research
∙
06/08/2023
Are fairness metric scores enough to assess discrimination biases in machine learning?
This paper presents novel experiments shedding light on the shortcomings...
share
research
∙
05/11/2023
COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP tasks
Transformer architectures are complex and their use in NLP, while it has...
research
∙
02/27/2023
How optimal transport can tackle gender biases in multi-class neural-network classifiers for job recommendations?
Automatic recommendation systems based on deep neural networks have beco...
research
∙
10/19/2022
p^3VAE: a physics-integrated generative model. Application to the semantic segmentation of optical remote sensing images
The combination of machine learning models with physical models is a rec...
research
∙
10/10/2022
A survey of Identification and mitigation of Machine Learning algorithmic biases in Image Analysis
The problem of algorithmic bias in machine learning has gained a lot of ...
research
∙
03/31/2020
A survey of bias in Machine Learning through the prism of Statistical Parity for the Adult Data Set
Applications based on Machine Learning models have now become an indispe...
research
∙
08/15/2019
Using Wasserstein-2 regularization to ensure fair decisions with Neural-Network classifiers
In this paper, we propose a new method to build fair Neural-Network clas...
research
∙
10/18/2018
Entropic Variable Boosting for Explainability and Interpretability in Machine Learning
In this paper, we present a new explainability formalism to make clear t...
research
∙
05/25/2018
COREclust: a new package for a robust and scalable analysis of complex data
In this paper, we present a new R package COREclust dedicated to the det...
research
∙
10/11/2017
Cytometry inference through adaptive atomic deconvolution
In this paper we consider a statistical estimation problem known as atom...
research
∙
03/03/2015
