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)
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}
}
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Keywords: |
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gaussian processes, kernel methods, machine learning, probabilistic numerics, probabilistic programming |
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Seminar: |
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Dagstuhl Reports, Volume 6, Issue 11
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Issue date: |
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2017 |
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Date of publication: |
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2017 |
2017
