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G
R. Guidotti, Soldani, J., Neri, D., and Brogi, A., “Explaining successful docker images using pattern mining analysis”, in Federation of International Conferences on Software Technologies: Applications and Foundations, 2018.
R. Guidotti and Rossetti, G., ““Know Thyself” How Personal Music Tastes Shape the Last. Fm Online Social Network”, in International Symposium on Formal Methods, 2019.
R. Guidotti, Monreale, A., and Pedreschi, D., “The AI black box Explanation Problem”, ERCIM NEWS, pp. 12–13, 2019.
R. Guidotti and Ruggieri, S., “Assessing the Stability of Interpretable Models”, arXiv preprint arXiv:1810.09352, 2018.
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.
R. Guidotti, Rossetti, G., Pappalardo, L., Giannotti, F., and Pedreschi, D., “Personalized Market Basket Prediction with Temporal Annotated Recurring Sequences”, IEEE Transactions on Knowledge and Data Engineering, 2018.
R. Guidotti, Monreale, A., Ruggieri, S., Pedreschi, D., Turini, F., and Giannotti, F., “Local Rule-Based Explanations of Black Box Decision Systems”, 2018.
R. Guidotti, Gabrielli, L., Monreale, A., Pedreschi, D., and Giannotti, F., “Discovering temporal regularities in retail customers’ shopping behavior”, EPJ Data Science, vol. 7, p. 6, 2018.
R. Guidotti, Monreale, A., Matwin, S., and Pedreschi, D., “Black Box Explanation by Learning Image Exemplars in the Latent Feature Space”, in Machine Learning and Knowledge Discovery in Databases, Cham, 2020.
R. Guidotti and Rossetti, G., ““Know Thyself” How Personal Music Tastes Shape the Last.Fm Online Social Network”, in Formal Methods. FM 2019 International Workshops, Cham, 2020.
R. Guidotti, Monreale, A., and Cariaggi, L., “Investigating Neighborhood Generation Methods for Explanations of Obscure Image Classifiers”, in Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2019.
R. Guidotti and Coscia, M., “On the Equivalence Between Community Discovery and Clustering”, in International Conference on Smart Objects and Technologies for Social Good, 2017.
H
S. Hajian, Monreale, A., Pedreschi, D., Domingo-Ferrer, J., and Giannotti, F., “Fair pattern discovery”, in Symposium on Applied Computing, {SAC} 2014, Gyeongju, Republic of Korea - March 24 - 28, 2014, 2014, pp. 113–120.
S. Hajian, Monreale, A., Pedreschi, D., Domingo-Ferrer, J., and Giannotti, F., “Injecting Discrimination and Privacy Awareness Into Pattern Discovery”, in 12th {IEEE} International Conference on Data Mining Workshops, {ICDM} Workshops, Brussels, Belgium, December 10, 2012, 2012, pp. 360–369.
S. Hajian, Domingo-Ferrer, J., Monreale, A., Pedreschi, D., and Giannotti, F., “Discrimination- and privacy-aware patterns”, Data Min. Knowl. Discov., vol. 29, pp. 1733–1782, 2015.
H. Hosni, Masserotti, M. V., and Renso, C., “Maximum Entropy Reasoning for GIS”, 2006.
J
D. Janssens, Giannotti, F., Nanni, M., Pedreschi, D., and Rinzivillo, S., “Data Science for Simulating the Era of Electric Vehicles”, KI - Künstliche Intelligenz, 2012.
S. Marina Joseph, Citraro, S., Morini, V., Rossetti, G., and Stella, M., “Cognitive network science quantifies feelings expressed in suicide letters and Reddit mental health communities”, arXiv preprint arXiv:2110.15269, 2021.
K
J. Kim, Sirbu, A., Giannotti, F., and Gabrielli, L., “Digital Footprints of International Migration on Twitter”, in International Symposium on Intelligent Data Analysis, 2020.
S. Kisilevich, Mansmann, F., Nanni, M., and Rinzivillo, S., “Spatio-temporal clustering”, in Data Mining and Knowledge Discovery Handbook, 2010, pp. 855-874.
A. Knott, Pedreschi, D., Chatila, R., Chakraborti, T., Leavy, S., Baeza-Yates, R., Eyers, D., Trotman, A., Teal, P. D., Biecek, P., Russell, S., and Bengio, Y., “Generative AI models should include detection mechanisms as a condition for public releaseAbstract”, Ethics and Information Technology, vol. 25, 2023.
L. Kotthoff, Nanni, M., Guidotti, R., and O'Sullivan, B., “Find Your Way Back: Mobility Profile Mining with Constraints”, in Principles and Practice of Constraint Programming, Cork, 2015.
L
O. Lampridis, Guidotti, R., and Ruggieri, S., “Explaining Sentiment Classification with Synthetic Exemplars and Counter-Exemplars”, in Discovery Science, Cham, 2020.
L. Leone, Marchitiello, M., Natilli, M., and Romano, M. Francesca, “Measuring the effectiveness of homeopathic care through objective and shared indicators”, Homeopathy, vol. 100, pp. 212–219, 2011.
F. Lillo and Ruggieri, S., “Estimating the Total Volume of Queries to a Search Engine”, IEEE Transactions on Knowledge and Data Engineering, pp. 1-1, 2021.

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