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Machine \emph{unlearning}, which involves erasing knowledge about a \emph{forget set} from a trained model, can prove to be costly and infeasible by existing techniques.
Incremental and decremental support vector machine learning
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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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Imagenet large scale visual recognition challenge
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Let machines unlearn - machine unlearning and the right to be forgotten
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Membership inference attacks against machine learning models
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Humans forget, machines remember: Artificial intelligence and the right to be forgotten
Eduard Fosch Villaronga, Peter Kieseberg, and Tiffany Li · 2017
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VGGFace2: A dataset for recognising faces across pose and age
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
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The california consumer privacy act: Towards a european-style privacy regime in the united states
Stuart L Pardau · 2018
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Making ai forget you: Data deletion in machine learning
Antonio Ginart, Melody Guan, Gregory Valiant, and James Y Zou · 2019
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Certified data removal from machine learning models
Chuan Guo, Tom Goldstein, Awni Hannun, and Laurens Van Der Maaten · 2019
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Right to be forgotten in light of regulation (eu) 2016/679 of the european parliament and of the council of 27 april 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46/ec
Małgorzata Magdziarczyk · 2019
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Generating diverse high-fidelity images with vq-vae-2
Ali Razavi, Aaron Van den Oord, and Oriol Vinyals · 2019
Learning transferable visual models from natural language supervision
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Machine unlearning of features and labels
Alexander Warnecke, Lukas Pirch, Christian Wressnegger, and Konrad Rieck · 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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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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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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An image is worth 16x16 words: Transformers for image recognition at scale
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Forgetting outside the box: Scrubbing deep networks of information accessible from input-output observations
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Right to be forgotten in the age of machine learning
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Taming transformers for high-resolution image synthesis
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Approximate data deletion from machine learning models
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Zero-shot machine unlearning
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Model sparsification can simplify machine unlearning
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