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Mathematical Foundations of Machine Learning (hybrid meeting)

dc.date.accessioned2021-06-17T09:05:04Z
dc.date.available2021-06-17T09:05:04Z
dc.date.issued2021
dc.identifier.urihttp://publications.mfo.de/handle/mfo/3868
dc.description.abstractMachine learning has achieved remarkable successes in various applications, but there is wide agreement that a mathematical theory for deep learning is missing. Recently, some first mathematical results have been derived in different areas such as mathematical statistics and statistical learning. Any mathematical theory of machine learning will have to combine tools from different fields such as nonparametric statistics, high-dimensional statistics, empirical process theory and approximation theory. The main objective of the workshop was to bring together leading researchers contributing to the mathematics of machine learning. A focus of the workshop was on theory for deep neural networks. Mathematically speaking, neural networks define function classes with a rich mathematical structure that are extremely difficult to analyze because of non-linearity in the parameters. Until very recently, most existing theoretical results could not cope with many of the distinctive characteristics of deep networks such as multiple hidden layers or the ReLU activation function. Other topics of the workshop are procedures for quantifying the uncertainty of machine learning methods and the mathematics of data privacy.
dc.titleMathematical Foundations of Machine Learning (hybrid meeting)
dc.rights.licenseDieses Dokument darf im Rahmen von § 53 UrhG zum eigenen Gebrauch kostenfrei heruntergeladen, gelesen, gespeichert und ausgedruckt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.de
dc.rights.licenseThis document may be downloaded, read, stored and printed for your own use within the limits of § 53 UrhG but it may not be distributed via the internet or passed on to external parties.en
dc.identifier.doi10.14760/OWR-2021-15
local.series.idOWR-2021-15
local.subject.msc62
local.date-range21 Mar - 27 Mar 2021
local.workshopcode2112
local.workshoptitleMathematical Foundations of Machine Learning (hybrid meeting)
local.organizersPeter Bartlett, Berkeley; Cristina Butucea, Palaiseau; Johannes Schmidt-Hieber, Enschede
local.report-nameWorkshop Report 2021,15
local.opc-photo-id2112
local.publishers-doi10.4171/OWR/2021/15
local.ems-referenceBartlett Peter, Butucea Cristina, Schmidt-Hieber Johannes: Mathematical Foundations of Machine Learning. Oberwolfach Rep. 18 (2021), 853-894. doi: 10.4171/OWR/2021/15


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