Andrés R. Masegosa

Publication Activity (10 Years)

Years Active: 2005-2024
Publications (10 Years): 38

Top Topics

Probabilistic Modeling

Top Venues

Int. J. Approx. Reason.

Knowl. Based Syst.

Publications

Ioar Casado, Luis A. Ortega, Andrés R. Masegosa, Aritz Pérez
PAC-Bayes-Chernoff bounds for unbounded losses. CoRR (2024)
Yijie Zhang, Yi-Shan Wu, Luis A. Ortega, Andrés R. Masegosa
If there is no underfitting, there is no Cold Posterior Effect. CoRR (2023)
Andrés R. Masegosa, Luis A. Ortega
Understanding Generalization in the Interpolation Regime using the Rate Function. CoRR (2023)
Daniel Russo, Andrés R. Masegosa, Klaas-Jan Stol
From anecdote to evidence: the relationship between personality and need for cognition of developers. Empir. Softw. Eng. 27 (3) (2022)
Antonio Salmerón, Helge Langseth, Andrés R. Masegosa, Thomas D. Nielsen
A Reparameterization of Mixtures of Truncated Basis Functions and its Applications. PGM (2022)
Luis A. Ortega, Rafael Cabañas, Andrés R. Masegosa
Diversity and Generalization in Neural Network Ensembles. AISTATS (2022)
Yi-Shan Wu, Andrés R. Masegosa, Stephan Sloth Lorenzen, Christian Igel, Yevgeny Seldin
Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote. CoRR (2021)
Luis A. Ortega, Rafael Cabañas, Andrés R. Masegosa
Diversity and Generalization in Neural Network Ensembles. CoRR (2021)
Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón
Probabilistic Models with Deep Neural Networks. Entropy 23 (1) (2021)
Daniel Russo, Andrés R. Masegosa, Klaas-Jan Stol
From Anecdote to Evidence: The Relationship Between Personality and Need for Cognition of Developers. CoRR (2021)
Yi-Shan Wu, Andrés R. Masegosa, Stephan Sloth Lorenzen, Christian Igel, Yevgeny Seldin
Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote. NeurIPS (2021)
Andrés R. Masegosa, Antonio Torres, María Morales, Antonio Salmerón
Comparing two multinomial samples using hierarchical Bayesian models. Prog. Artif. Intell. 9 (2) (2020)
Rafael Cabañas, Javier Cózar, Antonio Salmerón, Andrés R. Masegosa
Probabilistic Graphical Models with Neural Networks in InferPy. PGM (2020)
Andrés R. Masegosa, Stephan Sloth Lorenzen, Christian Igel, Yevgeny Seldin
Second Order PAC-Bayesian Bounds for the Weighted Majority Vote. CoRR (2020)
Andrés R. Masegosa, Stephan Sloth Lorenzen, Christian Igel, Yevgeny Seldin
Second Order PAC-Bayesian Bounds for the Weighted Majority Vote. NeurIPS (2020)
Andrés R. Masegosa
Learning under Model Misspecification: Applications to Variational and Ensemble methods. NeurIPS (2020)
Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón
Analyzing concept drift: A case study in the financial sector. Intell. Data Anal. 24 (3) (2020)
Javier Cózar, Rafael Cabañas, Antonio Salmerón, Andrés R. Masegosa
InferPy: Probabilistic modeling with deep neural networks made easy. Neurocomputing 415 (2020)
Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen
AMIDST: A Java toolbox for scalable probabilistic machine learning. Knowl. Based Syst. 163 (2019)
Andrés R. Masegosa
Learning from i.i.d. data under model miss-specification. CoRR (2019)
Rafael Cabañas, Antonio Salmerón, Andrés R. Masegosa
InferPy: Probabilistic modeling with Tensorflow made easy. Knowl. Based Syst. 168 (2019)
Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón
Probabilistic Models with Deep Neural Networks. CoRR (2019)
Javier Cózar, Rafael Cabañas, Andrés R. Masegosa, Antonio Salmerón
InferPy: Probabilistic Modeling with Deep Neural Networks Made Easy. CoRR (2019)
Rafael Cabañas, Andrés Cano, Manuel Gómez-Olmedo, Andrés R. Masegosa, Serafín Moral
Virtual Subconcept Drift Detection in Discrete Data Using Probabilistic Graphical Models. IPMU (3) (2018)
Darío Ramos-López, Andrés R. Masegosa, Antonio Salmerón, Rafael Rumí, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen
Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks. Int. J. Approx. Reason. 100 (2018)
Andrés R. Masegosa, Thomas D. Nielsen, Helge Langseth, Darío Ramos-López, Antonio Salmerón, Anders L. Madsen
Bayesian Models of Data Streams with Hierarchical Power Priors. ICML (2017)
Sergej Dogadov, Andrés R. Masegosa, Shinichi Nakajima
Variational Robust Subspace Clustering with Mean Update Algorithm. ICCV Workshops (2017)
Darío Ramos-López, Andrés R. Masegosa, Ana M. Martínez, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen
MAP inference in dynamic hybrid Bayesian networks. Prog. Artif. Intell. 6 (2) (2017)
Andrés R. Masegosa, Ana M. Martínez, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Darío Ramos-López, Anders L. Madsen
Scaling up Bayesian variational inference using distributed computing clusters. Int. J. Approx. Reason. 88 (2017)
Andrés R. Masegosa, Thomas D. Nielsen, Helge Langseth, Darío Ramos-López, Antonio Salmerón, Anders L. Madsen
Bayesian Models of Data Streams with Hierarchical Power Priors. CoRR (2017)
Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen
AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning. CoRR (2017)
Darío Ramos-López, Antonio Salmerón, Rafael Rumí, Ana M. Martínez, Thomas D. Nielsen, Andrés R. Masegosa, Helge Langseth, Anders L. Madsen
Scalable MAP inference in Bayesian networks based on a Map-Reduce approach. Probabilistic Graphical Models (2016)
Andrés R. Masegosa, A. J. Feelders, Linda C. van der Gaag
Learning from incomplete data in Bayesian networks with qualitative influences. Int. J. Approx. Reason. 69 (2016)
Antonio Salmerón, Anders L. Madsen, Frank Jensen, Helge Langseth, Thomas D. Nielsen, Darío Ramos-López, Ana M. Martínez, Andrés R. Masegosa
Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs. ECAI (2016)
Andrés R. Masegosa, Ana M. Martínez, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Darío Ramos-López, Anders L. Madsen
d-VMP: Distributed Variational Message Passing. Probabilistic Graphical Models (2016)
Rafael Cabañas, Ana M. Martínez, Andrés R. Masegosa, Darío Ramos-López, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen
Financial Data Analysis with PGMs Using AMIDST. ICDM Workshops (2016)
Andrés R. Masegosa, Ana M. Martínez, Hanen Borchani
Probabilistic Graphical Models on Multi-Core CPUs using Java 8. CoRR (2016)
Andrés R. Masegosa, Ana M. Martínez, Hanen Borchani
Probabilistic Graphical Models on Multi-Core CPUs Using Java 8. IEEE Comput. Intell. Mag. 11 (2) (2016)
Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Antonio Fernández, Anders L. Madsen, Ramón Sáez
Modeling Concept Drift: A Probabilistic Graphical Model Based Approach. IDA (2015)
Antonio Salmerón, Darío Ramos-López, Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Antonio Fernández, Helge Langseth, Anders L. Madsen, Thomas D. Nielsen
Parallel Importance Sampling in Conditional Linear Gaussian Networks. CAEPIA (2015)
Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Antonio Fernández, Anders L. Madsen, Ramón Sáez
Dynamic Bayesian modeling for risk prediction in credit operations. SCAI (2015)
Andrés R. Masegosa
Stochastic Discriminative EM. UAI (2014)
Thomas D. Nielsen, Sigve Hovda, Antonio Fernández, Helge Langseth, Anders L. Madsen, Andrés R. Masegosa, Antonio Salmerón
Requirement Engineering for a Small Project with Pre-Specified Scope. NIK (2014)
Andrés R. Masegosa
Stochastic Discriminative EM. CoRR (2014)
Joaquín Abellán, Rebecca M. Baker, Frank P. A. Coolen, Richard J. Crossman, Andrés R. Masegosa
Classification with decision trees from a nonparametric predictive inference perspective. Comput. Stat. Data Anal. 71 (2014)
Andrés R. Masegosa, Serafín Moral
Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks. Int. J. Approx. Reason. 55 (7) (2014)
Andrés R. Masegosa, Serafín Moral
Rejoinder on "Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks". Int. J. Approx. Reason. 55 (7) (2014)
Andrés R. Masegosa, Serafín Moral
An interactive approach for Bayesian network learning using domain/expert knowledge. Int. J. Approx. Reason. 54 (8) (2013)
Andrés Cano, Manuel Gómez-Olmedo, Andrés R. Masegosa, Serafín Moral
Locally averaged Bayesian Dirichlet metrics for learning the structure and the parameters of Bayesian networks. Int. J. Approx. Reason. 54 (4) (2013)
Andrés R. Masegosa, Serafín Moral
New skeleton-based approaches for Bayesian structure learning of Bayesian networks. Appl. Soft Comput. 13 (2) (2013)
Joaquín Abellán, Andrés R. Masegosa
Bagging schemes on the presence of class noise in classification. Expert Syst. Appl. 39 (8) (2012)
María M. Abad-Grau, Nuria Medina-Medina, Andrés R. Masegosa, Serafín Moral
Haplotype-based Classifiers to Predict Individual Susceptibility to Complex Diseases - An Example for Multiple Sclerosis. BIOINFORMATICS (2012)
Andrés R. Masegosa, Serafín Moral
A Bayesian stochastic search method for discovering Markov boundaries. Knowl. Based Syst. 35 (2012)
Joaquín Abellán, Andrés R. Masegosa
Imprecise Classification with Credal Decision Trees. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 20 (5) (2012)
Andrés Cano, Manuel Gómez-Olmedo, Andrés R. Masegosa, Serafín Moral
Locally Averaged Bayesian Dirichlet Metrics. ECSQARU (2011)
Andrés Cano, Manuel Gómez-Olmedo, Andrés R. Masegosa, Serafín Moral
Learning with Bayesian networks and probability trees to approximate a joint distribution. ISDA (2011)
Sergio Torres-Sánchez, Rosana Montes-Soldado, Nuria Medina-Medina, Andrés R. Masegosa, María del Mar Abad-Grau
Riskoweb: Web-Based Genetic Profiling to Complex Disease Using Genome-Wide SNP Markers. PACBB (2011)
Joaquín Abellán, Andrés Cano, Andrés R. Masegosa, Serafín Moral
A memory efficient semi-Naive Bayes classifier with grouping of cases. Intell. Data Anal. 15 (3) (2011)
Andrés Cano, Andrés R. Masegosa, Serafín Moral
A Method for Integrating Expert Knowledge When Learning Bayesian Networks From Data. IEEE Trans. Syst. Man Cybern. Part B 41 (5) (2011)
Joaquín Abellán, Andrés R. Masegosa
Bagging Decision Trees on Data Sets with Classification Noise. FoIKS (2010)
Joaquín Abellán, Andrés R. Masegosa
An ensemble method using credal decision trees. Eur. J. Oper. Res. 205 (1) (2010)
Andrés Cano, Andrés R. Masegosa, Serafín Moral
An Importance Sampling Approach to Integrate Expert Knowledge When Learning Bayesian Networks From Data. IPMU (2010)
Andrés Cano, Andrés R. Masegosa, Serafín Moral
A Bayesian Random Split to Build Ensembles of Classification Trees. ECSQARU (2009)
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Andrés R. Masegosa, Alfonso E. Romero
Link-Based Text Classification Using Bayesian Networks. INEX (2009)
Joaquín Abellán, Andrés R. Masegosa
A Filter-Wrapper Method to Select Variables for the Naive Bayes Classifier Based on Credal Decision Trees. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 17 (6) (2009)
Joaquín Abellán, Andrés R. Masegosa
An Experimental Study about Simple Decision Trees for Bagging Ensemble on Datasets with Classification Noise. ECSQARU (2009)
Joaquín Abellán, Andrés R. Masegosa
Requirements for total uncertainty measures in Dempster-Shafer theory of evidence. Int. J. Gen. Syst. 37 (6) (2008)
Joaquín Abellán, Andrés R. Masegosa
Split Criterions for Variable Selection Using Decision Trees. ECSQARU (2007)
Joaquín Abellán, Andrés R. Masegosa
Combining Decision Trees Based on Imprecise Probabilities and Uncertainty Measures. ECSQARU (2007)
Andrés R. Masegosa, Hideo Joho, Joemon M. Jose
Evaluating Query-Independent Object Features for Relevancy Prediction. ECIR (2007)
Andrés R. Masegosa, Hideo Joho, Joemon M. Jose
Effects of highly agreed documents in relevancy prediction. SIGIR (2007)
Joaquín Abellán, Andrés Cano, Andrés R. Masegosa, Serafín Moral
A Semi-naive Bayes Classifier with Grouping of Cases. ECSQARU (2007)
Joaquín Abellán, Serafín Moral, Manuel Gómez-Olmedo, Andrés R. Masegosa
Varying Parameter in Classification Based on Imprecise Probabilities. SMPS (2006)
Andrés Cano, Francisco Javier García Castellano, Andrés R. Masegosa, Serafín Moral
Methods to Determine the Branching Attribute in Bayesian Multinets Classifiers. ECSQARU (2005)
Andrés Cano, Francisco Javier García Castellano, Andrés R. Masegosa, Serafín Moral
Selective Gaussian Naïve Bayes Model for Diffuse Large-B-Cell Lymphoma Classification: Some Improvements in Preprocessing and Variable Elimination. ECSQARU (2005)