PhD in Information Engineering (Politecnico di Milano)
Probabilistic Graphical Models
Bayesian Statistics and Machine Learning
My list of Publications
My Google Scholar page.
Best Paper Prize at the International Environmental Modelling and Software Conference 2002 (Lugano, Switzerland).
Conferences organization: PGM 2016 & ISIPTA 2017
I have co-organized, co-chaired and co-edited the proceedings of two conferences, held in Lugano in respectively 2016 and 2017:
PGM 2016 (The Eighth International Conference on Probabilistic Graphical Models)
ISIPTA 2017 (The Tenth International Symposium on Imprecise Probability: Theories and Applications)
AISTATS (2020): The International Conference on Artificial Intelligence and Statistics
IJCAI (2015, 2016, 2018, 2019, 2021, 2022): Int. Joint Conference on Artificial Intelligence
UAI (2016, 2018, 2019, 2020, 2021): Conference on Uncertainty in Artificial Intelligence
NIPS (2018, 2019, 2020): Conference on Neural Information Processing Systems
PGM (2014, 2016, 2018, 2020): European Workshop on Probabilistic Graphical Models
ISIPTA (2009, 2011, 2013, 2015, 2017, 2019): International Symposium on Imprecise Probability: Theories and Applications:
Reviewer for many different journals such as Machine Learning, Artificial Intelligence, Artificial Intelligence in Medicine, etc.
Outstanding reviewer certificate from the journal Environmental Modelling and Software.
Certificate of reviewing from the Int. Journal of Approximate Reasoning.
Uncertain Reasoning and Data Mining (2010 --2019)
Master of Science in Information Technology (co -teacher).
Applied Statistics (2010 -- present)
Bachelor of Business Engineering.
Analysis of Sequential Data (2018 -- present)
Master of Science in Data Science (co -teacher).
Our method for automatic forecasting with Gaussian Processes, published at ECML PKDD 2021:
Interface for Python and R.
Bayesian hypothesis testing in machine learning
Slides and code of our tutorial at ECML/PKDD 2016:
G. Corani, A. Benavoli, J. Demsar
Naive Credal Classifier
The open source implementation of the naive credal classifier is the JNCC2.
Credal Model Averaging
The R implementation of credal model averaging for logistic regression is due to A. Mignatti. The algorithms are discussed in these two papers (link1, link2). The software and the marmot data set used in those papers is available here.