Machine Learning and Formal Methods (Dagstuhl Seminar 17351)

Authors Sanjit A. Seshia, Xianjin (Jerry) Zhu, Andreas Krause, Susmit Jha and all authors of the abstracts in this report



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Sanjit A. Seshia
Xianjin (Jerry) Zhu
Andreas Krause
Susmit Jha
and all authors of the abstracts in this report

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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)
https://doi.org/10.4230/DagRep.7.8.55

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.
Keywords
  • Formal Methods
  • Machine Learning

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