LiVe 2017: 1st Workshop on Learning in Verification


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).

Invited speakers

Invited talks will be delivered by the following two distinguished scientists: Looking forward to your submissions and seeing you LiVe in Uppsala!

Jan Kretinsky (organiser),