TY - JOUR
T1 - The Shapley Value in Machine Learning
AU - Rozemberczki, Benedek
AU - Watson, Lauren
AU - Bayer, Péter
AU - Yang, Hao-Tsung
AU - Kiss, Olivér
AU - Nilsson, Sebastian
AU - Sarkar, Rik
PY - 2022/7
Y1 - 2022/7
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85137921612
U2 - 10.24963/ijcai.2022/778
DO - 10.24963/ijcai.2022/778
M3 - Review article
SP - 5572
EP - 5579
JO - Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
JF - Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
ER -