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TZID:Europe/Paris
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DTSTART:20100328T010000
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DTSTART:20101031T010000
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DTSTART;TZID=Europe/Paris:20101011T133000
DTEND;TZID=Europe/Paris:20101011T143000
DTSTAMP:20260413T183053
CREATED:20101011T113000Z
LAST-MODIFIED:20211104T085524Z
UID:7935-1286803800-1286807400@www.math.ens.psl.eu
SUMMARY:Régression linéaire au sens des moindres carrés à partir de sous-espaces vectoriels aléatoires
DESCRIPTION:Exposé en français mais transparents en anglais   I will present recent works on least-squares regression using randomly generated subspaces.In this approach\, the regression function is the empirical risk minimizer in a low dimensional randomly generated subspace of a high (possibly infinite) dimensional function space. This approach can be seen as an alternative to usual penalization techniques. Approximation error and excess risk bounds are derived and the issue of numerical complexity will be discussed.This is joint work with Odalric Maillard and is described in the following papers: – Compressed Least-Squares Regression\, NIPS 2009 – Brownian Motions and Scrambled Wavelets for Least-Squares Regression\, NIPS 2010
URL:https://www.math.ens.psl.eu/evenement/regression-lineaire-au-sens-des-moindres-carres-a-partir-de-sous-espaces-vectoriels-aleatoires/
LOCATION:Ecole normale supérieure salle W
CATEGORIES:SMILE in Paris
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