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Influence functions efficiently estimate the effect of removing a single training data point on a model's learned parameters.
The proof and measurement of association between two things
Spearman, C · 1961
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The influence curve and its role in robust estimation
Hampel, F. R · 1974
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Influential observations in linear regression
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The implicit function theorem: history, theory, and applications
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Influence functions in deep learning are fragile
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Pearson’s correlation coefficient
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Very deep convolutional networks for large-scale image recognition
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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Optimizing neural networks with kronecker-factored approximate curvature
Martens, J. and Grosse, R · 2015
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Second-order stochastic optimization in linear time
Agarwal, N., Bullins, B., and Hazan, E · 2016
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Examples are not enough, learn to criticize! criticism for interpretability
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Dua, D. and Graff, C · 2017
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Understanding black-box predictions via influence functions
Koh, P. W. and Liang, P · 2017
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Can you trust this prediction? Auditing pointwise reliability after learning
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When does preconditioning help or hurt generalization?
Amari, S.-i., Ba, J., Grosse, R., Li, X., Nitanda, A., Suzuki, T., Wu, D., and Xu, J · 2020
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On warm-starting neural network training
Ash, J. and Adams, R. P · 2020
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RelatIF: Identifying explanatory training samples via relative influence
Barshan, E., Brunet, M.-E., and Dziugaite, G. K · 2020
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Benign overfitting in linear regression
Bartlett, P. L., Long, P. M., Lugosi, G., and Tsigler, A · 2020
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Do gans always have nash equilibria?
Farnia, F. and Ozdaglar, A · 2020
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Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Counterfactual explanations without opening the black box automated decisions and the GDPR
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Explaining black box predictions and unveiling data artifacts through influence functions
Han, X., Wallace, B. C., and Tsvetkov, Y · 2020
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Learning augmentation network via influence functions
Lee, D., Park, H., Pham, T., and Yoo, C. D · 2020
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Estimating training data influence by tracing gradient descent
Pruthi, G., Liu, F., Kale, S., and Sundararajan, M · 2020
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FastIF: Scalable influence functions for efficient model interpretation and debugging
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Evaluation of similarity-based explanations
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Approximate data deletion from machine learning models
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Revisiting methods for finding influential examples
K, K. and Søgaard, A · 2021
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Resolving training biases via influence-based data relabeling
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Scaling up influence functions
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Interactive label cleaning with example-based explanations
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Surprises in high-dimensional ridgeless least squares interpolation
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Stronger data poisoning attacks break data sanitization defenses
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Understanding rare spurious correlations in neural networks
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