Search Results for author:
Found 14 papers, 7 papers with code
Explaining Machine Learning Models using Entropic Variable Projection
In order to emphasize the impact of each input variable, this formalism uses an information theory framework that quantifies the influence of all input-output observations based on entropic projections.
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 indispensable part of the everyday life and the professional world.
p$^3$VAE: a physics-integrated generative model. Application to the pixel-wise classification of airborne hyperspectral images
The combination of machine learning models with physical models is a recent research path to learn robust data representations.
COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP tasks
COCKATIEL is a novel, post-hoc, concept-based, model-agnostic XAI technique that generates meaningful explanations from the last layer of a neural net model trained on an NLP classification task by using Non-Negative Matrix Factorization (NMF) to discover the concepts the model leverages to make predictions and by exploiting a Sensitivity Analysis to estimate accurately the importance of each of these concepts for the model.
Explainable Artificial Intelligence (XAI)
Sentiment Analysis
Toulouse Hyperspectral Data Set: a benchmark data set to assess semi-supervised spectral representation learning and pixel-wise classification techniques
While the spectral dimension of hyperspectral images is highly informative of the chemical composition of the land surface, the use of state-of-the-art machine learning algorithms to map the land cover has been dramatically limited by the availability of training data.
Transport-based Counterfactual Models
We address the problem of designing realistic and feasible counterfactuals in the absence of a causal model.
TaCo: Targeted Concept Removal in Output Embeddings for NLP via Information Theory and Explainability
In evaluation, we show that the proposed post-hoc approach significantly reduces gender-related associations in NLP models while preserving the overall performance and functionality of the models.
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 detection of representative variables in high dimensional spaces with a potentially limited number of observations.
Anisotropic Diffusion in ITK
Anisotropic Non-Linear Diffusion is a powerful image processing technique, which allows to simultaneously remove the noise and enhance sharp features in two or three dimensional images.
Tackling Algorithmic Bias in Neural-Network Classifiers using Wasserstein-2 Regularization
We then introduce in this paper a new method to temper the algorithmic bias in Neural-Network based classifiers.
A survey of Identification and mitigation of Machine Learning algorithmic biases in Image Analysis
Contrarily to societal applications where a set of proxy variables can be provided by the common sense or by regulations to draw the attention on potential risks, industrial and safety-critical applications are most of the times sailing blind.
How optimal transport can tackle gender biases in multi-class neural-network classifiers for job recommendations?
To anticipate the certification of recommendation systems using textual data, we then used it on the Bios dataset, for which the learning task consists in predicting the occupation of female and male individuals, based on their LinkedIn biography.
Are fairness metric scores enough to assess discrimination biases in machine learning?
This paper presents novel experiments shedding light on the shortcomings of current metrics for assessing biases of gender discrimination made by machine learning algorithms on textual data.
Debiasing Machine Learning Models by Using Weakly Supervised Learning
In our work, we then propose a bias mitigation strategy for continuous sensitive variables, based on the notion of endogeneity which comes from the field of econometrics.
