Over the last few years, the Shapley value, a solution concept from cooperative game theory,
has found numerous applications in machine learning. In this paper, we first discuss …

As data becomes the fuel driving technological and economic growth, a fundamental
challenge is how to quantify the value of data in algorithmic predictions and decisions. For …

The Shapley value has become popular in the Explainable AI (XAI) literature, thanks, to a
large extent, to a solid theoretical foundation, including four “favourable and fair” axioms for …

Classification and regression trees, as well as their variants, are off-the-shelf methods in
Machine Learning. In this paper, we review recent contributions within the Continuous …

In recent years, a large amount of model-agnostic methods to improve the transparency,
trustability, and interpretability of machine learning models have been developed. Based on …

The internet of reality or augmented reality has been considered a breakthrough and an
outstanding critical mutation with an emphasis on data mining leading to dismantling of …

In recent years, classifier ensemble techniques have drawn the attention of many
researchers in the machine learning research community. The ultimate goal of these …

Machine learning techniques are increasingly gaining attention due to their widespread use
in various disciplines across academia and industry. Despite their tremendous success …

We present a general method for explaining individual predictions of classification models.
The method is based on fundamental concepts from coalitional game theory and predictions …

Game-theoretic attribution techniques based on Shapley values are used to interpret black-
box machine learning models, but their exact calculation is generally NP-hard, requiring …