4 citations · 10 across the 5 of their papers we have counts for
5 papers
Probabilistic Scores of Classifiers, Calibration is not Enough
Agathe Fernandes Machado, Arthur Charpentier, Emmanuel Flachaire +2
In binary classification tasks, accurate representation of probabilistic predictions is essential for various real-world applications such as predicting payment defaults or assessi…
From Uncertainty to Precision: Enhancing Binary Classifier Performance through Calibration
Agathe Fernandes Machado, Arthur Charpentier, Emmanuel Flachaire +2
The assessment of binary classifier performance traditionally centers on discriminative ability using metrics, such as accuracy. However, these metrics often disregard the model's…
Geospatial Disparities: A Case Study on Real Estate Prices in Paris
Agathe Fernandes Machado, François Hu, Philipp Ratz +2
Driven by an increasing prevalence of trackers, ever more IoT sensors, and the declining cost of computing power, geospatial information has come to play a pivotal role in contempo…
Parametric Fairness with Statistical Guarantees
François HU, Philipp Ratz, Arthur Charpentier
Algorithmic fairness has gained prominence due to societal and regulatory concerns about biases in Machine Learning models. Common group fairness metrics like Equalized Odds for cl…
Fairness Explainability using Optimal Transport with Applications in Image Classification
Philipp Ratz, François Hu, Arthur Charpentier
Ensuring trust and accountability in Artificial Intelligence systems demands explainability of its outcomes. Despite significant progress in Explainable AI, human biases still tain…