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Machine unlearning aims to remove information derived from forgotten data while preserving that of the remaining dataset in a well-trained model.
Convolutional networks for images, speech, and time series
Yann LeCun, Yoshua Bengio, et al · 1995
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The mnist database of handwritten digits
Yann LeCun · 1998
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Evolutionary algorithms for solving multi-objective problems , volume 5 of Genetic algorithms and evolutionary computation
Carlos Artemio Coello Coello, David A. van Veldhuizen, and Gary B. Lamont · 2002
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Variational graph auto-encoders
Thomas N. Kipf and Max Welling · 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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The eu general data protection regulation (gdpr)
Paul Voigt and Axel Von dem Bussche · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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A guide to the california consumer privacy act of 2018
Lydia de la Torre · 2018
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Variational autoencoders for collaborative filtering
Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara · 2018
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Representation learning with contrastive predictive coding
Aäron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton · 2020
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Variational bayesian unlearning
Quoc Phong Nguyen, Bryan Kian Hsiang Low, and Patrick Jaillet · 2020
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From weakly supervised learning to biquality learning, a brief introduction
Pierre Nodet, Vincent Lemaire, Alexis Bondu, Antoine Cornuéjols, and Adam Ouorou · 2020
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Efficient two-stage model retraining for machine unlearning
Junyaup Kim and Simon S. Woo · 2022
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A survey of machine unlearning
Thanh Tam Nguyen, Thanh Trung Huynh, Phi Le Nguyen, Alan Wee-Chung Liew, Hongzhi Yin, and Quoc Viet Hung Nguyen · 2022
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Unrolling SGD: understanding factors influencing machine unlearning
Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, and Nicolas Papernot · 2022
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Machine unlearning for image retrieval: A generative scrubbing approach
Peng-Fei Zhang, Guangdong Bai, Zi Huang, and Xin-Shun Xu · 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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Can bad teaching induce forgetting? unlearning in deep networks using an incompetent teacher
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Forgetting outside the box: Scrubbing deep networks of information accessible from input-output observations
Aditya Golatkar, Alessandro Achille, and Stefano Soatto
Cited in the paper.
Vikram S. Chundawat, Ayush K. Tarun, Murari Mandal, and Mohan S. Kankanhalli · 2023
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Towards unbounded machine unlearning
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Fast yet effective machine unlearning
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Machine unlearning: A survey
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