference of the Spanish Association for Artificial Intelligence (CAEPIA), 2015.
Karshenas, H., R. Santana, C. Bielza, and P. Larrañaga, "Multi-Objective Optimization with Joint Probabilistic Modeling of Objectives and Variables", Lecture Notes in Computer Science, no. 6576: Springer, pp. 298-312, 2011.


Benjumeda, M., S. Luengo-Sanchez, P. Larrañaga, C. Bielza. "Bounding the Complexity of Structural Expectation-Maximization", Tractable Probabilistic Models (ICML), Stockholm, 2018.