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Machine Learning and Formal Methods (Dagstuhl Seminar 17351)

Authors: Sanjit A. Seshia, Xianjin (Jerry) Zhu, Andreas Krause, and Susmit Jha

Published in: Dagstuhl Reports, Volume 7, Issue 8 (2018)

Abstract

This report documents the program and the outcomes of Dagstuhl Seminar 17351 "Machine Learning and Formal Methods". The seminar brought together practitioners and reseachers in machine learning and related areas (such as robotics) with those working in formal methods and related areas (such as programming languages and control theory). The meeting highlighted the connections between the two disciplines, and created new links between the two research communities.

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Sanjit A. Seshia, Xianjin (Jerry) Zhu, Andreas Krause, and Susmit Jha. Machine Learning and Formal Methods (Dagstuhl Seminar 17351). In Dagstuhl Reports, Volume 7, Issue 8, pp. 55-73, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@Article{seshia_et_al:DagRep.7.8.55,
  author =	{Seshia, Sanjit A. and Zhu, Xianjin (Jerry) and Krause, Andreas and Jha, Susmit},
  title =	{{Machine Learning and Formal Methods (Dagstuhl Seminar 17351)}},
  pages =	{55--73},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2018},
  volume =	{7},
  number =	{8},
  editor =	{Seshia, Sanjit A. and Zhu, Xianjin (Jerry) and Krause, Andreas and Jha, Susmit},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.7.8.55},
  URN =		{urn:nbn:de:0030-drops-84302},
  doi =		{10.4230/DagRep.7.8.55},
  annote =	{Keywords: Formal Methods, Machine Learning}
}

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Machine Learning and Formal Methods (Dagstuhl Seminar 17351)

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