Machine learning applications such as finance and medicine demand accurate and
justifiable predictions, barring most deep learning methods from use. In response, previous …

We present an alternative layer to convolution layers in convolutional neural networks
(CNNs). Our approach reduces the complexity of convolutions by replacing it with binary …

This paper investigates the connections between two state of the art classifiers: decision
forests (DFs, including decision jungles) and convolutional neural networks (CNNs) …

We consider the use of decision trees for decision-making problems under the predict-then-
optimize framework. That is, we would like to first use a decision tree to predict unknown …

Deep neural networks and in particular, deep neural classifiers have become an integral
part of many modern applications. Despite their practical success, we still have limited …

Deep neural networks have been proven powerful at processing perceptual data, such as
images and audio. However for tabular data, tree-based models are more popular. A nice …

Recent explainability related studies have shown that state-of-the-art DNNs do not always
adopt correct evidences to make decisions. It not only hampers their generalization but also …

Deep neural networks have been well-known for their superb handling of various machine
learning and artificial intelligence tasks. However, due to their over-parameterized black-box …

In this manuscript, we show that any neural network with any activation function can be
represented as a decision tree. The representation is equivalence and not an …

Randomized decision trees and forests have a rich history in machine learning and have
seen considerable success in application, perhaps particularly so for computer vision …