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Mini-Workshop: Mathematical Foundations of Robust and Generalizable Learning

dc.date.accessioned2022-11-21T12:44:08Z
dc.date.available2022-11-21T12:44:08Z
dc.date.issued2022
dc.identifier.urihttp://publications.mfo.de/handle/mfo/3989
dc.description.abstractMachine learning has become an highly active field of research, but its mathematical underpinnings are still hardly understood. This workshop identified key challenges, and it discussed potential solutions. Bringing together a diverse group of researchers, the workshop established different views on the topic based on notions from statistics, probability theory, and optimization.
dc.titleMini-Workshop: Mathematical Foundations of Robust and Generalizable Learning
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-2022-46
local.series.idOWR-2022-46
local.subject.msc62
local.date-range02 Oct - 08 Oct 2022
local.workshopcode2240c
local.workshoptitleMini-Workshop: Mathematical Foundations of Robust and Generalizable Learning
local.organizersJohannes Lederer, Bochum; Po-Ling Loh, Cambridge; Yuting Wei, Philadelphia; Fanny Yang, Zürich
local.report-nameWorkshop Report 2022,46
local.opc-photo-id2240c
local.publishers-doi10.4171/OWR/2022/46


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