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Documents authored by Schölkopf, Bernhard

Document
DOI: 10.4230/DagRep.6.11.142
New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481)

Authors: Arthur Gretton, Philipp Hennig, Carl Edward Rasmussen, and Bernhard Schölkopf

Published in: Dagstuhl Reports, Volume 6, Issue 11 (2017)

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.
Cite as

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)

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@Article{gretton_et_al:DagRep.6.11.142,
  author =	{Gretton, Arthur and Hennig, Philipp and Rasmussen, Carl Edward and Sch\"{o}lkopf, Bernhard},
  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 =	{Gretton, Arthur and Hennig, Philipp and Rasmussen, Carl Edward and Sch\"{o}lkopf, Bernhard},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.6.11.142},
  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}
}
Document
DOI: 10.4230/DagSemProc.09401.1
09401 Abstracts Collection – Machine learning approaches to statistical dependences and causality

Authors: Dominik Janzing, Steffen Lauritzen, and Bernhard Schölkopf

Published in: Dagstuhl Seminar Proceedings, Volume 9401, Machine learning approaches to statistical dependences and causality (2010)

Abstract
From 27.09.2009 to 02.10.2009, the Dagstuhl Seminar 09401 ``Machine learning approaches to statistical dependences and causality'' was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.
Cite as

Dominik Janzing, Steffen Lauritzen, and Bernhard Schölkopf. 09401 Abstracts Collection – Machine learning approaches to statistical dependences and causality. In Machine learning approaches to statistical dependences and causality. Dagstuhl Seminar Proceedings, Volume 9401, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010)

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@InProceedings{janzing_et_al:DagSemProc.09401.1,
  author =	{Janzing, Dominik and Lauritzen, Steffen and Sch\"{o}lkopf, Bernhard},
  title =	{{09401 Abstracts Collection – Machine learning approaches to statistical dependences and causality }},
  booktitle =	{Machine learning approaches to statistical dependences and causality},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{9401},
  editor =	{Dominik Janzing and Steffen Lauritzen and Bernhard Sch\"{o}lkopf},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09401.1},
  URN =		{urn:nbn:de:0030-drops-23636},
  doi =		{10.4230/DagSemProc.09401.1},
  annote =	{Keywords: Machine learning, statistical dependences, causality}
}
Document
DOI: 10.4230/DagSemRep.315
Inference Principles and Model Selection (Dagstuhl Seminar 01301)

Authors: Joachim Buhmann and Bernhard Schölkopf

Published in: Dagstuhl Seminar Reports. Dagstuhl Seminar Reports, Volume 1 (2021)

Abstract
Cite as

Joachim Buhmann and Bernhard Schölkopf. Inference Principles and Model Selection (Dagstuhl Seminar 01301). Dagstuhl Seminar Report 315, pp. 1-22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2002)

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@TechReport{buhmann_et_al:DagSemRep.315,
  author =	{Buhmann, Joachim and Sch\"{o}lkopf, Bernhard},
  title =	{{Inference Principles and Model Selection (Dagstuhl Seminar 01301)}},
  pages =	{1--22},
  ISSN =	{1619-0203},
  year =	{2002},
  type = 	{Dagstuhl Seminar Report},
  number =	{315},
  institution =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemRep.315},
  URN =		{urn:nbn:de:0030-drops-151999},
  doi =		{10.4230/DagSemRep.315},
}
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