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Traditionally, data valuation (DV) is posed as a problem of equitably splitting the validation performance of a learning algorithm among the training data.
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 J Spanos, and Dawn Song · 1908
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On the translocation of masses
Leonid V Kantorovich · 1942
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On information and sufficiency
Solomon Kullback and Richard A Leibler · 1951
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Nonlinear programming
Dimitri P Bertsekas · 1997
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Introduction to linear optimization , volume 6
Dimitris Bertsimas and John N Tsitsiklis · 1997
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Hierarchical clustering via joint between-within distances: Extending ward’s minimum variance method
Gabor J Szekely, Maria L Rizzo, et al · 2005
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Improving academic performance prediction by dealing with class imbalance
Nguyen Thai-Nghe, Andre Busche, and Lars Schmidt-Thieme · 2009
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Optimal transport: old and new , volume 338
Cédric Villani · 2009
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
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Joint distribution optimal transportation for domain adaptation
Nicolas Courty, Rémi Flamary, Amaury Habrard, and Alain Rakotomamonjy · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Trojaning attack on neural networks
Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, Juan Zhai, Weihang Wang, and Xiangyu Zhang · 2017
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Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation
Bharath Bhushan Damodaran, Benjamin Kellenberger, Rémi Flamary, Devis Tuia, and Nicolas Courty · 2018
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Learning generative models with sinkhorn divergences
Aude Genevay, Gabriel Peyré, and Marco Cuturi · 2018
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
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Poison frogs! targeted clean-label poisoning attacks on neural networks
Ali Shafahi, W Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein · 2018
Cited alongside, same era.
Wasserstein distance guided representation learning for domain adaptation
Jian Shen, Yanru Qu, Weinan Zhang, and Yong Yu · 2018
Cited alongside, same era.
Interpolating between optimal transport and mmd using sinkhorn divergences
Jean Feydy, Thibault Séjourné, François-Xavier Vialard, Shun-ichi Amari, Alain Trouvé, and Gabriel Peyré · 2019
Cited alongside, same era.
Data shapley: Equitable valuation of data for machine learning
Data valuation using reinforcement learning
Jinsung Yoon, Sercan Arik, and Tomas Pfister · 2020
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Ota: Optimal transport assignment for object detection
Zheng Ge, Songtao Liu, Zeming Li, Osamu Yoshie, and Jian Sun · 2021
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Unlearnable examples: Making personal data unexploitable
Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani, James Bailey, and Yisen Wang · 2021
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Beta shapley: a unified and noise-reduced data valuation framework for machine learning
Yongchan Kwon and James Zou · 2021
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Pervasive label errors in test sets destabilize machine learning benchmarks
Curtis G Northcutt, Anish Athalye, and Jonas Mueller · 2021
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Amirata Ghorbani and James Zou · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Cited alongside, same era.
Frontier technology quarterly january 2019: Data economy - radical transformation or dystopia?
Gabe Scelta, Hamid Rashid, Hoi Wai Jackie Cheng, Marcelo LaFleur, Mariangela Parra-Lancourt, Alex Julca, Nicole Hunt, S. Islam, and Hiroshi Kawamura · 2019
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Geometric dataset distances via optimal transport
David Alvarez-Melis and Nicolo Fusi · 2020
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Array programming with NumPy
Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. van Kerkwijk, Matthew Brett, Allan Haldane, Jaime Fernández del Río, Mark Wiebe, Pearu Peterson, Pierre Gérard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, Christoph Gohlke, and Travis E. Oliphant · 2020
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Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis
Davood Karimi, Haoran Dou, Simon K Warfield, and Ali Gholipour · 2020
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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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Entropic optimal transport: Convergence of potentials
Marcel Nutz and Johannes Wiesel · 2021
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Topics in optimal transportation , volume 58
Cédric Villani · 2021
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Validation free and replication robust volume-based data valuation
Xinyi Xu, Zhaoxuan Wu, Chuan Sheng Foo, and Bryan Kian Hsiang Low · 2021
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If you like shapley then you’ll love the core
Tom Yan and Ariel D Procaccia · 2021
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Adversarial unlearning of backdoors via implicit hypergradient
Yi Zeng, Si Chen, Won Park, Zhuoqing Mao, Ming Jin, and Ruoxi Jia · 2021
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Amazon sagemaker data labeling pricing, 2019
Amazon Web Services Inc. AWS · 2022
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Narcissus: A practical clean-label backdoor attack with limited information
Minzhou Pan, Yi Zeng, Hoang Anh Just, Lingjuan Lyu, Meikang Qiu, and Ruoxi Jia · 2022
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Data banzhaf: A data valuation framework with maximal robustness to learning stochasticity
Tianhao Wang and Ruoxi Jia · 2022
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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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