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As the right to be forgotten has been legislated worldwide, many studies attempt to design unlearning mechanisms to protect users' privacy when they want to leave machine learning service platforms.
Decision trees and transient stability of electric power systems
Louis Wehenkel and Mania Pavella · 1991
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Exact calculation of the hessian matrix for the multilayer perceptron, 1992
Chris Bishop · 1992
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Representations of quasi-newton matrices and their use in limited memory methods
Richard H Byrd, Jorge Nocedal, and Robert B Schnabel · 1994
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Efficient noise-tolerant learning from statistical queries
Michael Kearns · 1998
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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A new benchmark collection for text categorization research
YYRTG Glewis, D David, and F Li · 2004
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Guidelines for performing systematic literature reviews in software engineering
Staffs Keele et al · 2007
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Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
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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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Kullback-leibler divergence
James M Joyce · 2011
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Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
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The german traffic sign recognition benchmark: a multi-class classification competition
Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel · 2011
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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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Analytic perturbation theory and its applications
Konstantin E Avrachenkov, Jerzy A Filar, and Phil G Howlett · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing
Matthew Fredrikson, Eric Lantz, Somesh Jha, Simon M. Lin, David Page, and Thomas Ristenpart · 2014
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Searching for exotic particles in high-energy physics with deep learning
Pierre Baldi, Peter Sadowski, and Daniel Whiteson · 2014
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Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang · 2015
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Scalable person re-identification: A benchmark
Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun · 2015
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Image-based recommendations on styles and substitutes
Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel · 2015
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Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
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Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William Cohen, and Ruslan Salakhudinov · 2016
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Federated learning of deep networks using model averaging
H Brendan McMahan, Eider Moore, Daniel Ramage, and Blaise Agüera y Arcas · 2016
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
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Efficient processing of deep neural networks: A tutorial and survey
Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, and Joel S Emer · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 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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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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Variational inference: A review for statisticians
David M Blei, Alp Kucukelbir, and Jon D McAuliffe · 2017
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Machine learning for internet of things data analysis: A survey
Mohammad Saeid Mahdavinejad, Mohammadreza Rezvan, Mohammadamin Barekatain, Peyman Adibi, Payam Barnaghi, and Amit P Sheth · 2018
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A guide to the california consumer privacy act of 2018
Lydia de la Torre · 2018
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Recent advances in convolutional neural networks
Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, Gang Wang, Jianfei Cai, et al · 2018
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Efficient repair of polluted machine learning systems via causal unlearning
Yinzhi Cao, Alexander Fangxiao Yu, Andrew Aday, Eric Stahl, Jon Merwine, and Junfeng Yang · 2018
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Pitfalls of graph neural network evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann · 2018
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Experience replay for continual learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne · 2019
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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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Making machine learning forget
Saurabh Shintre, Kevin A Roundy, and Jasjeet Dhaliwal · 2019
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Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y Zhao · 2019
Earlier work this paper cites.
Lifelong anomaly detection through unlearning
Min Du, Zhi Chen, Chang Liu, Rajvardhan Oak, and Dawn Song · 2019
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Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen · 2019
Cited alongside, same era.
Variational bayesian unlearning
Quoc Phong Nguyen, Bryan Kian Hsiang Low, and Patrick Jaillet · 2020
Cited alongside, same era.
Formalizing data deletion in the context of the right to be forgotten
Sanjam Garg, Shafi Goldwasser, and Prashant Nalini Vasudevan · 2020
Cited alongside, same era.
Deltagrad: Rapid retraining of machine learning models
Yinjun Wu, Edgar Dobriban, and Susan Davidson · 2020
Cited alongside, same era.
Certified data removal from machine learning models
Chuan Guo, Tom Goldstein, Awni Y. Hannun, and Laurens van der Maaten · 2020
Cited alongside, same era.
Analyzing information leakage of updates to natural language models
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Victor Rühle, Andrew Paverd, Olga Ohrimenko, Boris Köpf, and Marc Brockschmidt · 2020
Label-only membership inference attacks on machine unlearning without dependence of posteriors
Zhaobo Lu, Hai Liang, Minghao Zhao, Qingzhe Lv, Tiancai Liang, and Yilei Wang · 2022
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Deletion inference, reconstruction, and compliance in machine (un)learning
Ji Gao, Sanjam Garg, Mohammad Mahmoody, and Prashant Nalini Vasudevan · 2022
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Machine unlearning: Linear filtration for logit-based classifiers
Thomas Baumhauer, Pascal Schöttle, and Matthias Zeppelzauer · 2022
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Hard to forget: Poisoning attacks on certified machine unlearning
Neil G Marchant, Benjamin IP Rubinstein, and Scott Alfeld · 2022
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Hidden poison: Machine unlearning enables camouflaged poisoning attacks
Jimmy Z Di, Jack Douglas, Jayadev Acharya, Gautam Kamath, and Ayush Sekhari · 2022
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Backdoor defense with machine unlearning
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Cited alongside, same era.
Forgetting outside the box: Scrubbing deep networks of information accessible from input-output observations
Aditya Golatkar, Alessandro Achille, and Stefano Soatto · 2020
Cited alongside, same era.
Updates-leak: Data set inference and reconstruction attacks in online learning
Ahmed Salem, Apratim Bhattacharya, Michael Backes, Mario Fritz, and Yang Zhang · 2020
Cited alongside, same era.
On second-order group influence functions for black-box predictions
Samyadeep Basu, Xuchen You, and Soheil Feizi · 2020
Cited alongside, same era.
Have you forgotten? a method to assess if machine learning models have forgotten data
Xiao Liu and Sotirios A Tsaftaris · 2020
Cited alongside, same era.
Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
Cited alongside, same era.
Machine unlearning
Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 2021
Cited alongside, same era.
Yang Liu, Mingyuan Fan, Cen Chen, Ximeng Liu, Zhuo Ma, Li Wang, and Jianfeng Ma · 2022
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Learning with recoverable forgetting
Jingwen Ye, Yifang Fu, Jie Song, Xingyi Yang, Songhua Liu, Xin Jin, Mingli Song, and Xinchao Wang · 2022
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Unlearning protected user attributes in recommendations with adversarial training
Christian Ganhör, David Penz, Navid Rekabsaz, Oleg Lesota, and Markus Schedl · 2022
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An introduction to machine unlearning
Salvatore Mercuri, Raad Khraishi, Ramin Okhrati, Devesh Batra, Conor Hamill, Taha Ghasempour, and Andrew Nowlan · 2022
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Prescriptive process monitoring: Quo vadis?
Kateryna Kubrak, Fredrik Milani, Alexander Nolte, and Marlon Dumas · 2022
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Can bad teaching induce forgetting? unlearning in deep networks using an incompetent teacher
Vikram S Chundawat, Ayush K Tarun, Murari Mandal, and Mohan Kankanhalli · 2022
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Evaluating machine unlearning via epistemic uncertainty
Alexander Becker and Thomas Liebig · 2022
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Certifiable unlearning pipelines for logistic regression: An experimental study
Ananth Mahadevan and Michael Mathioudakis · 2022
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Forget me now: Fast and exact unlearning in neighborhood-based recommendation
Sebastian Schelter, Mozhdeh Ariannezhad, and Maarten de Rijke · 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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Machine unlearning of features and labels
Alexander Warnecke, Lukas Pirch, Christian Wressnegger, and Konrad Rieck · 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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Zero-shot machine unlearning
Vikram S Chundawat, Ayush K Tarun, Murari Mandal, and Mohan Kankanhalli · 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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Verifying in the dark: Verifiable machine unlearning by using invisible backdoor triggers
Yu Guo, Yu Zhao, Saihui Hou, Cong Wang, and Xiaohua Jia · 2023
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Gif: A general graph unlearning strategy via influence function
Jiancan Wu, Yi Yang, Yuchun Qian, Yongduo Sui, Xiang Wang, and Xiangnan He · 2023
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Bfu: Bayesian federated unlearning with parameter self-sharing
Weiqi Wang, Zhiyi Tian, Chenhan Zhang, An Liu, and Shui Yu · 2023
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Conditional matching gan guided reconstruction attack in machine unlearning
Kaiyue Zhang, Weiqi Wang, Zipei Fan, Xuan Song, and Shui Yu · 2023
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Shared adversarial unlearning: Backdoor mitigation by unlearning shared adversarial examples
Shaokui Wei, Mingda Zhang, Hongyuan Zha, and Baoyuan Wu · 2023
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Anwar Said, Tyler Derr, Mudassir Shabbir, Waseem Abbas, and Xenofon Koutsoukos · 2023
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Eraser: Machine unlearning in mlaas via an inference serving-aware approach
Yuke Hu, Jian Lou, Jiaqi Liu, Feng Lin, Zhan Qin, and Kui Ren · 2024
Closest in time.
Machine unlearning of features and labels
Alexander Warnecke, Lukas Pirch, Christian Wressnegger, and Konrad Rieck · 2024
Closest in time.
Learning to unlearn: Instance-wise unlearning for pre-trained classifiers
Sungmin Cha, Sungjun Cho, Dasol Hwang, Honglak Lee, Taesup Moon, and Moontae Lee · 2024
Closest in time.
Sifu: Sequential informed federated unlearning for efficient and provable client unlearning in federated optimization
Yann Fraboni, Martin Van Waerebeke, Kevin Scaman, Richard Vidal, Laetitia Kameni, and Marco Lorenzi · 2024
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Score forgetting distillation: A swift, data-free method for machine unlearning in diffusion models
Tianqi Chen, Shujian Zhang, and Mingyuan Zhou · 2024
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Boosting alignment for post-unlearning text-to-image generative models
Myeongseob Ko, Henry Li, Zhun Wang, Jonathan Patsenker, Jiachen Tianhao Wang, Qinbin Li, Ming Jin, Dawn Song, and Ruoxi Jia · 2024
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Rwku: Benchmarking real-world knowledge unlearning for large language models
Zhuoran Jin, Pengfei Cao, Chenhao Wang, Zhitao He, Hongbang Yuan, Jiachun Li, Yubo Chen, Kang Liu, and Jun Zhao · 2024
Closest in time.
Learn what you want to unlearn: Unlearning inversion attacks against machine unlearning
H. Hu, S. Wang, T. Dong, and M. Xue · 2024
Closest in time.
Static and sequential malicious attacks in the context of selective forgetting
Chenxu Zhao, Wei Qian, Rex Ying, and Mengdi Huai · 2024
Closest in time.
A duty to forget, a right to be assured? exposing vulnerabilities in machine unlearning services
Hongsheng Hu, Shuo Wang, Jiamin Chang, Haonan Zhong, Ruoxi Sun, Shuang Hao, Haojin Zhu, and Minhui Xue · 2024
Closest in time.
Data unlearning in diffusion models
Silas Alberti, Kenan Hasanaliyev, Manav Shah, and Stefano Ermon · 2025
Closest in time.
Saeuron: Interpretable concept unlearning in diffusion models with sparse autoencoders
Bartosz Cywiński and Kamil Deja · 2025
Closest in time.
The illusion of unlearning: The unstable nature of machine unlearning in text-to-image diffusion models
Naveen George, Karthik Nandan Dasaraju, Rutheesh Reddy Chittepu, and Konda Reddy Mopuri · 2025
Closest in time.
Tape: Tailored posterior difference for auditing of machine unlearning
Weiqi Wang, Zhiyi Tian, An Liu, and Shui Yu · 2025
Closest in time.
Truvrf: Towards triple-granularity verification on machine unlearning
Chunyi Zhou, Yansong Gao, Anmin Fu, Kai Chen, Zhi Zhang, Minhui Xue, Zhiyang Dai, Shouling Ji, and Yuqing Zhang · 2025
Closest in time.
Blindu: Blind machine unlearning without revealing erasing data
Weiqi Wang, Zhiyi Tian, Chenhan Zhang, and Shui Yu · 2026
Closest in time.