latest.bib

@article{NguRigLam21-ML-IJ,
  author = {Nguembang Fadja, Arnaud  and Fabrizio Riguzzi and Evelina Lamma},
  title = {Learning Hierarchical Probabilistic Logic Programs},
  journal = {Machine Learning},
  publisher = {Springer},
  copyright = {Springer},
  year = {2021},
  url = {http://ml.unife.it/wp-content/uploads/Papers/NguRigLam-ML21.pdf},
  abstract = {
Probabilistic logic programming (PLP) provides a powerful tool for reason- ing with uncertain relational models. However, learning probabilistic logic programs is expensive due to the high cost of inference. Among the proposals to overcome this problem, one of the most promising is lifted inference. In this paper we consider PLP models that are amenable to lifted inference and present an algorithm for performing parameter and structure learning of these models from positive and negative exam- ples. We discuss parameter learning with EM and LBFGS and structure learning with LIFTCOVER, an algorithm similar to SLIPCOVER. The results of the comparison of LIFTCOVER with SLIPCOVER on 12 datasets show that it can achieve solutions of similar or better quality in a fraction of the time.
},
  keywords = {Probabilistic Logic Programming, Distribution Semantics, Arithmetic Circuits, Gradient Descent, Back-propagation}
}
@article{AzzRigLam21-AIJ-IJ,
  title = {A Semantics for Hybrid Probabilistic Logic Programs with Function Symbols},
  author = {Azzolini, Damiano and Riguzzi, Fabrizio and Lamma, Evelina},
  journal = {Artificial Intelligence},
  year = {2021},
  copyright = {Elsevier},
  issn = {0004-3702},
  url = {http://ml.unife.it/wp-content/uploads/Papers/AzzRigLam21-AIJ-IJ.pdf},
  doi = {10.1016/j.artint.2021.103452},
  note = {The final publication is available at Elsevier via \url{https://doi.org/10.1016/j.artint.2021.103452} },
  volume = {294},
  pages = {103452}
}
@article{RigBelZesAlbLam21-ML-IJ,
  author = {Riguzzi, Fabrizio and Bellodi, Elena and Zese, Riccardo and Alberti, Marco and Lamma, Evelina},
  title = {Probabilistic inductive constraint logic},
  journal = {Machine Learning},
  year = {2021},
  volume = {110},
  issue = {4},
  pages = {723-754},
  doi = {10.1007/s10994-020-05911-6},
  pdf = {https://link.springer.com/content/pdf/10.1007/s10994-020-05911-6.pdf},
  publisher = {Springer},
  issn = {08856125}
}
@inproceedings{FraLamRig20-LOD-IC,
  author = {Fraccaroli, Michele
and Lamma, Evelina
and Riguzzi, Fabrizio},
  editor = {Nicosia, Giuseppe
and Ojha, Varun
and La Malfa, Emanuele
and Jansen, Giorgio
and Sciacca, Vincenzo
and Pardalos, Panos
and Giuffrida, Giovanni
and Umeton, Renato},
  title = {Automatic Setting of {DNN} Hyper-Parameters by Mixing {Bayesian Optimization} and Tuning Rules},
  booktitle = {Machine Learning, Optimization, and Data Science, 6th International Conference, LOD 2020, Siena, Italy, July 19–23, 2020, Revised Selected Papers, Part I},
  year = {2020},
  publisher = {Springer International Publishing},
  address = {Cham},
  pages = {477--488},
  abstract = {Deep learning techniques play an increasingly important role in industrial and research environments due to their outstanding results. However, the large number of hyper-parameters to be set may lead to errors if they are set manually. The state-of-the-art hyper-parameters tuning methods are grid search, random search, and Bayesian Optimization. The first two methods are expensive because they try, respectively, all possible combinations and random combinations of hyper-parameters. Bayesian Optimization, instead, builds a surrogate model of the objective function, quantifies the uncertainty in the surrogate using Gaussian Process Regression and uses an acquisition function to decide where to sample the new set of hyper-parameters. This work faces the field of Hyper-Parameters Optimization (HPO). The aim is to improve Bayesian Optimization applied to Deep Neural Networks. For this goal, we build a new algorithm for evaluating and analyzing the results of the network on the training and validation sets and use a set of tuning rules to add new hyper-parameters and/or to reduce the hyper-parameter search space to select a better combination.},
  isbn = {978-3-030-64583-0},
  doi = {10.1007/978-3-030-64583-0_43},
  note = {The final publication is available at Springer via \url{https://link.springer.com/chapter/10.1007/978-3-030-64583-0_43}},
  copyright = {Springer},
  pdf = {http://ml.unife.it/wp-content/uploads/Papers/FraLamRig-LOD20.pdf},
  series = {Lecture Notes in Computer Science},
  volume = {12565}
}
@article{AlbGavLam20-FI-IJ,
  author = {Marco Alberti and
               Marco Gavanelli and
               Evelina Lamma and
               Fabrizio Riguzzi and
               Ken Satoh and
               Riccardo Zese},
  title = {Dischargeable Obligations in the {SCIFF} Framework},
  journal = {Fundamenta Informaticae},
  volume = {176},
  number = {3-4},
  pages = {321--348},
  year = {2020},
  doi = {10.3233/FI-2020-1976},
  publisher = {IOS Press}
}
@article{CheCotGavLamMelRig20-EAAI-IJ,
  author = {Federico Chesani and
               Giuseppe Cota and
               Marco Gavanelli and
               Evelina Lamma and
               Paola Mello and
               Fabrizio Riguzzi},
  title = {Declarative and Mathematical Programming approaches to Decision Support
               Systems for food recycling},
  journal = {Engineering Applications of Artificial Intelligence},
  volume = {95},
  pages = {103861},
  year = {2020},
  doi = {10.1016/j.engappai.2020.103861},
  scopus = {2-s2.0-85089188550}
}
@incollection{CotZesBelLamRig20-SSWS-BC,
  title = {A Framework for Reasoning on Probabilistic Description Logics},
  author = {Cota, Giuseppe and Zese, Riccardo and Bellodi, Elena and Lamma, Evelina and Riguzzi, Fabrizio},
  booktitle = {Applications and Practices in Ontology Design, Extraction, and Reasoning},
  series = {Studies on the Semantic Web},
  volume = {49},
  editor = {Cota, Giuseppe and Daquino, Marilena and Pozzato, Gian Luca},
  isbn = {978-1-64368-142-9},
  doi = {10.3233/SSW200040},
  language = {English},
  pages = {127-144},
  year = {2020},
  publisher = {{IOS} Press},
  abstract = {While there exist several reasoners for Description Logics, very few of them can cope with uncertainty. BUNDLE is an inference framework that can exploit several OWL (non-probabilistic) reasoners to perform inference over Probabilistic Description Logics.
	In this chapter, we report the latest advances implemented in BUNDLE. In particular, BUNDLE can now interface with the reasoners of the TRILL system, thus providing a uniform method to execute probabilistic queries using different settings. BUNDLE can be easily extended and can be used either as a standalone desktop application or as a library in OWL API-based applications that need to reason over Probabilistic Description Logics.
	The reasoning performance heavily depends on the reasoner and method used to compute the probability. We provide a comparison of the different reasoning settings on several datasets.
	},
  copyright = {Akademische Verlagsgesellschaft AKA GmbH, Berlin}
}
@inproceedings{AzzRigLam20-PLP-IW,
  title = {An Analysis of {Gibbs} Sampling for Probabilistic Logic Programs},
  booktitle = {Workshop on Probabilistic Logic Programming (PLP 2020)},
  year = 2020,
  author = {Azzolini, Damiano and Fabrizio Riguzzi and Evelina Lamma},
  editor = {Carmine Dodaro and George Aristidis Elder and Wolfgang Faber and Jorge Fandinno and Martin Gebser and Markus Hecher and Emily LeBlanc and Michael Morak and Jessica Zangari},
  volume = {2678},
  series = {CEUR Workshop Proceedings},
  publisher = {Sun {SITE} Central Europe},
  address = {Aachen, Germany},
  issn = {1613-0073},
  venue = {Rende, Italy},
  eventdate = {September 19, 2020},
  copyright = {by the authors},
  url = {http://ceur-ws.org/Vol-2678/paper12.pdf},
  pages = {1-13},
  scopus = {2-s2.0-85092328818}
}
@inproceedings{AzzBelBraRigLam20-ICLP-IC,
  title = {Modeling Bitcoin Lightning Network by Logic Programming},
  booktitle = {Proceedings of the 36th International Conference on Logic Programming (Technical Communications)},
  year = 2020,
  author = {Damiano Azzolini and Elena Bellodi and Alessandro Brancaleoni and Fabrizio Riguzzi and Evelina Lamma},
  editor = {Francesco Ricca and Alessandra Russo and Sergio Greco and Nicola Leone and Alexander Artikis and Gerhard Friedrich and Paul Fodor and Angelika Kimmig and Francesca Lisi and Marco Maratea and Alessandra Mileo and Fabrizio Riguzzi},
  publisher = {Open Publishing Association},
  address = {Waterloo, Australia},
  issn = {2075-2180},
  venue = {Rende, Italy},
  eventdate = {September 18-25, 2020},
  copyright = {by the authors},
  url = {https://cgi.cse.unsw.edu.au/~eptcs/content.cgi?ICLP2020#EPTCS325.30},
  http = {https://arxiv.org/html/2009.09158v1/#EPTCS325.30},
  doi = {10.4204/EPTCS.325},
  scopus = {2-s2.0-85092656765},
  pages = {258-260}
}
@inproceedings{AzzRigLam20-BSCT-IW,
  author = {Azzolini, Damiano and Riguzzi, Fabrizio and Lamma, Evelina},
  editor = {Abramowicz, Witold and Klein, Gary},
  title = {Modeling Smart Contracts with Probabilistic Logic Programming},
  booktitle = {Business Information Systems Workshops},
  year = {2020},
  publisher = {Springer International Publishing},
  series = {Lecture Notes in Business Information Processing},
  volume = {394},
  address = {Cham},
  pages = {86--98},
  isbn = {978-3-030-61146-0},
  url = {http://ml.unife.it/wp-content/uploads/Papers/AzzRigLam-BSCT20.pdf},
  copyright = {Springer},
  doi = {10.1007/978-3-030-61146-0_7},
  note = {The final publication is available at Springer via \url{http://dx.doi.org/10.1007/978-3-030-61146-0_7}}
}

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