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Andrés R. Masegosa
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2020 – today
- 2024
- [i14]Ioar Casado, Luis A. Ortega, Andrés R. Masegosa, Aritz Pérez:
PAC-Bayes-Chernoff bounds for unbounded losses. CoRR abs/2401.01148 (2024) - 2023
- [i13]Andrés R. Masegosa, Luis A. Ortega:
Understanding Generalization in the Interpolation Regime using the Rate Function. CoRR abs/2306.10947 (2023) - [i12]Yijie Zhang, Yi-Shan Wu
, Luis A. Ortega, Andrés R. Masegosa:
If there is no underfitting, there is no Cold Posterior Effect. CoRR abs/2310.01189 (2023) - 2022
- [j26]Daniel Russo
From anecdote to evidence: the relationship between personality and need for cognition of developers. Empir. Softw. Eng. 27(3): 71 (2022) - [c35]Luis A. Ortega
Diversity and Generalization in Neural Network Ensembles. AISTATS 2022: 11720-11743 - [c34]Antonio Salmerón, Helge Langseth, Andrés R. Masegosa, Thomas D. Nielsen:
A Reparameterization of Mixtures of Truncated Basis Functions and its Applications. PGM 2022: 205-216 - 2021
- [j25]Andrés R. Masegosa, Rafael Cabañas
Probabilistic Models with Deep Neural Networks. Entropy 23(1): 117 (2021) - [c33]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: 12625-12636 - [i11]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 abs/2106.13624 (2021) - [i10]Luis A. Ortega, Rafael Cabañas, Andrés R. Masegosa:
Diversity and Generalization in Neural Network Ensembles. CoRR abs/2110.13786 (2021) - [i9]Daniel Russo, Andrés R. Masegosa, Klaas-Jan Stol:
From Anecdote to Evidence: The Relationship Between Personality and Need for Cognition of Developers. CoRR abs/2112.06610 (2021) - 2020
- [j24]Andrés R. Masegosa, Ana M. Martínez
Analyzing concept drift: A case study in the financial sector. Intell. Data Anal. 24(3): 665-688 (2020) - [j23]Javier Cózar, Rafael Cabañas, Antonio Salmerón
InferPy: Probabilistic modeling with deep neural networks made easy. Neurocomputing 415: 408-410 (2020) - [j22]Andrés R. Masegosa, Antonio Torres, María Morales
Comparing two multinomial samples using hierarchical Bayesian models. Prog. Artif. Intell. 9(2): 145-154 (2020) - [c32]Andrés R. Masegosa:
Learning under Model Misspecification: Applications to Variational and Ensemble methods. NeurIPS 2020 - [c31]Andrés R. Masegosa, Stephan Sloth Lorenzen, Christian Igel, Yevgeny Seldin:
Second Order PAC-Bayesian Bounds for the Weighted Majority Vote. NeurIPS 2020 - [c30]Rafael Cabañas, Javier Cózar, Antonio Salmerón, Andrés R. Masegosa:
Probabilistic Graphical Models with Neural Networks in InferPy. PGM 2020: 601-604 - [i8]Andrés R. Masegosa, Stephan Sloth Lorenzen, Christian Igel, Yevgeny Seldin:
Second Order PAC-Bayesian Bounds for the Weighted Majority Vote. CoRR abs/2007.13532 (2020)
2010 – 2019
- 2019
- [j21]Andrés R. Masegosa, Ana M. Martínez
AMIDST: A Java toolbox for scalable probabilistic machine learning. Knowl. Based Syst. 163: 595-597 (2019) - [j20]Rafael Cabañas
InferPy: Probabilistic modeling with Tensorflow made easy. Knowl. Based Syst. 168: 25-27 (2019) - [i7]Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón:
Probabilistic Models with Deep Neural Networks. CoRR abs/1908.03442 (2019) - [i6]Javier Cózar, Rafael Cabañas, Andrés R. Masegosa, Antonio Salmerón:
InferPy: Probabilistic Modeling with Deep Neural Networks Made Easy. CoRR abs/1908.11161 (2019) - [i5]Andrés R. Masegosa:
Learning from i.i.d. data under model miss-specification. CoRR abs/1912.08335 (2019) - 2018
- [j19]Darío Ramos-López
Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks. Int. J. Approx. Reason. 100: 115-134 (2018) - [c29]Rafael Cabañas
Virtual Subconcept Drift Detection in Discrete Data Using Probabilistic Graphical Models. IPMU (3) 2018: 616-628 - 2017
- [j18]Andrés R. Masegosa
Scaling up Bayesian variational inference using distributed computing clusters. Int. J. Approx. Reason. 88: 435-451 (2017) - [j17]Darío Ramos-López
MAP inference in dynamic hybrid Bayesian networks. Prog. Artif. Intell. 6(2): 133-144 (2017) - [c28]Sergej Dogadov, Andrés R. Masegosa, Shinichi Nakajima:
Variational Robust Subspace Clustering with Mean Update Algorithm. ICCV Workshops 2017: 1792-1799 - [c27]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: 2334-2343 - [i4]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 abs/1704.01427 (2017) - [i3]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 abs/1707.02293 (2017) - 2016
- [j16]Andrés R. Masegosa, Ana M. Martínez
Probabilistic Graphical Models on Multi-Core CPUs Using Java 8. IEEE Comput. Intell. Mag. 11(2): 41-54 (2016) - [j15]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: 18-34 (2016) - [c26]Antonio Salmerón, Anders L. Madsen
Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs. ECAI 2016: 743-750 - [c25]Rafael Cabañas
Financial Data Analysis with PGMs Using AMIDST. ICDM Workshops 2016: 1284-1287 - [c24]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: 321-332 - [c23]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: 415-425 - [i2]Andrés R. Masegosa, Ana M. Martínez, Hanen Borchani:
Probabilistic Graphical Models on Multi-Core CPUs using Java 8. CoRR abs/1604.07990 (2016) - 2015
- [c22]Antonio Salmerón, Darío Ramos-López
Parallel Importance Sampling in Conditional Linear Gaussian Networks. CAEPIA 2015: 36-46 - [c21]Hanen Borchani
Modeling Concept Drift: A Probabilistic Graphical Model Based Approach. IDA 2015: 72-83 - [c20]Hanen Borchani
Dynamic Bayesian modeling for risk prediction in credit operations. SCAI 2015: 17-26 - 2014
- [j14]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: 789-802 (2014) - [j13]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): 1548-1569 (2014) - [j12]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): 1618-1622 (2014) - [c19]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 - [c18]Andrés R. Masegosa:
Stochastic Discriminative EM. UAI 2014: 573-582 - [i1]Andrés R. Masegosa:
Stochastic Discriminative EM. CoRR abs/1410.1784 (2014) - 2013
- [j11]Andrés R. Masegosa, Serafín Moral
New skeleton-based approaches for Bayesian structure learning of Bayesian networks. Appl. Soft Comput. 13(2): 1110-1120 (2013) - [j10]Andrés Cano, Manuel Gómez-Olmedo
Locally averaged Bayesian Dirichlet metrics for learning the structure and the parameters of Bayesian networks. Int. J. Approx. Reason. 54(4): 526-540 (2013) - [j9]Andrés R. Masegosa, Serafín Moral:
An interactive approach for Bayesian network learning using domain/expert knowledge. Int. J. Approx. Reason. 54(8): 1168-1181 (2013) - 2012
- [j8]Joaquín Abellán, Andrés R. Masegosa:
Bagging schemes on the presence of class noise in classification. Expert Syst. Appl. 39(8): 6827-6837 (2012) - [j7]Joaquín Abellán, Andrés R. Masegosa:
Imprecise Classification with Credal Decision Trees. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 20(5): 763-788 (2012) - [j6]Andrés R. Masegosa, Serafín Moral
A Bayesian stochastic search method for discovering Markov boundaries. Knowl. Based Syst. 35: 211-223 (2012) - [c17]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: 360-366 - 2011
- [j5]Joaquín Abellán, Andrés Cano
A memory efficient semi-Naive Bayes classifier with grouping of cases. Intell. Data Anal. 15(3): 299-318 (2011) - [j4]Andrés Cano
A Method for Integrating Expert Knowledge When Learning Bayesian Networks From Data. IEEE Trans. Syst. Man Cybern. Part B 41(5): 1382-1394 (2011) - [c16]Andrés Cano
Locally Averaged Bayesian Dirichlet Metrics. ECSQARU 2011: 217-228 - [c15]Andrés Cano
Learning with Bayesian networks and probability trees to approximate a joint distribution. ISDA 2011: 624-629 - [c14]Sergio Torres-Sánchez, Rosana Montes-Soldado
Riskoweb: Web-Based Genetic Profiling to Complex Disease Using Genome-Wide SNP Markers. PACBB 2011: 1-8 - 2010
- [j3]Joaquín Abellán, Andrés R. Masegosa:
An ensemble method using credal decision trees. Eur. J. Oper. Res. 205(1): 218-226 (2010) - [c13]Joaquín Abellán, Andrés R. Masegosa:
Bagging Decision Trees on Data Sets with Classification Noise. FoIKS 2010: 248-265 - [c12]Andrés Cano
An Importance Sampling Approach to Integrate Expert Knowledge When Learning Bayesian Networks From Data. IPMU 2010: 685-695
2000 – 2009
- 2009
- [j2]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): 833-854 (2009) - [c11]Joaquín Abellán, Andrés R. Masegosa:
An Experimental Study about Simple Decision Trees for Bagging Ensemble on Datasets with Classification Noise. ECSQARU 2009: 446-456 - [c10]Andrés Cano
A Bayesian Random Split to Build Ensembles of Classification Trees. ECSQARU 2009: 469-480 - [c9]Luis M. de Campos
Link-Based Text Classification Using Bayesian Networks. INEX 2009: 397-406 - 2008
- [j1]Joaquín Abellán, Andrés R. Masegosa:
Requirements for total uncertainty measures in Dempster-Shafer theory of evidence. Int. J. Gen. Syst. 37(6): 733-747 (2008) - 2007
- [c8]Andrés R. Masegosa, Hideo Joho, Joemon M. Jose:
Evaluating Query-Independent Object Features for Relevancy Prediction. ECIR 2007: 283-294 - [c7]Joaquín Abellán, Andrés Cano
A Semi-naive Bayes Classifier with Grouping of Cases. ECSQARU 2007: 477-488 - [c6]Joaquín Abellán, Andrés R. Masegosa:
Split Criterions for Variable Selection Using Decision Trees. ECSQARU 2007: 489-500 - [c5]Joaquín Abellán, Andrés R. Masegosa:
Combining Decision Trees Based on Imprecise Probabilities and Uncertainty Measures. ECSQARU 2007: 512-523 - [c4]Andrés R. Masegosa, Hideo Joho, Joemon M. Jose
Effects of highly agreed documents in relevancy prediction. SIGIR 2007: 883-884 - 2006
- [c3]Joaquín Abellán, Serafín Moral, Manuel Gómez-Olmedo, Andrés R. Masegosa:
Varying Parameter in Classification Based on Imprecise Probabilities. SMPS 2006: 231-239 - 2005
- [c2]Andrés Cano
Selective Gaussian Naïve Bayes Model for Diffuse Large-B-Cell Lymphoma Classification: Some Improvements in Preprocessing and Variable Elimination. ECSQARU 2005: 908-920 - [c1]Andrés Cano
Methods to Determine the Branching Attribute in Bayesian Multinets Classifiers. ECSQARU 2005: 932-943
Coauthor Index
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