most citedBeyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models

1 citations · 1 across the 4 of their papers we have counts for

collaborators

6 papers

econ.TH2025

Linear Risk Sharing on Networks

Arthur Charpentier, Philipp Ratz

Over the past decade alternatives to traditional insurance and banking have grown in popularity. The desire to encourage local participation has lead products such as peer-to-peer…

stat.ML20251 cited

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models

Marouane Il Idrissi, Agathe Fernandes Machado, Arthur Charpentier

Cooperative game theory has become a cornerstone of post-hoc interpretability in machine learning, largely through the use of Shapley values. Yet, despite their widespread adoption…

cs.AI2025

Unveil Sources of Uncertainty: Feature Contribution to Conformal Prediction Intervals

Marouane Il Idrissi, Agathe Fernandes Machado, Ewen Gallic +1

Cooperative game theory methods, notably Shapley values, have significantly enhanced machine learning (ML) interpretability. However, existing explainable AI (XAI) frameworks mainl…

cs.LG2025

EquiPy: Sequential Fairness using Optimal Transport in Python

Agathe Fernandes Machado, Suzie Grondin, Philipp Ratz +2

Algorithmic fairness has received considerable attention due to the failures of various predictive AI systems that have been found to be unfairly biased against subgroups of the po…

econ.TH2025

Beyond Human Intervention: Algorithmic Collusion through Multi-Agent Learning Strategies

Suzie Grondin, Arthur Charpentier, Philipp Ratz

Collusion in market pricing is a concept associated with human actions to raise market prices through artificially limited supply. Recently, the idea of algorithmic collusion was p…

cs.LG2025

Optimal Transport on Categorical Data for Counterfactuals using Compositional Data and Dirichlet Transport

Agathe Fernandes Machado, Arthur Charpentier, Ewen Gallic

Recently, optimal transport-based approaches have gained attention for deriving counterfactuals, e.g., to quantify algorithmic discrimination. However, in the general multivariate…