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New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481) Gretton, Arthur Hennig, Philipp Rasmussen, Carl Edward Schölkopf, Bernhard gaussian processes kernel methods machine learning probabilistic numerics probabilistic programming 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Arthur Gretton and Philipp Hennig and Carl Edward Rasmussen and Bernhard Schölkopf 2017 Is Part Of Dagstuhl Reports, Volume 6, Issue 11 (2017) Article Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DagRep.6.11.142 urn:nbn:de:0030-drops-71064 https://drops.dagstuhl.de/entities/document/10.4230/DagRep.6.11.142 eng https://creativecommons.org/licenses/by/3.0/legalcode