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S. Ruggieri, Enumerating Distinct Decision Trees, in International Conference on Machine Learning, 2017.
S. Ruggieri and Mesnard, F., Typing Linear Constraints for Moding CLP() Programs, in SAS, 2008, pp. 128-143.
S. Ruggieri, Introduction to the special issue on Artificial Intelligence for Society and Economy, Intelligenza Artificiale, vol. 9, pp. 23–23, 2015.
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.
S. Ruggieri, Using t-closeness anonymity to control for non-discrimination., Trans. Data Privacy, vol. 7, pp. 99–129, 2014.
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.
S. Ruggieri, Data Anonymity Meets Non-discrimination, in Data Mining Workshops (ICDMW), 2013 IEEE 13th International Conference on, 2013.
S. Ruggieri, Learning from polyhedral sets, in Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, 2013.
S. Ruggieri and Turini, F., A KDD process for discrimination discovery, in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2016.
A. Rossi, Pedreschi, D., Clifton, D. A., and Morelli, D., Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts, Sensors, vol. 20, p. 7122, 2020.
A. Rossi, Perri, E., Pappalardo, L., Cintia, P., and F Iaia, M., Relationship between External and Internal Workloads in Elite Soccer Players: Comparison between Rate of Perceived Exertion and Training Load, Applied Sciences, vol. 9, p. 5174, 2019.
A. Rossi, Pappalardo, L., Cintia, P., F Iaia, M., Fernàndez, J., and Medina, D., Effective injury forecasting in soccer with GPS training data and machine learning, PloS one, vol. 13, p. e0201264, 2018.
G. Rossetti, Guidotti, R., Miliou, I., Pedreschi, D., and Giannotti, F., A supervised approach for intra-/inter-community interaction prediction in dynamic social networks, Social Network Analysis and Mining, vol. 6, p. 86, 2016.
G. Rossetti, Pedreschi, D., and Giannotti, F., Node-centric Community Discovery: From static to dynamic social network analysis, Online Social Networks and Media, vol. 3, pp. 32–48, 2017.
G. Rossetti, Pappalardo, L., and Pedreschi, D., Measuring tie strength in multidimensional networks, in SEDB 2013, 2013.
G. Rossetti, Milli, L., and Cazabet, R., CDLIB: a python library to extract, compare and evaluate communities from complex networks, Applied Network Science, vol. 4, p. 52, 2019.
G. Rossetti, Berlingerio, M., and Giannotti, F., Scalable Link Prediction on Multidimensional Networks, in ICDM Workshops, Vancouver, 2011, pp. 979-986.
G. Rossetti, Milli, L., Rinzivillo, S., Sirbu, A., Pedreschi, D., and Giannotti, F., NDlib: a python library to model and analyze diffusion processes over complex networks, International Journal of Data Science and Analytics, vol. 5, pp. 61–79, 2018.
G. Rossetti, Guidotti, R., Pennacchioli, D., Pedreschi, D., and Giannotti, F., Interaction Prediction in Dynamic Networks exploiting Community Discovery, in International conference on Advances in Social Network Analysis and Mining, ASONAM 2015, Paris, France, 2015.
G. Rossetti, Citraro, S., and Milli, L., Conformity: A Path-Aware Homophily Measure for Node-Attributed Networks, arXiv preprint arXiv:2012.05195, 2020.
G. Rossetti, Milli, L., Citraro, S., and Morini, V., UTLDR: an agent-based framework for modeling infectious diseases and public interventions, arXiv preprint arXiv:2011.05606, 2020.
G. Rossetti, Pappalardo, L., Kikas, R., Pedreschi, D., Giannotti, F., and Dumas, M., Community-centric analysis of user engagement in Skype social network, in International conference on Advances in Social Network Analysis and Mining, Paris, France, 2015.
G. Rossetti, Pappalardo, L., Pedreschi, D., and Giannotti, F., Tiles: an online algorithm for community discovery in dynamic social networks, Machine Learning, vol. 106, pp. 1213–1241, 2017.

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