Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

A Review of Formal Methods applied to Machine Learning

Abstract : We review state-of-the-art formal methods applied to the emerging field of the verification of machine learning systems. Formal methods can provide rigorous correctness guarantees on hardware and software systems. Thanks to the availability of mature tools, their use is well established in the industry, and in particular to check safety-critical applications as they undergo a stringent certification process. As machine learning is becoming more popular, machine-learned components are now considered for inclusion in critical systems. This raises the question of their safety and their verification. Yet, established formal methods are limited to classic, i.e. non machine-learned software. Applying formal methods to verify systems that include machine learning has only been considered recently and poses novel challenges in soundness, precision, and scalability. We first recall established formal methods and their current use in an exemplar safety-critical field, avionic software, with a focus on abstract interpretation based techniques as they provide a high level of scalability. This provides a golden standard and sets high expectations for machine learning verification. We then provide a comprehensive and detailed review of the formal methods developed so far for machine learning, highlighting their strengths and limitations. The large majority of them verify trained neural networks and employ either SMT, optimization, or abstract interpretation techniques. We also discuss methods for support vector machines and decision tree ensembles, as well as methods targeting training and data preparation, which are critical but often neglected aspects of machine learning. Finally, we offer perspectives for future research directions towards the formal verification of machine learning systems.
Document type :
Preprints, Working Papers, ...
Complete list of metadata
Contributor : Antoine Miné Connect in order to contact the contributor
Submitted on : Wednesday, April 7, 2021 - 8:17:30 PM
Last modification on : Wednesday, June 8, 2022 - 12:50:03 PM


Distributed under a Creative Commons Attribution 4.0 International License

Links full text


  • HAL Id : hal-03192255, version 1
  • ARXIV : 2104.02466


Caterina Urban, Antoine Miné. A Review of Formal Methods applied to Machine Learning. 2021. ⟨hal-03192255⟩



Record views