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Growing concerns over data privacy and security highlight the importance of machine unlearning--removing specific data influences from trained models without full retraining.
Sur les équations algébriques ayant toutes leurs racines réelles
Tiberiu Popoviciu · 1935
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Statistical theory of extreme values and some practical applications: a series of lectures
Emil Julius Gumbel · 1954
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Perplexity—a measure of the difficulty of speech recognition tasks
F. Jelinek, R. L. Mercer, L. R. Bahl, and J. K. Baker · 1977
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang · 2015
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Deep Learning
Ian J. Goodfellow, Yoshua Bengio, and Aaron C. Courville · 2016
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 2016
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A closer look at memorization in deep networks
Devansh Arpit, Stanislaw Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron C. Courville, Yoshua Bengio, and Simon Lacoste-Julien · 2017
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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, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 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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CINIC-10 is not imagenet or CIFAR-10
Luke Nicholas Darlow, Elliot J. Crowley, Antreas Antoniou, and Amos J. Storkey · 2018
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Does learning require memorization? a short tale about a long tail
Vitaly Feldman · 2020
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Eternal sunshine of the spotless net: Selective forgetting in deep networks
Aditya Golatkar, Alessandro Achille, and Stefano Soatto · 2020
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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 · 2020
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Certified data removal from machine learning models
Chuan Guo, Tom Goldstein, Awni Y. Hannun, and Laurens van der Maaten · 2020
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Have you forgotten? A method to assess if machine learning models have forgotten data
Xiao Liu and Sotirios A. Tsaftaris · 2020
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Influence functions in deep learning are fragile
Samyadeep Basu, Phillip Pope, and Soheil Feizi · 2021
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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 Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B. Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, and Colin Raffel · 2021
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When machine unlearning jeopardizes privacy
Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, and Yang Zhang · 2021
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A mathematical framework for transformer circuits, 2021
Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Chris Olah · 2021
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Mixed-privacy forgetting in deep networks
Aditya Golatkar, Alessandro Achille, Avinash Ravichandran, Marzia Polito, and Stefano Soatto · 2021
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Amnesiac machine learning
Laura Graves, Vineel Nagisetty, and Vijay Ganesh · 2021
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Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites · 2021
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EMA: auditing data removal from trained models
Yangsibo Huang, Xiaoxiao Li, and Kai Li · 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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Proof-of-learning: Definitions and practice
Hengrui Jia, Mohammad Yaghini, Christopher A. Choquette-Choo, Natalie Dullerud, Anvith Thudi, Varun Chandrasekaran, and Nicolas Papernot · 2021
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Fast yet effective machine unlearning
Ayush K. Tarun, Vikram S. Chundawat, Murari Mandal, and Mohan S. Kankanhalli · 2021
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Machine unlearning via algorithmic stability
Enayat Ullah, Tung Mai, Anup Rao, Ryan A. Rossi, and Raman Arora · 2021
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What does it mean for a language model to preserve privacy?
Hannah Brown, Katherine Lee, Fatemehsadat Mireshghallah, Reza Shokri, and Florian Tramèr · 2022
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Membership inference attacks from first principles
Croissantllm: A truly bilingual french-english language model
Manuel Faysse, Patrick Fernandes, Nuno Miguel Guerreiro, António Loison, Duarte M. Alves, Caio F. Corro, Nicolas Boizard, João Alves, Ricardo Rei, Pedro Henrique Martins, Antoni Bigata Casademunt, François Yvon, André F. T. Martins, Gautier Viaud, Céline Hudelot, and Pierre Colombo · 2024
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Fast machine unlearning without retraining through selective synaptic dampening
Jack Foster, Stefan Schoepf, and Alexandra Brintrup · 2024
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Pleak: Prompt leaking attacks against large language model applications
Bo Hui, Haolin Yuan, Neil Gong, Philippe Burlina, and Yinzhi Cao · 2024
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Latesteval: Addressing data contamination in language model evaluation through dynamic and time-sensitive test construction
Yucheng Li, Frank Guerin, and Chenghua Lin · 2024
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Paloma: A benchmark for evaluating language model fit
Ian Magnusson, Akshita Bhagia, Valentin Hofmann, Luca Soldaini, Ananya Harsh Jha, Oyvind Tafjord, Dustin Schwenk, Evan Pete Walsh, Yanai Elazar, Kyle Lo, Dirk Groeneveld, Iz Beltagy, Hanna Hajishirzi, Noah A. Smith, Kyle Richardson, and Jesse Dodge · 2024
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Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramèr · 2022
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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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Unrolling SGD: understanding factors influencing machine unlearning
Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, and Nicolas Papernot · 2022
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On the necessity of auditable algorithmic definitions for machine unlearning
Anvith Thudi, Hengrui Jia, Ilia Shumailov, and Nicolas Papernot · 2022
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Enhanced membership inference attacks against machine learning models
Jiayuan Ye, Aadyaa Maddi, Sasi Kumar Murakonda, Vincent Bindschaedler, and Reza Shokri · 2022
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Quantifying memorization across neural language models
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramèr, and Chiyuan Zhang · 2023
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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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In-context unlearning: Language models as few-shot unlearners
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Detecting pretraining data from large language models
Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, and Luke Zettlemoyer · 2024
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Communication efficient and provable federated unlearning
Youming Tao, Cheng-Long Wang, Miao Pan, Dongxiao Yu, Xiuzhen Cheng, and Di Wang · 2024
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Guardrail baselines for unlearning in llms
Pratiksha Thaker, Yash Maurya, and Virginia Smith · 2024
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Google fined eur250 million in france over dispute with news publishers, 2024
The Wall Street Journal · 2024
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Proof of unlearning: Definitions and instantiation
Jia-Si Weng, Shenglong Yao, Yuefeng Du, Junjie Huang, Jian Weng, and Cong Wang · 2024
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Revisiting differentially private hyper-parameter tuning
Zihang Xiang, Tianhao Wang, Cheng-Long Wang, and Di Wang · 2024
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Preserving node-level privacy in graph neural networks
Zihang Xiang, Tianhao Wang, and Di Wang · 2024
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Large language model unlearning
Yuanshun Yao, Xiaojun Xu, and Yang Liu · 2024
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Low-cost high-power membership inference attacks
Sajjad Zarifzadeh, Philippe Liu, and Reza Shokri · 2024
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Adaptive pre-training data detection for large language models via surprising tokens
Anqi Zhang and Chaofeng Wu · 2024
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Fedmua: Exploring the vulnerabilities of federated learning to malicious unlearning attacks
Jian Chen, Zehui Lin, Wanyu Lin, Wenlong Shi, Xiaoyan Yin, and Di Wang · 2025
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Shaopeng Fu, Liang Ding, and Di Wang · 2025
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Rethinking machine unlearning for large language models
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Sok: Membership inference attacks on llms are rushing nowhere (and how to fix it)
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Safety alignment should be made more than just a few tokens deep
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MUSE: machine unlearning six-way evaluation for language models
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Privacy audit as bits transmission:(im) possibilities for audit by one run
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Min-k%++: Improved baseline for pre-training data detection from large language models
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