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[13]
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Arnaud Nguembang Fadja, Giuseppe Cota, Francesco Bertasi, Fabrizio Riguzzi,
Enzo Losi, Lucrezia Manservigi, Mauro Venturini, and Giovanni Bechini.
Machine learning approaches for the prediction of gas turbine
transients.
Journal of Computer Science, 20(5):495--510, Feb 2024.
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[12]
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Damiano Azzolini, Elena Bellodi, and Fabrizio Riguzzi.
Learning the parameters of probabilistic answer set programs.
In Stephen H. Muggleton and Alireza Tamaddoni-Nezhad, editors,
Inductive Logic Programming - ILP 2022, volume 14363 of Lecture Notes
in Computer Science, pages 1--14, Cham, 2024. Springer Nature Switzerland.
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[11]
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Elisabetta Gentili, Alice Bizzarri, Damiano Azzolini, Riccardo Zese, and
Fabrizio Riguzzi.
Regularization in probabilistic inductive logic programming.
In Elena Bellodi, Francesca Alessandra Lisi, and Riccardo Zese,
editors, Inductive Logic Programming - ILP 2023, volume 14363 of
Lecture Notes in Computer Science, pages 16--29, Cham, 2023. Springer Nature
Switzerland.
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[10]
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Fabrizio Riguzzi.
Foundations of Probabilistic Logic Programming: Languages,
Semantics, Inference and Learning, Second Edition.
River Publishers, Gistrup, Denmark, 2023.
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[9]
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Damiano Azzolini, Elena Bellodi, and Fabrizio Riguzzi.
Summary of statistical statements in probabilistic logic programming.
volume 385, pages 384 -- 385, 2023.
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[8]
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Damiano Azzolini, Elena Bellodi, and Fabrizio Riguzzi.
Towards a representation of decision theory problems with
probabilistic answer set programs.
volume 385, pages 190 -- 191, 2023.
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[7]
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Damiano Azzolini and Fabrizio Riguzzi.
Inference in probabilistic answer set programming under the credal
semantics.
In Roberto Basili, Domenico Lembo, Carla Limongelli, and Andrea
Orlandini, editors, AIxIA 2023 - Advances in Artificial Intelligence,
volume 14318 of Lecture Notes in Artificial Intelligence, pages
367--380, Heidelberg, Germany, 2023. Springer.
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[6]
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Damiano Azzolini and Fabrizio Riguzzi.
Lifted inference for statistical statements in probabilistic answer
set programming.
International Journal of Approximate Reasoning, 163:109040,
2023.
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DOI |
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[5]
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Tom Schrijvers, Birthe Van Den Berg, and Fabrizio Riguzzi.
Automatic differentiation in prolog.
Theory and Practice of Logic Programming, 23(4):900–917,
2023.
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DOI |
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[4]
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Damiano Azzolini, Elisabetta Gentili, and Fabrizio Riguzzi.
Link prediction in knowledge graphs with probabilistic logic
programming: Work in progress.
In Joaquín Arias, Sotiris Batsakis, Wolfgang Faber, Gopal Gupta,
Francesco Pacenza, Emmanuel Papadakis, Livio Robaldo, Kilian Ruckschloss,
Elmer Salazar, Zeynep G. Saribatur, Ilias Tachmazidis, Felix Weitkamper, and
Adam Wyner, editors, Proceedings of the International Conference on
Logic Programming 2023 Workshops co-located with the 39th International
Conference on Logic Programming (ICLP 2023), volume 3437 of CEUR
Workshop Proceedings, pages 1--4. CEUR-WS.org, 2023.
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[3]
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Damiano Azzolini, Elena Bellodi, and Fabrizio Riguzzi.
MAP inference in probabilistic answer set programs.
In Agostino Dovier, Angelo Montanari, and Andrea Orlandini, editors,
AIxIA 2022 -- Advances in Artificial Intelligence, pages 413--426,
Cham, 2023. Springer International Publishing.
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[2]
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Damiano Azzolini, Elena Bellodi, and Fabrizio Riguzzi.
Approximate inference in probabilistic answer set programming for
statistical probabilities.
In Agostino Dovier, Angelo Montanari, and Andrea Orlandini, editors,
AIxIA 2022 -- Advances in Artificial Intelligence, pages 33--46, Cham,
2023. Springer International Publishing.
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[1]
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Salvatore Greco, Alessandro Salatiello, Nicolò Fabbri, Fabrizio Riguzzi,
Emanuele Locorotondo, Riccardo Spaggiari, Alfredo De Giorgi, and Angelina
Passaro.
Rapid assessment of COVID-19 mortality risk with GASS
classifiers.
Biomedicines, 11(3), 2023.
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