rafael cabañas

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I am assitant professor in Statistics at the Department of Mathematics at the University of Almería (Spain). I have broad interests in probabilistic graphical models, machine learning, causality and counterfactual reasoning.

rcabanas@ual.es

Publications

I highlight my most relevant publications. You can visit my google scholar profile for the complete list.

  1. Cabañas, R., Gómez-Olmedo, M., & Cano, A. (2016). Using binary trees for the evaluation of influence diagrams. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 24(01), 59-89.
    COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE [Q3]

  2. Cabañas, R., Cano, A., Gómez-Olmedo, M., & Madsen, A. L. (2016). Improvements to variable elimination and symbolic probabilistic inference for evaluating influence diagrams. International Journal of Approximate Reasoning, 70, 13-35.
    COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE [Q2]

  3. Cabañas, R., Antonucci, A., Cano, A., & Gómez-Olmedo, M. (2017). Evaluating interval-valued influence diagrams. International Journal of Approximate Reasoning, 80, 393-411.
    COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE [Q2]

  4. Masegosa, A. R., Martinez, A. M., Ramos-López, D., Cabañas, R., Salmerón, A., Langseth, H., ... & Madsen, A. L. (2019). AMIDST: A Java toolbox for scalable probabilistic machine learning. Knowledge-Based Systems, 163, 595-597.
    COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE [Q1]

  5. Cabañas, R., Salmerón, A., & Masegosa, A. R. (2019). InferPy: Probabilistic modeling with Tensorflow made easy. Knowledge-Based Systems, 168, 25-27.
    COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE [Q1]

  6. Cózar, J., Cabañas, R., Salmerón, A., and Masegosa, A. R. InferPy: Probabilistic modeling with deep neural networks made easy. Neurocomputing, 415 (2020), 408-410.
    COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE [Q1]

  7. Masegosa, A. R., Cabañas, R., Lanseth, H., Nielsen, T. D., and Salmerón, A. Probabilistic Models with Deep Neural Networks. Entropy, 23 (2021), 117
    PHYSICS, MULTIDISCIPLINARY - SCIE [Q2]

  8. Gómez-Olmedo, M., Cabañas, R., Cano, A., Moral, S., and Retamero, O. P. Value-Based Potentials: Exploiting Quantitative Information Regularity Patterns in Probabilistic Graphical Models. International Journal of Intelligent Systems. 2021
    COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE [Q1]

  9. Zaffalon, M., Antonucci, A., Cabañas, R., & Huber, D. (2023). Approximating counterfactual bounds while fusing observational, biased and randomised data sources. International Journal of Approximate Reasoning, 162, 109023.
    COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE [Q2]

  10. Zaffalon, M., Antonucci, A., Cabañas, R., Huber, D., & Azzimonti, D. (2024). Efficient computation of counterfactual bounds. International Journal of Approximate Reasoning, 109111
    COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE [Q2]

  11. L.A. Ortega, R. Cabañas, A.R. Masegosa. (2022). Diversity and Generalization in Neural Network Ensembles. In International Conference on Artificial Intelligence and Statistics  PMLR
    Conference paper [Core A]