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Machine unlearning algorithms, designed for selective removal of training data from models, have emerged as a promising approach to growing privacy concerns.
The perceptron: a probabilistic model for information storage and organization in the brain
Frank Rosenblatt · 1958
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Incremental and decremental support vector machine learning
Gert Cauwenberghs and Tomaso Poggio · 2000
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Incremental and decremental proximal support vector classification using decay coefficients
Amund Tveit, Magnus Lie Hetland, and Håavard Engum · 2003
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Incremental and decremental learning for linear support vector machines
Enrique Romero, Ignacio Barrio, and Lluís Belanche · 2007
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky et al · 2009
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Multiple incremental decremental learning of support vector machines
Masayuki Karasuyama and Ichiro Takeuchi · 2010
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Intriguing properties of neural networks
C Szegedy · 2013
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Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang · 2015
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Optimal rates for zero-order convex optimization: The power of two function evaluations
John C Duchi, Michael I Jordan, Martin J Wainwright, and Andre Wibisono · 2015
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Regulation (EU) 2016/679 of the European Parliament and of the Council
European Parliament and Council of the European Union · 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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Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2017
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Random gradient-free minimization of convex functions
Yurii Nesterov and Vladimir Spokoiny · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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Adversarial examples in the physical world
Alexey Kurakin, Ian J Goodfellow, and Samy Bengio · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Mądry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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High-Dimensional Probability: An Introduction with Applications in Data Science
Roman Vershynin · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 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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Formalizing data deletion in the context of the right to be forgotten
Sanjam Garg, Shafi Goldwasser, and Prashant Nalini Vasudevan · 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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Zero-shot machine unlearning
Vikram S Chundawat, Ayush K Tarun, Murari Mandal, and Mohan Kankanhalli · 2023
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Control, confidentiality, and the right to be forgotten
Aloni Cohen, Adam Smith, Marika Swanberg, and Prashant Nalini Vasudevan · 2023
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Who’s Harry Potter? Approximate Unlearning in LLMs
Ronen Eldan and Mark Russinovich · 2023
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Foundation models and fair use
Peter Henderson, Xuechen Li, Dan Jurafsky, Tatsunori Hashimoto, Mark A Lemley, and Percy Liang · 2023
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Editing models with task arithmetic
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi · 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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Gaoyang Liu, Xiaoqiang Ma, Yang Yang, Chen Wang, and Jiangchuan Liu · 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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Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al · 2021
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Amnesiac machine learning
Laura Graves, Vineel Nagisetty, and Vijay Ganesh · 2021
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In NeurIPS , 2021
Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites · 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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Neurips 2023 - machine unlearning
Eleni Triantafillou, Fabian Pedregosa, Jamie Hayes, Peter Kairouz, Isabelle Guyon, Meghdad Kurmanji, Gintare Karolina Dziugaite, Peter Triantafillou, Kairan Zhao, Lisheng Sun Hosoya, Julio C. S. Jacques Junior, Vincent Dumoulin, Ioannis Mitliagkas, Sergio Escalera, Jun Wan, Sohier Dane, Maggie Demkin, and Walter Reade · 2023
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Depn: Detecting and editing privacy neurons in pretrained language models
Xinwei Wu, Junzhuo Li, Minghui Xu, Weilong Dong, Shuangzhi Wu, Chao Bian, and Deyi Xiong · 2023
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How to compute Hessian-vector products?, 2024
Mathieu Dagréou, Pierre Ablin, Samuel Vaiter, and Thomas Moreau · 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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Fantastic copyrighted beasts and how (not) to generate them
Luxi He, Yangsibo Huang, Weijia Shi, Tinghao Xie, Haotian Liu, Yue Wang, Luke Zettlemoyer, Chiyuan Zhang, Danqi Chen, and Peter Henderson · 2024
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Talkin’ ’bout AI generation: Copyright and the generative-AI supply chain, 2024
Katherine Lee, A. Feder Cooper, and James Grimmelmann · 2024
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Imagehash: A perceptual image hashing library
Benjamin Little · 2024
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An adversarial perspective on machine unlearning for AI safety
Jakub Łucki, Boyi Wei, Yangsibo Huang, Peter Henderson, Florian Tramèr, and Javier Rando · 2024
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TOFU: a task of fictitious unlearning for LLMs
Pratyush Maini, Zhili Feng, Avi Schwarzschild, Zachary Chase Lipton, and J. Zico Kolter · 2024
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Muse: Machine unlearning six-way evaluation for language models
Weijia Shi, Jaechan Lee, Yangsibo Huang, Sadhika Malladi, Jieyu Zhao, Ari Holtzman, Daogao Liu, Luke Zettlemoyer, Noah A Smith, and Chiyuan Zhang · 2024
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Ununlearning: Unlearning is not sufficient for content regulation in advanced generative AI
Ilia Shumailov, Jamie Hayes, Eleni Triantafillou, Guillermo Ortiz-Jimenez, Nicolas Papernot, Matthew Jagielski, Itay Yona, Heidi Howard, and Eugene Bagdasaryan · 2024
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Evaluating copyright takedown methods for language models
Boyi Wei, Weijia Shi, Yangsibo Huang, Noah A Smith, Chiyuan Zhang, Luke Zettlemoyer, Kai Li, and Peter Henderson · 2024
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Negative preference optimization: From catastrophic collapse to effective unlearning
Ruiqi Zhang, Licong Lin, Yu Bai, and Song Mei · 2024
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