8 research outputs found
Understanding Interventional TreeSHAP : How and Why it Works
Shapley values are ubiquitous in interpretable Machine Learning due to their
strong theoretical background and efficient implementation in the SHAP library.
Computing these values used to induce an exponential cost with respect to the
number of input features of an opaque model. Now, with efficient
implementations such as Interventional TreeSHAP, this exponential burden is
alleviated assuming one is explaining ensembles of decision trees. Although
Interventional TreeSHAP has risen in popularity, it still lacks a formal proof
of how/why it works. We provide such proof with the aim of not only increasing
the transparency of the algorithm but also to encourage further development of
these ideas. Notably, our proof for Interventional TreeSHAP is easily adapted
to Shapley-Taylor indices
How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review
Context: Machine Learning (ML) has been at the heart of many innovations over
the past years. However, including it in so-called 'safety-critical' systems
such as automotive or aeronautic has proven to be very challenging, since the
shift in paradigm that ML brings completely changes traditional certification
approaches.
Objective: This paper aims to elucidate challenges related to the
certification of ML-based safety-critical systems, as well as the solutions
that are proposed in the literature to tackle them, answering the question 'How
to Certify Machine Learning Based Safety-critical Systems?'.
Method: We conduct a Systematic Literature Review (SLR) of research papers
published between 2015 to 2020, covering topics related to the certification of
ML systems. In total, we identified 217 papers covering topics considered to be
the main pillars of ML certification: Robustness, Uncertainty, Explainability,
Verification, Safe Reinforcement Learning, and Direct Certification. We
analyzed the main trends and problems of each sub-field and provided summaries
of the papers extracted.
Results: The SLR results highlighted the enthusiasm of the community for this
subject, as well as the lack of diversity in terms of datasets and type of
models. It also emphasized the need to further develop connections between
academia and industries to deepen the domain study. Finally, it also
illustrated the necessity to build connections between the above mention main
pillars that are for now mainly studied separately.
Conclusion: We highlighted current efforts deployed to enable the
certification of ML based software systems, and discuss some future research
directions.Comment: 60 pages (92 pages with references and complements), submitted to a
journal (Automated Software Engineering). Changes: Emphasizing difference
traditional software engineering / ML approach. Adding Related Works, Threats
to Validity and Complementary Materials. Adding a table listing papers
reference for each section/subsection
A Wadge hierarchy for second countable spaces
We define a notion of reducibility for subsets of a second countable T 0 topological space based on relatively continuous relations and admissible representations. This notion of reducibility induces a hierarchy that refines the Baire classes and the Hausdorff-Kuratowski classes of differences. It coincides with Wadge reducibility on zero dimensional spaces. However in virtually every second countable T 0 space, it yields a hierarchy on Borel sets, namely it is well founded and antichains are of length at most 2. It thus differs from the Wadge reducibility in many important cases, for example on the real line R or the Scott Domain P ω