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Data valuation aims to quantify the usefulness of individual data sources in training machine learning (ML) models, and is a critical aspect of data-centric ML research.
A value for n-person games
Lloyd S Shapley · 1953
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Survey of techniques for fixed radius near neighbor searching
Jon L Bentley · 1975
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Value theory without efficiency
Pradeep Dubey, Abraham Neyman, and Robert James Weber · 1981
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Protocols for secure computations
Andrew C Yao · 1982
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The mnist database of handwritten digits
Yann LeCun · 1998
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Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression
Pierangela Samarati and Latanya Sweeney · 1998
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A distribution-free theory of nonparametric regression
László Györfi, Michael Köhler, Adam Krzyżak, and Harro Walk · 2002
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Revealing information while preserving privacy
Irit Dinur and Kobbi Nissim · 2003
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Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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How to break anonymity of the netflix prize dataset
Arvind Narayanan and Vitaly Shmatikov · 2006
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Dbpedia: A nucleus for a web of open data
Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives · 2007
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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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Boosting and differential privacy
Cynthia Dwork, Guy N Rothblum, and Salil Vadhan · 2010
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Mechanism design in large games: Incentives and privacy
Michael Kearns, Mallesh Pai, Aaron Roth, and Jonathan Ullman · 2014
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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 learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Concentrated differential privacy
Cynthia Dwork and Guy N. Rothblum · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Exposed! a survey of attacks on private data
Cynthia Dwork, Adam Smith, Thomas Steinke, and Jonathan Ullman · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Understanding the sparse vector technique for differential privacy
Min Lyu, Dong Su, and Ninghui Li · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Privacy amplification by subsampling: Tight analyses via couplings and divergences
Borja Balle, Gilles Barthe, and Marco Gaboardi · 2018
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Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov · 2019
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Data shapley: Equitable valuation of data for machine learning
Amirata Ghorbani and James Zou · 2019
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Estimation of the shapley value by ergodic sampling
Ferenc Illés and Péter Kerényi · 2019
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Efficient task-specific data valuation for nearest neighbor algorithms
Chapter 1: K nearest neighbors (supervised machine learning algorithm), 2021
Sabita Rajbanshi · 2021
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Online class-incremental continual learning with adversarial shapley value
Dongsub Shim, Zheda Mai, Jihwan Jeong, Scott Sanner, Hyunwoo Kim, and Jongseong Jang · 2021
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N24news: A new dataset for multimodal news classification
Zhen Wang, Xu Shan, Xiangxie Zhang, and Jie Yang · 2021
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Membership inference attacks from first principles
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramer · 2022
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Optimal and differentially private data acquisition: Central and local mechanisms
Alireza Fallah, Ali Makhdoumi, Azarakhsh Malekian, and Asuman Ozdaglar · 2022
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Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nezihe Merve Gurel, Bo Li, Ce Zhang, Costas J Spanos, and Dawn Song · 2019
Cited alongside, same era.
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.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 2019
Cited alongside, same era.
Warner & hawley introduce bill to force social media companies to disclose how they are monetizing user data, 2019
Mark Warner · 2019
Cited alongside, same era.
A programming framework for opendp
Marco Gaboardi, Michael Hay, and Salil Vadhan · 2020
Cited alongside, same era.
A distributional framework for data valuation
Amirata Ghorbani, Michael Kim, and James Zou · 2020
Cited alongside, same era.
Computing tight differential privacy guarantees using fft
Antti Koskela, Joonas Jälkö, and Antti Honkela · 2020
Cited alongside, same era.
Data shapley valuation for efficient batch active learning
Amirata Ghorbani, James Zou, and Andre Esteva · 2022
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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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Feature inference attack on shapley values
Xinjian Luo, Yangfan Jiang, and Xiaokui Xiao · 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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Private data valuation and fair payment in data marketplaces
Zhihua Tian, Jian Liu, Jingyu Li, Xinle Cao, Ruoxi Jia, and Kui Ren · 2022
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Differentially private shapley values for data evaluation
Lauren Watson, Rayna Andreeva, Hao-Tsung Yang, and Rik Sarkar · 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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Improving cooperative game theory-based data valuation via data utility learning
Tianhao Wang, Yu Yang, and Ruoxi Jia · 2022
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Optimal data acquisition with privacy-aware agents
Rachel Cummings, Hadi Elzayn, Emmanouil Pountourakis, Vasilis Gkatzelis, and Juba Ziani · 2023
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The fair value of data under heterogeneous privacy constraints
Justin Kang, Ramtin Pedarsani, and Kannan Ramchandran · 2023
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Data-oob: Out-of-bag estimate as a simple and efficient data value
Yongchan Kwon and James Zou · 2023
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Planning for agi and beyond, 2023
OpenAI · 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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A note on" efficient task-specific data valuation for nearest neighbor algorithms"
Jiachen T Wang and Ruoxi Jia · 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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Private prediction strikes back! Private kernelized nearest neighbors with individual Rényi filter
Yuqing Zhu, Xuandong Zhao, Chuan Guo, and Yu-Xiang Wang · 2023
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