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Data Attribution (DA) methods quantify the influence of individual training data points on model outputs and have broad applications such as explainability, data selection, and noisy label identification.
Bias and confidence in not quite large samples
John Tukey · 1958
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Stability of randomized learning algorithms
Andre Elisseeff, Theodoros Evgeniou, and Massimiliano Pontil · 2005
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Influence functions in deep learning are fragile
Samyadeep Basu, Philip Pope, and Soheil Feizi · 2006
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Differential privacy: A survey of results
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Learning multiple layers of features from tiny images
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Optimizing non-decomposable performance measures: A tale of two classes
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Stanford question answering dataset
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Data shapley: Equitable valuation of data for machine learning
Amirata Ghorbani and James Zou · 2019
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Estimating training data influence by tracing gradient descent
Garima Pruthi, Frederick Liu, Satyen Kale, and Mukund Sundararajan · 2020
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FastIF: Scalable influence functions for efficient model interpretation and debugging
Han Guo, Nazneen Rajani, Peter Hase, Mohit Bansal, and Caiming Xiong · 2021
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Scalability vs. utility: Do we have to sacrifice one for the other in data importance quantification?
Ruoxi Jia, Fan Wu, Xuehui Sun, Jiacen Xu, David Dao, Bhavya Kailkhura, Ce Zhang, Bo Li, and Dawn Song · 2021
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Datamodels: Predicting predictions from training data
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Beta shapley: a unified and noise-reduced data valuation framework for machine learning
Yongchan Kwon and James Zou · 2022
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Towards efficient data valuation based on the shapley value
Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nick Hynes, Nezihe Merve Gürel, Bo Li, Ce Zhang, Dawn Song, and Costas J Spanos · 2019
Cited alongside, same era.
On the accuracy of influence functions for measuring group effects
Pang Wei W Koh, Kai-Siang Ang, Hubert Teo, and Percy S Liang · 2019
Cited alongside, same era.
Limitations of the empirical fisher approximation for natural gradient descent
Frederik Kunstner, Philipp Hennig, and Lukas Balles · 2019
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Relatif: Identifying explanatory training samples via relative influence
Elnaz Barshan, Marc-Etienne Brunet, and Gintare Karolina Dziugaite · 2020
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On second-order group influence functions for black-box predictions
Samyadeep Basu, Xuchen You, and Soheil Feizi
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Scaling up influence functions
Andrea Schioppa, Polina Zablotskaia, David Vilar, and Artem Sokolov · 2022
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Trak: Attributing model behavior at scale
Sung Min Park, Kristian Georgiev, Andrew Ilyas, Guillaume Leclerc, and Aleksander Madry · 2023
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Data banzhaf: A robust data valuation framework for machine learning
Jiachen T Wang and Ruoxi Jia · 2023
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