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Privacy concerns associated with machine learning models have driven research into machine unlearning, which aims to erase the memory of specific target training data from already trained models.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998) · 1998
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Finding frequent items in data streams
Charikar, M., Chen, K., and Farach-Colton, M. (2002) · 2002
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G. (2009) · 2009
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Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y. (2010) · 2010
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Principal component analysis
Bro, R. and Smilde, A. K. (2014) · 2014
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J. (2015) · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X. (2015) · 2015
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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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The biology of forgetting—a perspective
Davis, R. L. and Zhong, Y. (2017) · 2017
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Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A. (2017) · 2017
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Memory aware synapses: Learning what (not) to forget
Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., and Tuytelaars, T. (2018) · 2018
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General data protection regulation
European Union (2018) · 2018
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Progress & compress: A scalable framework for continual learning
Schwarz, J., Czarnecki, W., Luketina, J., Grabska-Barwinska, A., Teh, Y. W., Pascanu, R., and Hadsell, R. (2018) · 2018
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California consumer privacy act (ccpa)
State of California Department of Justice (2018) · 2018
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Memory replay gans: Learning to generate new categories without forgetting
Wu, C., Herranz, L., Liu, X., van de Weijer, J., Raducanu, B., et al. (2018) · 2018
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Making ai forget you: Data deletion in machine learning
Ginart, T., Guan, M., Valiant, G., and Zou, J. (2019) · 2019
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Badnets: Evaluating backdooring attacks on deep neural networks
Gu, T., Liu, K., Dolan-Gavitt, B., and Garg, S. (2019) · 2019
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Overcoming catastrophic forgetting for continual learning via model adaptation
Hu, W., Lin, Z., Liu, B., Tao, C., Tao, Z., Ma, J., Zhao, D., and Yan, R. (2019) · 2019
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Learn to grow: A continual structure learning framework for overcoming catastrophic forgetting
Li, X., Zhou, Y., Wu, T., Socher, R., and Xiong, C. (2019) · 2019
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Sliced cramer synaptic consolidation for preserving deeply learned representations
Kolouri, S., Ketz, N. A., Soltoggio, A., and Pilly, P. K. (2020) · 2020
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New insights and perspectives on the natural gradient method
Martens, J. (2020) · 2020
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Active forgetting: Adaptation of memory by prefrontal control
Anderson, M. C. and Hulbert, J. C. (2021) · 2021
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Influence functions in deep learning are fragile
Basu, S., Pope, P., and Feizi, S. (2021) · 2021
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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
Federated unlearning: How to efficiently erase a client in fl?
Halimi, A. and et al. (2022) · 2022
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Membership inference via backdooring
Hu, H. and et al. (2022) · 2022
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The right to be forgotten in federated learning: An efficient realization with rapid retraining
Liu, Y., Xu, L., Yuan, X., Wang, C., and Li, B. (2022) · 2022
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Deep unlearning via randomized conditionally independent hessians
Mehta, R., Pal, S., Singh, V., and Ravi, S. N. (2022) · 2022
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A survey of machine unlearning
Nguyen, T. T., Huynh, T. T., Nguyen, P. L., Liew, A. W.-C., Yin, H., and Nguyen, Q. V. H. (2022) · 2022
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Overcoming catastrophic forgetting by bayesian generative regularization
Chen, P.-H., Wei, W., Hsieh, C.-J., and Dai, B. (2021) · 2021
Cited alongside, same era.
Explaining classification performance and bias via network structure and sampling technique
Espín-Noboa, L., Karimi, F., Ribeiro, B., Lerman, K., and Wagner, C. (2021) · 2021
Cited alongside, same era.
Amnesiac machine learning
Graves, L., Nagisetty, V., and Ganesh, V. (2021) · 2021
Cited alongside, same era.
Fastif: Scalable influence functions for efficient model interpretation and debugging
Guo, H., Rajani, N., Hase, P., Bansal, M., and Xiong, C. (2021) · 2021
Cited alongside, same era.
Lifelong learning with sketched structural regularization
Li, H., Krishnan, A., Wu, J., Kolouri, S., Pilly, P. K., and Braverman, V. (2021) · 2021
Cited alongside, same era.
Federaser: Enabling efficient client-level data removal from federated learning models
Liu, G. and et al. (2021) · 2021
Cited alongside, same era.
Forgetting as a form of adaptive engram cell plasticity
Ryan, T. J. and Frankland, P. W. (2022) · 2022
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Athena: Probabilistic verification of machine unlearning
Sommer, D. M. and et al. (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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Arcane: An efficient architecture for exact machine unlearning
Yan, H., Li, X., Guo, Z., Li, H., Li, F., and Lin, X. (2022) · 2022
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California senate bill 362
California Legislative Information (2023) · 2023
Closest in time.
Fast federated machine unlearning with nonlinear functional theory
Che, T., Zhou, Y., Zhang, Z., Lyu, L., Liu, J., Yan, D., Dou, D., and Huan, J. (2023) · 2023
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A survey on federated unlearning: Challenges, methods, and future directions
Liu, Z., Jiang, Y., Shen, J., Peng, M., Lam, K.-Y., and Yuan, X. (2023) · 2023
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Bfu: Bayesian federated unlearning with parameter self-sharing
Wang, W., Tian, Z., Zhang, C., Liu, A., and Yu, S. (2023) · 2023
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Fedme 2: Memory evaluation & erase promoting federated unlearning in dtmn
Xia, H., Xu, S., Pei, J., Zhang, R., Yu, Z., Zou, W., Wang, L., and Liu, C. (2023) · 2023
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Federated unlearning for on-device recommendation
Yuan, W. and et al. (2023) · 2023
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Heterogeneous federated knowledge graph embedding learning and unlearning
Zhu, X., Li, G., and Hu, W. (2023) · 2023
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Making recommender systems forget: Learning and unlearning for erasable recommendation
Li, Y., Chen, C., Zheng, X., Liu, J., and Wang, J. (2024) · 2024
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