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The attribution problem, that is the problem of attributing a model's prediction to its base features, is well-studied.
Did the model understand the question?
Mudrakarta, P. K., Taly, A., Sundararajan, M., and Dhamdhere, K · 1906
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A value of n-person games
Shapley, L. S · 1953
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Multilinear extensions of games
Owen, G · 1972
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Values of Non-Atomic Games
Aumann, R. J., and Shapley, L. S · 1974
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Hedonic prices and the demand for clean air
Harrison, D., and Rubinfeld, D. L · 1978
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Regression shrinkage and selection via the lasso
Tibshirani, R · 1996
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An axiomatic approach to the concept of interaction among players in cooperative games
Grabisch, M., and Roubens, M · 1999
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Feature selection based on the shapley value
Cohen, S., Ruppin, E., and Dror, G · 2005
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Analysis of variance—why it is more important than ever
Gelman, A., et al · 2005
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How to explain individual classification decisions
Baehrens, D., Schroeter, T., Harmeling, S., Kawanabe, M., Hansen, K., and Müller, K.-R · 2009
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An efficient explanation of individual classifications using game theory
Strumbelj, E., and Kononenko, I · 2010
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Bounding the estimation error of sampling-based shapley value approximation with/without stratifying
Maleki, S., Tran-Thanh, L., Hines, G., Rahwan, T., and Rogers, A · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2013
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Convolutional neural networks for sentence classification
Kim, Y · 2014
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Sobol’ indices and Shapley value
Owen, A. B · 2014
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Striving for simplicity: The all convolutional net
Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M. A · 2014
A unified approach to interpreting model predictions
Lundberg, S., and Lee, S.-I · 2017
Later among the works it cites.
A unified approach to interpreting model predictions
Lundberg, S. M., and Lee, S.-I · 2017
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Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017
Precup, D., and Teh, Y. W · 2017
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Consistent individualized feature attribution for tree ensembles
Lundberg, S. M., Erion, G. G., and Lee, S · 2018
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Detecting statistical interactions from neural network weights
Tsang, M., Cheng, D., and Liu, Y · 2018
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Neural interaction transparency (nit): Disentangling learned interactions for improved interpretability
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Influence in classification via cooperative game theory
Datta, A., Datta, A., Procaccia, A. D., and Zick, Y · 2015
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Layer-wise relevance propagation for neural networks with local renormalization layers
Binder, A., Montavon, G., Lapuschkin, S., Müller, K.-R., and Samek, W · 2016
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Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems
Datta, A., Sen, S., and Zick, Y · 2016
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Squad: 100, 000+ questions for machine comprehension of text
Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P · 2016
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Learning important features through propagating activation differences
Shrikumar, A., Greenside, P., and Kundaje, A
Cited in the paper.
Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q
Cited in the paper.
Tsang, M., Liu, H., Purushotham, S., Murali, P., and Liu, Y · 2018
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Fast and accurate reading comprehension by combining self-attention and convolution
Yu, A. W., Dohan, D., Le, Q., Luong, T., Zhao, R., and Chen, K · 2018
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Recovering pairwise interactions using neural networks
Cui, T., Marttinen, P., and Kaski, S · 2019
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Hierarchical interpretations for neural network predictions
Singh, C., Murdoch, W. J., and Yu, B · 2019
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Axiomatic characterization of data-driven influence measures for classification
Sliwinski, J., Strobel, M., and Zick, Y · 2019
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