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


Publications

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.


ISIPTA 2017


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


Awards


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.


Teaching


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.


Code


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.