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Machine unlearning is a crucial tool for enabling a classification model to forget specific data that are used in the training time.
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 · 1901
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Learning multiple layers of features from tiny images
Krizhevsky, A.; Hinton, G.; et al. 2009 · 2009
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Reading digits in natural images with unsupervised feature learning
Netzer, Y.; Wang, T.; Coates, A.; Bissacco, A.; Wu, B.; and Ng, A. Y. 2011 · 2011
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Membership inference attacks against machine learning models
Shokri, R.; Stronati, M.; Song, C.; and Shmatikov, V. 2017 · 2017
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Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
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Large-scale celebfaces attributes (celeba) dataset
Liu, Z.; Luo, P.; Wang, X.; and Tang, X. 2018 · 2018
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Kenton, J. D. M.-W. C.; and Toutanova, L. K. 2019 · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Tan, M.; and Le, Q. 2019 · 2019
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Forgetting outside the box: Scrubbing deep networks of information accessible from input-output observations
Golatkar, A.; Achille, A.; and Soatto, S. 2020b · 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 · 2021
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Extracting training data from large language models
Carlini, N.; Tramer, F.; Wallace, E.; Jagielski, M.; Herbert-Voss, A.; Lee, K.; Roberts, A.; Brown, T.; Song, D.; Erlingsson, U.; et al. 2021 · 2021
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Adaptive machine unlearning
Gupta, V.; Jung, C.; Neel, S.; Roth, A.; Sharifi-Malvajerdi, S.; and Waites, C. 2021 · 2021
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Descent-to-delete: Gradient-based methods for machine unlearning
Neel, S.; Roth, A.; and Sharifi-Malvajerdi, S. 2021 · 2021
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Remember what you want to forget: Algorithms for machine unlearning
Deep unlearning via randomized conditionally independent hessians
Mehta, R.; Pal, S.; Singh, V.; and Ravi, S. N. 2022 · 2022
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Unrolling sgd: Understanding factors influencing machine unlearning
Thudi, A.; Deza, G.; Chandrasekaran, V.; and Papernot, N. 2022 · 2022
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Federated unlearning via class-discriminative pruning
Wang, J.; Guo, S.; Xie, X.; and Qi, H. 2022 · 2022
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Machine unlearning for image retrieval: A generative scrubbing approach
Zhang, P.-F.; Bai, G.; Huang, Z.; and Xu, X.-S. 2022 · 2022
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Towards Unbounded Machine Unlearning
Kurmanji, M.; Triantafillou, P.; and Triantafillou, E. 2023 · 2023
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Sekhari, A.; Acharya, J.; Kamath, G.; and Suresh, A. T. 2021 · 2021
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Machine unlearning via algorithmic stability
Ullah, E.; Mai, T.; Rao, A.; Rossi, R. A.; and Arora, R. 2021 · 2021
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Evaluating inexact unlearning requires revisiting forgetting
Goel, S.; Prabhu, A.; and Kumaraguru, P. 2022 · 2022
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A survey on vision transformer
Han, K.; Wang, Y.; Chen, H.; Chen, X.; Guo, J.; Liu, Z.; Tang, Y.; Xiao, A.; Xu, C.; Xu, Y.; et al. 2022 · 2022
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Can bad teaching induce forgetting? Unlearning in deep networks using an incompetent teacher
Chundawat, V. S.; Tarun, A. K.; Mandal, M.; and Kankanhalli, M. 2023a
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Zero-shot machine unlearning
Chundawat, V. S.; Tarun, A. K.; Mandal, M.; and Kankanhalli, M. 2023b
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Eternal sunshine of the spotless net: Selective forgetting in deep networks
Golatkar, A.; Achille, A.; and Soatto, S. 2020a
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Liu, X.; Zheng, Y.; Du, Z.; Ding, M.; Qian, Y.; Yang, Z.; and Tang, J. 2023 · 2023
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Fast yet effective machine unlearning
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Llama: Open and efficient foundation language models
Touvron, H.; Lavril, T.; Izacard, G.; Martinet, X.; Lachaux, M.-A.; Lacroix, T.; Rozière, B.; Goyal, N.; Hambro, E.; Azhar, F.; et al. 2023 · 2023
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Bag of Tricks for Training Data Extraction from Language Models
Yu, W.; Pang, T.; Liu, Q.; Du, C.; Kang, B.; Huang, Y.; Lin, M.; and Yan, S. 2023 · 2023
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