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Shapley Values, a solution to the credit assignment problem in cooperative game theory, are a popular type of explanation in machine learning, having been used to explain the importance of features, embeddings, and even neurons.
A value for n-person games
Lloyd Shapley. 1953 · 1953
Earlier work this paper cites.
Maximal flow through a network
Lester Randolph Ford and Delbert R Fulkerson. 1956 · 1956
Earlier work this paper cites.
Graphs and cooperation in games
Roger B Myerson. 1977 · 1977
Earlier work this paper cites.
Monotonic solutions of cooperative games
H Peyton Young. 1985 · 1985
Earlier work this paper cites.
Neuron shapley: Discovering the responsible neurons
Amirata Ghorbani and James Zou. 2020 · 2002
Earlier work this paper cites.
Bounding the estimation error of sampling-based shapley value approximation
Sasan Maleki, Long Tran-Thanh, Greg Hines, Talal Rahwan, and Alex Rogers. 2013 · 2013
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, KyungHyun Cho, and Yoshua Bengio. 2015 · 2015
Earlier work this paper cites.
Understanding neural networks through representation erasure
Jiwei Li, Will Monroe, and Dan Jurafsky. 2016 · 2016
Cited alongside, same era.
” what is relevant in a text document?”: An interpretable machine learning approach
Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek. 2017 · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee. 2017 · 2017
Cited alongside, same era.
Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg. 2017 · 2017
Cited alongside, same era.
Pathologies of neural models make interpretations difficult
Shi Feng, Eric Wallace, Alvin Grissom II, Mohit Iyyer, Pedro Rodriguez, and Jordan Boyd-Graber. 2018 · 2018
Cited alongside, same era.
Attention is not explanation
Sarthak Jain and Byron C Wallace. 2019 · 2019
Later among the works it cites.
Revealing the dark secrets of bert
Olga Kovaleva, Alexey Romanov, Anna Rogers, and Anna Rumshisky. 2019 · 2019
Later among the works it cites.
Are sixteen heads really better than one?
Paul Michel, Omer Levy, and Graham Neubig. 2019 · 2019
Later among the works it cites.
Is attention interpretable?
Sofia Serrano and Noah A Smith. 2019 · 2019
Later among the works it cites.
Attention is not not explanation
Sarah Wiegreffe and Yuval Pinter. 2019 · 2019
Later among the works it cites.
Quantifying attention flow in transformers
Samira Abnar and Willem Zuidema. 2020 · 2020
Later among the works it cites.
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How contextual are contextualized word representations? comparing the geometry of bert, elmo, and gpt-2 embeddings
Kawin Ethayarajh. 2019 · 2019
Cited alongside, same era.
Data shapley: Equitable valuation of data for machine learning
Amirata Ghorbani and James Zou. 2019 · 2019
Cited alongside, same era.