no code implementations • 5 Oct 2022 • Lorenzo Zambon, Dario Azzimonti, Giorgio Corani
The forecasts for these time series are required to be coherent, that is, to satisfy the constraints given by the hierarchy.
no code implementations • 19 Jul 2022 • Giorgio Corani, Dario Azzimonti, Nicolò Rubattu
Forecast reconciliation is an important research topic.
1 code implementation • 17 Sep 2020 • Giorgio Corani, Alessio Benavoli, Marco Zaffalon
Automatic forecasting is the task of receiving a time series and returning a forecast for the next time steps without any human intervention.
no code implementations • 4 Aug 2020 • Laura Azzimonti, Giorgio Corani, Marco Scutari
In this paper we propose a new Bayesian Dirichlet score, which we call Bayesian Hierarchical Dirichlet (BHD).
no code implementations • 7 Feb 2018 • Mauro Scanagatta, Giorgio Corani, Marco Zaffalon, Jaemin Yoo, U Kang
We present a novel anytime algorithm (k-MAX) method for this task, which scales up to thousands of variables.
no code implementations • 19 Jul 2017 • Cassio P. de Campos, Mauro Scanagatta, Giorgio Corani, Marco Zaffalon
For decomposable score-based structure learning of Bayesian networks, existing approaches first compute a collection of candidate parent sets for each variable and then optimize over this collection by choosing one parent set for each variable without creating directed cycles while maximizing the total score.
no code implementations • NeurIPS 2016 • Mauro Scanagatta, Giorgio Corani, Cassio P. de Campos, Marco Zaffalon
We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables.
1 code implementation • 28 Sep 2016 • Giorgio Corani, Alessio Benavoli, Janez Demšar, Francesca Mangili, Marco Zaffalon
Usually one compares the accuracy of two competing classifiers via null hypothesis significance tests (nhst).
1 code implementation • 14 Jun 2016 • Alessio Benavoli, Giorgio Corani, Janez Demsar, Marco Zaffalon
The machine learning community adopted the use of null hypothesis significance testing (NHST) in order to ensure the statistical validity of results.
no code implementations • 11 May 2016 • Mauro Scanagatta, Giorgio Corani, Cassio P. de Campos, Marco Zaffalon
We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables.
no code implementations • NeurIPS 2015 • Mauro Scanagatta, Cassio P. de Campos, Giorgio Corani, Marco Zaffalon
We present a method for learning Bayesian networks from data sets containingthousands of variables without the need for structure constraints.
no code implementations • 9 May 2015 • Alessio Benavoli, Giorgio Corani, Francesca Mangili
In other words, the outcome of the comparison between algorithms A and B depends also on the performance of the other algorithms included in the original experiment.
no code implementations • 14 May 2014 • Giorgio Corani, Andrea Mignatti
CMA detects prior-dependent instances, namely instances in which a different class is more probable depending on the prior over the models.