Rafael Cabañas

Orcid: 0000-0002-5034-582X

Affiliations:

University of Almería, Spain
Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, Manno-Lugano, Switzerland (2019 - 2021)
University of Granada,Department of Computer Science and Artificial Intelligence CITIC, Spain (PhD 2017)

According to our database¹, Rafael Cabañas authored at least 30 papers between 2013 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

2014

2016

2018

2020

2022

2024

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Bibliography

2024

Efficient computation of counterfactual bounds.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2024

How do Machine Learning Models Change?

[BibT_eX]

[DOI]

Silverio Martínez-Fernández

CoRR, 2024

Counterfactual Reasoning with Probabilistic Graphical Models for Analyzing Socioecological Systems.

[BibT_eX]

[DOI]

Rafael Cabañas

Ana D. Maldonado

María Morales

Pedro Aguilera Aguilera

Antonio Salmerón

CoRR, 2024

On Counterfactual Explainations of Cardiovascular Risk in Adolescents and Young Adults Breast Cancer Survivors.

[BibT_eX]

[DOI]

Proceedings of the 3rd AIxIA Workshop on Artificial Intelligence For Healthcare (HC@AIxIA 2024) co-located with the 23rd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2024), 2024

2023

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

[BibT_eX]

[DOI]

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

2022

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

[BibT_eX]

[DOI]

CoRR, 2022

Bounding Counterfactuals under Selection Bias.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Probabilistic Graphical Models, 2022

Diversity and Generalization in Neural Network Ensembles.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Int. J. Intell. Syst., 2021

Probabilistic Models with Deep Neural Networks.

[BibT_eX]

[DOI]

Entropy, 2021

CREPO: An Open Repository to Benchmark Credal Network Algorithms.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Neurocomputing, 2020

EM Based Bounding of Unidentifiable Queries in Structural Causal Models.

[BibT_eX]

[DOI]

Marco Zaffalon

Alessandro Antonucci

Rafael Cabañas

CoRR, 2020

Structural Causal Models Are (Solvable by) Credal Networks.

[BibT_eX]

[DOI]

Marco Zaffalon

Alessandro Antonucci

Rafael Cabañas

Proceedings of the International Conference on Probabilistic Graphical Models, 2020

CREMA: A Java Library for Credal Network Inference.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Probabilistic Graphical Models, 2020

Probabilistic Graphical Models with Neural Networks in InferPy.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Probabilistic Graphical Models, 2020

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

[BibT_eX]

[DOI]

Proceedings of the International Conference on Probabilistic Graphical Models, 2020

2019

AMIDST: A Java toolbox for scalable probabilistic machine learning.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2019

InferPy: Probabilistic modeling with Tensorflow made easy.

[BibT_eX]

[DOI]

Rafael Cabañas

Antonio Salmerón

Andrés R. Masegosa

Knowl. Based Syst., 2019

2018

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

[BibT_eX]

[DOI]

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

2017

Evaluating interval-valued influence diagrams.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2017

2016

Using Binary Trees for the Evaluation of Influence Diagrams.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2016

Financial Data Analysis with PGMs Using AMIDST.

[BibT_eX]

[DOI]

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

2015

Variable Elimination for Interval-Valued Influence Diagrams.

[BibT_eX]

[DOI]

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

2014

On SPI-Lazy Evaluation of Influence Diagrams.

[BibT_eX]

[DOI]

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

On SPI for Evaluating Influence Diagrams.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the Twelfth Scandinavian Conference on Artificial Intelligence, 2013

Evaluating Asymmetric Decision Problems with Binary Constraint Trees.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the Advances in Artificial Intelligence, 2013

Rafael Cabañas

Timeline

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