Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data

Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data. Catav, A., Fu, B., Zoabi, Y., Weiss-Meilik, A., Shomron, N., Ernst, J., Sankararaman, S., & Gilad-Bachrach, R. In Meila, M. & Zhang, T., editors, Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, volume 139, of Proceedings of Machine Learning Research, pages 1324–1335, 2021. PMLR.

Paper bibtex

@inproceedings{DBLP:conf/icml/CatavFZWSESG21,
  author    = {Amnon Catav and
               Boyang Fu and
               Yazeed Zoabi and
               Ahuva Weiss{-}Meilik and
               Noam Shomron and
               Jason Ernst and
               Sriram Sankararaman and
               Ran Gilad{-}Bachrach},
  editor    = {Marina Meila and
               Tong Zhang},
  title     = {Marginal Contribution Feature Importance - an Axiomatic Approach for
               Explaining Data},
  booktitle = {Proceedings of the 38th International Conference on Machine Learning,
               {ICML} 2021, 18-24 July 2021, Virtual Event},
  series    = {Proceedings of Machine Learning Research},
  volume    = {139},
  pages     = {1324--1335},
  publisher = {{PMLR}},
  year      = {2021},
  url       = {http://proceedings.mlr.press/v139/catav21a.html},
  timestamp = {Wed, 14 Jul 2021 14:10:33 +0200},
  biburl    = {https://dblp.org/rec/conf/icml/CatavFZWSESG21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

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