Proceedings

2024

• R. Sojo, P. Larrañaga, C. Bielza, J. Díaz-Rozo (2024). Fast bridge health monitoring deployment with transferable expertise.Bridge Maintenance, Safety, Management, Digitalization and Sustainability – Proceedings of the 12th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2024

2023

• S.R. Ram, B.A. Strange, L. Zhang, T. del Ser, E. Lucia, V. Lorenzo, M. Valent, M.A. Zea-Sevilla, B. Frades, T. Heskes, P. Larrañaga, C. Bielza, P. Sanchez-Juan (2023). Structural modeling of clinical factors for validation and prediction of future conversion to mild cognitive impairment . Alzheimer’s Association International Conference (AAIC23).
• D. Lozano-Paredes, L. Bote-Curiel, C. Bielza, P. Larrañaga, M. Sabater-Molina, J.R. Gimeno-Blanes, S. Muñoz-Romero, F.J. Gimeno-Blanes, J.L. Rojo-Alvarez (2023). High-dimensional feature characterization of single nucleotide variants in hypertrophic cardiomyopathy. 50th Computing in Cardiology Conference (CinC-2023). DOI:10.22489/CinC.2023.172
• V. P. Soloviev, P. Larrañaga, C. Bielza (2023). Variational quantum algorithm parameter tuning with estimation of distribution algorithms. IEEE Congress on Evolutionary Computation.DOI: 10.1109/CEC53210.2023.10254138
• V. P. Soloviev, C. Bielza, P. Larrañaga (2023). A probabilistic perspective for optimizing the parameters of quantum heuristics using evolutionary algorithms. Quantum Information in Spain ICE-8
• J. Casajús-Setién, C. Bielza, P. Larrañaga (2023). Anomaly-based intrusion detection in IIoT networks using transformer models. IEEE International Conference on Cyber Security and Resilience

2022

• E Valero-Leal, P Larrañaga, C Bielza, Interpreting Time-Varying Dynamic Bayesian Networks for Earth Climate Modelling, International Conference on Probabilistic Graphical Models, 373-384
• C Villa-Blanco, A Bregoli, C Bielza, P Larrañaga, F Stella, Structure learning algorithms for multidimensional continuous-time Bayesian network classifiers , International Conference on Probabilistic Graphical Models, 313-324
• J Casajús-Setién, C Bielza, P Larrañaga , Evolutive Adversarially-Trained Bayesian Network Autoencoder for Interpretable Anomaly Detection , International Conference on Probabilistic Graphical Models, 397-408
• VP Soloviev, P Larrañaga, C Bielza , Quantum parametric circuit optimization with estimation of distribution algorithms , Proceedings of the Genetic and Evolutionary Computation Conference Companion .
• E Valero-Leal, P Larranaga, C Bielza , Extending MAP-independence for Bayesian network explainability.

2021

• C Puerto-Santana, P Larrañaga, J Diaz-Rozo, C Bielza, An Online Feature Selection Methodology for Ball-Bearing Harmonic Frequencies Based on HMMs, International Workshop on Soft Computing Models in Industrial and Environmental Applications.
• D Quesada, C Bielza, P Larrañaga, Structure Learning of High-Order Dynamic Bayesian Networks via Particle Swarm Optimization with Order Invariant Encoding, International Conference on Hybrid Artificial Intelligence Systems, 158-171
• VP Soloviev, C Bielza, P Larrañaga, Quantum-Inspired Estimation of Distribution Algorithm to solve the travelling salesman problem, 2021 IEEE Congress on Evolutionary Computation (CEC), 416-425

2020

• N Bernaola, M Michiels, C Bielza, P Larrañaga, BayesSuites: An Open Web Framework for Visualization of Massive Bayesian Networks, International Conference on Probabilistic Graphical Models, 593-596

2019

• N. Bernaola, P. Larrañaga, C. Bielza (2019).  Using Bayesian networks for differential analysis of gene regulatory networks. 3rd HBP Student Conference on Interdisciplinary Brain Research.
• B. Mihaljevic, C. Bielza, and P. Larrañaga (2019). Multivariate comparison of human and mouse pyramidal cell dendritic morphologies. 3rd HBP Student Conference on Interdisciplinary Brain Research

2018

• Córdoba-Sánchez, I., G. Varando, C. Bielza, and P. Larrañaga, “A fast Metropolis-Hastings method for generating random correlation matrices (Slides)“, International Conference on Intelligent Data Engineering and Automated Learning , vol. Lecture Notes in Computer Science 11314: Springer, pp. 117-124, 2018.
• Córdoba-Sánchez, I., G. Varando, C. Bielza, and P. Larrañaga, “A partial orthogonalization method for simulating covariance and concentration graph matrices (Slides)“, Proceedings of Machine Learning Research, vol. 72, pp. 61-72, 2018.
• Córdoba-Sánchez, I., E.C. Garrido-Merchán, D. Hernández-Lobato, C. Bielza, and P. Larrañaga, “Bayesian optimization of the PC algorithm for learning Gaussian Bayesian networks (Slides)“, Conference of the Spanish Association for Artificial Intelligence 2018, vol. Lecture Notes in Artificial Intelligence 11160: Springer, pp. 44-54, 2018.
• Gil-Begue, S., P. Larrañaga, and C. Bielza, “Multi-dimensional Bayesian network classifier trees (Slides)“, Proceedings of the 19th International Conference on Intelligent Data Engineering and Automated Learning, vol. Lecture Notes in Computer Science 11314: Springer, pp. 354-363, 2018.
• G. Varando, C. Bielza, P. Larrañaga (2018). MultiMap: An application to visualize, edit and analyze spatial data. 2nd HBP Student Conference on Transdisciplinary Research Linking Neuroscience, Brain Medicine and Computer Science
• M. Benjumeda, S. Luengo-Sánchez, P. Larrañaga, C. Bielza (2018). Bounding the complexity of structural expectation-maximization. Workshop on Tractable Probabilistic Models (TPM-2018) within the 35th ICML, 1-3
• Mihaljevic, B., C. Bielza, and P. Larrañaga, “Learning Bayesian network classifiers with completed partially directed acyclic graphs (Slides)“, Proceedings of Machine Learning Research, vol. 72, pp. 272-283, 2018.
• Puerto-Santana, C., C. Bielza, and P. Larrañaga, “Asymmetric Hidden Markov Models with Continuous Variables“, CAEPIA 2018: Springer LNAI, Accepted, 2018.
• Rodriguez-Sanchez, F., P. Larrañaga, and C. Bielza, “Discrete model-based clustering with overlapping subsets of attributes“, Proceedings of Machine Learning Research, vol. 72, pp. 392-403, 2018.

2017

• Mesonero, C. Bielza, P. Larrañaga (2017). Architecture for anomaly detection in a laser heating surface process. Proceedings of 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA-2017). Workshop on Cyber-Physical Systems and Smart Networked Systems, 1-4.

• I. Leguey, S. Kato, C. Bielza, P. Larrañaga (2017). Hybrid mutual information. Proceedings of Advances in Directional Statistics. The Second Triennial Workshop (ADISTA-17), 47.
• S. Luengo-Sanchez, C. Bielza, P. Larrañaga (2017). Directional-linear data clustering using structural expectation-maximization algorithm. Proceedings of Advances in Directional Statistics. The Second Triennial Workshop (ADISTA-17), 48-49.

2016

• Atienza, D., C. Bielza, and P. Larrañaga, “Anomaly Detection with a Spatio-Temporal Tracking of the Laser Spot“, Proceedings of the Eight European Starting AI Researcher Symposium (STAIRS 2016), pp. 137-142, 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.
• Diaz, J., C. Bielza, J.L. Ocaña, and P. Larrañaga, “Development of a Cyber-Physical System based on selective Gaussian naïve Bayes model for a self-predict laser surface heat treatment process control“, Machine Learning for Cyber Physical Systems: Selected papers from the International Conference ML4CPS 2015: Springer Berlin Heidelberg, pp. 1-8, 2016.
• Fernandez-Gonzalez, P., P. Larrañaga, and C. Bielza, “Bayesian Gaussian networks for multidimensional classification of morphologically characterized neurons in the NeuroMorpho repository“, CAEPIA, 17th, 2016.
• Larrañaga, P., “Bayesian networks for neuroscience”, Workshop on Advances and Applications on Data Science and Engineering, Madrid, Real Academia de Ingenieria, 2016.
• Leguey, I., C. Bielza, and P. Larrañaga, “Tree-structured Bayesian networks for wrapped Cauchy directional distributions“, CAEPIA, Salamanca, Springer, pp. 207-216, 2016.
• Luengo-Sanchez, S., C. Bielza, and P. Larrañaga, “Hybrid Gaussian and von Mises model-based clustering“, European Conference on Artificial Intelligence, vol. 285, pp. 855-862, 2016.
• Ogbechie, A., J. Díaz-Rozo, P. Larrañaga, and C. Bielza, “Dynamic Bayesian Network-Based Anomaly Detection for In-Process Visual Inspection of Laser Surface Heat Treatment“, ML4CPS – Machine Learning for Cyber ​​Physical Systems and Industry 4.0, Karlsruhe, Germany, Springer, 2016.

2015

• 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.
• Córdoba-Sánchez, I., C. Bielza, and P. Larrañaga, “Towards Gaussian Bayesian Network Fusion“, Symbolic and Quantitative Approaches to Reasoning with Uncertainty, LNAI 9161, vol. 9161: Springer International Publishing, pp. 519-528, July, 2015.
• Diaz-Rozo, J., J. Posada, I. Barandiaran, and C. Toro, “Recommendations for sustainability in production from a machine-tool manufacturer“, KES-SDM, 2015.
• Diaz-Rozo, J., C. Bielza, J. Luis Ocaña, and P. Larrañaga, “Machine Learning for Cyber Physical Systems ML4CPS“, Development of a Cyber-Physical System based on selective Gaussian naive Bayes model for a self-predict laser surface heat treatment process control: Springer Vieweg, 2015.
• Fernandez-Gonzalez, P., C. Bielza, and P. Larrañaga, “Multidimensional classifiers for neuroanatomical data”, ICML Workshop on Statistics, Machine Learning and Neuroscience (Stamlins 2015), Lille, France, ICML, Jul 2015.
• Ochoa, A., J. Diaz-Rozo, and B. Kamp, “Insights for Industry 4.0 Implementation in machine tool servitization”, International Virtual Concept Workshop on INDUSTRIE 4.0, 2015.
• Ochoa, A., J. Diaz-Rozo, and B. Kamp, “Challenges and opportunities for servitization in the machine tool industry in the era of Industry 4.0”, 4th International Conference on Business Servitization, 2015.
• Rodriguez-Lujan, L., C. Bielza, and P. Larrañaga, “Regularized Multivariate von Mises Distribution“, Proc. of the Conference of the Spanish Association for Artificial Intelligence (CAEPIA), 2015.

2014

• Varando, G., C. Bielza, and P. Larrañaga, “Expressive Power of Binary Relevance and Chain Classifiers Based on Bayesian Networks for Multi-Label Classification“, Lecture Notes in Artificial Intelligence, 8754: Springer, pp. 519-534, 2014.

2013

• Anton-Sanchez, L., C. Bielza, and P. Larrañaga, “Towards optimal neuronal wiring through estimation of distribution algorithms“, Proceedings of the 15h Annual Conference companion on Genetic and Evolutionary Computation Conference Companion, New York, NY, USA, ACM, pp. 1647-1650, 2013.
• Guerra, L., R. Benavides-Piccione, C. Bielza, V. Robles, J. DeFelipe, and P. Larrañaga, “Semi-supervised projected clustering for classifying GABAergic interneurons“, Artificial Intelligence in Medicine 2013, LNAI 7885: Springer, pp. 156–165, 2013.
• Lopez-Cruz, P. L., C. Bielza, and P. Larrañaga, “Learning conditional linear Gaussian classifiers with probabilistic class labels“, Advances in Artificial Intelligence, Proceedings of the 15th MultiConference of the Spanish Association for Artificial Intelligence, LNCS 8109: Springer, pp. 139-148, 2013.
• Lopez-Cruz, P. L., T. D. Nielsen, C. Bielza, and P. Larrañaga, “Learning mixtures of polynomials of conditional densities from data“, Advances in Artificial Intelligence, Proceedings of the 15th MultiConference of the Spanish Association for Artificial Intelligence, LNCS 8109: Springer, pp. 363-372, 2013.
• Mihaljevic, B., P. Larrañaga, and C. Bielza, “Augmented Semi-naive Bayes Classifier“, Advances in Artificial Intelligence, Proceedings of the 15th MultiConference of the Spanish Association for Artificial Intelligence, LNAI 8109: Springer, pp. 159-167, 2013.

2012

• Armañanzas, R., P. Martínez-Martín, C. Bielza, and P. Larrañaga, “Restating clinical impression of severity index for Parkinson’s disease using just non-motor criteria“, Proceedings of the 25th European Conference on Operational Research, Vilnius, Lithuania, pp. 231-232, 2012.
• García-Bilbao, A., R. Armañanzas, Z. Ispizua, B. Calvo, A. Alonso-Varona, I. Inza, P. Larrañaga, G. López Vivanco, B. Suárez-Merino, and M. Betanzos, “Identification of a biomarker panel for colorectal cancer diagnosis“, Proceedings of the 6th International Meeting on Biotechnology, Bilbao, Spain, pp. 77, 2012.
• Lopez-Cruz, P. L., C. Bielza, and P. Larrañaga, “Learning mixtures of polynomials from data using B-spline interpolation“, Sixth European Workshop on Probabilistic Graphical Models (PGM 2012), Granada, Spain, pp. 271-278, 2012.
• Santana, R., C. Bielza, and P. Larrañaga, “Maximizing the number of polychronous groups in spiking networks“, Companion Material Proceedings of the 14th Annual Genetic and Evolutionary Computation Conference (GECCO-2012): ACM Digital Library, pp. 1499-1500, 2012.

2011

• Borchani, H., C. Bielza, and P. Larrañaga, “Learning multi-dimensional Bayesian network classifiers using Markov blankets: A case study in the prediction of HIV protease inhibitors“, AIME’11 Workshop on Probabilistic Problem Solving in Biomedicine, 2011.
• Ibañez, A., P. Larrañaga, and C. Bielza, “Predicting the h-index with cost-sensitive naive Bayes“, 11th International Conference on Intelligent Systems Design and Applications: IEEE SMC, pp. 599-604, 2011.
• 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.
• Lopez-Cruz, P. L., C. Bielza, and P. Larrañaga, “The von Mises naive Bayes classifier for directional data“, Advances in Artificial Intelligence, 14th Conference of the Spanish Association for Artificial Intelligence, vol. 7023: Springer, pp. 145-154, 2011.
• Santana, R., H. Karshenas, C. Bielza, and P. Larrañaga, “Regularized k-order Markov Models in EDAs“, Proceedings of the 2011 Genetic and Evolutionary Conference (GECCO-2011): ACM Digital Library, pp. 593–600, 2011.
• Santana, R., H. Karshenas, C. Bielza, and P. Larrañaga, “Quantitative genetics in multi-objective optimization algorithms: From useful insights to effective methods“, Proceedings of the 2011 Genetic and Evolutionary Conference (GECCO-2011): ACM Digital Library, pp. 91-92, 2011.
• Santana, R., C. Bielza, and P. Larrañaga, “Affinity Propagation Enhanced by Estimation of Distribution Algorithms“, Proceedings of the 2011 Genetic and Evolutionary Conference (GECCO-2011): ACM Digital Library, pp. 331–338, 2011.
• Zaragoza, J. H., L. E. Sucar, E. F. Morales, C. Bielza, and P. Larrañaga, “Bayesian chain classifiers for multidimensional classification“, Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI’11): AAAI Press, pp. 2192–-2197, 2011.

2010

• Borchani, H., P. Larrañaga, and C. Bielza, “Mining Concept-Drifting Data Streams Containing Labeled and Unlabeled Instances“, The Twenty Third International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE’10), vol. 1, pp. 531-540, 2010.
• Borchani, H., C. Bielza, and P. Larrañaga, “Learning CB-decomposable multi-dimensional Bayesian network classifiers”, Proceedings of the 5th European Workshop on Probabilistic Graphical Models (PGM’10), pp. 25-32, 2010. Research Gate.
• Cuesta-Infante, R. Santana, J.I. Hidalgo, C. Bielza, P. Larrañaga (2010). Bivariate empirical and n-variate Archimedean copulas in estimation of distribution algorithms. 2010 IEEE Congress on Evolutionary Computation (IEEE-CEC-2010), 1355-1362, IEEE.
• H. Karshenas, R. Santana, C. Bielza, P. Larrañaga (2010). Multi-Objective decomposition with Gaussian Bayesian networks. Proceedings of the International Conference on Metaheuristics and Nature Inspired Computing (META’10), paper 119.
• P. López, C. Bielza, P. Larrañaga, R. Benavides-Piccione, J. DeFelipe (2010). 3D simulation of dendritic morphology using Bayesian networks. 16th Annual Meeting of the Organization for Human Brain Mapping (HBM-2010)

2009

• Diaz, E., E. Ponce-de-Leon, P. Larrañaga, and C. Bielza, “Probabilistic graphical Markov model learning: an adaptive strategy“, MICAI, Lecture Notes in Artificial Intelligence, 5845, vol. 5845: Springer, pp. 225-236, 2009.
• I. Cuesta, C. Bielza, P. Larrañaga, M. Cuenca-Estrella, J.L. Rodríguez-Tudela (2009). Evaluación de los puntos de corte de fluconazol del CLSI y el EUCAST mediante técnicas de minería de datos. Enfermedades Infecciosas y Microbiología Clínica, Vol. 27, 104-105, Elsevier Doyma
• Santana, R., C. Bielza, J. A. Lozano, and P. Larrañaga, “Mining Probabilistic Models Learned by EDAs in the Optimization of Multi-Objective Problems“, Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation: ACM, pp. 445-452, 2009.
• Vidaurre, D., C. Bielza, and P. Larrañaga, “Variable Selection in Local Regression Models via an Iterative LASSO“, The Eighth Workshop on Uncertainty Processing (WUPES’09), pp. 237-250, 2009.

2008

• Armañanzas, R., Y. Saeys, I. Inza, M. García-Torres, Y. van de Peer, C. Bielza, and P. Larrañaga, “Mass spectrometry data analysis: it’s all in the preprocessing“, Proceedings of the Benelux Bioinformatics Conference, Maastricht, The Netherlands, pp. 92, 2008.
• Correa, M., J. R. Alique, and C. Bielza, “Comparativa de Modelos con Aprendizaje Supervisado: Aplicación a un Proceso Industrial”, IV Simposio de Control Inteligente, 2008.
• Correa, M., C. Bielza, J. Pamies-Teixeira, and J. R. Alique, “Redes Bayesianas vs Redes Neuronales en Modelos para la Predicción del Acabado Superficial”, XVII Congreso de Máquinas-Herramienta y Tecnologías de Fabricación, 2008.

2007

• Armañanzas, R., B. Calvo, I. Inza, P. Larrañaga, I. Bernales, A. Fullaondo, and A. M. Zubiaga, “Bayesian Classifiers with Consensus Gene Selection: a case study in the Systemic Lupus Erythematosus“, Progress in Industrial Mathematics at ECMI 2006, vol. 12: Springer, pp. 560-565, 2007.
• Correa, M., M. J. Ramírez, C. Bielza, J. Pamies, and J. R. Alique, “Predicción de la Calidad Superficial Usando Modelos Probabilísticos”, VII Congreso de la Asociación Colombiana de Automática, pp. 1-6, 2007.
• García, A., A. Freije, R. Armañanzas, I. Inza, Z. Ispizua, P. Heredia, P. Larrañaga, G. López Vivanco, T. Suárez, and M. Betanzos, “Gene expression model for the classification of human colorectal cancer and potential CRC biomarkers search“, Drug Discovery Technology, London, UK, 2007.

2006

• Correa, M., C. Bielza, M. J. Ramírez, and J. Pamies, “Modelado y Predicción con Redes Probabilísticas. Caso de Estudio: La Rugosidad Superficial”, XXVII Jornadas de Automática, pp. 1356-1361, 2006.

• Correa, M., M. J. Ramírez, C. Bielza, and J. R. Alique, “Modelos Probabilísticos para la Predicción de la Rugosidad Superficial en Fresado a Alta Velocidad”, XVI Congreso de Máquinas-Herramienta y Tecnologías de Fabricación, vol. 1, pp. 347-365, 2006.
• Fernández del Pozo, J.A., Bielza, C. (2006). Gestión de tablas de decisiones óptimas, XXIX Congreso Nacional S.E.I.O., 753-768
• García, A., A. Freije, R. Armañanzas, I. Inza, Z. Ispizua, P. Heredia, P. Larrañaga, G. López Vivanco, T. Suárez, and M. Betanzos, “Simultaneous Search of Genomic and Proteomic Biomarkers in Human Colorectal Cancer”, Genomes to Systems Conference, Manchester, UK, 2006.

2005

• Armañanzas, R., B. Calvo, I. Inza, P. Larrañaga, I. Bernales, A. Fullaondo, and A. M. Zubiaga, “Selección de genes asociados a dos enfermedades autoinmunes a partir de microarrays de ADN”, VI Jornadas de Transferencia Tecnológica de Inteligencia Artificial, TTIA (AEPIA), Granada, Spain, pp. 63-70, 2005.
• Armañanzas, R., I. Inza, and P. Larrañaga, “Consensus Gene Selection on DNA Microarrays“, European Conference on Computational Biology, Madrid, Spain, 2005.
• Barreiro, P., M. Ruiz-Altisent, C. Bielza, and A. Moya-González, “Multivariate Analysis of an On-Line NIR Spectrometer under Industrial Use”, Acta Horticulturae, vol. 674, no. 513-519: International Society for Horticultural Science, 2005.

2004

• Fernandez del Pozo, J. A., and C. Bielza, “Sensitivity Analysis and Explanation of Optimal Decisions”, Second European Workshop on Probabilistic Graphical Models, pp. 81-88, 2004.

• Fernández del Pozo, J.A., Bielza, C., Gómez, M. (2004). Heurísticas para síntesis de conocimiento multidimensional, XXVIII Congreso Nacional S.E.I.O., 18 pages in CD-ROM.

2003

• Barreiro, P., R. Alonso, E. C. Correa, M. Ruiz-Altisent, J. C. Fabero, L. Casasus, M. Calles, and C. Bielza, “Simulation of Gases in Fruit Storage Chambers with Lattice Boltzman”, Acta Horticulturae, vol. 599: International Society for Horticultural Science, pp. 413-419, 2003. Research Gate.

2002

• Fernandez del Pozo, J. A., and C. Bielza, “New Structures for Conditional Probability Tables”, First European Workshop on Probabilistic Graphical Models: Univ. de Castilla-La Mancha, pp. 61-70, 2002.
• Puch, R. O., J. Q. Smith, and C. Bielza, “Inferentially Efficient Propagation in Non-Decomposable Bayesian Networks with Hierarchical Junction Trees”, First European Workshop on Probabilistic Graphical Models: Univ. de Castilla-La Mancha, pp. 152-160, 2002. Research Gate.

2001

• Barreiro, P., F. García, M. Ruiz-Altisent, and C. Bielza, “Desarrollo de un Instrumento de Ayuda a la Decisión para la Mejora de las Líneas de Confección“, Actas de Horticultura 28, IV Congreso Ibérico de Ciencias Hortícolas, vol. 1, pp. 61-69, 2001.
• Barreiro, P., J. C. Fabero, C. Bielza, S. Bielza, R. Sanz, E. Correa, and M. Ruiz-Altisent, “Simulación de Gases en Cámaras de Almacenamiento de Fruta“, 1er Congreso Nacional de Ingeniería para la Agricultura y el Medio Rural, vol. 1, pp. 321-327, 2001.
• Bielza, C., S. Ríos-Insua, M. Gómez, and J. A. Fernandez del Pozo, “Sensitivity Analysis in IctNeo“, Third International Symposium on Sensitivity Analysis of Model Output: CIEMAT, pp. 287-291, 2001.
• Bielza, C., Rodríguez-Galiano, M.I., Barreiro, P. (2001). Minimización de daños a la fruta en líneas de clasificación, XXVI Congreso Nacional S.E.I.O., 12 pages in CD-ROM. Dialnet.
• Fernandez del Pozo, J. A., C. Bielza, and M. Gómez, “Knowledge Synthesis Optimising the Combinatorial Storage of Multidimensional Matrices“, XIXth EURO Summer Institute -Decision Analysis and Artificial Intelligence-, pp. 49-57, 2001.
• Fernández del Pozo, J.A., Bielza, C., Gómez, M. (2001). Construcción de bases de conocimiento en sistemas de ayuda a la decisión, XXVI Congreso Nacional S.E.I.O., 34 pages in CD-ROM
• Fernandez del Pozo, J. A., and C. Bielza, “Knowledge Synthesis on Multidimensional Matrices“, Operational Research Peripatetic Postgraduate Programme, pp. 1-13, 2001.

2000

• Ballestero, E., J. M. Antón, and C. Bielza, “Bayesian Approach to Road Selection with Compromise Utility Functions”, 8th International Conference “Information Processing and Management of Uncertainty in Knowledge-based Systems”, vol. 2, pp. 813-820, 2000.
• Bielza, C., P. Barreiro, M. I. Rodríguez-Galiano, and J. Martín, “Logistic Regression for Simulating Damage Ocurrence on a Fruit Grading Line“, Congreso Latinoiberoamericano de Investigación de Operaciones y Sistemas, pp. 49-50, 2000.
• Gómez, M., and C. Bielza, “Node Deletion Sequences in Influence Diagrams using Genetic Algorithms“, 8th International Conference “Information Processing and Management of Uncertainty in Knowledge-based Systems”, vol. 3, pp. 1291-1298, 2000.

1999

• Gómez, M., and C. Bielza, Algoritmo Genético para Secuencias de Eliminación de Nodos en Diagramas de Influencia, , vol. 1, pp. 116-124, 1999.
• Mateos, A., S. Ríos-Insua, C. Bielza, M. Gómez, M. Sánchez, and D. Blanco, “A Decision Analysis Approach to Extracorporeal Life Support”, 5th International Conference of the Decision Sciences Institute, Integrating Technology and Human Decisions: Global Bridges into the 21st Century, pp. 665-667, 1999.

1997

• Bielza, C., and P. P. Shenoy, “A Comparison of Decision Trees, Influence Diagrams and Valuation Networks for Asymmetric Decision Problems“, 6th International Workshop on Artificial Intelligence and Statistics, pp. 39-46, 1997.
• Bielza, C., P. Müller, and D. Ríos Insua, “Markov Chain Monte Carlo Methods for Decision Analysis”, 6th International Workshop on Artificial Intelligence and Statistics, pp. 31-38, 1997.