Rafael Cabañas

According to our database, Rafael Cabañas authored at least 28 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Counterfactual Reasoning with Probabilistic Graphical Models for Analyzing Socioecological Systems.

Rafael Cabañas, Ana D. Maldonado, María Morales, Pedro Aguilera Aguilera, Antonio Salmerón

CoRR, 2024

2023

Approximating counterfactual bounds while fusing observational, biased and randomised data sources.

Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas, David Huber

Int. J. Approx. Reason., November, 2023

Efficient Computation of Counterfactual Bounds.

Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas, David Huber, Dario Azzimonti

CoRR, 2023

2022

Learning to Bound Counterfactual Inference in Structural Causal Models from Observational and Randomised Data.

Marco Zaffalon, Alessandro Antonucci, David Huber, Rafael Cabañas

CoRR, 2022

Bounding Counterfactuals under Selection Bias.

Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas, David Huber, Dario Azzimonti

Proceedings of the International Conference on Probabilistic Graphical Models, 2022

Diversity and Generalization in Neural Network Ensembles.

Luis A. Ortega, Rafael Cabañas, Andrés R. Masegosa

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Value-based potentials: Exploiting quantitative information regularity patterns in probabilistic graphical models.

Manuel Gómez-Olmedo, Rafael Cabañas, Andrés Cano, Serafín Moral, Ofelia P. Retamero

Int. J. Intell. Syst., 2021

Probabilistic Models with Deep Neural Networks.

Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón

Entropy, 2021

CREPO: An Open Repository to Benchmark Credal Network Algorithms.

Rafael Cabañas, Alessandro Antonucci

Proceedings of the International Symposium on Imprecise Probability: Theories and Applications, 2021

2020

InferPy: Probabilistic modeling with deep neural networks made easy.

Javier Cózar, Rafael Cabañas, Antonio Salmerón, Andrés R. Masegosa

Neurocomputing, 2020

EM Based Bounding of Unidentifiable Queries in Structural Causal Models.

Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas

CoRR, 2020

Structural Causal Models Are (Solvable by) Credal Networks.

Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas

Proceedings of the International Conference on Probabilistic Graphical Models, 2020

CREMA: A Java Library for Credal Network Inference.

David Huber, Rafael Cabañas, Alessandro Antonucci, Marco Zaffalon

Proceedings of the International Conference on Probabilistic Graphical Models, 2020

Probabilistic Graphical Models with Neural Networks in InferPy.

Rafael Cabañas, Javier Cózar, Antonio Salmerón, Andrés R. Masegosa

Proceedings of the International Conference on Probabilistic Graphical Models, 2020

CREDICI: A Java Library for Causal Inference by Credal Networks.

Rafael Cabañas, Alessandro Antonucci, David Huber, Marco Zaffalon

Proceedings of the International Conference on Probabilistic Graphical Models, 2020

2019

AMIDST: A Java toolbox for scalable probabilistic machine learning.

Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen

Knowl. Based Syst., 2019

InferPy: Probabilistic modeling with Tensorflow made easy.

Rafael Cabañas, Antonio Salmerón, Andrés R. Masegosa

Knowl. Based Syst., 2019

2018

Virtual Subconcept Drift Detection in Discrete Data Using Probabilistic Graphical Models.

Rafael Cabañas, Andrés Cano, Manuel Gómez-Olmedo, Andrés R. Masegosa, Serafín Moral

Proceedings of the Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications, 2018

2017

Evaluating interval-valued influence diagrams.

Rafael Cabañas, Alessandro Antonucci, Andrés Cano, Manuel Gómez-Olmedo

Int. J. Approx. Reason., 2017

2016

Using Binary Trees for the Evaluation of Influence Diagrams.

Rafael Cabañas, Manuel Gómez-Olmedo, Andrés Cano

Int. J. Uncertain. Fuzziness Knowl. Based Syst., 2016

Improvements to Variable Elimination and Symbolic Probabilistic Inference for evaluating Influence Diagrams.

Rafael Cabañas, Andrés Cano, Manuel Gómez-Olmedo, Anders L. Madsen

Int. J. Approx. Reason., 2016

Financial Data Analysis with PGMs Using AMIDST.

Rafael Cabañas, Ana M. Martínez, Andrés R. Masegosa, Darío Ramos-López, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen

Proceedings of the IEEE International Conference on Data Mining Workshops, 2016

2015

Variable Elimination for Interval-Valued Influence Diagrams.

Rafael Cabañas, Alessandro Antonucci, Andrés Cano, Manuel Gómez-Olmedo

Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2015

2014

On SPI-Lazy Evaluation of Influence Diagrams.

Rafael Cabañas, Andrés Cano, Manuel Gómez-Olmedo, Anders L. Madsen

Proceedings of the Probabilistic Graphical Models - 7th European Workshop, 2014

On SPI for Evaluating Influence Diagrams.

Rafael Cabañas, Anders L. Madsen, Andrés Cano, Manuel Gómez-Olmedo

Proceedings of the Information Processing and Management of Uncertainty in Knowledge-Based Systems, 2014

2013

Heuristics for Determining the Elimination Ordering in the Influence Diagram Evaluation with Binary Trees.

Rafael Cabañas, Andrés Cano, Manuel Gómez-Olmedo, Anders L. Madsen

Proceedings of the Twelfth Scandinavian Conference on Artificial Intelligence, 2013

Evaluating Asymmetric Decision Problems with Binary Constraint Trees.

Rafael Cabañas, Manuel Gómez-Olmedo, Andrés Cano

Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2013

Approximate Lazy Evaluation of Influence Diagrams.

Rafael Cabañas, Andrés Cano, Manuel Gómez-Olmedo, Anders L. Madsen

Proceedings of the Advances in Artificial Intelligence, 2013