1 citations · 1 across the 3 of their papers we have counts for
4 papers
Learning Global Sensitivity Indices from Observational Data: A Metamodel-Based Approach
Giulia Vannucci
Classical variance-based Global Sensitivity Analysis (GSA) assumes that the input--output mechanism can be repeatedly evaluated under a designed sampling scheme, which is infeasibl…
From Efficient Frontier to Fragile Frontier: A Global Sensitivity Analysis of Markowitz Portfolios
Stefano Pellegrino, Giulia Vannucci, Roberta Siciliano
In mean-variance portfolio analysis, the efficient frontier represents the optimal trade-off between expected return and risk, assuming stable underlying parameters. This paper inv…
Simultaneous Latent Budget Trees for Stratified Classification
Simultaneous Latent Budget Trees for Stratified Classification Cristian Buoncompagni, Cristian Buoncompagni, Stefano Pellegrino +3
In the era of Explainable Artificial Intelligence, there is a renewed focus on single trees for their ease of interpretation. This paper introduces Simultaneous Latent Budget Trees…
Enhancing Variable Importance in Random Forests: A Novel Application of Global Sensitivity Analysis
Giulia Vannucci, Roberta Siciliano, Andrea Saltelli
The present work provides an application of Global Sensitivity Analysis to supervised machine learning methods such as Random Forests. These methods act as black boxes, selecting f…