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Machine unlearning aims to erase the impact of specific training samples upon deleted requests from a trained model.
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
Torralba, A.; Fergus, R.; and Freeman, W. T. 2008 · 1970
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Incremental and Decremental Support Vector Machine Learning
Cauwenberghs, G.; and Poggio, T. 2001 · 2001
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Monte Carlo sampling methods
Shapiro, A. 2003 · 2003
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Multiple Incremental Decremental Learning of Support Vector Machines
Karasuyama, M.; and Takeuchi, I. 2009 · 2009
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The EU Proposal for a General Data Protection Regulation and the roots of the ’right to be forgotten’
Alessandro; and Mantelero. 2013 · 2013
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Towards making systems forget with machine unlearning
Cao, Y.; and Yang, J. 2015 · 2015
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; and Bernstein, M. 2015 · 2015
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Identity mappings in deep residual networks
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Approximate Data Deletion from Machine Learning Models
Izzo, Z.; Smart, M. A.; Chaudhuri, K.; and Zou, J. 2021 · 2016
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Expert gate: Lifelong learning with a network of experts
Aljundi, R.; Chakravarty, P.; and Tuytelaars, T. 2017 · 2017
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Membership inference attacks against machine learning models
Shokri, R.; Stronati, M.; Song, C.; and Shmatikov, V. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
Cited alongside, same era.
Privacy risk in machine learning: Analyzing the connection to overfitting
Yeom, S.; Giacomelli, I.; Fredrikson, M.; and Jha, S. 2018 · 2018
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The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks
Carlini, N.; Liu, C.; Erlingsson, U.; Kos, J.; and Song, D. 2019 · 2019
Cited alongside, same era.
Making AI Forget You: Data Deletion in Machine Learning
Ginart, A.; Guan, M.; Valiant, G.; and Zou, J. Y. 2019 · 2019
Cited alongside, same era.
Do better imagenet models transfer better?
Kornblith, S.; Shlens, J.; and Le, Q. V. 2019 · 2019
Machine unlearning
Bourtoule, L.; Chandrasekaran, V.; Choquette-Choo, C. A.; Jia, H.; Travers, A.; Zhang, B.; Lie, D.; and Papernot, N. 2021 · 2021
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Extracting Training Data from Large Language Models
Carlini, N.; Tramèr, F.; Wallace, E.; Jagielski, M.; Herbert-Voss, A.; Lee, K.; Roberts, A.; Brown, T.; Song, D.; Erlingsson, Ú.; Oprea, A.; and Raffel, C. 2021 · 2021
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Chen, M.; Zhang, Z.; Wang, T.; Backes, M.; Humbert, M.; and Zhang, Y. 2021 · 2021
Later among the works it cites.
Adaptive machine unlearning
Gupta, V.; Jung, C.; Neel, S.; Roth, A.; Sharifi-Malvajerdi, S.; and Waites, C. 2021 · 2021
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Certifiable machine unlearning for linear models
Mahadevan, A.; and Mathioudakis, M. 2021 · 2021
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Cited alongside, same era.
Demystifying membership inference attacks in machine learning as a service
Truex, S.; Liu, L.; Gursoy, M. E.; Yu, L.; and Wei, W. 2019 · 2019
Cited alongside, same era.
Certified Data Removal from Machine Learning Models
Guo, C.; Goldstein, T.; Hannun, A.; and Van Der Maaten, L. 2020 · 2020
Cited alongside, same era.
”Amnesia” - Machine Learning Models That Can Forget User Data Very Fast
Schelter, S. 2020 · 2020
Cited alongside, same era.
Analyzing Information Leakage of Updates to Natural Language Models
Zanella-Béguelin, S.; Wutschitz, L.; Tople, S.; Rühle, V.; Paverd, A.; Ohrimenko, O.; Köpf, B.; and Brockschmidt, M. 2020 · 2020
Cited alongside, same era.
Eternal sunshine of the spotless net: Selective forgetting in deep networks
Golatkar, A.; Achille, A.; and Soatto, S. 2020a
Cited in the paper.
Forgetting Outside the Box: Scrubbing Deep Networks of Information Accessible from Input-Output Observations
Golatkar, A.; Achille, A.; and Soatto, S. 2020b
Cited in the paper.
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning
Neel, S.; Roth, A.; and Sharifi-Malvajerdi, S. 2021 · 2021
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K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
Wang, R.; Tang, D.; Duan, N.; Wei, Z.; and Zhou, M. 2021 · 2021
Later among the works it cites.
Membership Inference Attacks Against Recommender Systems
Zhang, M.; Ren, Z.; Wang, Z.; Ren, P.; Chen, Z.; Hu, P.; and Zhang, Y. 2021 · 2021
Later among the works it cites.
Recommendation Unlearning
Chen, C.; Sun, F.; Zhang, M.; and Ding, B. 2022 · 2022
Closest in time.
Chundawat, V. S.; Tarun, A. K.; Mandal, M.; and Kankanhalli, M. 2022 · 2022
Closest in time.