What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled
  Safety Critical Systems
Saddek Bensalem Chih-Hong Cheng Wei Huang Xiaowei Huang Changshun Wu Xingyu Zhao
Machine learning has made remarkable advancements, but confidently utilising
learning-enabled components in safety-critical domains still poses challenges.
Among the challenges, it is known that a rigorous, yet practical, way of
achieving safety guarantees is one of the most prominent. In this paper, we
first discuss the engineering and research challenges associated with the
design and verification of such systems. Then, based on the observation that
existing works cannot actually achieve provable guarantees, we promote a
two-step verification method for the ultimate achievement of provable
statistical guarantees.