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 forests (also known as random forests) are an indispensable tool for automatic
image analysis. This practical and easy-to-follow text explores the theoretical underpinnings …

Despite the popularity of explainable AI, there is limited work on effective methods for
unsupervised learning. We study algorithms for $ k $-means clustering, focusing on a trade …

Abstract We propose Tree Variational Autoencoder (TreeVAE), a new generative
hierarchical clustering model that learns a flexible tree-based posterior distribution over …

MLPACK is a state-of-the-art, scalable, multi-platform C++ machine learning library released
in late 2011 offering both a simple, consistent API accessible to novice users and high …

When it is acknowledged that all candidate parameterised statistical models are
misspecified relative to the data generating process, the decision maker (DM) must currently …

Artificial intelligence (AI) has rapidly developed through advancements in computational
power and the growth of massive datasets. However, this progress has also heightened …

Anomaly detection in streaming data is of high interest in numerous application domains. In
this paper, we propose a novel one-class semi-supervised algorithm to detect anomalies in …

We propose methods for density estimation and data synthesis using a novel form of
unsupervised random forests. Inspired by generative adversarial networks, we implement a …

Abstract Decision Trees (DTs) and Random Forests (RFs) are powerful discriminative
learners and tools of central importance to the everyday machine learning practitioner and …