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Machine Unlearning aims to remove undesired information from trained models without full retraining from scratch.
Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky · 2009
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
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Essentially no barriers in neural network energy landscape
Felix Draxler, Kambis Veschgini, Manfred Salmhofer, and Fred Hamprecht · 2018
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Loss surfaces, mode connectivity, and fast ensembling of dnns
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P Vetrov, and Andrew G Wilson · 2018
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A corpus for reasoning about natural language grounded in photographs
Alane Suhr, Stephanie Zhou, Ally Zhang, Iris Zhang, Huajun Bai, and Yoav Artzi · 2019
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Linear mode connectivity and the lottery ticket hypothesis
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, and Michael Carbin · 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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DeltaGrad: Rapid retraining of machine learning models
Yinjun Wu, Edgar Dobriban, and Susan Davidson · 2020
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Bridging mode connectivity in loss landscapes and adversarial robustness
Pu Zhao, Pin-Yu Chen, Payel Das, Karthikeyan Natesan Ramamurthy, and Xue Lin · 2020
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Loss surface simplexes for mode connecting volumes and fast ensembling
Gregory Benton, Wesley Maddox, Sanae Lotfi, and Andrew Gordon Gordon Wilson · 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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Sharpness-aware minimization for efficiently improving generalization
Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur · 2021
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Amnesiac machine learning
Laura Graves, Vineel Nagisetty, and Vijay Ganesh · 2021
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 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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Time-aware language models as temporal knowledge bases
Bhuwan Dhingra, Jeremy R. Cole, Julian Martin Eisenschlos, Daniel Gillick, Jacob Eisenstein, and William W. Cohen · 2022
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Exploring mode connectivity for pre-trained language models
Yujia Qin, Cheng Qian, Jing Yi, Weize Chen, Yankai Lin, Xu Han, Zhiyuan Liu, Maosong Sun, and Jie Zhou · 2022
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Adversarial unlearning: Reducing confidence along adversarial directions
Amrith Setlur, Benjamin Eysenbach, Virginia Smith, and Sergey Levine · 2022
Cited alongside, same era.
GNNDelete: A general strategy for unlearning in graph neural networks
Jiali Cheng, George Dasoulas, Huan He, Chirag Agarwal, and Marinka Zitnik · 2023
Cited alongside, same era.
Can bad teaching induce forgetting? unlearning in deep networks using an incompetent teacher
Vikram S Chundawat, Ayush K Tarun, Murari Mandal, and Mohan Kankanhalli · 2023
Cited alongside, same era.
Who’s harry potter? approximate unlearning in llms
Ronen Eldan and Mark Russinovich · 2023
Cited alongside, same era.
Model sparsity can simplify machine unlearning
Jinghan Jia, Jiancheng Liu, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, and Sijia Liu · 2023
Cited alongside, same era.
SOUL: Unlocking the power of second-order optimization for LLM unlearning
Jinghan Jia, Yihua Zhang, Yimeng Zhang, Jiancheng Liu, Bharat Runwal, James Diffenderfer, Bhavya Kailkhura, and Sijia Liu · 2024
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The WMDP benchmark: Measuring and reducing malicious use with unlearning
Nathaniel Li, Alexander Pan, Anjali Gopal, Summer Yue, Daniel Berrios, Alice Gatti, Justin D. Li, Ann-Kathrin Dombrowski, Shashwat Goel, Gabriel Mukobi, Nathan Helm-Burger, Rassin Lababidi, Lennart Justen, Andrew Bo Liu, Michael Chen, Isabelle Barrass, Oliver Zhang, Xiaoyuan Zhu, Rishub Tamirisa, Bhrugu Bharathi, Ariel Herbert-Voss, Cort B Breuer, Andy Zou, Mantas Mazeika, Zifan Wang, Palash Oswal, Weiran Lin, Adam Alfred Hunt, Justin Tienken-Harder, Kevin Y. Shih, Kemper Talley, John Guan, Ian Steneker, David Campbell, Brad Jokubaitis, Steven Basart, Stephen Fitz, Ponnurangam Kumaraguru, Kallol Krishna Karmakar, Uday Tupakula, Vijay Varadharajan, Yan Shoshitaishvili, Jimmy Ba, Kevin M. Esvelt, Alexandr Wang, and Dan Hendrycks · 2024
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Exploring neural network landscapes: Star-shaped and geodesic connectivity
Zhanran Lin, Puheng Li, and Lei Wu · 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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Preserving privacy through dememorization: An unlearning technique for mitigating memorization risks in language models
Aly Kassem, Omar Mahmoud, and Sherif Saad · 2023
Cited alongside, same era.
Mechanistic mode connectivity
Ekdeep Singh Lubana, Eric J Bigelow, Robert P. Dick, David Krueger, and Hidenori Tanaka · 2023
Cited alongside, same era.
Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2023
Cited alongside, same era.
Exploring diversified adversarial robustness in neural networks via robust mode connectivity
Ren Wang, Yuxuan Li, and Sijia Liu · 2023
Cited alongside, same era.
Shared adversarial unlearning: Backdoor mitigation by unlearning shared adversarial examples
Shaokui Wei, Mingda Zhang, Hongyuan Zha, and Baoyuan Wu · 2023
Cited alongside, same era.
To each (textual sequence) its own: Improving memorized-data unlearning in large language models
George-Octavian Barbulescu and Peter Triantafillou · 2024
Cited alongside, same era.
Mu-bench: A multitask multimodal benchmark for machine unlearning
Jiali Cheng and Hadi Amiri · 2024
Cited alongside, same era.
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Eight methods to evaluate robust unlearning in llms
Aengus Lynch, Phillip Guo, Aidan Ewart, Stephen Casper, and Dylan Hadfield-Menell · 2024
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Tofu: A task of fictitious unlearning for llms, 2024
Pratyush Maini, Zhili Feng, Avi Schwarzschild, Zachary C. Lipton, and J. Zico Kolter · 2024
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What makes unlearning hard and what to do about it
Kairan Zhao, Meghdad Kurmanji, George-Octavian Bărbulescu, Eleni Triantafillou, and Peter Triantafillou · 2024
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Speech Unlearning
Jiali Cheng and Hadi Amiri · 2025
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Openunlearning: Accelerating llm unlearning via unified benchmarking of methods and metrics
Vineeth Dorna, Anmol Mekala, Wenlong Zhao, Andrew McCallum, Zachary C Lipton, J Zico Kolter, and Pratyush Maini · 2025
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Chongyu Fan, Jinghan Jia, Yihua Zhang, Anil Ramakrishna, Mingyi Hong, and Sijia Liu · 2025
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Tilted sharpness-aware minimization
Tian Li, Tianyi Zhou, and Jeff Bilmes · 2025
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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 · 2025
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Input space mode connectivity in deep neural networks
Jakub Vrabel, Ori Shem-Ur, Yaron Oz, and David Krueger · 2025
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Machine unlearning across tasks and modalities
Jiali Cheng and Hadi Amiri · 2026
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A mechanistic perspective and circuit-guided difficulty metric for unlearning
Jiali Cheng, Ziheng Chen, Chirag Agarwal, and Hadi Amiri · 2026
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Blur: A bi-level optimization approach for llm unlearning
Hadi Reisizadeh, Jinghan Jia, Zhiqi Bu, Bhanukiran Vinzamuri, Anil Ramakrishna, Kai-Wei Chang, Volkan Cevher, Sijia Liu, and Mingyi Hong · 2026
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SemEval-2013 task 9 : Extraction of drug-drug interactions from biomedical texts (DDIExtraction 2013)
Isabel Segura-Bedmar, Paloma Martínez, and María Herrero-Zazo · 2056
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