License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagRep.6.9.94
URN: urn:nbn:de:0030-drops-69542
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2017/6954/
Go back to Dagstuhl Reports

Balcan, Maria-Florina ; Ben-David, Shai ; Urner, Ruth ; von Luxburg, Ulrike
Weitere Beteiligte (Hrsg. etc.): Maria-Florina Balcan and Shai Ben-David and Ruth Urner and Ulrike von Luxburg

Foundations of Unsupervised Learning (Dagstuhl Seminar 16382)

pdf-format:
dagrep_v006_i009_p094_s16382.pdf (0.9 MB)

Abstract

This report documents the program and the outcomes of Dagstuhl Seminar 16382 "Foundations of Unsupervised Learning". Unsupervised learning techniques are frequently used in practice of data analysis. However, there is currently little formal guidance as to how, when and to what effect to use which unsupervised learning method. The goal of the seminar was to initiate a broader and more systematic research on the foundations of unsupervised learning with the ultimate aim to provide more support to practitioners. The seminar brought together academic researchers from the fields of theoretical computer science and statistics as well as some researchers from industry.

BibTeX - Entry

@Article{balcan_et_al:DR:2017:6954,
  author =	{Maria-Florina Balcan and Shai Ben-David and Ruth Urner and Ulrike von Luxburg},
  title =	{{Foundations of Unsupervised Learning (Dagstuhl Seminar 16382)}},
  pages =	{94--109},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2017},
  volume =	{6},
  number =	{9},
  editor =	{Maria-Florina Balcan and Shai Ben-David and Ruth Urner and Ulrike von Luxburg},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2017/6954},
  URN =		{urn:nbn:de:0030-drops-69542},
  doi =		{10.4230/DagRep.6.9.94},
  annote =	{Keywords: Machine learning, theory of computing, unsupervised learning, representation learning}
}

Keywords: Machine learning, theory of computing, unsupervised learning, representation learning
Collection: Dagstuhl Reports, Volume 6, Issue 9
Issue Date: 2017
Date of publication: 17.02.2017

DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI