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With the rapid adoption of machine learning systems in sensitive applications, there is an increasing need to make black-box models explainable.
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M. Brunet, C. Alkalay-Houlihan, A. Anderson, and R. S. Zemel · 2018
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Why is my classifier discriminatory?
I. Chen, F. D. Johansson, and D. Sontag · 2018
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A swiss army infinitesimal jackknife
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An overview of deep learning in medical imaging focusing on MRI
A. S. Lundervold and A. Lundervold · 2018
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Representer point selection for explaining deep neural networks
C. Yeh, J. S. Kim, I. E. Yen, and P. Ravikumar · 2018
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K. E. Avrachenkov, J. A. Filar, and P. G. Howlett · 2013
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Why l1 is a good approximation to l0: A geometric explanation
C. Ramirez · 2013
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Higher order influence functions and minimax estimation of nonlinear functionals
R. James, L. Lingling, T. Eric, and A. van der Vaart · 2017
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Understanding black-box predictions via influence functions
P. W. Koh and P. Liang · 2017
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Should we treat data as labor? moving beyond "free"
I. Arrieta-Ibarra, L. Goff, D. Jiménez-Hernández, J. Lanier, and E. G. Weyl · 2018
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Stronger data poisoning attacks break data sanitization defenses
P. W. Koh, J. Steinhardt, and P. Liang
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Validating causal inference models via influence functions
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A higher-order swiss army infinitesimal jackknife
R. Giordano, M. I. Jordan, and T. Broderick · 2019
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Detecting extrapolation with influence functions
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Can you trust this prediction? auditing pointwise reliability after learning
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