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Data valuation is a growing research field that studies the influence of individual data points for machine learning (ML) models.
A value for n-person games
Lloyd S Shapley · 1953
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The mnist database of handwritten digits
Yann LeCun · 1998
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Locality-sensitive hashing scheme based on p-stable distributions
Mayur Datar, Nicole Immorlica, Piotr Indyk, and Vahab S Mirrokni · 2004
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Vehicle classification in distributed sensor networks
Marco F Duarte and Yu Hen Hu · 2004
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients
I-Cheng Yeh and Che-hui Lien · 2009
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On the difficulty of nearest neighbor search
Junfeng He, Sanjiv Kumar, and Shih-Fu Chang · 2012
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Calibrating probability with undersampling for unbalanced classification
Andrea Dal Pozzolo, Olivier Caelen, Reid A Johnson, and Gianluca Bontempi · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Cited alongside, same era.
Data shapley: Equitable valuation of data for machine learning
Amirata Ghorbani and James Zou · 2019
Cited alongside, same era.
Estimation of the shapley value by ergodic sampling
Ferenc Illés and Péter Kerényi · 2019
Cited alongside, same era.
Collaborative machine learning markets with data-replication-robust payments
Olga Ohrimenko, Shruti Tople, and Sebastian Tschiatschek · 2019
Cited alongside, same era.
A multilinear sampling algorithm to estimate shapley values
Ramin Okhrati and Aldo Lipani · 2021
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Data debugging with shapley importance over end-to-end machine learning pipelines
Bojan Karlaš, David Dao, Matteo Interlandi, Bo Li, Sebastian Schelter, Wentao Wu, and Ce Zhang · 2022
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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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Measuring the effect of training data on deep learning predictions via randomized experiments
Jinkun Lin, Anqi Zhang, Mathias Lécuyer, Jinyang Li, Aurojit Panda, and Siddhartha Sen · 2022
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Sampling permutations for shapley value estimation
Rory Mitchell, Joshua Cooper, Eibe Frank, and Geoffrey Holmes · 2022
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A distributional framework for data valuation
Amirata Ghorbani, Michael Kim, and James Zou · 2020
Cited alongside, same era.
A principled approach to data valuation for federated learning
Tianhao Wang, Johannes Rausch, Ce Zhang, Ruoxi Jia, and Dawn Song · 2020
Cited alongside, same era.
Yatao Bian, Yu Rong, Tingyang Xu, Jiaxiang Wu, Andreas Krause, and Junzhou Huang · 2021
Cited alongside, same era.
Approximating the shapley value using stratified empirical bernstein sampling
Mark Alexander Burgess and Archie C Chapman · 2021
Cited alongside, same era.
Efficient task-specific data valuation for nearest neighbor algorithms
Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nezihe Merve Gurel, Bo Li, Ce Zhang, Costas Spanos, and Dawn Song
Cited in the paper.
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
Cited in the paper.
Tianhao Wang and Ruoxi Jia · 2022
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Davinz: Data valuation using deep neural networks at initialization
Zhaoxuan Wu, Yao Shu, and Bryan Kian Hsiang Low · 2022
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Approximating the shapley value without marginal contributions
Patrick Kolpaczki, Viktor Bengs, and Eyke Hüllermeier · 2023
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A note on" towards efficient data valuation based on the shapley value”
Jiachen T Wang and Ruoxi Jia · 2023
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