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Machine unlearning aims to eliminate the influence of a subset of training samples (i.e., unlearning samples) from a trained model.
Properties of cross-entropy minimization
J. Shore and R. Johnson · 1981
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An Empirical Investigation of Catastrophic Forgeting in Gradient-Based Neural Networks
I. Goodfellow, Mehdi Mirza, Xia Da, Aaron C. Courville, and Yoshua Bengio · 2013
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Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang · 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 closer look at memorization in deep networks
Devansh Arpit, Stanislaw Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, and Simon Lacoste-Julien · 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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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Eternal sunshine of the spotless net: Selective forgetting in deep networks
Aditya Golatkar, Alessandro Achille, and Stefano Soatto · 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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Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 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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Dissecting supervised contrastive learning
Florian Graf, Christoph Hofer, Marc Niethammer, and Roland Kwitt · 2021
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Adaptive machine unlearning
Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites · 2021
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Deep unlearning via randomized conditionally independent hessians
Ronak Mehta, Sourav Pal, Vikas Singh, and Sathya N Ravi · 2022
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On the necessity of auditable algorithmic definitions for machine unlearning
Anvith Thudi, Hengrui Jia, Ilia Shumailov, and Nicolas Papernot · 2022
Model sparsity can simplify machine unlearning
Jinghan Jia, Jiancheng Liu, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, and Sijia Liu · 2023
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No matter how you slice it: Machine unlearning with SISA comes at the expense of minority classes
Korbinian Koch and Marcus Soll · 2023
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Towards unbounded machine unlearning, 2023
Meghdad Kurmanji, Peter Triantafillou, Jamie Hayes, and Eleni Triantafillou · 2023
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Erm-ktp: Knowledge-level machine unlearning via knowledge transfer
Shen Lin, Xiaoyu Zhang, Chenyang Chen, Xiaofeng Chen, and Willy Susilo · 2023
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Fast yet effective machine unlearning
Ayush K. Tarun, Vikram S. Chundawat, Murari Mandal, and Mohan Kankanhalli · 2023
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Certified edge unlearning for graph neural networks
Kun Wu, Jie Shen, Yue Ning, Ting Wang, and Wendy Hui Wang · 2023
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Arcane: An efficient architecture for exact machine unlearning
Haonan Yan, Xiaoguang Li, Ziyao Guo, Hui Li, Fenghua Li, and Xiaodong Lin · 2022
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Boundary unlearning: Rapid forgetting of deep networks via shifting the decision boundary
Min Chen, Weizhuo Gao, Gaoyang Liu, Kai Peng, and Chen Wang · 2023
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DUCK: Distance-based Unlearning via Centroid Kinematics, December 2023
Marco Cotogni, Jacopo Bonato, Luigi Sabetta, Francesco Pelosin, and Alessandro Nicolosi · 2023
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On the separability of classes with the cross-entropy loss function, 2024
Rudrajit Das and Subhasis Chaudhuri · 2024
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The EU proposal for a general data protection regulation and the roots of the ‘right to be forgotten’
Alessandro Mantelero · 2024
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Descent-to-delete: Gradient-based methods for machine unlearning
Seth Neel, Aaron Roth, and Saeed Sharifi-Malvajerdi · 2024
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