Andres R. Masegosa

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Serafín MoralDept. Computer Science and Artificial Intelligence, University of GranadaVerified email at decsai.ugr.es
Antonio Salmerón (ORCID: 0000-0003...University of Almería, SpainVerified email at ual.es
Thomas Dyhre NielsenAalborg UniversityVerified email at cs.aau.dk
Helge LangsethNorwegian University of Science and TechnologyVerified email at ntnu.no
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
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
Linda C. van der GaagSenior researcher, Dalle Molle Institute for Artificial Intelligence Research, Lugano, SwitzerlandVerified email at idsia.ch

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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Learning under Model Misspecification: Applications to Variational and Ensemble methods A Masegosa Advances in Neural Information Processing Systems 33, 2020	108	2020
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	103	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	91	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	90	2013
Bagging decision trees on data sets with classification noise J Abellán, AR Masegosa International Symposium on Foundations of Information and Knowledge Systems …, 2010	83	2010
Bagging schemes on the presence of class noise in classification J Abellán, AR Masegosa Expert Systems with Applications 39 (8), 6827-6837, 2012	77	2012
An ensemble method using credal decision trees J Abellan, AR Masegosa European journal of operational research 205 (1), 218-226, 2010	68	2010
Diversity and Generalization in Neural Network Ensembles LA Ortega, R Cabañas, A Masegosa International Conference on Artificial Intelligence and Statistics, 11720-11743, 2022	64	2022
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	54	2020
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	41	2014
An experimental study about simple decision trees for bagging ensemble on datasets with classification noise J Abellán, AR Masegosa European Conference on Symbolic and Quantitative Approaches to Reasoning and …, 2009	37	2009
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	36	2016
Modeling concept drift: A probabilistic graphical model based approach H Borchani, AM Martínez, AR Masegosa, H Langseth, TD Nielsen, ... Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA …, 2015	34	2015
Imprecise classification with credal decision trees J Abellán, AR Masegosa International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems …, 2012	33	2012
New skeleton-based approaches for Bayesian structure learning of Bayesian networks AR Masegosa, S Moral Applied Soft Computing 13 (2), 1110-1120, 2013	31	2013
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	27	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	26	2019
From anecdote to evidence: the relationship between personality and need for cognition of developers D Russo, AR Masegosa, KJ Stol Empirical Software Engineering 27 (3), 71, 2022	25	2022
Analyzing concept drift: A case study in the financial sector AR Masegosa, AM Martínez, D Ramos-López, H Langseth, TD Nielsen, ... Intelligent Data Analysis 24 (3), 665-688, 2020	22	2020
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	20	2014

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