Picture for Laurent Risser

Laurent Risser

IMT

Debiasing Machine Learning Models by Using Weakly Supervised Learning

Add code
Feb 23, 2024
Viaarxiv icon

TaCo: Targeted Concept Removal in Output Embeddings for NLP via Information Theory and Explainability

Add code
Dec 11, 2023
Figure 1 for TaCo: Targeted Concept Removal in Output Embeddings for NLP via Information Theory and Explainability
Figure 2 for TaCo: Targeted Concept Removal in Output Embeddings for NLP via Information Theory and Explainability
Figure 3 for TaCo: Targeted Concept Removal in Output Embeddings for NLP via Information Theory and Explainability
Figure 4 for TaCo: Targeted Concept Removal in Output Embeddings for NLP via Information Theory and Explainability
Viaarxiv icon

Toulouse Hyperspectral Data Set: a benchmark data set to assess semi-supervised spectral representation learning and pixel-wise classification techniques

Add code
Nov 15, 2023
Viaarxiv icon

Are fairness metric scores enough to assess discrimination biases in machine learning?

Add code
Jun 08, 2023
Viaarxiv icon

COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP tasks

Add code
May 14, 2023
Viaarxiv icon

How optimal transport can tackle gender biases in multi-class neural-network classifiers for job recommendations?

Add code
Feb 27, 2023
Viaarxiv icon

p$^3$VAE: a physics-integrated generative model. Application to the semantic segmentation of optical remote sensing images

Add code
Oct 19, 2022
Figure 1 for p$^3$VAE: a physics-integrated generative model. Application to the semantic segmentation of optical remote sensing images
Figure 2 for p$^3$VAE: a physics-integrated generative model. Application to the semantic segmentation of optical remote sensing images
Figure 3 for p$^3$VAE: a physics-integrated generative model. Application to the semantic segmentation of optical remote sensing images
Figure 4 for p$^3$VAE: a physics-integrated generative model. Application to the semantic segmentation of optical remote sensing images
Viaarxiv icon

A survey of Identification and mitigation of Machine Learning algorithmic biases in Image Analysis

Add code
Oct 10, 2022
Figure 1 for A survey of Identification and mitigation of Machine Learning algorithmic biases in Image Analysis
Figure 2 for A survey of Identification and mitigation of Machine Learning algorithmic biases in Image Analysis
Figure 3 for A survey of Identification and mitigation of Machine Learning algorithmic biases in Image Analysis
Figure 4 for A survey of Identification and mitigation of Machine Learning algorithmic biases in Image Analysis
Viaarxiv icon

A survey of bias in Machine Learning through the prism of Statistical Parity for the Adult Data Set

Add code
Apr 06, 2020
Figure 1 for A survey of bias in Machine Learning through the prism of Statistical Parity for the Adult Data Set
Figure 2 for A survey of bias in Machine Learning through the prism of Statistical Parity for the Adult Data Set
Figure 3 for A survey of bias in Machine Learning through the prism of Statistical Parity for the Adult Data Set
Figure 4 for A survey of bias in Machine Learning through the prism of Statistical Parity for the Adult Data Set
Viaarxiv icon

Using Wasserstein-2 regularization to ensure fair decisions with Neural-Network classifiers

Add code
Aug 15, 2019
Figure 1 for Using Wasserstein-2 regularization to ensure fair decisions with Neural-Network classifiers
Figure 2 for Using Wasserstein-2 regularization to ensure fair decisions with Neural-Network classifiers
Figure 3 for Using Wasserstein-2 regularization to ensure fair decisions with Neural-Network classifiers
Figure 4 for Using Wasserstein-2 regularization to ensure fair decisions with Neural-Network classifiers
Viaarxiv icon