Fetching the paper…
Reading the bibliography…
We offer a new formalism for global explanations of pairwise feature dependencies and interactions in supervised models.
A value for n-person games
Shapley, L · 1953
Earlier work this paper cites.
Explaining prediction models and individual predictions with feature contributions
Štrumbelj, E. and Kononenko, I · 2014
Earlier work this paper cites.
”why should I trust you?” explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
Earlier work this paper cites.
Towards a rigorous science of interpretable machine learning
Doshi-Velez, F. and Kim, B · 2017
Earlier work this paper cites.
A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I · 2017
Earlier work this paper cites.
Explaining explanations: An overview of interpretability of machine learning
Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., and Kagal, L · 2018
Cited alongside, same era.
The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery
Lipton, Z. C · 2018
Cited alongside, same era.
Consistent individualized feature attribution for tree ensembles
Lundberg, S. M., Erion, G. G., and Lee, S.-I · 2018
Cited alongside, same era.
Explainable AI for trees: From local explanations to global understanding
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I · 2019
Cited alongside, same era.
Generating counterfactual and contrastive explanations using SHAP
Rathi, S · 2019
Cited alongside, same era.
Shapley residuals: Quantifying the limits of the Shapley value for explanations
Kumar, I. E., Scheidegger, C., Venkatasubramanian, S., and Friedler, S
Cited in the paper.
Problems with Shapley-value-based explanations as feature importance measures
Kumar, I. E., Venkatasubramanian, S., Scheidegger, C., and Friedler, S
Cited in the paper.
Hodge decomposition and the Shapley value of a cooperative game
Stern, A. and Tettenhorst, A · 2019
Later among the works it cites.
True to the model or true to the data?
Chen, H., Janizek, J. D., Lundberg, S., and Lee, S.-I · 2020
Later among the works it cites.
Understanding global feature contributions with additive importance measures
Covert, I., Lundberg, S., and Lee, S.-I · 2020
Later among the works it cites.
The explanation game: Explaining Machine Learning models using Shapley values
Merrick, L. and Taly, A · 2020
Later among the works it cites.
The many Shapley values for model explanation
Sundararajan, M. and Najmi, A · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…