DagRep.7.8.55.pdf
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Machine Learning and Formal Methods (Dagstuhl Seminar 17351)
Authors and all authors of the abstracts in this report
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Part of:
Issue:
Dagstuhl Reports, Volume 7, Issue 8
Volume: Dagstuhl Reports, Volume 7
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 3.0 Unported license
- Publication Date: 2018-02-06
Cite AsGet BibTex
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
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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