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The high cost of model training makes it increasingly desirable to develop techniques for unlearning.
Membership inference attacks from first principles
N. Carlini, S. Chien, M. Nasr, S. Song, A. Terzis, and F. Tramer · 1914
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Ix. on the problem of the most efficient tests of statistical hypotheses
J. Neyman and E. S. Pearson · 1933
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Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
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Resolving individuals contributing trace amounts of dna to highly complex mixtures using high-density snp genotyping microarrays
N. Homer, S. Szelinger, M. Redman, D. Duggan, W. Tembe, J. Muehling, J. V. Pearson, D. A. Stephan, S. F. Nelson, and D. W. Craig · 2008
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Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
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On evaluating adversarial robustness, 2019
N. Carlini, A. Athalye, N. Papernot, W. Brendel, J. Rauber, D. Tsipras, I. Goodfellow, A. Madry, and A. Kurakin · 2019
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Making ai forget you: Data deletion in machine learning
A. Ginart, M. Guan, G. Valiant, and J. Y. Zou · 2019
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Decoupled weight decay regularization, 2019
I. Loshchilov and F. Hutter · 2019
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Forgetting outside the box: Scrubbing deep networks of information accessible from input-output observations
A. Golatkar, A. Achille, and S. Soatto · 2020
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Online forgetting process for linear regression models
Y. Li, C.-H. Wang, and G. Cheng · 2020
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Deltagrad: Rapid retraining of machine learning models
Y. Wu, E. Dobriban, and S. Davidson · 2020
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Coded machine unlearning
N. Aldaghri, H. Mahdavifar, and A. Beirami · 2021
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Machine unlearning
L. Bourtoule, V. Chandrasekaran, C. A. Choquette-Choo, H. Jia, A. Travers, B. Zhang, D. Lie, and N. Papernot · 2021
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Mixed-privacy forgetting in deep networks
A. Golatkar, A. Achille, A. Ravichandran, M. Polito, and S. Soatto · 2021
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Amnesiac machine learning
L. Graves, V. Nagisetty, and V. Ganesh · 2021
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Adaptive machine unlearning
V. Gupta, C. Jung, S. Neel, A. Roth, S. Sharifi-Malvajerdi, and C. Waites · 2021
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Approximate data deletion from machine learning models
Z. Izzo, M. A. Smart, K. Chaudhuri, and J. Zou · 2021
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Descent-to-delete: Gradient-based methods for machine unlearning
S. Neel, A. Roth, and S. Sharifi-Malvajerdi · 2021
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Remember what you want to forget: Algorithms for machine unlearning
A. Sekhari, J. Acharya, G. Kamath, and A. T. Suresh · 2021
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Unrolling SGD: Understanding factors influencing machine unlearning
A. Thudi, G. Deza, V. Chandrasekaran, and N. Papernot · 2022
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Arcane: An efficient architecture for exact machine unlearning
H. Yan, X. Li, Z. Guo, H. Li, F. Li, and X. Lin · 2022
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Enhanced membership inference attacks against machine learning models
J. Ye, A. Maddi, S. K. Murakonda, V. Bindschaedler, and R. Shokri · 2022
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Palm 2 technical report, 2023
R. A. Google and, A. M. Dai, O. Firat, M. Johnson, D. Lepikhin, A. Passos, S. Shakeri, E. Taropa, P. Bailey, Z. Chen, E. Chu, J. H. Clark, L. E. Shafey, Y. Huang, K. Meier-Hellstern, G. Mishra, E. Moreira, M. Omernick, K. Robinson, S. Ruder, Y. Tay, K. Xiao, Y. Xu, Y. Zhang, G. H. Abrego, J. Ahn, J. Austin, P. Barham, J. Botha, J. Bradbury, S. Brahma, K. Brooks, M. Catasta, Y. Cheng, C. Cherry, C. A. Choquette-Choo, A. Chowdhery, C. Crepy, S. Dave, M. Dehghani, S. Dev, J. Devlin, M. Díaz, N. Du, E. Dyer, V. Feinberg, F. Feng, V. Fienber, M. Freitag, X. Garcia, S. Gehrmann, L. Gonzalez, G. Gur-Ari, S. Hand, H. Hashemi, L. Hou, J. Howland, A. Hu, J. Hui, J. Hurwitz, M. Isard, A. Ittycheriah, M. Jagielski, W. Jia, K. Kenealy, M. Krikun, S. Kudugunta, C. Lan, K. Lee, B. Lee, E. Li, M. Li, W. Li, Y. Li, J. Li, H. Lim, H. Lin, Z. Liu, F. Liu, M. Maggioni, A. Mahendru, J. Maynez, V. Misra, M. Moussalem, Z. Nado, J. Nham, E. Ni, A. Nystrom, A. Parrish, M. Pellat, M. Polacek, A. Polozov, R. Pope, S. Qiao, E. Reif, B. Richter, P. Riley, A. C. Ros, A. Roy, B. Saeta, R. Samuel, R. Shelby, A. Slone, D. Smilkov, D. R. So, D. Sohn, S. Tokumine, D. Valter, V. Vasudevan, K. Vodrahalli, X. Wang, P. Wang, Z. Wang, T. Wang, J. Wieting, Y. Wu, K. Xu, Y. Xu, L. Xue, P. Yin, J. Yu, Q. Zhang, S. Zheng, C. Zheng, W. Zhou, D. Zhou, S. Petrov, and Y. Wu · 2023
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Manipulating sgd with data ordering attacks
I. Shumailov, Z. Shumaylov, D. Kazhdan, Y. Zhao, N. Papernot, M. A. Erdogdu, and R. J. Anderson · 2021
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Reconstructing training data with informed adversaries
B. Balle, G. Cherubin, and J. Hayes · 2022
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The privacy onion effect: Memorization is relative
N. Carlini, M. Jagielski, C. Zhang, N. Papernot, A. Terzis, and F. Tramer · 2022
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Towards adversarial evaluations for inexact machine unlearning
S. Goel, A. Prabhu, A. Sanyal, S.-N. Lim, P. Torr, and P. Kumaraguru · 2022
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Measuring forgetting of memorized training examples
M. Jagielski, O. Thakkar, F. Tramer, D. Ippolito, K. Lee, N. Carlini, E. Wallace, S. Song, A. Thakurta, N. Papernot, et al · 2022
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Knowledge unlearning for mitigating privacy risks in language models
J. Jang, D. Yoon, S. Yang, S. Cha, M. Lee, L. Logeswaran, and M. Seo · 2022
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Model sparsity can simplify machine unlearning
J. Jia, J. Liu, P. Ram, Y. Yao, G. Liu, Y. Liu, P. Sharma, and S. Liu · 2023
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Openai’s ceo says the age of giant ai models is already over, April 2023
W. Knight · 2023
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Deep unlearning: Fast and efficient training-free approach to controlled forgetting
S. Kodge, G. Saha, and K. Roy · 2023
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Towards unbounded machine unlearning
M. Kurmanji, P. Triantafillou, J. Hayes, and E. Triantafillou · 2023
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Scalable extraction of training data from (production) language models
M. Nasr, N. Carlini, J. Hayase, M. Jagielski, A. F. Cooper, D. Ippolito, C. A. Choquette-Choo, E. Wallace, F. Tramèr, and K. Lee · 2023
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In-context unlearning: Language models as few shot unlearners
M. Pawelczyk, S. Neel, and H. Lakkaraju · 2023
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Gradients look alike: Sensitivity is often overestimated in dp-sgd, 2023
A. Thudi, H. Jia, C. Meehan, I. Shumailov, and N. Papernot · 2023
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Neurips 2023 - machine unlearning, 2023
E. Triantafillou, F. Pedregosa, J. Hayes, P. Kairouz, I. Guyon, M. Kurmanji, G. K. Dziugaite, P. Triantafillou, K. Zhao, L. S. Hosoya, J. C. S. J. Junior, V. Dumoulin, I. Mitliagkas, S. Escalera, J. Wan, S. Dane, M. Demkin, and W. Reade · 2023
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Lima: Less is more for alignment
C. Zhou, P. Liu, P. Xu, S. Iyer, J. Sun, Y. Mao, X. Ma, A. Efrat, P. Yu, L. Yu, et al · 2023
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