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DOI: 10.4230/DagRep.6.11.142
URN: urn:nbn:de:0030-drops-71064
URL: https://drops.dagstuhl.de/opus/volltexte/2017/7106/
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 | |
| Seminar: | Dagstuhl Reports, Volume 6, Issue 11 | |
| Issue date: | 2017 | |
| Date of publication: | 12.04.2017 |
