This article provides a birds-eye view on the role of decision trees in machine learning and
data science over roughly four decades. It sketches the evolution of decision tree research …

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 …

Formal approaches can provide strict correctness guarantees for the development of both
hardware and software systems. In this work, we examine state-of-the-art formal methods for …

Random Forest (RFs) are among the most widely used Machine Learning (ML) classifiers.
Even though RFs are not interpretable, there are no dedicated non-heuristic approaches for …

We study the robustness verification problem of tree based models, including random forest
(RF) and gradient boosted decision tree (GBDT). Formal robustness verification of decision …

Bayesian neural networks harness the power of Bayesian inference which provides an
approach to neural learning that not only focuses on accuracy but also uncertainty …

We study the problem of formally and automatically verifying robustness properties of
decision tree ensemble classifiers such as random forests and gradient boosted decision …

In this paper, we present Forest GUMP (for Generalized, Unifying Merge Process) a tool for
verification and precise explanation of Random forests. Besides pre/post-condition-based …

In nonstationary environments, data distributions can change over time. This phenomenon is
known as concept drift, and the related models need to adapt if they are to remain accurate …

Recent advances in machine learning are now being considered for integration in safety-
critical systems such as vehicles, medical equipment and critical infrastructure. However …