DagRep.1.7.53.pdf
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Mathematical and Computational Foundations of Learning Theory (Dagstuhl Seminar 11291)
Authors and all authors of the abstracts in this report
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Dagstuhl Reports, Volume 1, Issue 7
Volume: Dagstuhl Reports, Volume 1
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported license
- Publication Date: 2011-11-25
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Matthias Hein, Gabor Lugosi, Lorenzo Rosasco, and Steve Smale. Mathematical and Computational Foundations of Learning Theory (Dagstuhl Seminar 11291). In Dagstuhl Reports, Volume 1, Issue 7, pp. 53-69, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)
https://doi.org/10.4230/DagRep.1.7.53
https://doi.org/10.4230/DagRep.1.7.53
Abstract
The main goal of the seminar ``Mathematical and Computational Foundations of Learning Theory'' was to bring together experts from computer science, mathematics and statistics to discuss the state of the art in machine learning broadly construed and identify and formulate the key challenges in learning which have to be addressed in the future. This Dagstuhl seminar was one of the first meetings to cover the full broad range of facets of modern learning theory. The meeting was very successful and all participants agreed that such a meeting should take place on a regular basis.
Keywords
- learning theory
- machine learning
- sparsity
- high-dimensional geometry
- manifold learning
- online learning
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