DagRep.6.11.142.pdf
- Filesize: 0.9 MB
- 26 pages
Document
New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481)
Authors and all authors of the abstracts in this report
-
Part of:
Issue:
Dagstuhl Reports, Volume 6, Issue 11
Volume: Dagstuhl Reports, Volume 6
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 3.0 Unported license
- Publication Date: 2017-04-12
Cite As Get BibTex
Arthur Gretton, Philipp Hennig, Carl Edward Rasmussen, and Bernhard Schölkopf. New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481). In Dagstuhl Reports, Volume 6, Issue 11, pp. 142-167, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)
https://doi.org/10.4230/DagRep.6.11.142
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.
Subject Classification
Keywords
- gaussian processes
- kernel methods
- machine learning
- probabilistic numerics
- probabilistic programming
Metrics
- Access Statistics
-
Total Accesses (updated on a weekly basis)
0PDF Downloads