Topics in robust statistical learning

Autor: Brecheteau Claire, Genetay Edouard, Mathieu Timothee, Saumard Adrien

Publikováno v: ESAIM: Proceedings and Surveys, Vol 74, Pp 119-136 (2023)

Some recent contributions to robust inference are presented. Firstly, the classical problem of robust M-estimation of a location parameter is revisited using an optimal transport approach - with specifically designed Wasserstein-type distances - that

Externí odkaz: https://doaj.org/article/31095b126eca43378524c3facaeae5e0

K-bMOM: a robust Lloyd-type clustering algorithm based on bootstrap Median-of-Means

Autor: Brunet-Saumard, Camille, Genetay, Edouard, Saumard, Adrien

We propose a new clustering algorithm that is robust to the presence of outliers in the dataset. We perform Lloyd-type iterations with robust estimates of the centroids. More precisely, we build on the idea of median-of-means statistics to estimate t

Externí odkaz: http://arxiv.org/abs/2002.03899

Topics in robust statistical learning

Autor: Brécheteau, Claire, Genetay, Edouard, Mathieu, Timothee, Saumard, Adrien

Publikováno v: ESAIM: Proceedings and Surveys
Journées MAS de la SMAI, édition 2021.
ESAIM: Proceedings and Surveys, EDP Sciences, In press
ESAIM: Proceedings and Surveys, In press

Some recent contributions to robust inference are presented. Firstly, the classical problem of robust M-estimation of a location parameter is revisited using an optimal transport approach-with specifically designed Wasserstein-type distances-that red

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::ddc68629d0f4e16842f07b0f1516c64f
https://hal.archives-ouvertes.fr/hal-03605702/file/Session_Robust_Learning_ESAIM_PROC.pdf