LiVe 2017: 1st Workshop on Learning in Verification
- Satellite event of ETAPS 2017, Uppsala, Sweden
The success of machine learning has recently motivated researchers in formal methods to adapt the highly scalable learning methods to the verification setting, where correctness guarantees on the result are essential.
The aim of this workshop is to bring together researchers from the formal verification community that are developing approaches to exploit learning methods in verification as well as researchers from machine learning area interested in applications in verification and synthesis.
The general topic of machine learning in verification includes, for instance, the use of learning techniques (e.g. reinforcement learning) for speeding up verification (e.g. rigorous analysis of complex systems combining non-determinism, stochasticity, timing etc.), the use of machine learning data structures and algorithms (e.g. decision trees) for enhancing results of verification (e.g. generating simple invariants of programs, generating small controllers of systems), or meta-usage of machine learning (e.g. to predict the best tools to be applied to a verification problem).
Submissions and selection procedure
Since the aim of the workshop is to stimulate discussion on the potential of learning techniques in verification and to report on recent advancements, the program will consist of presentations of work recently accepted to top conferences and ongoing work.
The submissions will thus be abstracts of such work, limited to at most two pages, and will only be published in the informal pre-proceedings for the convenience of the participants. There will be no formal publication or post-proceedings.
The tentative important dates are set as follows:
The PC will be chaired by Joost-Pieter Katoen (RWTH Aachen) and Jan Kretinsky (Technical University of Munich).
- Abstract submission: 3 February 2017
- Notification: 21 February 2017
- Final versions of abstracts for informal pre-proceedings: 1 March 2017
Invited talks will be delivered by the following two distinguished scientists:
Looking forward to your submissions and seeing you LiVe in Uppsala!
- Kim G. Larsen is a professor in the Department of Computer Science at Aalborg University within the Distributed and Embedded Systems Unit.
He is one of the leaders of using machine learning in verification, with an ongoing ERC Advanced Grant LASSO (Learning, analysis, synthesis and optimization of cyber-physical systems) and 20 thousand citations and h-index 68.
- Pushmeet Kohli is a principal research scientist in Microsoft Research and the Machine Learning advisor to the Chief Research Officer of Microsoft.
As a head of machine learning in Microsoft, with recent ground-breaking results and 9 thousand citations and h-index 39, his interests have recently reached to applications in verification.
Jan Kretinsky (organiser), firstname.lastname@example.org