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Machine Learning

The Machine Learning research area is designed to study a range of topics related to theoretical foundations and applications of machine learning in various contexts such as computer vision, natural language processing, finance, mobility, health care and bioinformatics. The work is interdisciplinary and deeply rooted in the research areas of complex networks, complex systems and computer science. The research focuses on modelling and simulation of systems that involve technological and social factors, using both supervised and unsupervised machine learning techniques to investigate the properties of such systems.

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Doctor XAI: An ontology-based approach to black-box sequential data classification explanations

C. Panigutti, A. Perotti, D. Pedreschi

Conference on Fairness, Accountability, and Transparency (FAT* 2020) (2020)

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Quantitative and Ontology-Based Comparison of Explanations for Image Classification

V. Ghidini, A. Perotti, R. Schifanella

International Conference on Machine Learning, Optimization, and Data Science LNCS 11943 (2020)

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FaiRecSys: mitigating algorithmic bias in recommender systems

B. Edizel, F. Bonchi, S. Hajian, A. Panisson, T. Tassa

International Journal of Data Science and Analytics 9 (2019)

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