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Found 9 papers, 6 papers with code
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.
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.
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.
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.
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
