Giorgio Corani

About me

Senior researcher at IDSIA, member of the Imprecise Probability Group.

Research interests

Probabilistic Graphical Models

Machine Learning

Bayesian Hypothesis Testing and Bayesian Hierarchical Models

Imprecise Probability and Credal Classification


My list of Publications

My Google Scholar page.

PGM 2016

I co-chaired the Eighth International Conference on Probabilistic Graphical Models (Lugano, September 2016) and co-edited its proceedings.


I co-chaired the Tenth International Symposium on Imprecise Probability: Theories and Applications (Lugano, July 2017) and co-edited its proceedings.

Program committees

AAAI (2018): AAAI Conference on Artificial Intelligence

DMIN (2011 , 2012 , 2013 , 2014 ): International Conference on Data Mining

ECAI (2014, 2016, 2018): European Conference on Artificial Intelligence

IJCAI (2015, 2016, 2018): Int. Joint Conference on Artificial Intelligence

ISIPTA (2009, 2011, 2013, 2015, 2017): International Symposium on Imprecise Probability: Theories and Applications:

NIPS (2018): Conference on Neural Information Processing Systems

PGM (2014, 2016, 2018): European Workshop on Probabilistic Graphical Models

UAI (2016, 2018): Conference on Uncertainty in Artificial Intelligence


Best Paper Prize at the International Environmental Modelling and Software Conference 2002 (Lugano, Switzerland).

Outstanding reviewer certificate from the journal Environmental Modelling and Software.

Certificate of reviewing from the Int. Journal of Approximate Reasoning.


Applied Statistics

Bach. of Management Engineering (SUPSI). Main topics: statistical inference and statistical process control.

Uncertain Reasoning and Data Mining

Master of Science in Engineering (SUPSI). Main topics: Bayesian networks and data mining. Co-teacher.


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.