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Influence functions estimate the effect of removing a training point on a model without the need to retrain.
A higher-order Swiss Army infinitesimal jackknife
R. Giordano, M. I. Jordan, and T. Broderick · 1907
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The infinitesimal jackknife
L. A. Jaeckel · 1972
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The influence curve and its role in robust estimation
F. R. Hampel · 1974
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Detection of influential observation in linear regression
R. D. Cook · 1977
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Logistic regression diagnostics
D. Pregibon et al · 1981
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Robust Statistics: The Approach Based on Influence Functions
F. R. Hampel, E. M. Ronchetti, P. J. Rousseeuw, and W. A. Stahel · 1986
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Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Convex Optimization
S. Boyd and L. Vandenberghe · 2004
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Spam filtering with naive Bayes – which naive Bayes?
V. Metsis, I. Androutsopoulos, and G. Paliouras · 2006
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Model selection in kernel based regression using the influence function
M. Debruyne, M. Hubert, and J. A. Suykens · 2008
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Tackling the widespread and critical impact of batch effects in high-throughput data
J. T. Leek, R. B. Scharpf, H. C. Bravo, D. Simcha, B. Langmead, W. E. Johnson, D. Geman, K. Baggerly, and R. A. Irizarry · 2010
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Efficient approximation of cross-validation for kernel methods using Bouligand influence function
Y. Liu, S. Jiang, and S. Liao · 2014
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Impact of HbA1c measurement on hospital readmission rates: Analysis of 70,000 clinical database patient records
B. Strack, J. P. DeShazo, C. Gennings, J. L. Olmo, S. Ventura, K. J. Cios, and J. N. Clore · 2014
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Overview of the BioCreative V chemical disease relation (cdr) task
C. Wei, Y. Peng, R. Leaman, A. P. Davis, C. J. Mattingly, J. Li, T. C. Wiegers, and Z. Lu · 2015
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Data programming: Creating large training sets, quickly
A. J. Ratner, C. M. D. Sa, S. Wu, D. Selsam, and C. Ré · 2016
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Understanding black-box predictions via influence functions
P. W. Koh and P. Liang · 2017
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Certified defenses for data poisoning attacks
A scalable estimate of the extra-sample prediction error via approximate leave-one-out
K. R. Rad and A. Maleki · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
A. Williams, N. Nangia, and S. Bowman · 2018
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Are we modeling the task or the annotator? an investigation ofannotator bias in natural language understanding datasets
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Data shapley: Equitable valuation of data for machine learning
A. Ghorbani and J. Zou · 2019
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Towards efficient data valuation based on the shapley value
R. Jia, D. Dao, B. Wang, F. A. Hubis, N. Hynes, N. M. Gurel, B. Li, C. Zhang, D. Song, and C. Spanos · 2019
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A Swiss Army infinitesimal jackknife
R. Giordano, W. Stephenson, R. Liu, M. Jordan, and T. Broderick
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Interpreting black box predictions using Fisher kernels
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Effects of influence on user trust in predictive decision making
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