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 …

The last decade witnessed an ever-increasing stream of successes in Machine Learning
(ML). These successes offer clear evidence that ML is bound to become pervasive in a wide …

Decision trees are among the most popular machine learning models and are used routinely
in applications ranging from revenue management and medicine to bioinformatics. In this …

Learning with adversarial robustness has been a challenge in contemporary machine
learning, and recent years have witnessed increasing attention on robust decision trees and …

Decision trees are traditionally trained using greedy heuristics that locally optimize an
impurity or information metric. Recently there has been a surge of interest in optimal …

Decision trees are popular models for their interpretation properties and their success in
ensemble models for structured data. However, common decision tree learning algorithms …

Adversarial examples, inputs designed to induce worst-case behavior in machine learning
models, have been extensively studied over the past decade. Yet, our understanding of this …

Global optimization of decision trees has shown to be promising in terms of accuracy, size,
and consequently human comprehensibility. However, many of the methods used rely on …

Research on adversarial evasion attacks primarily focuses on neural network models due to
their popularity in fields such as computer vision and natural language processing, as well …

In many high-stakes domains, the data used to drive machine learning algorithms is noisy
(due to eg, the sensitive nature of the data being collected, limited resources available to …