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BEGIN:VEVENT
UID:5830@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20221212T000000
DTEND;TZID=Europe/Paris:20221216T000000
DTSTAMP:20241221T194150Z
URL:https://www.i2m.univ-amu.fr/evenements/meeting-in-mathematical-statist
 ics-2022/
SUMMARY:Conference (CIRM\, Luminy\, Marseille): Meeting in Mathematical Sta
 tistics 2022
DESCRIPTION:Conference: \n\n\n\n&nbsp\;\n\n\n\n\n\n Time Schedule \n\n\n A
 bstracts \n\n\n Participants \n\n\n\n\n Archives MMS 2020 \n\n\n\n\n\n\n\n
 \n\n\n\n\n\n\n\n\nMULTIYEAR PROGRAM\nCONFERENCE\n​\nMeeting in Mathemati
 cal Statistics   /   Rencontres de Statistique Mathématique\nMachine 
 learning and nonparametric statistics\n12 - 16 December 2021\n\n\n\n\n  \
 n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nScientific Committee &amp\; Organizi
 ng Committee\nComité scientifique &amp\; Comité d'organisation\nCristin
 a Butucea (Université Paris-Est Marne-la-Vallée)\nStanislav Minsker (Un
 iversity of Southern California)\nChristophe Pouet  (École Centrale de 
 Marseille)\nVladimir Spokoiny (Humboldt University of Berlin)\n\n\n\n\n\n
 \n  \n\n\n\n\n\n\n\n\n\nDescription\nContemporary machine learning algori
 thms define the state of the art in diverse areas (computer vision\, robot
 ics and speech recognition\, to name a few)\, but in many cases theoretica
 l justification behind the success of these methods is still missing. Math
 ematical results\, in particular statistical and probabilistic properties\
 , are being actively developed\, but many challenges still remain. Deep le
 arning and generative models are particular examples of the areas with sig
 nificant gaps between the engineering success and theoretical understandin
 g. To fill this gap\, tools from diverse areas such as nonparametric stati
 stics\, approximation theory\, empirical process theory and computational 
 efficiency are needed. This conference aims at establishing new fruitful c
 ollaborations among the experts in nonparametric statistics and theoretica
 l computer science. Expected outcome of such collaborations are new develo
 pments in the theory of machine learning\, including the topics such as de
 ep learning\, robustness\, privacy and estimation under fairness constrain
 ts.\n\nSPONSORS\n\n\n\n\n\n\n\n\n\n\n\n  \n\n\n\n\n\n\n\n  \n\n\n\n\n\n\n\
 n  \n\n\n\n\n\n\n\n  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n  \n\n\n\n\n\
 n\n\n  \n\n\n\n\n\n\n\n  \nPROJET ANR-17-CE40-0003 HIDITSA\n\n\n\n\n\n\n  
 \n\n\n\n\n\n\n\n\n\n\n
ATTACH;FMTTYPE=image/jpeg:https://www.i2m.univ-amu.fr/wp-content/uploads/2
 022/02/image_alea-sta-hal-02293008-Log-tail-approximation-errors-fig.1-Gol
 ubev-Pouet.png
CATEGORIES:Colloque
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TZID:Europe/Paris
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DTSTART:20221030T020000
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