The Shapley Value in Machine Learning

Benedek Rozemberczki*, Lauren Watson, Péter Bayer, Hao Tsung Yang, Olivér Kiss, Sebastian Nilsson, Rik Sarkar

*Corresponding author for this work

    Research output: Contribution to Book/Report typesConference contributionpeer-review

    Abstract (may include machine translation)

    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.

    Original languageEnglish
    Title of host publicationProceedings of the 31st International Joint Conference on Artificial Intelligence, IJCAI 2022
    EditorsLuc De Raedt, Luc De Raedt
    PublisherInternational Joint Conferences on Artificial Intelligence
    Pages5572-5579
    Number of pages8
    ISBN (Electronic)9781956792003
    DOIs
    StatePublished - 2022
    Event31st International Joint Conference on Artificial Intelligence, IJCAI 2022 - Vienna, Austria
    Duration: 23 Jul 202229 Jul 2022

    Publication series

    NameIJCAI International Joint Conference on Artificial Intelligence
    ISSN (Print)1045-0823

    Conference

    Conference31st International Joint Conference on Artificial Intelligence, IJCAI 2022
    Country/TerritoryAustria
    CityVienna
    Period23/07/2229/07/22

    Fingerprint

    Dive into the research topics of 'The Shapley Value in Machine Learning'. Together they form a unique fingerprint.

    Cite this