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.



Awards


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



Scientific events: PGM 2016 and ISIPTA 2017



I am co-chair and co-organizer of  PGM 2016 , the Eighth International Conference on Probabilistic Graphical Models (Lugano,  September 2016). 
The  proceedings of PGM 2016 are published by JMLR.


I am co-chair and co-organizer of  ISIPTA 2017 , the Tenth International Symposium on Imprecise Probability: Theories and Applications (Lugano,  July 2017). 
The  proceedings of ISIPTA 2017 are published by JMLR.

Program committees 


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


UAI (2016): Conference on Uncertainty in Artificial Intelligence


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


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


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


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


Reviewing


Reviewer for Machine Learning,  Int. J. of Approximate ReasoningInt. J. of Artificial Intelligence Research


Outstanding reviewer certificate  from  Environmental Modelling and Software



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

Comparing competing algorithms: Bayesian versus frequentist hypothesis testing.



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.