IJCAI 2022poster268 citations
The Shapley Value in Machine Learning
Benedek Rozemberczki, Lauren Watson, Péter Bayer, Hao-Tsung Yang, Olivér Kiss, Sebastian Nilsson, Rik Sarkar
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.
Survey Track: -Survey Track: Data MiningSurvey Track: Machine LearningSurvey Track: Constraint Satisfaction and Optimization
BibTeX
@inproceedings{ijcai2022p778,
title = {The Shapley Value in Machine Learning},
author = {Rozemberczki, Benedek and Watson, Lauren and Bayer, Péter and Yang, Hao-Tsung and Kiss, Olivér and Nilsson, Sebastian and Sarkar, Rik},
booktitle = {Proceedings of the Thirty-First International Joint Conference on
Artificial Intelligence, {IJCAI-22}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Lud De Raedt},
pages = {5572--5579},
year = {2022},
month = {7},
note = {Survey Track},
doi = {10.24963/ijcai.2022/778},
url = {https://doi.org/10.24963/ijcai.2022/778},
}