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 …

Decision trees have the particularity of being machine learning models that are visually easy
to interpret and understand. Therefore, they are primarily suited for sensitive domains like …

An emerging trend in network security consists in the adoption of programmable switches for
performing various security tasks in large-scale, high-speed networks. However, since …

Explainable Artificial Intelligence (XAI) aims to improve the transparency of machine
learning (ML) pipelines. We systematize the increasingly growing (but fragmented) …

Learning-based classifiers are found to be susceptible to adversarial examples. Recent
studies suggested that ensemble classifiers tend to be more robust than single classifiers …

Current state-of-the-art algorithms for training robust decision trees have high runtime costs
and require hours to run. We present GROOT, an efficient algorithm for training robust …

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

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

Decision trees are a popular choice of explainable model, but just like neural networks, they
suffer from adversarial examples. Existing algorithms for fitting decision trees robust against …

Many safety-critical applications of machine learning, such as fraud or abuse detection, use
data in tabular domains. Adversarial examples can be particularly damaging for these …