ETAPS 2019
Sat 6 - Thu 11 April 2019 Prague, Czech Republic

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); verification of machine-learning artefacts (e.g. verification of neural networks); or meta-usage of machine learning (e.g. to predict the best tools to be applied to a verification problem).

Accepted Papers

Title
Daniel Neider: Horn-ICE Learning
LiVe
Industrial talk: Martin Neuhäußer (Siemens): Verifying the approximate: Challenges in neural network analysis
LiVe
Industrial talk: Vahid Hashemi (AUDI): Industrial View on Safety of Learning
LiVe
Invited talk: Bettina Könighofer: Safe and Optimized Learning via Shielding
LiVe
Invited talk: Kristian Kersting: Exploiting Symmetries for Modelling and Solving Quadratic Programs
LiVe
Marielle Stoelinga: Learning from failures: generating reliability models from data
LiVe
Maximilian Weininger: BRTDP for Stochastic Games
LiVe
Nathanaël Fijalkow: Data generation for programming by example
LiVe
Nils Jansen: Counterexample-Guided Strategy Improvement for POMDPs Using Recurrent Neural Networks
LiVe
Opening
LiVe
Stefan Ratschan: Learning Certificates
LiVe
Yutaka Nagashima: Towards Machine Learning Induction
LiVe

Call for Papers

Since the aim of the workshop is to stimulate discussion on the potential of learning techniques in verification and to report on recent advancements, we invite presentations of possibly already published as well as ongoing work. The submissions should be abstracts of such work, limited to at most two pages in the llncs style, 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 submission are to be done over Easychair.

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