Andrés R. Masegosa

According to our database, Andrés R. Masegosa authored at least 75 papers between 2005 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

UncertainGen: Uncertainty-Aware Representations of DNA Sequences for Metagenomic Binning.

Abdulkadir Çelikkanat, Andrés R. Masegosa, Mads Albertsen, Thomas D. Nielsen

CoRR, September, 2025

Deep Actor-Critics with Tight Risk Certificates.

Bahareh Tasdighi, Manuel Haussmann, Yi-Shan Wu, Andrés R. Masegosa, Melih Kandemir

CoRR, May, 2025

PAC-Chernoff Bounds: Understanding Generalization in the Interpolation Regime.

Andrés R. Masegosa, Luis A. Ortega

J. Artif. Intell. Res., 2025

A Bagging algorithm for imprecise classification in cost-sensitive scenarios.

Serafín Moral-García, Andrés R. Masegosa, Joaquín Abellán

Inf. Sci., 2025

Understanding the Role of Diversity in Ensemble-Based AutoML Methods for Classification Tasks.

Salomey Osei, Andrés R. Masegosa, Antonio D. Masegosa

IEEE Access, 2025

Toward Variational Structural Learning of Bayesian Networks.

Andrés R. Masegosa, Manuel Gómez-Olmedo

IEEE Access, 2025

2024

The Cold Posterior Effect Indicates Underfitting, and Cold Posteriors Represent a Fully Bayesian Method to Mitigate It.

Yijie Zhang, Yi-Shan Wu, Luis A. Ortega, Andrés R. Masegosa

Trans. Mach. Learn. Res., 2024

Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning.

Abdulkadir Çelikkanat, Andrés R. Masegosa, Thomas D. Nielsen

CoRR, 2024

Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning.

Abdulkadir Çelikkanat, Andrés R. Masegosa, Thomas Nielsen

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

PAC-Bayes-Chernoff bounds for unbounded losses.

Ioar Casado, Luis A. Ortega Andrés, Aritz Pérez, Andrés R. Masegosa

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

If there is no underfitting, there is no Cold Posterior Effect.

Yijie Zhang, Yi-Shan Wu, Luis A. Ortega, Andrés R. Masegosa

CoRR, 2023

Understanding Generalization in the Interpolation Regime using the Rate Function.

Andrés R. Masegosa, Luis A. Ortega

CoRR, 2023

2022

From anecdote to evidence: the relationship between personality and need for cognition of developers.

Daniel Russo, Andrés R. Masegosa, Klaas-Jan Stol

Empir. Softw. Eng., 2022

A Reparameterization of Mixtures of Truncated Basis Functions and its Applications.

Antonio Salmerón, Helge Langseth, Andrés R. Masegosa, Thomas D. Nielsen

Proceedings of the International Conference on Probabilistic Graphical Models, 2022

Diversity and Generalization in Neural Network Ensembles.

Luis A. Ortega, Rafael Cabañas, Andrés R. Masegosa

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Probabilistic Models with Deep Neural Networks.

Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón

Entropy, 2021

Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote.

Yi-Shan Wu, Andrés R. Masegosa, Stephan Sloth Lorenzen, Christian Igel, Yevgeny Seldin

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Comparing two multinomial samples using hierarchical Bayesian models.

Andrés R. Masegosa, Antonio Torres, María Morales, Antonio Salmerón

Prog. Artif. Intell., 2020

InferPy: Probabilistic modeling with deep neural networks made easy.

Javier Cózar, Rafael Cabañas, Antonio Salmerón, Andrés R. Masegosa

Neurocomputing, 2020

Analyzing concept drift: A case study in the financial sector.

Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón

Intell. Data Anal., 2020

Probabilistic Graphical Models with Neural Networks in InferPy.

Rafael Cabañas, Javier Cózar, Antonio Salmerón, Andrés R. Masegosa

Proceedings of the International Conference on Probabilistic Graphical Models, 2020

Second Order PAC-Bayesian Bounds for the Weighted Majority Vote.

Andrés R. Masegosa, Stephan Sloth Lorenzen, Christian Igel, Yevgeny Seldin

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning under Model Misspecification: Applications to Variational and Ensemble methods.

Andrés R. Masegosa

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

AMIDST: A Java toolbox for scalable probabilistic machine learning.

Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen

Knowl. Based Syst., 2019

InferPy: Probabilistic modeling with Tensorflow made easy.

Rafael Cabañas, Antonio Salmerón, Andrés R. Masegosa

Knowl. Based Syst., 2019

Learning from i.i.d. data under model miss-specification.

Andrés R. Masegosa

CoRR, 2019

2018

Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks.

Darío Ramos-López, Andrés R. Masegosa, Antonio Salmerón, Rafael Rumí, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen

Int. J. Approx. Reason., 2018

Virtual Subconcept Drift Detection in Discrete Data Using Probabilistic Graphical Models.

Rafael Cabañas, Andrés Cano, Manuel Gómez-Olmedo, Andrés R. Masegosa, Serafín Moral

Proceedings of the Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications, 2018

2017

MAP inference in dynamic hybrid Bayesian networks.

Darío Ramos-López, Andrés R. Masegosa, Ana M. Martínez, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen

Prog. Artif. Intell., 2017

Scaling up Bayesian variational inference using distributed computing clusters.

Andrés R. Masegosa, Ana M. Martínez, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Darío Ramos-López, Anders L. Madsen

Int. J. Approx. Reason., 2017

Bayesian Models of Data Streams with Hierarchical Power Priors.

Andrés R. Masegosa, Thomas D. Nielsen, Helge Langseth, Darío Ramos-López, Antonio Salmerón, Anders L. Madsen

Proceedings of the 34th International Conference on Machine Learning, 2017

Variational Robust Subspace Clustering with Mean Update Algorithm.

Sergej Dogadov, Andrés R. Masegosa, Shinichi Nakajima

Proceedings of the 2017 IEEE International Conference on Computer Vision Workshops, 2017

2016

Learning from incomplete data in Bayesian networks with qualitative influences.

Andrés R. Masegosa, A. J. Feelders, Linda C. van der Gaag

Int. J. Approx. Reason., 2016

Probabilistic Graphical Models on Multi-Core CPUs Using Java 8.

Andrés R. Masegosa, Ana M. Martínez, Hanen Borchani

IEEE Comput. Intell. Mag., 2016

Scalable MAP inference in Bayesian networks based on a Map-Reduce approach.

Darío Ramos-López, Antonio Salmerón, Rafael Rumí, Ana M. Martínez, Thomas D. Nielsen, Andrés R. Masegosa, Helge Langseth, Anders L. Madsen

Proceedings of the Probabilistic Graphical Models - Eighth International Conference, 2016

d-VMP: Distributed Variational Message Passing.

Andrés R. Masegosa, Ana M. Martínez, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Darío Ramos-López, Anders L. Madsen

Proceedings of the Probabilistic Graphical Models - Eighth International Conference, 2016

Financial Data Analysis with PGMs Using AMIDST.

Rafael Cabañas, Ana M. Martínez, Andrés R. Masegosa, Darío Ramos-López, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen

Proceedings of the IEEE International Conference on Data Mining Workshops, 2016

Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs.

Antonio Salmerón, Anders L. Madsen, Frank Jensen, Helge Langseth, Thomas D. Nielsen, Darío Ramos-López, Ana M. Martínez, Andrés R. Masegosa

Proceedings of the ECAI 2016 - 22nd European Conference on Artificial Intelligence, 29 August-2 September 2016, The Hague, The Netherlands, 2016

2015

Dynamic Bayesian modeling for risk prediction in credit operations.

Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Antonio Fernández, Anders L. Madsen, Ramón Sáez

Proceedings of the Thirteenth Scandinavian Conference on Artificial Intelligence, 2015

Modeling Concept Drift: A Probabilistic Graphical Model Based Approach.

Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Antonio Fernández, Anders L. Madsen, Ramón Sáez

Proceedings of the Advances in Intelligent Data Analysis XIV, 2015

Parallel Importance Sampling in Conditional Linear Gaussian Networks.

Antonio Salmerón, Darío Ramos-López, Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Antonio Fernández, Helge Langseth, Anders L. Madsen, Thomas D. Nielsen

Proceedings of the Advances in Artificial Intelligence, 2015

2014

Rejoinder on "Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks".

Andrés R. Masegosa, Serafín Moral

Int. J. Approx. Reason., 2014

Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks.

Andrés R. Masegosa, Serafín Moral

Int. J. Approx. Reason., 2014

Classification with decision trees from a nonparametric predictive inference perspective.

Joaquín Abellán, Rebecca M. Baker, Frank P. A. Coolen, Richard J. Crossman, Andrés R. Masegosa

Comput. Stat. Data Anal., 2014

Stochastic Discriminative EM.

Andrés R. Masegosa

Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Requirement Engineering for a Small Project with Pre-Specified Scope.

Thomas D. Nielsen, Sigve Hovda, Antonio Fernández, Helge Langseth, Anders L. Madsen, Andrés R. Masegosa, Antonio Salmerón

Proceedings of the 27th Norsk Informatikkonferanse, 2014

2013

An interactive approach for Bayesian network learning using domain/expert knowledge.

Andrés R. Masegosa, Serafín Moral

Int. J. Approx. Reason., 2013

Locally averaged Bayesian Dirichlet metrics for learning the structure and the parameters of Bayesian networks.

Andrés Cano, Manuel Gómez-Olmedo, Andrés R. Masegosa, Serafín Moral

Int. J. Approx. Reason., 2013

New skeleton-based approaches for Bayesian structure learning of Bayesian networks.

Andrés R. Masegosa, Serafín Moral

Appl. Soft Comput., 2013

Learning classifiers from discretized expression quantitative trait loci.

Andres Masegosa Arredondo, María del Mar Abad-Grau, Serafín Moral, Fuencisla Matesanz

Proceedings of the International Work-Conference on Bioinformatics and Biomedical Engineering, 2013

2012

A Bayesian stochastic search method for discovering Markov boundaries.

Andrés R. Masegosa, Serafín Moral

Knowl. Based Syst., 2012

Imprecise Classification with Credal Decision Trees.

Joaquín Abellán, Andrés R. Masegosa

Int. J. Uncertain. Fuzziness Knowl. Based Syst., 2012

Bagging schemes on the presence of class noise in classification.

Joaquín Abellán, Andrés R. Masegosa

Expert Syst. Appl., 2012

Haplotype-based Classifiers to Predict Individual Susceptibility to Complex Diseases - An Example for Multiple Sclerosis.

María M. Abad-Grau, Nuria Medina-Medina, Andrés R. Masegosa, Serafín Moral

Proceedings of the BIOINFORMATICS 2012 - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms, Vilamoura, Algarve, Portugal, 1, 2012

2011

A Method for Integrating Expert Knowledge When Learning Bayesian Networks From Data.

Andrés Cano, Andrés R. Masegosa, Serafín Moral

IEEE Trans. Syst. Man Cybern. Part B, 2011

A memory efficient semi-Naive Bayes classifier with grouping of cases.

Joaquín Abellán, Andrés Cano, Andrés R. Masegosa, Serafín Moral

Intell. Data Anal., 2011

Riskoweb: Web-Based Genetic Profiling to Complex Disease Using Genome-Wide SNP Markers.

Sergio Torres-Sánchez, Rosana Montes-Soldado, Nuria Medina-Medina, Andrés R. Masegosa, María del Mar Abad-Grau

Proceedings of the 5th International Conference on Practical Applications of Computational Biology & Bioinformatics, 2011

Learning with Bayesian networks and probability trees to approximate a joint distribution.

Andrés Cano, Manuel Gómez-Olmedo, Andrés R. Masegosa, Serafín Moral

Proceedings of the 11th International Conference on Intelligent Systems Design and Applications, 2011

Locally Averaged Bayesian Dirichlet Metrics.

Andrés Cano, Manuel Gómez-Olmedo, Andrés R. Masegosa, Serafín Moral

Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2011

2010

An ensemble method using credal decision trees.

Joaquín Abellán, Andrés R. Masegosa

Eur. J. Oper. Res., 2010

An Importance Sampling Approach to Integrate Expert Knowledge When Learning Bayesian Networks From Data.

Andrés Cano, Andrés R. Masegosa, Serafín Moral

Proceedings of the Computational Intelligence for Knowledge-Based Systems Design, 2010

Bagging Decision Trees on Data Sets with Classification Noise.

Joaquín Abellán, Andrés R. Masegosa

Proceedings of the Foundations of Information and Knowledge Systems, 2010

2009

A Filter-Wrapper Method to Select Variables for the Naive Bayes Classifier Based on Credal Decision Trees.

Joaquín Abellán, Andrés R. Masegosa

Int. J. Uncertain. Fuzziness Knowl. Based Syst., 2009

Link-Based Text Classification Using Bayesian Networks.

Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Andrés R. Masegosa, Alfonso E. Romero

Proceedings of the Focused Retrieval and Evaluation, 2009

A Bayesian Random Split to Build Ensembles of Classification Trees.

Andrés Cano, Andrés R. Masegosa, Serafín Moral

Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2009

An Experimental Study about Simple Decision Trees for Bagging Ensemble on Datasets with Classification Noise.

Joaquín Abellán, Andrés R. Masegosa

Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2009

2008

Requirements for total uncertainty measures in Dempster-Shafer theory of evidence.

Joaquín Abellán, Andrés R. Masegosa

Int. J. Gen. Syst., 2008

2007

Effects of highly agreed documents in relevancy prediction.

Andrés R. Masegosa, Hideo Joho, Joemon M. Jose

Proceedings of the SIGIR 2007: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2007

Combining Decision Trees Based on Imprecise Probabilities and Uncertainty Measures.

Joaquín Abellán, Andrés R. Masegosa

Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2007

Split Criterions for Variable Selection Using Decision Trees.

Joaquín Abellán, Andrés R. Masegosa

Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2007

A Semi-naive Bayes Classifier with Grouping of Cases.

Joaquín Abellán, Andrés Cano, Andrés R. Masegosa, Serafín Moral

Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2007

Evaluating Query-Independent Object Features for Relevancy Prediction.

Andrés R. Masegosa, Hideo Joho, Joemon M. Jose

Proceedings of the Advances in Information Retrieval, 2007

2006

Varying Parameter in Classification Based on Imprecise Probabilities.

Joaquín Abellán, Serafín Moral, Manuel Gómez-Olmedo, Andrés R. Masegosa

Proceedings of the Soft Methods for Integrated Uncertainty Modelling, 2006

2005

Methods to Determine the Branching Attribute in Bayesian Multinets Classifiers.

Andrés Cano, Francisco Javier García Castellano, Andrés R. Masegosa, Serafín Moral

Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2005

Selective Gaussian Naïve Bayes Model for Diffuse Large-B-Cell Lymphoma Classification: Some Improvements in Preprocessing and Variable Elimination.

Andrés Cano, Francisco Javier García Castellano, Andrés R. Masegosa, Serafín Moral

Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2005