Frédéric Koriche — accepted AI-conference papers

2025

Probabilistic Explanations for Regression Models

Frédéric Koriche, Jean-Marie Lagniez, Chi Tran

UAI 2025

Formal explainability is an emerging field that aims to provide mathematically guaranteed explanations for the predictions made by machine learning models. Recent work in this area focuses on computing “probabilistic explanations” for the predictions made by classifiers based on specific data instan

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2022

Trading Complexity for Sparsity in Random Forest Explanations

Gilles Audemard, Steve Bellart, Louènas Bounia, Frédéric Koriche, Jean-Marie Lagniez, Pierre Marquis

AAAI 2022technical

Random forests have long been considered as powerful model ensembles in machine learning. By training multiple decision trees, whose diversity is fostered through data and feature subsampling, the resulting random forest can lead to more stable and reliable predictions than a single decision tree. T…

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