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Modern machine learning models are expensive to train, and there is a growing concern about the challenge of retroactively removing specific training data.
The eu proposal for a general data protection regulation and the roots of the ‘right to be forgotten’
Mantelero, A · 2013
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Towards making systems forget with machine unlearning
Cao, Y. and Yang, J · 2015
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Adam: A method for stochastic optimization, 2017
Kingma, D. P. and Ba, J · 2017
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Making ai forget you: Data deletion in machine learning
Ginart, A., Guan, M., Valiant, G., and Zou, J. Y · 2019
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Certified data removal from machine learning models
Guo, C., Goldstein, T., Hannun, A., and Van Der Maaten, L · 2019
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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Eternal sunshine of the spotless net: Selective forgetting in deep networks
Golatkar, A., Achille, A., and Soatto, S · 2020
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Machine unlearning
Bourtoule, L., Chandrasekaran, V., Choquette-Choo, C. A., Jia, H., Travers, A., Zhang, B., Lie, D., and Papernot, N · 2021
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Machine unlearning for random forests
Brophy, J. and Lowd, D · 2021
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Amnesiac machine learning
Graves, L., Nagisetty, V., and Ganesh, V · 2021
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Adaptive machine unlearning
Gupta, V., Jung, C., Neel, S., Roth, A., Sharifi-Malvajerdi, S., and Waites, C · 2021
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Manipulating sgd with data ordering attacks
Shumailov, I., Shumaylov, Z., Kazhdan, D., Zhao, Y., Papernot, N., Erdogdu, M. A., and Anderson, R. J · 2021
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An explanation of in-context learning as implicit bayesian inference
Xie, S. M., Raghunathan, A., Liang, P., and Ma, T · 2021
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Differentially private fine-tuning of language models
Yu, D., Naik, S., Backurs, A., Gopi, S., Inan, H. A., Kamath, G., Kulkarni, J., Lee, Y. T., Manoel, A., Wutschitz, L., et al · 2021
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Machine unlearning: Linear filtration for logit-based classifiers
Baumhauer, T., Schöttle, P., and Zeppelzauer, M · 2022
Cited alongside, same era.
Unlocking high-accuracy differentially private image classification through scale
De, S., Berrada, L., Hayes, J., Smith, S. L., and Balle, B · 2022
Cited alongside, same era.
Knowledge unlearning for mitigating privacy risks in language models
Jang, J., Yoon, D., Yang, S., Cha, S., Lee, M., Logeswaran, L., and Seo, M · 2022
Cited alongside, same era.
Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
Are emergent abilities of large language models a mirage?, 2023
Schaeffer, R., Miranda, B., and Koyejo, S · 2023
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models, 2023
Srivastava, A., Rastogi, A., Rao, A., and et al · 2023
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Llama: Open and efficient foundation language models, 2023
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., and Lample, G · 2023
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Large language model unlearning
Yao, Y., Xu, X., and Liu, Y · 2023
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A survey of large language models, 2023
Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., Du, Y., Yang, C., Chen, Y., Chen, Z., Jiang, J., Ren, R., Li, Y., Tang, X., Liu, Z., Liu, P., Nie, J.-Y., and Wen, J.-R · 2023
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Liu, H., Tam, D., Muqeeth, M., Mohta, J., Huang, T., Bansal, M., and Raffel, C. A · 2022
Cited alongside, same era.
Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Lu, Y., Bartolo, M., Moore, A., Riedel, S., and Stenetorp, P · 2022
Cited alongside, same era.
Rethinking the role of demonstrations: What makes in-context learning work?
Min, S., Lyu, X., Holtzman, A., Artetxe, M., Lewis, M., Hajishirzi, H., and Zettlemoyer, L · 2022
Cited alongside, same era.
Unrolling sgd: Understanding factors influencing machine unlearning
Thudi, A., Deza, G., Chandrasekaran, V., and Papernot, N · 2022
Cited alongside, same era.
Automatic chain of thought prompting in large language models
Zhang, Z., Zhang, A., Li, M., and Smola, A · 2022
Cited alongside, same era.
Alpa: Automating inter- and intra-operator parallelism for distributed deep learning, 2022
Zheng, L., Li, Z., Zhang, H., Zhuang, Y., Chen, Z., Huang, Y., Wang, Y., Xu, Y., Zhuo, D., Xing, E. P., Gonzalez, J. E., and Stoica, I · 2022
Cited alongside, same era.
Unlearn what you want to forget: Efficient unlearning for llms
Chen, J. and Yang, D · 2023
Cited alongside, same era.
Flops profiler python package, 2023
Li, C · 2023
Cited alongside, same era.
Large language models are human-level prompt engineers, 2023
Zhou, Y., Muresanu, A. I., Han, Z., Paster, K., Pitis, S., Chan, H., and Ba, J · 2023
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Does your data spark joy? performance gains from domain upsampling at the end of training
Blakeney, C., Paul, M., Larsen, B. W., Owen, S., and Frankle, J · 2024
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Cooper, A. F., Choquette-Choo, C. A., Bogen, M., Jagielski, M., Filippova, K., Liu, K. Z., Chouldechova, A., Hayes, J., Huang, Y., Mireshghallah, N., et al · 2024
Closest in time.
Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A., et al · 2024
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Gradients look alike: Sensitivity is often overestimated in { \{ DP-SGD } \}
Thudi, A., Jia, H., Meehan, C., Shumailov, I., and Papernot, N · 2024
Closest in time.
Are we making progress in unlearning? findings from the first neurips unlearning competition
Triantafillou, E., Kairouz, P., Pedregosa, F., Hayes, J., Kurmanji, M., Zhao, K., Dumoulin, V., Junior, J. J., Mitliagkas, I., Wan, J., et al · 2024
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Towards scalable exact machine unlearning using parameter-efficient fine-tuning, 2025
Chowdhury, S. B. R., Choromanski, K., Sehanobish, A., Dubey, A., and Chaturvedi, S · 2025
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Inexact unlearning needs more careful evaluations to avoid a false sense of privacy
Hayes, J., Shumailov, I., Triantafillou, E., Khalifa, A., and Papernot, N · 2025
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