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.6.11.142
URN: urn:nbn:de:0030-drops-71064
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2017/7106/
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Gretton, Arthur ; Hennig, Philipp ; Rasmussen, Carl Edward ; Schölkopf, Bernhard
Weitere Beteiligte (Hrsg. etc.): Arthur Gretton and Philipp Hennig and Carl Edward Rasmussen and Bernhard Schölkopf

New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481)

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Abstract

The Dagstuhl Seminar on 16481 "New Directions for Learning with Kernels and Gaussian Processes" brought together two principal theoretical camps of the machine learning community at a crucial time for the field. Kernel methods and Gaussian process models together form a significant part of the discipline's foundations, but their prominence is waning while more elaborate but poorly understood hierarchical models are ascendant. In a lively, amiable seminar, the participants re-discovered common conceptual ground (and some continued points of disagreement) and productively discussed how theoretical rigour can stay relevant during a hectic phase for the subject.

BibTeX - Entry

@Article{gretton_et_al:DR:2017:7106,
  author =	{Arthur Gretton and Philipp Hennig and Carl Edward Rasmussen and Bernhard Sch{\"o}lkopf},
  title =	{{New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481)}},
  pages =	{142--167},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2017},
  volume =	{6},
  number =	{11},
  editor =	{Arthur Gretton and Philipp Hennig and Carl Edward Rasmussen and Bernhard Sch{\"o}lkopf},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2017/7106},
  URN =		{urn:nbn:de:0030-drops-71064},
  doi =		{10.4230/DagRep.6.11.142},
  annote =	{Keywords: gaussian processes, kernel methods, machine learning, probabilistic numerics, probabilistic programming}
}

Keywords: gaussian processes, kernel methods, machine learning, probabilistic numerics, probabilistic programming
Collection: Dagstuhl Reports, Volume 6, Issue 11
Issue Date: 2017
Date of publication: 12.04.2017

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