Rafael Cabañas

44 papers A 1C 3Journal 26Unranked 14

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Bayesian networks for causal analysis in socioecological systems. Ecol. Informatics Rafael Cabañas, Ana D. Maldonado, María Morales, Pedro Aguilera Aguilera, Antonio Salmerón
2025	J	`jnl`	Causal computations in Semi Markovian Structural Causal Models using divide and conquer. CoRR Anna Rodum Bjøru, Rafael Cabañas, Helge Langseth, Antonio Salmerón
2025	J	`jnl`	Divide and conquer for causal computation. Int. J. Approx. Reason. Anna Rodum Bjøru, Rafael Cabañas, Helge Langseth, Antonio Salmerón
2025	C	`conf`	Noise-Robust Weighted Logistic Regression Based on Outlier Detection with Expectation Maximization. ECSQARU Serafín Moral-García, Rafael Cabañas, Antonio Salmerón
2024	—	`conf`	A Divide and Conquer Approach for Solving Structural Causal Models. PGM Anna Rodum Bjøru, Rafael Cabañas, Helge Langseth, Antonio Salmerón
2024	J	`jnl`	Counterfactual Reasoning with Probabilistic Graphical Models for Analyzing Socioecological Systems. CoRR Rafael Cabañas, Ana D. Maldonado, María Morales, Pedro Aguilera Aguilera, Antonio Salmerón
2024	J	`jnl`	Efficient computation of counterfactual bounds. Int. J. Approx. Reason. Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas, David Huber, Dario Azzimonti
2024	J	`jnl`	How do Machine Learning Models Change? CoRR Joel Castaño, Rafael Cabañas, Antonio Salmerón, David Lo, Silverio Martínez-Fernández
2024	—	`conf`	On Counterfactual Explainations of Cardiovascular Risk in Adolescents and Young Adults Breast Cancer Survivors. HC@AIxIA Alice Bernasconi, Antonio Balordi, Alessio Zanga, Rafael Cabañas
2023	J	`jnl`	Approximating Counterfactual Bounds while Fusing Observational, Biased and Randomised Data Sources. CoRR Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas, David Huber
2023	J	`jnl`	Approximating counterfactual bounds while fusing observational, biased and randomised data sources. Int. J. Approx. Reason. Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas, David Huber
2023	J	`jnl`	Efficient Computation of Counterfactual Bounds. CoRR Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas, David Huber, Dario Azzimonti
2022	—	`conf`	Bounding Counterfactuals under Selection Bias. PGM Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas, David Huber, Dario Azzimonti
2022	J	`jnl`	Bounding Counterfactuals under Selection Bias. CoRR Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas, David Huber, Dario Azzimonti
2022	A	`conf`	Diversity and Generalization in Neural Network Ensembles. AISTATS Luis A. Ortega, Rafael Cabañas, Andrés R. Masegosa
2022	J	`jnl`	Learning to Bound Counterfactual Inference in Structural Causal Models from Observational and Randomised Data. CoRR Marco Zaffalon, Alessandro Antonucci, David Huber, Rafael Cabañas
2021	—	`conf`	CREPO: An Open Repository to Benchmark Credal Network Algorithms. ISIPTA Rafael Cabañas, Alessandro Antonucci
2021	J	`jnl`	CREPO: An Open Repository to Benchmark Credal Network Algorithms. CoRR Rafael Cabañas, Alessandro Antonucci
2021	J	`jnl`	Diversity and Generalization in Neural Network Ensembles. CoRR Luis A. Ortega, Rafael Cabañas, Andrés R. Masegosa
2021	J	`jnl`	Probabilistic Models with Deep Neural Networks. Entropy Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón
2021	J	`jnl`	Value-based potentials: Exploiting quantitative information regularity patterns in probabilistic graphical models. Int. J. Intell. Syst. Manuel Gómez-Olmedo, Rafael Cabañas, Andrés Cano, Serafín Moral, Ofelia P. Retamero
2020	—	`conf`	CREDICI: A Java Library for Causal Inference by Credal Networks. PGM Rafael Cabañas, Alessandro Antonucci, David Huber, Marco Zaffalon
2020	—	`conf`	CREMA: A Java Library for Credal Network Inference. PGM David Huber, Rafael Cabañas, Alessandro Antonucci, Marco Zaffalon
2020	J	`jnl`	EM Based Bounding of Unidentifiable Queries in Structural Causal Models. CoRR Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas
2020	J	`jnl`	InferPy: Probabilistic modeling with deep neural networks made easy. Neurocomputing Javier Cózar, Rafael Cabañas, Antonio Salmerón, Andrés R. Masegosa
2020	—	`conf`	Probabilistic Graphical Models with Neural Networks in InferPy. PGM Rafael Cabañas, Javier Cózar, Antonio Salmerón, Andrés R. Masegosa
2020	—	`conf`	Structural Causal Models Are (Solvable by) Credal Networks. PGM Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas
2020	J	`jnl`	Structural Causal Models Are (Solvable by) Credal Networks. CoRR Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas
2019	J	`jnl`	AMIDST: A Java toolbox for scalable probabilistic machine learning. Knowl. Based Syst. Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen
2019	J	`jnl`	InferPy: Probabilistic Modeling with Deep Neural Networks Made Easy. CoRR Javier Cózar, Rafael Cabañas, Andrés R. Masegosa, Antonio Salmerón
2019	J	`jnl`	InferPy: Probabilistic modeling with Tensorflow made easy. Knowl. Based Syst. Rafael Cabañas, Antonio Salmerón, Andrés R. Masegosa
2019	J	`jnl`	Probabilistic Models with Deep Neural Networks. CoRR Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón
2018	—	`conf`	Virtual Subconcept Drift Detection in Discrete Data Using Probabilistic Graphical Models. IPMU (3) Rafael Cabañas, Andrés Cano, Manuel Gómez-Olmedo, Andrés R. Masegosa, Serafín Moral
2017	J	`jnl`	AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning. CoRR Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen
2017	J	`jnl`	Evaluating interval-valued influence diagrams. Int. J. Approx. Reason. Rafael Cabañas, Alessandro Antonucci, Andrés Cano, Manuel Gómez-Olmedo
2016	—	`conf`	Financial Data Analysis with PGMs Using AMIDST. ICDM Workshops Rafael Cabañas, Ana M. Martínez, Andrés R. Masegosa, Darío Ramos-López, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen
2016	J	`jnl`	Improvements to Variable Elimination and Symbolic Probabilistic Inference for evaluating Influence Diagrams. Int. J. Approx. Reason. Rafael Cabañas, Andrés Cano, Manuel Gómez-Olmedo, Anders L. Madsen
2016	J	`jnl`	Using Binary Trees for the Evaluation of Influence Diagrams. Int. J. Uncertain. Fuzziness Knowl. Based Syst. Rafael Cabañas, Manuel Gómez-Olmedo, Andrés Cano
2015	C	`conf`	Variable Elimination for Interval-Valued Influence Diagrams. ECSQARU Rafael Cabañas, Alessandro Antonucci, Andrés Cano, Manuel Gómez-Olmedo
2014	—	`conf`	On SPI for Evaluating Influence Diagrams. IPMU (1) Rafael Cabañas, Anders L. Madsen, Andrés Cano, Manuel Gómez-Olmedo
2014	—	`conf`	On SPI-Lazy Evaluation of Influence Diagrams. Probabilistic Graphical Models Rafael Cabañas, Andrés Cano, Manuel Gómez-Olmedo, Anders L. Madsen
2013	—	`conf`	Approximate Lazy Evaluation of Influence Diagrams. CAEPIA Rafael Cabañas, Andrés Cano, Manuel Gómez-Olmedo, Anders L. Madsen
2013	C	`conf`	Evaluating Asymmetric Decision Problems with Binary Constraint Trees. ECSQARU Rafael Cabañas, Manuel Gómez-Olmedo, Andrés Cano
2013	—	`conf`	Heuristics for Determining the Elimination Ordering in the Influence Diagram Evaluation with Binary Trees. SCAI Rafael Cabañas, Andrés Cano, Manuel Gómez-Olmedo, Anders L. Madsen