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Machine unlearning, the study of efficiently removing the impact of specific training instances on a model, has garnered increased attention in recent years due to regulatory guidelines such as the \emph{Right to be Forgotten}.
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Language models are few-shot learners
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Data minimization for gdpr compliance in machine learning models
Goldsteen, A., Ezov, G., Shmelkin, R., Moffie, M., and Farkash, A · 2021
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Amnesiac machine learning
Graves, L., Nagisetty, V., and Ganesh, V · 2021
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Approximate data deletion from machine learning models
Izzo, Z., Anne Smart, M., Chaudhuri, K., and Zou, J · 2021
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Remember what you want to forget: Algorithms for machine unlearning
Sekhari, A., Acharya, J., Kamath, G., and Suresh, A. T · 2021
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Longnet: Scaling transformers to 1,000,000,000 tokens
Ding, J., Ma, S., Dong, L., Zhang, X., Huang, S., Wang, W., Zheng, N., and Wei, F · 2023
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Dong, Q., Li, L., Dai, D., Zheng, C., Wu, Z., Chang, B., Sun, X., Xu, J., and Sui, Z · 2023
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Fast machine unlearning without retraining through selective synaptic dampening, 2023
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Rethinking influence functions of neural networks in the over-parameterized regime
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Membership inference attacks from first principles
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What can transformers learn in-context? a case study of simple function classes
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Towards adversarial evaluations for inexact machine unlearning
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Measuring forgetting of memorized training examples
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Gaussian membership inference privacy
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Scalable extraction of training data from (production) language models
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Larger language models do in-context learning differently
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Model sparsity can simplify machine unlearning, 2024
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