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Machine unlearning updates machine learning models to remove information from specific training samples, complying with data protection regulations that allow individuals to request the removal of their personal data.
The mnist database of handwritten digits
Yann LeCun · 1998
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Differential privacy
Cynthia Dwork · 2006
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Introduction to modern cryptography: principles and protocols
Jonathan Katz and Yehuda Lindell · 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 eu proposal for a general data protection regulation and the roots of the ‘right to be forgotten’
Alessandro Mantelero · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts · 2013
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Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang · 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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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An overview of gradient descent optimization algorithms
Sebastian Ruder · 2016
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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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California consumer privacy act (ccpa), 2018
CCPA · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Eternal sunshine of the spotless net: Selective forgetting in deep networks
A. Golatkar, A. Achille, and S. Soatto · 2020
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Amnesiac machine learning
Laura Graves, Vineel Nagisetty, and Vijay Ganesh · 2020
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Certified data removal from machine learning models
Chuan Guo, Tom Goldstein, Awni Hannun, and Laurens Van Der Maaten · 2020
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Towards probabilistic verification of machine unlearning
David Marco Sommer, Liwei Song, Sameer Wagh, and Prateek Mittal · 2020
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Deltagrad: Rapid retraining of machine learning models
Yinjun Wu, Edgar Dobriban, and Susan Davidson · 2020
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Machine unlearning
Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 2021
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When machine unlearning jeopardizes privacy
Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, and Yang Zhang · 2021
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Mixed-privacy forgetting in deep networks
Aditya Golatkar, Alessandro Achille, Avinash Ravichandran, Marzia Polito, and Stefano Soatto · 2021
Graph unlearning
Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, and Yang Zhang · 2022
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Scalable membership inference attacks via quantile regression
Martin Andres Bertran, Shuai Tang, Aaron Roth, Michael Kearns, Jamie Heather Morgenstern, and Steven Wu · 2023
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Efficient model updates for approximate unlearning of graph-structured data
Eli Chien, Chao Pan, and Olgica Milenkovic · 2023
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Forget unlearning: Towards true data-deletion in machine learning
Rishav Chourasia and Neil Shah · 2023
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Re-aligning shadow models can improve white-box membership inference attacks
Ana-Maria Cretu, Daniel Jones, Yves-Alexandre de Montjoye, and Shruti Tople · 2023
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Towards adversarial evaluations for inexact machine unlearning, 2023
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Deepobliviate: a powerful charm for erasing data residual memory in deep neural networks
Yingzhe He, Guozhu Meng, Kai Chen, Jinwen He, and Xingbo Hu · 2021
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Approximate data deletion from machine learning models
Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, and James Zou · 2021
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Descent-to-delete: Gradient-based methods for machine unlearning
Seth Neel, Aaron Roth, and Saeed Sharifi-Malvajerdi · 2021
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Ssse: Efficiently erasing samples from trained machine learning models, 2021
Alexandra Peste, Dan Alistarh, and Christoph H. Lampert · 2021
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Remember what you want to forget: Algorithms for machine unlearning
Ayush Sekhari, Jayadev Acharya, Gautam Kamath, and Ananda Theertha Suresh · 2021
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Systematic evaluation of privacy risks of machine learning models
Liwei Song and Prateek Mittal · 2021
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Shashwat Goel, Ameya Prabhu, Amartya Sanyal, Ser-Nam Lim, Philip Torr, and Ponnurangam Kumaraguru · 2023
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Towards unbounded machine unlearning
Meghdad Kurmanji, Peter Triantafillou, Jamie Hayes, and Eleni Triantafillou · 2023
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Machine unlearning: A survey, 2023
Heng Xu, Tianqing Zhu, Lefeng Zhang, Wanlei Zhou, and Philip S. Yu · 2023
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Salun: Empowering machine unlearning via gradient-based weight saliency in both image classification and generation
Chongyu Fan, Jiancheng Liu, Yihua Zhang, Eric Wong, Dennis Wei, and Sijia Liu · 2024
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Fast machine unlearning without retraining through selective synaptic dampening
Jack Foster, Stefan Schoepf, and Alexandra Brintrup · 2024
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Inexact unlearning needs more careful evaluations to avoid a false sense of privacy
Jamie Hayes, Ilia Shumailov, Eleni Triantafillou, Amr Khalifa, and Nicolas Papernot · 2024
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Learn to unlearn: Insights into machine unlearning
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Evaluation for the NeurIPS Machine Unlearning Competition, 2023
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Brennon Brimhall, Philip Mathew, Neil Fendley, Yinzhi Cao, and Matthew Green · 2025
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