5 papers
Optimizing Multidimensional Scaling in Gini Metric Spaces
Cassandra Mussard, Stéphane Mussard
The Gini Multidimensional Scaling (Gini MDS) framework extends the Euclidean multidimensional scaling. We introduce a Gini pseudo-distance based on values and their ranks that depe…
Shapley meets Rawls: an integrated framework for measuring and explaining unfairness
Fadoua Amri-Jouidel, Emmanuel Kemel, Stéphane Mussard
Explainability and fairness have mainly been considered separately, with recent exceptions trying the explain the sources of unfairness. This paper shows that the Shapley value can…
Aumann-SHAP: The Geometry of Counterfactual Interaction Explanations in Machine Learning
Adam Belahcen, Stéphane Mussard
We introduce Aumann-SHAP, an interaction-aware framework that decomposes counterfactual transitions by restricting the model to a local hypercube connecting baseline and counterfac…
KNN and K-means in Gini Prametric Spaces
Cassandra Mussard, Arthur Charpentier, Stéphane Mussard
This paper introduces enhancements to the K-means and K-nearest neighbors (KNN) algorithms based on the concept of Gini prametric spaces, instead of traditional metric spaces. Unli…
Principal Component Analysis: A Generalized Gini Approach
Charpentier, Arthur, Mussard +2
A principal component analysis based on the generalized Gini correlation index is proposed (Gini PCA). The Gini PCA generalizes the standard PCA based on the variance. It is shown,…