Andres R. Masegosa

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Serafín MoralDept. Computer Science and Artificial Intelligence, University of GranadaVerified email at decsai.ugr.es
Antonio SalmerónUniversity of Almería, SpainVerified email at ual.es
Helge LangsethNorwegian University of Science and TechnologyVerified email at ntnu.no
Thomas Dyhre NielsenAalborg UniversityVerified email at cs.aau.dk
Joaquín AbellánProfesor del departamento de Ciencias de la Computación e Inteligencia Artificial (UGR)Verified email at decsai.ugr.es
Anders L MadsenCEO at HUGIN EXPERT and Adjunct professor at Aalborg UniversityVerified email at hugin.com
Ana M. MartínezJoint Research Center, European CommissionVerified email at ec.europa.eu
Darío Ramos-LópezProfessor of Applied Mathematics, Department of Mathematics, University of Almería, Almería (Spain)Verified email at ual.es
Andrés CanoDepartment of Computer Science and Artificial Intelligence, University of GranadaVerified email at decsai.ugr.es
Rafael CabañasUniversity of AlmeríaVerified email at ual.es
Hanen BorchaniSimCorp A/SVerified email at simcorp.com
Manuel Gómez-Olmedo (mgomez at d...Department of Computer Science and Artificial Intelligence, Universidad de GranadaVerified email at decsai.ugr.es
Antonio FernándezSenior Data Scientist in Banking (Cajamar)Verified email at bcc.es
Christian IgelDepartment of Computer Science, University of CopenhagenVerified email at diku.dk
Stephan Sloth Lorenzenph.d. student, Department of Computer Science, University of CopenhagenVerified email at di.ku.dk
Yevgeny SeldinProfessor, Department of Computer Science, University of CopenhagenVerified email at di.ku.dk
Rafael RumíProfessor, University of AlmeríaVerified email at ual.es
Luis A. OrtegaAutonomous University of MadridVerified email at uam.es
Javier García CastellanoDepartment of Computer Science and Artificial Intelligence at University of GranadaVerified email at decsai.ugr.es
Frank CoolenProfessor of Statistics, Durham UniversityVerified email at durham.ac.uk

Andres R. Masegosa

University of Aalborg

Verified email at cs.aau.dk - Homepage

Machine Learning Probabilistic Programming Bayesian Deep Learning Variational Inference


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Requirements for total uncertainty measures in Dempster–Shafer theory of evidence J Abellán, A Masegosa International journal of general systems 37 (6), 733-747, 2008	93	2008
A method for integrating expert knowledge when learning Bayesian networks from data A Cano, AR Masegosa, S Moral IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 41 …, 2011	82	2011
An interactive approach for Bayesian network learning using domain/expert knowledge AR Masegosa, S Moral International Journal of Approximate Reasoning 54 (8), 1168-1181, 2013	75	2013
Bagging schemes on the presence of class noise in classification J Abellán, AR Masegosa Expert Systems with Applications 39 (8), 6827-6837, 2012	69	2012
Learning under Model Misspecification: Applications to Variational and Ensemble methods A Masegosa Advances in Neural Information Processing Systems 33, 2020	68	2020
Bagging decision trees on data sets with classification noise J Abellán, AR Masegosa Foundations of Information and Knowledge Systems: 6th International …, 2010	64	2010
An ensemble method using credal decision trees J Abellan, AR Masegosa European journal of operational research 205 (1), 218-226, 2010	56	2010
Classification with decision trees from a nonparametric predictive inference perspective J Abellán, RM Baker, F Coolen, RJ Crossman, AR Masegosa Computational Statistics & Data Analysis 71, 789-802, 2014	34	2014
Second order PAC-Bayesian bounds for the weighted majority vote A Masegosa, S Lorenzen, C Igel, Y Seldin Advances in Neural Information Processing Systems 33, 2020	33	2020
Learning from incomplete data in Bayesian networks with qualitative influences AR Masegosa, AJ Feelders, LC van der Gaag International Journal of Approximate Reasoning 69, 18-34, 2016	33	2016
An experimental study about simple decision trees for bagging ensemble on datasets with classification noise J Abellán, AR Masegosa Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 10th …, 2009	33	2009
New skeleton-based approaches for Bayesian structure learning of Bayesian networks AR Masegosa, S Moral Applied Soft Computing 13 (2), 1110-1120, 2013	29	2013
Modeling concept drift: A probabilistic graphical model based approach H Borchani, AM Martínez, AR Masegosa, H Langseth, TD Nielsen, ... International Symposium on Intelligent Data Analysis, 72-83, 2015	27	2015
Imprecise classification with credal decision trees J Abellan, AR Masegosa International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems …, 2012	27	2012
Bayesian models of data streams with hierarchical power priors A Masegosa, TD Nielsen, H Langseth, D Ramos-López, A Salmerón, ... Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017	25	2017
AMIDST: A Java toolbox for scalable probabilistic machine learning AR Masegosa, AM Martínez, D Ramos-López, R Cabañas, A Salmerón, ... Knowledge-Based Systems 163, 595-597, 2019	22	2019
Diversity and Generalization in Neural Network Ensembles LA Ortega, R Cabañas, A Masegosa International Conference on Artificial Intelligence and Statistics, 11720-11743, 2022	19	2022
Probabilistic Graphical Models on Multi-Core CPUs Using Java 8 AR Masegosa, AM Martinez, H Borchani IEEE Computational Intelligence Magazine 11 (2), 41-54, 2016	18	2016
Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks AR Masegosa, S Moral International Journal of Approximate Reasoning 55 (7), 1548-1569, 2014	18	2014
Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks D Ramos-López, AR Masegosa, A Salmerón, R Rumí, H Langseth, ... International Journal of Approximate Reasoning 100, 115-134, 2018	16	2018

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