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Found 37 papers, 12 papers with code
Fair Graph Neural Network with Supervised Contrastive Regularization
In recent years, Graph Neural Networks (GNNs) have made significant advancements, particularly in tasks such as node classification, link prediction, and graph representation.
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.
Hoeffding decomposition of black-box models with dependent inputs
The elements of this decomposition can be expressed using oblique projections and allow for novel interpretability indices for evaluation and variance decomposition purposes.
Improved learning theory for kernel distribution regression with two-stage sampling
In this paper, we improve the learning theory of kernel distribution regression.
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.
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.
When Mitigating Bias is Unfair: A Comprehensive Study on the Impact of Bias Mitigation Algorithms
With comparable performances in fairness and accuracy, are the different bias mitigation approaches impacting a similar number of individuals?
Gaussian Processes on Distributions based on Regularized Optimal Transport
We present a novel kernel over the space of probability measures based on the dual formulation of optimal regularized transport.
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.
Quantile-constrained Wasserstein projections for robust interpretability of numerical and machine learning models
Numerical experiments on real case studies, from the UQ and ML fields, highlight the computational feasibility of such studies and provide local and global insights on the robustness of black-box models to input perturbations.
Dimension Reduction for time series with Variational AutoEncoders
In this work, we explore dimensionality reduction techniques for univariate and multivariate time series data.
An improved central limit theorem and fast convergence rates for entropic transportation costs
We prove a central limit theorem for the entropic transportation cost between subgaussian probability measures, centered at the population cost.
GAN Estimation of Lipschitz Optimal Transport Maps
This paper introduces the first statistically consistent estimator of the optimal transport map between two probability distributions, based on neural networks.
Fairness constraint in Structural Econometrics and Application to fair estimation using Instrumental Variables
A {\it fair} solution is obtained by projecting the unconstrained index into the null space of this operator or by directly finding the closest solution of the functional equation into this null space.
Transport-based Counterfactual Models
We address the problem of designing realistic and feasible counterfactuals in the absence of a causal model.
Diversity-Preserving K-Armed Bandits, Revisited
We consider the bandit-based framework for diversity-preserving recommendations introduced by Celis et al. (2019), who approached it in the case of a polytope mainly by a reduction to the setting of linear bandits.
Achieving robustness in classification using optimal transport with hinge regularization
This loss function has a direct interpretation in terms of adversarial robustness together with certifiable robustness bound.
The statistical effect of entropic regularization in optimal transportation
We propose to tackle the problem of understanding the effect of regularization in Sinkhorn algotihms.
Review of Mathematical frameworks for Fairness in Machine Learning
A review of the main fairness definitions and fair learning methodologies proposed in the literature over the last years is presented from a mathematical point of view.
Projection to Fairness in Statistical Learning
In the context of regression, we consider the fundamental question of making an estimator fair while preserving its prediction accuracy as much as possible.
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.
Minimax optimal goodness-of-fit testing for densities and multinomials under a local differential privacy constraint
To the best of our knowledge, we provide the first minimax optimal test and associated private transformation under a local differential privacy constraint over Besov balls in the continuous setting, quantifying the price to pay for data privacy.
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.
optimalFlow: Optimal-transport approach to flow cytometry gating and population matching
We also present $optimalFlowClassification$, which uses a database of gated cytometries and optimalFlowTemplates to assign cell types to a new cytometry.
Attraction-Repulsion clustering with applications to fairness
We consider the problem of diversity enhancing clustering, i. e, developing clustering methods which produce clusters that favour diversity with respect to a set of protected attributes such as race, sex, age, etc.
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.
Can everyday AI be ethical. Fairness of Machine Learning Algorithms
Combining big data and machine learning algorithms, the power of automatic decision tools induces as much hope as fear.
Confidence Intervals for Testing Disparate Impact in Fair Learning
We provide the asymptotic distribution of the major indexes used in the statistical literature to quantify disparate treatment in machine learning.
Obtaining fairness using optimal transport theory
\textit{Fairness} is generally studied in a probabilistic framework where it is assumed that there exists a protected variable, whose use as an input of the algorithm may imply discrimination.
Statistics Theory Statistics Theory 62H30, 68T01
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.
Gaussian Processes indexed on the symmetric group: prediction and learning
In the framework of the supervised learning of a real function defined on a space X , the so called Kriging method stands on a real Gaussian field defined on X.
Multiple testing for outlier detection in functional data
We propose a novel procedure for outlier detection in functional data, in a semi-supervised framework.
Deep Learning applied to Road Traffic Speed forecasting
In this paper, we propose deep learning architectures (FNN, CNN and LSTM) to forecast a regression model for time dependent data.
A Gaussian Process Regression Model for Distribution Inputs
We prove that the Gaussian processes indexed by distributions corresponding to these kernels can be efficiently forecast, opening new perspectives in Gaussian process modeling.
Big Data analytics. Three use cases with R, Python and Spark
Management and analysis of big data are systematically associated with a data distributed architecture in the Hadoop and now Spark frameworks.
Destination Prediction by Trajectory Distribution Based Model
We present how this model can be used to predict the final destination of a new trajectory based on their first locations using a two step procedure: We first assign the new trajectory to the clusters it mot likely belongs.
Review and Perspective for Distance Based Trajectory Clustering
Using clustering technics based on the choice of a distance between the observations, we first provide a comprehensive review of the different distances used in the literature to compare trajectories.
