Andrés R. Masegosa

86 papers A* 7A 4B 1C 10Misc 1Journal 49Unranked 14

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A large deviation theory analysis on the implicit bias of SGD. Neurocomputing Luis A. Ortega, Andrés R. Masegosa
2025	J	`jnl`	A Bagging algorithm for imprecise classification in cost-sensitive scenarios. Inf. Sci. Serafín Moral-García, Andrés R. Masegosa, Joaquín Abellán
2025	J	`jnl`	Deep Actor-Critics with Tight Risk Certificates. CoRR Bahareh Tasdighi, Manuel Haussmann, Yi-Shan Wu, Andrés R. Masegosa, Melih Kandemir
2025	J	`jnl`	PAC-Chernoff Bounds: Understanding Generalization in the Interpolation Regime. J. Artif. Intell. Res. Andrés R. Masegosa, Luis A. Ortega
2025	J	`jnl`	Toward Variational Structural Learning of Bayesian Networks. IEEE Access Andrés R. Masegosa, Manuel Gómez-Olmedo
2025	J	`jnl`	UncertainGen: Uncertainty-Aware Representations of DNA Sequences for Metagenomic Binning. CoRR Abdulkadir Çelikkanat, Andrés R. Masegosa, Mads Albertsen, Thomas D. Nielsen
2025	J	`jnl`	Understanding the Role of Diversity in Ensemble-Based AutoML Methods for Classification Tasks. IEEE Access Salomey Osei, Andrés R. Masegosa, Antonio D. Masegosa
2024	A*	`conf`	PAC-Bayes-Chernoff bounds for unbounded losses. NeurIPS Ioar Casado, Luis A. Ortega Andrés, Aritz Pérez, Andrés R. Masegosa
2024	J	`jnl`	PAC-Bayes-Chernoff bounds for unbounded losses. CoRR Ioar Casado, Luis A. Ortega, Andrés R. Masegosa, Aritz Pérez
2024	A*	`conf`	Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning. NeurIPS Abdulkadir Çelikkanat, Andrés R. Masegosa, Thomas Nielsen
2024	J	`jnl`	Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning. CoRR Abdulkadir Çelikkanat, Andrés R. Masegosa, Thomas D. Nielsen
2024	J	`jnl`	The Cold Posterior Effect Indicates Underfitting, and Cold Posteriors Represent a Fully Bayesian Method to Mitigate It. Trans. Mach. Learn. Res. Yijie Zhang, Yi-Shan Wu, Luis A. Ortega, Andrés R. Masegosa
2023	J	`jnl`	If there is no underfitting, there is no Cold Posterior Effect. CoRR Yijie Zhang, Yi-Shan Wu, Luis A. Ortega, Andrés R. Masegosa
2023	J	`jnl`	Understanding Generalization in the Interpolation Regime using the Rate Function. CoRR Andrés R. Masegosa, Luis A. Ortega
2022	—	`conf`	A Reparameterization of Mixtures of Truncated Basis Functions and its Applications. PGM Antonio Salmerón, Helge Langseth, Andrés R. Masegosa, Thomas D. Nielsen
2022	A	`conf`	Diversity and Generalization in Neural Network Ensembles. AISTATS Luis A. Ortega, Rafael Cabañas, Andrés R. Masegosa
2022	J	`jnl`	From anecdote to evidence: the relationship between personality and need for cognition of developers. Empir. Softw. Eng. Daniel Russo, Andrés R. Masegosa, Klaas-Jan Stol
2021	A*	`conf`	Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote. NeurIPS Yi-Shan Wu, Andrés R. Masegosa, Stephan Sloth Lorenzen, Christian Igel, Yevgeny Seldin
2021	J	`jnl`	Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote. CoRR Yi-Shan Wu, Andrés R. Masegosa, Stephan Sloth Lorenzen, Christian Igel, Yevgeny Seldin
2021	J	`jnl`	Diversity and Generalization in Neural Network Ensembles. CoRR Luis A. Ortega, Rafael Cabañas, Andrés R. Masegosa
2021	J	`jnl`	From Anecdote to Evidence: The Relationship Between Personality and Need for Cognition of Developers. CoRR Daniel Russo, Andrés R. Masegosa, Klaas-Jan Stol
2021	J	`jnl`	Probabilistic Models with Deep Neural Networks. Entropy Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón
2020	J	`jnl`	Analyzing concept drift: A case study in the financial sector. Intell. Data Anal. Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón
2020	J	`jnl`	Comparing two multinomial samples using hierarchical Bayesian models. Prog. Artif. Intell. Andrés R. Masegosa, Antonio Torres, María Morales, Antonio Salmerón
2020	J	`jnl`	InferPy: Probabilistic modeling with deep neural networks made easy. Neurocomputing Javier Cózar, Rafael Cabañas, Antonio Salmerón, Andrés R. Masegosa
2020	A*	`conf`	Learning under Model Misspecification: Applications to Variational and Ensemble methods. NeurIPS Andrés R. Masegosa
2020	—	`conf`	Probabilistic Graphical Models with Neural Networks in InferPy. PGM Rafael Cabañas, Javier Cózar, Antonio Salmerón, Andrés R. Masegosa
2020	A*	`conf`	Second Order PAC-Bayesian Bounds for the Weighted Majority Vote. NeurIPS Andrés R. Masegosa, Stephan Sloth Lorenzen, Christian Igel, Yevgeny Seldin
2020	J	`jnl`	Second Order PAC-Bayesian Bounds for the Weighted Majority Vote. CoRR Andrés R. Masegosa, Stephan Sloth Lorenzen, Christian Igel, Yevgeny Seldin
2019	J	`jnl`	AMIDST: A Java toolbox for scalable probabilistic machine learning. Knowl. Based Syst. Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen
2019	J	`jnl`	InferPy: Probabilistic Modeling with Deep Neural Networks Made Easy. CoRR Javier Cózar, Rafael Cabañas, Andrés R. Masegosa, Antonio Salmerón
2019	J	`jnl`	InferPy: Probabilistic modeling with Tensorflow made easy. Knowl. Based Syst. Rafael Cabañas, Antonio Salmerón, Andrés R. Masegosa
2019	J	`jnl`	Learning from i.i.d. data under model miss-specification. CoRR Andrés R. Masegosa
2019	J	`jnl`	Probabilistic Models with Deep Neural Networks. CoRR Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón
2018	J	`jnl`	Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks. Int. J. Approx. Reason. Darío Ramos-López, Andrés R. Masegosa, Antonio Salmerón, Rafael Rumí, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen
2018	—	`conf`	Virtual Subconcept Drift Detection in Discrete Data Using Probabilistic Graphical Models. IPMU (3) Rafael Cabañas, Andrés Cano, Manuel Gómez-Olmedo, Andrés R. Masegosa, Serafín Moral
2017	J	`jnl`	AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning. CoRR Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen
2017	A*	`conf`	Bayesian Models of Data Streams with Hierarchical Power Priors. ICML Andrés R. Masegosa, Thomas D. Nielsen, Helge Langseth, Darío Ramos-López, Antonio Salmerón, Anders L. Madsen
2017	J	`jnl`	Bayesian Models of Data Streams with Hierarchical Power Priors. CoRR Andrés R. Masegosa, Thomas D. Nielsen, Helge Langseth, Darío Ramos-López, Antonio Salmerón, Anders L. Madsen
2017	J	`jnl`	MAP inference in dynamic hybrid Bayesian networks. Prog. Artif. Intell. Darío Ramos-López, Andrés R. Masegosa, Ana M. Martínez, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen
2017	J	`jnl`	Scaling up Bayesian variational inference using distributed computing clusters. Int. J. Approx. Reason. Andrés R. Masegosa, Ana M. Martínez, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Darío Ramos-López, Anders L. Madsen
2017	—	`conf`	Variational Robust Subspace Clustering with Mean Update Algorithm. ICCV Workshops Sergej Dogadov, Andrés R. Masegosa, Shinichi Nakajima
2016	—	`conf`	Financial Data Analysis with PGMs Using AMIDST. ICDM Workshops Rafael Cabañas, Ana M. Martínez, Andrés R. Masegosa, Darío Ramos-López, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen
2016	J	`jnl`	Learning from incomplete data in Bayesian networks with qualitative influences. Int. J. Approx. Reason. Andrés R. Masegosa, A. J. Feelders, Linda C. van der Gaag
2016	A	`conf`	Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs. ECAI Antonio Salmerón, Anders L. Madsen, Frank Jensen, Helge Langseth, Thomas D. Nielsen, Darío Ramos-López, Ana M. Martínez, Andrés R. Masegosa
2016	J	`jnl`	Probabilistic Graphical Models on Multi-Core CPUs Using Java 8. IEEE Comput. Intell. Mag. Andrés R. Masegosa, Ana M. Martínez, Hanen Borchani
2016	J	`jnl`	Probabilistic Graphical Models on Multi-Core CPUs using Java 8. CoRR Andrés R. Masegosa, Ana M. Martínez, Hanen Borchani
2016	—	`conf`	Scalable MAP inference in Bayesian networks based on a Map-Reduce approach. Probabilistic Graphical Models Darío Ramos-López, Antonio Salmerón, Rafael Rumí, Ana M. Martínez, Thomas D. Nielsen, Andrés R. Masegosa, Helge Langseth, Anders L. Madsen
2016	—	`conf`	d-VMP: Distributed Variational Message Passing. Probabilistic Graphical Models Andrés R. Masegosa, Ana M. Martínez, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Darío Ramos-López, Anders L. Madsen
2015	—	`conf`	Dynamic Bayesian modeling for risk prediction in credit operations. SCAI Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Antonio Fernández, Anders L. Madsen, Ramón Sáez
2015	B	`conf`	Modeling Concept Drift: A Probabilistic Graphical Model Based Approach. IDA Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Antonio Fernández, Anders L. Madsen, Ramón Sáez
2015	—	`conf`	Parallel Importance Sampling in Conditional Linear Gaussian Networks. CAEPIA Antonio Salmerón, Darío Ramos-López, Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Antonio Fernández, Helge Langseth, Anders L. Madsen, Thomas D. Nielsen
2014	J	`jnl`	Classification with decision trees from a nonparametric predictive inference perspective. Comput. Stat. Data Anal. Joaquín Abellán, Rebecca M. Baker, Frank P. A. Coolen, Richard J. Crossman, Andrés R. Masegosa
2014	J	`jnl`	Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks. Int. J. Approx. Reason. Andrés R. Masegosa, Serafín Moral
2014	J	`jnl`	Rejoinder on "Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks". Int. J. Approx. Reason. Andrés R. Masegosa, Serafín Moral
2014	—	`conf`	Requirement Engineering for a Small Project with Pre-Specified Scope. NIK Thomas D. Nielsen, Sigve Hovda, Antonio Fernández, Helge Langseth, Anders L. Madsen, Andrés R. Masegosa, Antonio Salmerón
2014	A	`conf`	Stochastic Discriminative EM. UAI Andrés R. Masegosa
2014	J	`jnl`	Stochastic Discriminative EM. CoRR Andrés R. Masegosa
2013	J	`jnl`	An interactive approach for Bayesian network learning using domain/expert knowledge. Int. J. Approx. Reason. Andrés R. Masegosa, Serafín Moral
2013	J	`jnl`	Locally averaged Bayesian Dirichlet metrics for learning the structure and the parameters of Bayesian networks. Int. J. Approx. Reason. Andrés Cano, Manuel Gómez-Olmedo, Andrés R. Masegosa, Serafín Moral
2013	J	`jnl`	New skeleton-based approaches for Bayesian structure learning of Bayesian networks. Appl. Soft Comput. Andrés R. Masegosa, Serafín Moral
2012	J	`jnl`	A Bayesian stochastic search method for discovering Markov boundaries. Knowl. Based Syst. Andrés R. Masegosa, Serafín Moral
2012	J	`jnl`	Bagging schemes on the presence of class noise in classification. Expert Syst. Appl. Joaquín Abellán, Andrés R. Masegosa
2012	—	`conf`	Haplotype-based Classifiers to Predict Individual Susceptibility to Complex Diseases - An Example for Multiple Sclerosis. BIOINFORMATICS María M. Abad-Grau, Nuria Medina-Medina, Andrés R. Masegosa, Serafín Moral
2012	J	`jnl`	Imprecise Classification with Credal Decision Trees. Int. J. Uncertain. Fuzziness Knowl. Based Syst. Joaquín Abellán, Andrés R. Masegosa
2011	J	`jnl`	A Method for Integrating Expert Knowledge When Learning Bayesian Networks From Data. IEEE Trans. Syst. Man Cybern. Part B Andrés Cano, Andrés R. Masegosa, Serafín Moral
2011	J	`jnl`	A memory efficient semi-Naive Bayes classifier with grouping of cases. Intell. Data Anal. Joaquín Abellán, Andrés Cano, Andrés R. Masegosa, Serafín Moral
2011	C	`conf`	Learning with Bayesian networks and probability trees to approximate a joint distribution. ISDA Andrés Cano, Manuel Gómez-Olmedo, Andrés R. Masegosa, Serafín Moral
2011	C	`conf`	Locally Averaged Bayesian Dirichlet Metrics. ECSQARU Andrés Cano, Manuel Gómez-Olmedo, Andrés R. Masegosa, Serafín Moral
2011	Misc	`conf`	Riskoweb: Web-Based Genetic Profiling to Complex Disease Using Genome-Wide SNP Markers. PACBB Sergio Torres-Sánchez, Rosana Montes-Soldado, Nuria Medina-Medina, Andrés R. Masegosa, María del Mar Abad-Grau
2010	C	`conf`	An Importance Sampling Approach to Integrate Expert Knowledge When Learning Bayesian Networks From Data. IPMU Andrés Cano, Andrés R. Masegosa, Serafín Moral
2010	J	`jnl`	An ensemble method using credal decision trees. Eur. J. Oper. Res. Joaquín Abellán, Andrés R. Masegosa
2010	—	`conf`	Bagging Decision Trees on Data Sets with Classification Noise. FoIKS Joaquín Abellán, Andrés R. Masegosa
2009	C	`conf`	A Bayesian Random Split to Build Ensembles of Classification Trees. ECSQARU Andrés Cano, Andrés R. Masegosa, Serafín Moral
2009	J	`jnl`	A Filter-Wrapper Method to Select Variables for the Naive Bayes Classifier Based on Credal Decision Trees. Int. J. Uncertain. Fuzziness Knowl. Based Syst. Joaquín Abellán, Andrés R. Masegosa
2009	C	`conf`	An Experimental Study about Simple Decision Trees for Bagging Ensemble on Datasets with Classification Noise. ECSQARU Joaquín Abellán, Andrés R. Masegosa
2009	—	`conf`	Link-Based Text Classification Using Bayesian Networks. INEX Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Andrés R. Masegosa, Alfonso E. Romero
2008	J	`jnl`	Requirements for total uncertainty measures in Dempster-Shafer theory of evidence. Int. J. Gen. Syst. Joaquín Abellán, Andrés R. Masegosa
2007	C	`conf`	A Semi-naive Bayes Classifier with Grouping of Cases. ECSQARU Joaquín Abellán, Andrés Cano, Andrés R. Masegosa, Serafín Moral
2007	C	`conf`	Combining Decision Trees Based on Imprecise Probabilities and Uncertainty Measures. ECSQARU Joaquín Abellán, Andrés R. Masegosa
2007	A*	`conf`	Effects of highly agreed documents in relevancy prediction. SIGIR Andrés R. Masegosa, Hideo Joho, Joemon M. Jose
2007	A	`conf`	Evaluating Query-Independent Object Features for Relevancy Prediction. ECIR Andrés R. Masegosa, Hideo Joho, Joemon M. Jose
2007	C	`conf`	Split Criterions for Variable Selection Using Decision Trees. ECSQARU Joaquín Abellán, Andrés R. Masegosa
2006	—	`conf`	Varying Parameter in Classification Based on Imprecise Probabilities. SMPS Joaquín Abellán, Serafín Moral, Manuel Gómez-Olmedo, Andrés R. Masegosa
2005	C	`conf`	Methods to Determine the Branching Attribute in Bayesian Multinets Classifiers. ECSQARU Andrés Cano, Francisco Javier García Castellano, Andrés R. Masegosa, Serafín Moral
2005	C	`conf`	Selective Gaussian Naïve Bayes Model for Diffuse Large-B-Cell Lymphoma Classification: Some Improvements in Preprocessing and Variable Elimination. ECSQARU Andrés Cano, Francisco Javier García Castellano, Andrés R. Masegosa, Serafín Moral