ferryjul

clavardage

Projet POO clavardage

fairCORELS

Algorithm for learning fair rule lists

fairCORELS_expe

This repository contains the code we used for our experiments presented in the paper "Learning fair rule lists"

fairCORELSV2

FairCORELSV2, an enhanced version of the FairCORELS algorithm for learning optimal fair rule lists

FairnessSampleRobustness

This repository contains the code to reproduce our experiments for our Machine Learning journal paper "Improving Fairness Generalization through a Sample-Robust Optimization Method"

HybridCORELS

Python module for Hybrid Rule-List/Black-Box models. This implementation provides methods for both the "Interpr-then-BB training" paradigm and the "BB-then-Interpr training" paradigm.

ieee-pandoc-template

IEEE paper template for pandoc

Metaheuristique

ProbabilisticDatasetsReconstruction

projet-integrateur-5sdbd

ferryjul/fairCORELS

Algorithm for learning fair rule lists

ferryjul/fairCORELS_expe

This repository contains the code we used for our experiments presented in the paper "Learning fair rule lists"

ferryjul/fairCORELSV2

FairCORELSV2, an enhanced version of the FairCORELS algorithm for learning optimal fair rule lists

ferryjul/clavardage

Projet POO clavardage

ferryjul/FairnessSampleRobustness

This repository contains the code to reproduce our experiments for our Machine Learning journal paper "Improving Fairness Generalization through a Sample-Robust Optimization Method"

ferryjul/HybridCORELS

Python module for Hybrid Rule-List/Black-Box models. This implementation provides methods for both the "Interpr-then-BB training" paradigm and the "BB-then-Interpr training" paradigm.

ferryjul/ieee-pandoc-template

IEEE paper template for pandoc

ferryjul/Metaheuristique

ferryjul/ProbabilisticDatasetsReconstruction

ferryjul/projet-integrateur-5sdbd

ferryjul/projet-r

ferryjul/pycorels

Hybrid Rule List/Black Box model based on the CORELS algorithm

ferryjul/SensitiveAttributesReconstructionCorrector

This repository contains the implementation of the Reconstruction Corrector component introduced in our article "Exploiting Fairness to Enhance Sensitive Attributes Reconstruction"

ferryjul/sklearn-expertsys

Highly interpretable classifiers for scikit learn, producing easily understood decision rules instead of black box models

ferryjul/TP_Apprentissage