Document Open Access Logo

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

Authors Arthur Gretton, Philipp Hennig, Carl Edward Rasmussen, Bernhard Schölkopf and all authors of the abstracts in this report



PDF
Thumbnail PDF

File

DagRep.6.11.142.pdf
  • Filesize: 0.9 MB
  • 26 pages

Document Identifiers

Author Details

Arthur Gretton
Philipp Hennig
Carl Edward Rasmussen
Bernhard Schölkopf
and all authors of the abstracts in this report

Cite AsGet 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.
Keywords
  • gaussian processes
  • kernel methods
  • machine learning
  • probabilistic numerics
  • probabilistic programming

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail