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Salvatore Ruggieri
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2013
D. Pedreschi
,
Ruggieri, S.
, and
Turini, F.
,
“
The discovery of discrimination
”
, in
Discrimination and privacy in the information society
, Springer, 2013, pp. 91–108.
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A. Romei
,
Ruggieri, S.
, and
Turini, F.
,
“
Discrimination discovery in scientific project evaluation: A case study
”
,
Expert Systems with Applications
, vol. 40, pp. 6064–6079, 2013.
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S. Ruggieri
,
“
Learning from polyhedral sets
”
, in
Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
, 2013.
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2014
S. Ruggieri
,
Hajian, S.
,
Kamiran, F.
, and
Zhang, X.
,
“
Anti-discrimination analysis using privacy attack strategies
”
, in
Joint European Conference on Machine Learning and Knowledge Discovery in Databases
, 2014.
DOI
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S. Ruggieri
,
Eirinakis, P.
,
Subramani, K.
, and
Wojciechowski, P.
,
“
On the complexity of quantified linear systems
”
,
Theoretical Computer Science
, vol. 518, pp. 128–134, 2014.
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M. Aldinucci
,
Ruggieri, S.
, and
Torquati, M.
,
“
Decision tree building on multi-core using FastFlow
”
,
Concurrency and Computation: Practice and Experience
, vol. 26, pp. 800–820, 2014.
DOI
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S. Mascetti
,
Ricci, A.
, and
Ruggieri, S.
,
“
Introduction to special issue on computational methods for enforcing privacy and fairness in the knowledge society
”
,
Artificial Intelligence and Law
, vol. 22, pp. 109–111, 2014.
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A. Romei
and
Ruggieri, S.
,
“
A multidisciplinary survey on discrimination analysis
”
,
The Knowledge Engineering Review
, vol. 29, pp. 582–638, 2014.
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P. Eirinakis
,
Ruggieri, S.
,
Subramani, K.
, and
Wojciechowski, P.
,
“
On quantified linear implications
”
,
Annals of Mathematics and Artificial Intelligence
, vol. 71, pp. 301–325, 2014.
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S. Ruggieri
,
“
Using t-closeness anonymity to control for non-discrimination.
”
,
Trans. Data Privacy
, vol. 7, pp. 99–129, 2014.
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2015
S. Ruggieri
,
“
Introduction to the special issue on Artificial Intelligence for Society and Economy
”
,
Intelligenza Artificiale
, vol. 9, pp. 23–23, 2015.
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A. Romei
,
Ruggieri, S.
, and
Turini, F.
,
“
The layered structure of company share networks
”
, in
Data Science and Advanced Analytics (DSAA), 2015. 36678 2015. IEEE International Conference on
, 2015.
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A. Baroni
and
Ruggieri, S.
,
“
Segregation Discovery in a Social Network of Companies
”
, in
International Symposium on Intelligent Data Analysis
, 2015.
DOI
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2016
S. Bergamaschi
,
Carlini, E.
,
Ceci, M.
,
Furletti, B.
,
Giannotti, F.
,
Malerba, D.
,
Mezzanzanica, M.
,
Monreale, A.
,
Pasi, G.
,
Pedreschi, D.
,
Perego, R.
, and
Ruggieri, S.
,
“
Big Data Research in Italy: A Perspective
”
,
Engineering
, vol. 2, p. 163, 2016.
DOI
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B. Qureshi
,
Kamiran, F.
,
Karim, A.
, and
Ruggieri, S.
,
“
Causal Discrimination Discovery Through Propensity Score Analysis
”
,
arXiv preprint arXiv:1608.03735
, 2016.
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B. Thanh Luong
,
Ruggieri, S.
, and
Turini, F.
,
“
Classification Rule Mining Supported by Ontology for Discrimination Discovery
”
, in
Data Mining Workshops (ICDMW), 2016 IEEE 16th International Conference on
, 2016.
DOI
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S. Ruggieri
and
Turini, F.
,
“
A KDD process for discrimination discovery
”
, in
Joint European Conference on Machine Learning and Knowledge Discovery in Databases
, 2016.
DOI
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2017
A. Baroni
,
Conte, A.
,
Patrignani, M.
, and
Ruggieri, S.
,
“
Efficiently Clustering Very Large Attributed Graphs
”
,
arXiv preprint arXiv:1703.08590
, 2017.
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S. Ruggieri
,
“
Enumerating Distinct Decision Trees
”
, in
International Conference on Machine Learning
, 2017.
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A. Baroni
and
Ruggieri, S.
,
“
Segregation discovery in a social network of companies
”
,
Journal of Intelligent Information Systems
, 2017.
DOI
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2018
R. Guidotti
and
Ruggieri, S.
,
“
Assessing the Stability of Interpretable Models
”
,
arXiv preprint arXiv:1810.09352
, 2018.
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G. Amato
,
Candela, L.
,
Castelli, D.
,
Esuli, A.
,
Falchi, F.
,
Gennaro, C.
,
Giannotti, F.
,
Monreale, A.
,
Nanni, M.
,
Pagano, P.
,
Pappalardo, L.
,
Pedreschi, D.
,
Pratesi, F.
,
Rabitti, F.
,
Rinzivillo, S.
,
Rossetti, G.
,
Ruggieri, S.
,
Sebastiani, F.
, and
Tesconi, M.
,
“
How Data Mining and Machine Learning Evolved from Relational Data Base to Data Science
”
, in
A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years
,
S. Flesca
,
Greco, S.
,
Masciari, E.
, and
Saccà, D.
, Eds.
Cham: Springer International Publishing, 2018, pp. 287 - 306.
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R. Guidotti
,
Monreale, A.
,
Ruggieri, S.
,
Pedreschi, D.
,
Turini, F.
, and
Giannotti, F.
,
“
Local Rule-Based Explanations of Black Box Decision Systems
”
, 2018.
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D. Pedreschi
,
Giannotti, F.
,
Guidotti, R.
,
Monreale, A.
,
Pappalardo, L.
,
Ruggieri, S.
, and
Turini, F.
,
“
Open the Black Box Data-Driven Explanation of Black Box Decision Systems
”
, 2018.
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R. Guidotti
,
Monreale, A.
,
Ruggieri, S.
,
Turini, F.
,
Giannotti, F.
, and
Pedreschi, D.
,
“
A survey of methods for explaining black box models
”
,
ACM computing surveys (CSUR)
, vol. 51, p. 93, 2018.
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