Fetching the paper…
Reading the bibliography…
High-performing predictive models, such as neural nets, usually operate as black boxes, which raises serious concerns about their interpretability.
The many Shapley values for model explanation
Sundararajan, M.; and Najmi, A. 2019 · 1908
Earlier work this paper cites.
A Baseline for Shapely Values in MLPs: from Missingness to Neutrality
Izzo, C.; Lipani, A.; Okhrati, R.; and Medda, F. 2020 · 2006
Earlier work this paper cites.
Learning Parameter Distributions to Detect Concept Drift in Data Streams
Haug, J.; and Kasneci, G. 2020 · 2010
Earlier work this paper cites.
Learned lessons in credit card fraud detection from a practitioner perspective
Dal Pozzolo, A.; Caelen, O.; Le Borgne, Y.-A.; Waterschoot, S.; and Bontempi, G. 2014 · 2014
Earlier work this paper cites.
Machine Bias: There’s software used across the country to predict future criminals. And it’s biased against blacks
Angwin, J.; Larson, J.; Mattu, S.; and Kirchner, L. 2016 · 2016
Earlier work this paper cites.
Licon: A linear weighting scheme for the contribution of input variables in deep artificial neural networks
Kasneci, G.; and Gottron, T. 2016 · 2016
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 · 2016
Cited alongside, same era.
UCI Machine Learning Repository
Dua, D.; and Graff, C. 2017 · 2017
Cited alongside, same era.
Interpretable explanations of black boxes by meaningful perturbation
Fong, R. C.; and Vedaldi, A. 2017 · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Lundberg, S. M.; and Lee, S.-I. 2017 · 2017
Cited alongside, same era.
Learning important features through propagating activation differences
Shrikumar, A.; Greenside, P.; and Kundaje, A. 2017 · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
Sundararajan, M.; Taly, A.; and Yan, Q. 2017 · 2017
Later among the works it cites.
Sanity checks for saliency maps
Adebayo, J.; Gilmer, J.; Muelly, M.; Goodfellow, I.; Hardt, M.; and Kim, B. 2018 · 2018
Later among the works it cites.
Consistent individualized feature attribution for tree ensembles
Lundberg, S. M.; Erion, G. G.; and Lee, S.-I. 2018 · 2018
Later among the works it cites.
Leveraging Model Inherent Variable Importance for Stable Online Feature Selection
Haug, J.; Pawelczyk, M.; Broelemann, K.; and Kasneci, G. 2020 · 2020
Later among the works it cites.
Visualizing the impact of feature attribution baselines
Sturmfels, P.; Lundberg, S.; and Lee, S.-I. 2020 · 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…
Smilkov, D.; Thorat, N.; Kim, B.; Viégas, F.; and Wattenberg, M. 2017 · 2017
Cited alongside, same era.