Manuele Leonelli

According to our database, Manuele Leonelli authored at least 26 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear.

Manuele Leonelli, Gherardo Varando

Appl. Intell., 2024

2023

Sensitivity and robustness analysis in Bayesian networks with the bnmonitor R package.

Manuele Leonelli, Ramsiya Ramanathan, Rachel L. Wilkerson

Knowl. Based Syst., October, 2023

The YODO algorithm: An efficient computational framework for sensitivity analysis in Bayesian networks.

Rafael Ballester-Ripoll, Manuele Leonelli

Int. J. Approx. Reason., August, 2023

A new class of generative classifiers based on staged tree models.

Federico Carli, Manuele Leonelli, Gherardo Varando

Knowl. Based Syst., 2023

AI and the creative realm: A short review of current and future applications.

Fabio Crimaldi, Manuele Leonelli

CoRR, 2023

Context-Specific Causal Discovery for Categorical Data Using Staged Trees.

Manuele Leonelli, Gherardo Varando

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Computing Sobol indices in probabilistic graphical models.

Rafael Ballester-Ripoll, Manuele Leonelli

Reliab. Eng. Syst. Saf., 2022

The <i>R</i> Package stagedtrees for Structural Learning of Stratified Staged Trees.

Federico Carli, Manuele Leonelli, Eva Riccomagno, Gherardo Varando

J. Stat. Softw., 2022

A geometric characterization of sensitivity analysis in monomial models.

Manuele Leonelli, Eva Riccomagno

Int. J. Approx. Reason., 2022

Structural Learning of Simple Staged Trees.

Manuele Leonelli, Gherardo Varando

CoRR, 2022

Highly Efficient Structural Learning of Sparse Staged Trees.

Manuele Leonelli, Gherardo Varando

Proceedings of the International Conference on Probabilistic Graphical Models, 2022

You Only Derive Once (YODO): Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networks.

Rafael Ballester-Ripoll, Manuele Leonelli

Proceedings of the International Conference on Probabilistic Graphical Models, 2022

2021

Global sensitivity analysis in probabilistic graphical models.

Rafael Ballester-Ripoll, Manuele Leonelli

CoRR, 2021

Staged trees and asymmetry-labeled DAGs.

Gherardo Varando, Federico Carli, Manuele Leonelli

CoRR, 2021

2020

Semiparametric bivariate modelling with flexible extremal dependence.

Manuele Leonelli, Dani Gamerman

Stat. Comput., 2020

Coherent combination of probabilistic outputs for group decision making: an algebraic approach.

Manuele Leonelli, Eva Riccomagno, Jim Q. Smith

OR Spectr., 2020

Model-Preserving Sensitivity Analysis for Families of Gaussian Distributions.

Christiane Görgen, Manuele Leonelli

J. Mach. Learn. Res., 2020

2019

Sensitivity analysis beyond linearity.

Manuele Leonelli

Int. J. Approx. Reason., 2019

A geometric characterisation of sensitivity analysis in monomial models.

Manuele Leonelli, Eva Riccomagno

CoRR, 2019

2017

Sensitivity analysis in multilinear probabilistic models.

Manuele Leonelli, Christiane Görgen, Jim Q. Smith

Inf. Sci., 2017

Directed Expected Utility Networks.

Manuele Leonelli, Jim Q. Smith

Decis. Anal., 2017

A symbolic algebra for the computation of expected utilities in multiplicative influence diagrams.

Manuele Leonelli, Eva Riccomagno, Jim Q. Smith

Ann. Math. Artif. Intell., 2017

2015

Sensitivity analysis, multilinearity and beyond.

Manuele Leonelli, Christiane Görgen, Jim Q. Smith

CoRR, 2015

Bayesian decision support for complex systems with many distributed experts.

Manuele Leonelli, James Q. Smith

Ann. Oper. Res., 2015

A Differential Approach for Staged Trees.

Christiane Görgen, Manuele Leonelli, James Q. Smith

Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2015

2013

Using graphical models and multi-attribute utility theory for probabilistic uncertainty handling in large systems, with application to the nuclear emergency management.

Manuele Leonelli, James Q. Smith

Proceedings of the Workshops Proceedings of the 29th IEEE International Conference on Data Engineering, 2013