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Machine unlearning is the process of efficiently removing the influence of a training data instance from a trained machine learning model without retraining it from scratch.
The hungarian method for the assignment problem
Harold W Kuhn · 1955
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On some techniques useful for solution of transportation network problems
Nobuaki Tomizawa · 1971
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Efficient algorithms for finding maximum matching in graphs
Zvi Galil · 1986
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The representation of permutations by trees
P Bhattacharya · 1994
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7.5 coupon collecting i, 7.6 coupon collecting ii, and 15.4 coupon collecting iii
Gunnar Blom, Lars Holst, and Dennis Sandell · 1994
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Poisoning attacks against support vector machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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Euler’s constant: Euler’s work and modern developments
Jeffrey Lagarias · 2013
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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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Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang · 2015
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Tiny imagenet visual recognition challenge
Ya Le and Xuan Yang · 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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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Certified defenses for data poisoning attacks
Jacob Steinhardt, Pang Wei W Koh, and Percy S Liang · 2017
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Think you have solved question answering? try arc, the ai2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord · 2018
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Can a suit of armor conduct electricity? a new dataset for open book question answering
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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The seven sins of { \{ Personal-Data } \} processing systems under { \{ GDPR } \}
Supreeth Shastri, Melissa Wasserman, and Vijay Chidambaram · 2019
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Superglue: A stickier benchmark for general-purpose language understanding systems
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman · 2019
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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al · 2019
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Hellaswag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi · 2019
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Piqa: Reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Jianfeng Gao, Yejin Choi, et al · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Unlearn what you want to forget: Efficient unlearning for llms
Jiaao Chen and Diyi Yang · 2023
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Safe: Machine unlearning with shard graphs
Yonatan Dukler, Benjamin Bowman, Alessandro Achille, Aditya Golatkar, Ashwin Swaminathan, and Stefano Soatto · 2023
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Who’s harry potter? approximate unlearning in llms
Ronen Eldan and Mark Russinovich · 2023
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A framework for few-shot language model evaluation, 12 2023
Leo Gao, Jonathan Tow, Baber Abbasi, Stella Biderman, Sid Black, Anthony DiPofi, Charles Foster, Laurence Golding, Jeffrey Hsu, Alain Le Noac’h, Haonan Li, Kyle McDonell, Niklas Muennighoff, Chris Ociepa, Jason Phang, Laria Reynolds, Hailey Schoelkopf, Aviya Skowron, Lintang Sutawika, Eric Tang, Anish Thite, Ben Wang, Kevin Wang, and Andy Zou · 2023
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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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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2020
Cited alongside, same era.
Secure and robust machine learning for healthcare: A survey
Adnan Qayyum, Junaid Qadir, Muhammad Bilal, and Ala Al-Fuqaha · 2020
Cited alongside, same era.
Coded machine unlearning
Nasser Aldaghri, Hessam Mahdavifar, and Ahmad Beirami · 2021
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
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Amnesiac machine learning
Laura Graves, Vineel Nagisetty, and Vijay Ganesh · 2021
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Adaptive machine unlearning
Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites · 2021
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Training data protection with compositional diffusion models
Aditya Golatkar, Alessandro Achille, Ashwin Swaminathan, and Stefano Soatto · 2023
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Model sparsification can simplify machine unlearning
Jinghan Jia, Jiancheng Liu, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, and Sijia Liu · 2023
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Privacy adhering machine un-learning in nlp
Vinayshekhar Bannihatti Kumar, Rashmi Gangadharaiah, and Dan Roth · 2023
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Can sensitive information be deleted from llms? objectives for defending against extraction attacks
Vaidehi Patil, Peter Hase, and Mohit Bansal · 2023
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Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto · 2023
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Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Machine unlearning: A survey
Heng Xu, Tianqing Zhu, Lefeng Zhang, Wanlei Zhou, and Philip S. Yu · 2023
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Ai model disgorgement: Methods and choices
Alessandro Achille, Michael Kearns, Carson Klingenberg, and Stefano Soatto · 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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Towards unbounded machine unlearning
Meghdad Kurmanji, Peter Triantafillou, Jamie Hayes, and Eleni Triantafillou · 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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Direct unlearning optimization for robust and safe text-to-image models
Yong-Hyun Park, Sangdoo Yun, Jin-Hwa Kim, Junho Kim, Geonhui Jang, Yonghyun Jeong, Junghyo Jo, and Gayoung Lee · 2024
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Has approximate machine unlearning been evaluated properly? from auditing to side effects
Cheng-Long Wang, Qi Li, Zihang Xiang, and Di Wang · 2024
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Machine unlearning: Solutions and challenges
Jie Xu, Zihan Wu, Cong Wang, and Xiaohua Jia · 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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