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.5.8.54
URN: urn:nbn:de:0030-drops-56783
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2016/5678/
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Hein, Matthias ; Lugosi, Gabor ; Rosasco, Lorenzo
Weitere Beteiligte (Hrsg. etc.): Matthias Hein and Gabor Lugosi and Lorenzo Rosasco

Mathematical and Computational Foundations of Learning Theory (Dagstuhl Seminar 15361)

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dagrep_v005_i008_p054_s15361.pdf (0.9 MB)

Abstract

Machine learning has become a core field in computer science. Over
the last decade the statistical machine learning approach has been successfully applied in many areas such as bioinformatics, computer vision, robotics and information retrieval. The main reasons for the success of machine learning are its strong theoretical foundations and its
multidisciplinary approach integrating aspects of computer science, applied mathematics, and statistics among others. The goal of the seminar was to bring together again experts from computer science, mathematics and statistics to discuss the state of the art in machine learning and identify and formulate the key challenges in learning which have to be addressed in the future.
The main topics of this seminar were:
- Interplay between Optimization and Learning,
- Learning Data Representations.

BibTeX - Entry

@Article{hein_et_al:DR:2016:5678,
  author =	{Matthias Hein and Gabor Lugosi and Lorenzo Rosasco},
  title =	{{Mathematical and Computational Foundations of Learning Theory (Dagstuhl Seminar 15361)}},
  pages =	{54--73},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2016},
  volume =	{5},
  number =	{8},
  editor =	{Matthias Hein and Gabor Lugosi and Lorenzo Rosasco},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2016/5678},
  URN =		{urn:nbn:de:0030-drops-56783},
  doi =		{10.4230/DagRep.5.8.54},
  annote =	{Keywords: learning theory, non-smooth optimization (convex and non-convex), signal processing}
}

Keywords: learning theory, non-smooth optimization (convex and non-convex), signal processing
Collection: Dagstuhl Reports, Volume 5, Issue 8
Issue Date: 2016
Date of publication: 15.01.2016

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