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Rafael Cabañas
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Diversity and generalization in neural network ensembles
LA Ortega, R Cabañas, A Masegosa
International Conference on Artificial Intelligence and Statistics, 11720-11743, 2022
282022
AMIDST: A Java toolbox for scalable probabilistic machine learning
AR Masegosa, AM Martinez, D Ramos-López, R Cabañas, A Salmerón, ...
Knowledge-Based Systems 163, 595-597, 2019
222019
Structural causal models are (solvable by) credal networks
M Zaffalon, A Antonucci, R Cabañas
International Conference on Probabilistic Graphical Models, 581-592, 2020
212020
InferPy: Probabilistic modeling with TensorFlow made easy
R Cabañas, A Salmerón, AR Masegosa
Knowledge-Based Systems 168, 25-27, 2019
142019
Evaluating interval-valued influence diagrams
R Cabañas, A Antonucci, A Cano, M Gómez-Olmedo
International Journal of Approximate Reasoning 80, 393-411, 2017
132017
Approximate inference in influence diagrams using binary trees
RC de Paz, M Gómez-Olmedo, A Cano
Proceedings of the 6th European Workshop on Probabilistic Graphical Models …, 2012
112012
Causal expectation-maximisation
M Zaffalon, A Antonucci, R Cabañas
arXiv preprint arXiv:2011.02912, 2020
102020
CREMA: a Java library for credal network inference
D Huber, R Cabañas, A Antonucci, M Zaffalon
International Conference on Probabilistic Graphical Models, 613-616, 2020
102020
Probabilistic models with deep neural networks
AR Masegosa, R Cabañas, H Langseth, TD Nielsen, A Salmerón
Entropy 23 (1), 117, 2021
92021
Financial data analysis with PGMs using AMIDST
R Cabañas, AM Martínez, AR Masegosa, D Ramos-López, A Samerón, ...
2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW …, 2016
92016
CREDICI: A Java Library for Causal Inference by Credal Networks
R Cabañas, A Antonucci, D Huber, M Zaffalon
Proceedings of the 10th International Conference on Probabilistic Graphical …, 2020
72020
Bounding counterfactuals under selection bias
M Zaffalon, A Antonucci, R Cabanas, D Huber, D Azzimonti
International Conference on Probabilistic Graphical Models, 289-300, 2022
62022
Using binary trees for the evaluation of influence diagrams
R Cabanas, M Gómez-Olmedo, A Cano
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems …, 2016
62016
InferPy: Probabilistic Modeling with Deep Neural Networks Made Easy
J Cózar, R Cabañas, A Salmerón, AR Masegosa
Neurocomputing 415, 408, 2020
52020
Virtual subconcept drift detection in discrete data using probabilistic graphical models
R Cabañas, A Cano, M Gómez-Olmedo, AR Masegosa, S Moral
Information Processing and Management of Uncertainty in Knowledge-Based …, 2018
52018
Improvements to variable elimination and symbolic probabilistic inference for evaluating influence diagrams
R Cabañas, A Cano, M Gómez-Olmedo, AL Madsen
International Journal of Approximate Reasoning 70, 13-35, 2016
52016
On SPI for evaluating influence diagrams
R Cabanas, AL Madsen, A Cano, M Gómez-Olmedo
Information Processing and Management of Uncertainty in Knowledge-Based …, 2014
52014
Approximating counterfactual bounds while fusing observational, biased and randomised data sources
M Zaffalon, A Antonucci, R Cabañas, D Huber
International Journal of Approximate Reasoning 162, 109023, 2023
4*2023
Value-based potentials: exploiting quantitative information regularity patterns in probabilistic graphical models
M Gómez-Olmedo, R Cabañas, A Cano, S Moral, OP Retamero
International Journal of Intelligent Systems, 2021
42021
On SPI-lazy evaluation of influence diagrams
R Cabañas, A Cano, M Gómez-Olmedo, AL Madsen
Probabilistic Graphical Models: 7th European Workshop, PGM 2014, Utrecht …, 2014
42014
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Articles 1–20