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08/28/2023
Improved learning theory for kernel distribution regression with two-stage sampling
The distribution regression problem encompasses many important statistic...
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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...
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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...
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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...
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02/14/2023
When Mitigating Bias is Unfair: A Comprehensive Study on the Impact of Bias Mitigation Algorithms
Most works on the fairness of machine learning systems focus on the blin...
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01/06/2023
On the coalitional decomposition of parameters of interest
Understanding the behavior of a black-box model with probabilistic input...
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10/12/2022
Gaussian Processes on Distributions based on Regularized Optimal Transport
We present a novel kernel over the space of probability measures based o...
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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 ...
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09/23/2022
Quantile-constrained Wasserstein projections for robust interpretability of numerical and machine learning models
Robustness studies of black-box models is recognized as a necessary task...
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07/15/2022
Weak limits of entropy regularized Optimal Transport; potentials, plans and divergences
This work deals with the asymptotic distribution of both potentials and ...
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06/28/2022
Diffeomorphic Registration using Sinkhorn Divergences
The diffeomorphic registration framework enables to define an optimal ma...
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04/23/2022
Dimension Reduction for time series with Variational AutoEncoders
In this work, we explore dimensionality reduction techniques for univari...
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04/19/2022
An improved central limit theorem and fast convergence rates for entropic transportation costs
We prove a central limit theorem for the entropic transportation cost be...
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02/16/2022
Fairness constraint in Structural Econometrics and Application to fair estimation using Instrumental Variables
A supervised machine learning algorithm determines a model from a learni...
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02/16/2022
GAN Estimation of Lipschitz Optimal Transport Maps
This paper introduces the first statistically consistent estimator of th...
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02/13/2022
Central Limit Theorems for Semidiscrete Wasserstein Distances
We prove a Central Limit Theorem for the empirical optimal transport cos...
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08/30/2021
Transport-based Counterfactual Models
Counterfactual frameworks have grown popular in explainable and fair mac...
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03/08/2021
Fairness seen as Global Sensitivity Analysis
Ensuring that a predictor is not biased against a sensible feature is th...
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02/17/2021
A Consistent Extension of Discrete Optimal Transport Maps for Machine Learning Applications
Optimal transport maps define a one-to-one correspondence between probab...
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02/12/2021
Central Limit Theorems for General Transportation Costs
We consider the problem of optimal transportation with general cost betw...
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10/05/2020
Diversity-Preserving K-Armed Bandits, Revisited
We consider the bandit-based framework for diversity-preserving recommen...
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08/13/2020
Risk Measures Estimation Under Wasserstein Barycenter
Randomness in financial markets requires modern and robust multivariate ...
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06/11/2020
Achieving robustness in classification using optimal transport with hinge regularization
We propose a new framework for robust binary classification, with Deep N...
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06/09/2020
The statistical effect of entropic regularization in optimal transportation
We propose to tackle the problem of understanding the effect of regulari...
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05/26/2020
Review of Mathematical frameworks for Fairness in Machine Learning
A review of the main fairness definitions and fair learning methodologie...
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05/24/2020
Projection to Fairness in Statistical Learning
In the context of regression, we consider the fundamental question of ma...
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05/24/2020
The price for fairness in a regression framework
We consider the problem of achieving fairness in a regression framework....
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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...
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02/11/2020
Minimax optimal goodness-of-fit testing for densities under a local differential privacy constraint
Finding anonymization mechanisms to protect personal data is at the hear...
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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...
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07/18/2019
optimalFlow: Optimal-transport approach to flow cytometry gating and population matching
Data used in Flow Cytometry present pronounced variability due to biolog...
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04/10/2019
Attraction-Repulsion clustering with applications to fairness
In the framework of fair learning, we consider clustering methods that a...
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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...
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10/03/2018
Can everyday AI be ethical. Fairness of Machine Learning Algorithms
Combining big data and machine learning algorithms, the power of automat...
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07/18/2018
A Central Limit Theorem for L_p transportation cost with applications to Fairness Assessment in Machine Learning
We provide a Central Limit Theorem for the Monge-Kantorovich distance be...
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07/17/2018
Confidence Intervals for Testing Disparate Impact in Fair Learning
We provide the asymptotic distribution of the major indexes used in the ...
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06/28/2018
Risk Measures and Credit Risk Under the Beta-Kotz Distribution
This paper considers the use for Value-at-Risk computations of the so-ca...
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06/08/2018
Obtaining fairness using optimal transport theory
Statistical algorithms are usually helping in making decisions in many a...
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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...
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05/02/2018
Gaussian Process Forecast with multidimensional distributional entries
In this work, we propose to define Gaussian Processes indexed by multidi...
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03/16/2018
Gaussian Processes indexed on the symmetric group: prediction and learning
In the framework of the supervised learning of a real function defined o...
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12/13/2017
Multiple testing for outlier detection in functional data
We propose a novel procedure for outlier detection in functional data, i...
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10/02/2017
Deep Learning applied to Road Traffic Speed forecasting
In this paper, we propose deep learning architectures (FNN, CNN and LSTM...
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01/31/2017
Gaussian Process Regression Model for Distribution Inputs
Monge-Kantorovich distances, otherwise known as Wasserstein distances, h...
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05/10/2016
Destination Prediction by Trajectory Distribution Based Model
In this paper we propose a new method to predict the final destination o...
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08/20/2015
Review and Perspective for Distance Based Trajectory Clustering
In this paper we tackle the issue of clustering trajectories of geolocal...
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10/08/2012