2 citations · 5 across the 10 of their papers we have counts for
11 papers
Decomposing Probabilistic Scores: Reliability, Information Loss and Uncertainty
Arthur Charpentier, Agathe Fernandes Machado
Calibration is a conditional property that depends on the information retained by a predictor. We develop decomposition identities for arbitrary proper losses that make this depend…
Sequential Transport for Causal Mediation Analysis
Agathe Fernandes Machado, Iryna Voitsitska, Arthur Charpentier +1
We propose sequential transport (ST), a distributional framework for mediation analysis that combines optimal transport (OT) with a mediator directed acyclic graph (DAG). Instead o…
Federated Measurement of Demographic Disparities from Quantile Sketches
Arthur Charpentier, Agathe Fernandes Machado, Olivier Côté +1
Many fairness goals are defined at a population level that misaligns with siloed data collection, which remains unsharable due to privacy regulations. Horizontal federated learning…
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…
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…
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…