J
Jean-Michel Loubes
Researcher at Institut de Mathématiques de Toulouse
Publications - 203
Citations - 10539
Jean-Michel Loubes is an academic researcher from Institut de Mathématiques de Toulouse. The author has contributed to research in topics: Estimator & Inverse problem. The author has an hindex of 23, co-authored 184 publications receiving 9133 citations. Previous affiliations of Jean-Michel Loubes include Centre national de la recherche scientifique & Département de Mathématiques.
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A data driven trimming procedure for robust classification
TL;DR: A new method based on trimming is introduced to produce classification rules with guaranteed performance on a significant fraction of the data and provides an automatic way of determining the right trimming proportion and obtaining in this setting oracle bounds for the classification error on the new data set.
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Entropic Variable Boosting for Explainability and Interpretability in Machine Learning.
TL;DR: A new explainability formalism is presented to make clear the impact of each variable on the predictions given by black-box decision rules and proposes a new computation-ally efficient algorithm to stress the variables, which only reweights the reference observations and predictions.
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Gaussian Process Forecast with multidimensional distributional entries
TL;DR: It is proved that the kernel defined as the quadratic distance between the transportation maps, that transport each distribution to the barycenter of the distributions, provides a valid covariance function.
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Modeling Weather Conditions Consequences on Road Trafficking Behaviors
TL;DR: A thresholded linear model corresponding to an application of a MARS model to road trafficking is considered, which adapts itself locally to the whole road network and provides accurate unbiased forecasted speed using live or short term forecasted weather data information.
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Gaussian field on the symmetric group: Prediction and learning
TL;DR: This paper proposes and studies an harmonic analysis of the covariance operators that allows to put into action the full machinery of Gaussian processes learning in the less classical case where X is the non commutative finite group of permutations.