Michele Lungaroni

Orcid: 0000-0002-1500-363X

According to our database1, Michele Lungaroni authored at least 12 papers between 2015 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

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Bibliography

2023
A practical utility-based but objective approach to model selection for regression in scientific applications.
Artif. Intell. Rev., November, 2023

Information theoretic and neural computational tools for meta-analysis of cumulative databases in the age of Big Physics experiments.
Neural Comput. Appl., 2023

2022
Complexity: Frontiers in Data-Driven Methods for Understanding, Prediction, and Control of Complex Systems 2022 on the Development of Information Theoretic Model Selection Criteria for the Analysis of Experimental Data.
Complex., 2022

A systemic approach to classification for knowledge discovery with applications to the identification of boundary equations in complex systems.
Artif. Intell. Rev., 2022

2020
Quantifying Total Influence between Variables with Information Theoretic and Machine Learning Techniques.
Entropy, 2020

2019
On the Use of Entropy to Improve Model Selection Criteria.
Entropy, 2019

Geodesic Distance on Gaussian Manifolds to Reduce the Statistical Errors in the Investigation of Complex Systems.
Complex., 2019

2018
On the Use of Transfer Entropy to Investigate the Time Horizon of Causal Influences between Signals.
Entropy, 2018

2017
Deriving Realistic Mathematical Models from Support Vector Machines for Scientific Applications.
Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2017

Complex networks for the analysis of the synchronization of time series relevant for plasma fusion diagnostics.
Proceedings of the 2017 European Conference on Circuit Theory and Design, 2017

2016
A Metric to Improve the Robustness of Conformal Predictors in the Presence of Error Bars.
Proceedings of the Conformal and Probabilistic Prediction with Applications, 2016

2015
How to Handle Error Bars in Symbolic Regression for Data Mining in Scientific Applications.
Proceedings of the Statistical Learning and Data Sciences - Third International Symposium, 2015


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