@inproceedings{0e5c33e4d2374466b100c172cc79d02b,
title = "The Shapley Value in Machine Learning",
abstract = "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 fundamental concepts of cooperative game theory and axiomatic properties of the Shapley value. Then, we give an overview of the most important applications of the Shapley value in machine learning: feature selection, explainability, multi-agent reinforcement learning, ensemble pruning, and data valuation. We examine the most crucial limitations of the Shapley value and point out directions for future research.",
author = "Benedek Rozemberczki and Lauren Watson and P{\'e}ter Bayer and Yang, {Hao Tsung} and Oliv{\'e}r Kiss and Sebastian Nilsson and Rik Sarkar",
note = "Publisher Copyright: {\textcopyright} 2022 International Joint Conferences on Artificial Intelligence. All rights reserved.; 31st International Joint Conference on Artificial Intelligence, IJCAI 2022 ; Conference date: 23-07-2022 Through 29-07-2022",
year = "2022",
language = "???core.languages.en_GB???",
series = "IJCAI International Joint Conference on Artificial Intelligence",
publisher = "International Joint Conferences on Artificial Intelligence",
pages = "5572--5579",
editor = "{De Raedt}, Luc and {De Raedt}, Luc",
booktitle = "Proceedings of the 31st International Joint Conference on Artificial Intelligence, IJCAI 2022",
}