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 (ECSQARU, 2005)

Methods to Determine the Branching Attribute in Bayesian Multinets Classifiers. - Andrés Cano; Francisco Javier García Castellano; Andrés R. Masegosa; Serafín Moral (ECSQARU, 2005)

Varying Parameter in Classification Based on Imprecise Probabilities. - Joaquín Abellán; Serafín Moral; Manuel Gómez-Olmedo; Andrés R. Masegosa (SMPS, 2006)

Evaluating Query-Independent Object Features for Relevancy Prediction. - Andrés R. Masegosa; Hideo Joho; Joemon M. Jose (ECIR, 2007)

A Semi-naive Bayes Classifier with Grouping of Cases. - Joaquín Abellán; Andrés Cano; Andrés R. Masegosa; Serafín Moral (ECSQARU, 2007)

Split Criterions for Variable Selection Using Decision Trees. - Joaquín Abellán; Andrés R. Masegosa (ECSQARU, 2007)

Combining Decision Trees Based on Imprecise Probabilities and Uncertainty Measures. - Joaquín Abellán; Andrés R. Masegosa (ECSQARU, 2007)

Effects of highly agreed documents in relevancy prediction. - Andrés R. Masegosa; Hideo Joho; Joemon M. Jose (SIGIR, 2007)

Requirements for total uncertainty measures in Dempster-Shafer theory of evidence. - Joaquín Abellán; Andrés R. Masegosa (Int. J. Gen. Syst., 2008)

An Experimental Study about Simple Decision Trees for Bagging Ensemble on Datasets with Classification Noise. - Joaquín Abellán; Andrés R. Masegosa (ECSQARU, 2009)

A Bayesian Random Split to Build Ensembles of Classification Trees. - Andrés Cano; Andrés R. Masegosa; Serafín Moral (ECSQARU, 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 (INEX, 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)

Bagging Decision Trees on Data Sets with Classification Noise. - Joaquín Abellán; Andrés R. Masegosa (FoIKS, 2010)

An Importance Sampling Approach to Integrate Expert Knowledge When Learning Bayesian Networks From Data. - Andrés Cano; Andrés R. Masegosa; Serafín Moral (IPMU, 2010)

An ensemble method using credal decision trees. - Joaquín Abellán; Andrés R. Masegosa (Eur. J. Oper. Res., 2010)

Locally Averaged Bayesian Dirichlet Metrics. - Andrés Cano; Manuel Gómez-Olmedo; Andrés R. Masegosa; Serafín Moral (ECSQARU, 2011)

Learning with Bayesian networks and probability trees to approximate a joint distribution. - Andrés Cano; Manuel Gómez-Olmedo; Andrés R. Masegosa; Serafín Moral (ISDA, 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 (PACBB, 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)

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)

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 (BIOINFORMATICS, 2012)

Bagging schemes on the presence of class noise in classification. - Joaquín Abellán; Andrés R. Masegosa (Expert Syst. Appl., 2012)

Imprecise Classification with Credal Decision Trees. - Joaquín Abellán; Andrés R. Masegosa (Int. J. Uncertain. Fuzziness Knowl. Based Syst., 2012)

A Bayesian stochastic search method for discovering Markov boundaries. - Andrés R. Masegosa; Serafín Moral (Knowl. Based Syst., 2012)

Learning classifiers from discretized expression quantitative trait loci. - Andres Masegosa Arredondo; María del Mar Abad-Grau; Serafín Moral; Fuencisla Matesanz (IWBBIO, 2013)

New skeleton-based approaches for Bayesian structure learning of Bayesian networks. - Andrés R. Masegosa; Serafín Moral (Appl. Soft Comput., 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)

An interactive approach for Bayesian network learning using domain/expert knowledge. - Andrés R. Masegosa; Serafín Moral (Int. J. Approx. Reason., 2013)

Requirement Engineering for a Small Project with Pre-Specified Scope. - Thomas D. Nielsen; Sigve Hovda; Antonio Fernández 002; Helge Langseth; Anders L. Madsen; Andrés R. Masegosa; Antonio Salmerón (NIK, 2014)

Stochastic Discriminative EM. - Andrés R. Masegosa (UAI, 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)

Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks. - Andrés R. Masegosa; Serafín Moral (Int. J. Approx. Reason., 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)

Parallel Importance Sampling in Conditional Linear Gaussian Networks. - Antonio Salmerón; Darío Ramos-López; Hanen Borchani; Ana M. Martínez; Andrés R. Masegosa; Antonio Fernández 002; Helge Langseth; Anders L. Madsen; Thomas D. Nielsen (CAEPIA, 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 002; Anders L. Madsen; Ramón Sáez (IDA, 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 002; Anders L. Madsen; Ramón Sáez (SCAI, 2015)

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 (ECAI, 2016)

Financial Data Analysis with PGMs Using AMIDST. - Rafael Cabañas 001; Ana M. Martínez; Andrés R. Masegosa; Darío Ramos-López; Antonio Salmerón; Thomas D. Nielsen; Helge Langseth; Anders L. Madsen (ICDM Workshops, 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 (Probabilistic Graphical Models, 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 (Probabilistic Graphical Models, 2016)

Probabilistic Graphical Models on Multi-Core CPUs Using Java 8. - Andrés R. Masegosa; Ana M. Martínez; Hanen Borchani (IEEE Comput. Intell. Mag., 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)

Variational Robust Subspace Clustering with Mean Update Algorithm. - Sergej Dogadov; Andrés R. Masegosa; Shinichi Nakajima (ICCV Workshops, 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 (ICML, 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)

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)

Virtual Subconcept Drift Detection in Discrete Data Using Probabilistic Graphical Models. - Rafael Cabañas 001; Andrés Cano; Manuel Gómez-Olmedo; Andrés R. Masegosa; Serafín Moral (IPMU (3), 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)

Learning from i.i.d. data under model miss-specification. - Andrés R. Masegosa (CoRR, 2019)

InferPy: Probabilistic modeling with Tensorflow made easy. - Rafael Cabañas 001; Antonio Salmerón; Andrés R. Masegosa (Knowl. Based Syst., 2019)

AMIDST: A Java toolbox for scalable probabilistic machine learning. - Andrés R. Masegosa; Ana M. Martínez; Darío Ramos-López; Rafael Cabañas 001; Antonio Salmerón; Helge Langseth; Thomas D. Nielsen; Anders L. Madsen (Knowl. Based Syst., 2019)

Learning under Model Misspecification: Applications to Variational and Ensemble methods. - Andrés R. Masegosa (NeurIPS, 2020)

Second Order PAC-Bayesian Bounds for the Weighted Majority Vote. - Andrés R. Masegosa; Stephan Sloth Lorenzen; Christian Igel; Yevgeny Seldin (NeurIPS, 2020)

Probabilistic Graphical Models with Neural Networks in InferPy. - Rafael Cabañas 001; Javier Cózar; Antonio Salmerón; Andrés R. Masegosa (PGM, 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)

InferPy: Probabilistic modeling with deep neural networks made easy. - Javier Cózar; Rafael Cabañas 001; Antonio Salmerón; Andrés R. Masegosa (Neurocomputing, 2020)

Comparing two multinomial samples using hierarchical Bayesian models. - Andrés R. Masegosa; Antonio Torres; María Morales; Antonio Salmerón (Prog. Artif. Intell., 2020)

Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote. - Yi-Shan Wu 003; Andrés R. Masegosa; Stephan Sloth Lorenzen; Christian Igel; Yevgeny Seldin (NeurIPS, 2021)

Probabilistic Models with Deep Neural Networks. - Andrés R. Masegosa; Rafael Cabañas 001; Helge Langseth; Thomas D. Nielsen; Antonio Salmerón (Entropy, 2021)

Diversity and Generalization in Neural Network Ensembles. - Luis A. Ortega 001; Rafael Cabañas 001; Andrés R. Masegosa (AISTATS, 2022)

A Reparameterization of Mixtures of Truncated Basis Functions and its Applications. - Antonio Salmerón; Helge Langseth; Andrés R. Masegosa; Thomas D. Nielsen (PGM, 2022)

From anecdote to evidence: the relationship between personality and need for cognition of developers. - Daniel Russo 002; Andrés R. Masegosa; Klaas-Jan Stol (Empir. Softw. Eng., 2022)

Understanding Generalization in the Interpolation Regime using the Rate Function. - Andrés R. Masegosa; Luis A. Ortega 001 (CoRR, 2023)

If there is no underfitting, there is no Cold Posterior Effect. - Yijie Zhang; Yi-Shan Wu 003; Luis A. Ortega 001; Andrés R. Masegosa (CoRR, 2023)

PAC-Bayes-Chernoff bounds for unbounded losses. - Ioar Casado; Luis A. Ortega Andrés; Aritz Pérez; Andrés R. Masegosa (NeurIPS, 2024)

Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning. - Abdulkadir Çelikkanat; Andrés R. Masegosa; Thomas Nielsen (NeurIPS, 2024)

Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning. - Abdulkadir Çelikkanat; Andrés R. Masegosa; Thomas D. Nielsen (CoRR, 2024)

The Cold Posterior Effect Indicates Underfitting, and Cold Posteriors Represent a Fully Bayesian Method to Mitigate It. - Yijie Zhang; Yi-Shan Wu 003; Luis A. Ortega 001; Andrés R. Masegosa (Trans. Mach. Learn. Res., 2024)

Toward Variational Structural Learning of Bayesian Networks. - Andrés R. Masegosa; Manuel Gómez-Olmedo (IEEE Access, 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)

A Bagging algorithm for imprecise classification in cost-sensitive scenarios. - Serafín Moral-García; Andrés R. Masegosa; Joaquín Abellán (Inf. Sci., 2025)

PAC-Chernoff Bounds: Understanding Generalization in the Interpolation Regime. - Andrés R. Masegosa; Luis A. Ortega 001 (J. Artif. Intell. Res., 2025)

Deep Actor-Critics with Tight Risk Certificates. - Bahareh Tasdighi; Manuel Haussmann; Yi-Shan Wu 003; Andrés R. Masegosa; Melih Kandemir (CoRR, 2025)