DagRep.6.9.94.pdf
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Foundations of Unsupervised Learning (Dagstuhl Seminar 16382)
Authors and all authors of the abstracts in this report
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Part of:
Issue:
Dagstuhl Reports, Volume 6, Issue 9
Volume: Dagstuhl Reports, Volume 6
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 3.0 Unported license
- Publication Date: 2017-02-17
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Maria-Florina Balcan, Shai Ben-David, Ruth Urner, and Ulrike von Luxburg. Foundations of Unsupervised Learning (Dagstuhl Seminar 16382). In Dagstuhl Reports, Volume 6, Issue 9, pp. 94-109, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)
https://doi.org/10.4230/DagRep.6.9.94
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.
Subject Classification
Keywords
- Machine learning
- theory of computing
- unsupervised learning
- representation learning
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