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Machine unlearning refers to the task of removing a subset of training data, thereby removing its contributions to a trained model.
Transforming neural-net output levels to probability distributions
Denker, J. S. and LeCun, Y · 1990
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A practical bayesian framework for backpropagation networks
MacKay, D. J. C · 1992
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Variational inference in probabilistic models
Lawrence, N. D · 2001
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Weight uncertainty in neural networks
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
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Towards making systems forget with machine unlearning
Cao, Y. and Yang, J · 2015
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Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N. C., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., Hassabis, D., Clopath, C., Kumaran, D., and Hadsell, R · 2016
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The eu general data protection regulation (gdpr)
Voigt, P. and Von dem Bussche, A · 2017
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Variational continual learning
Nguyen, C. V., Li, Y., Bui, T. D., and Turner, R. E · 2018
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A scalable laplace approximation for neural networks
Ritter, H., Botev, A., and Barber, D · 2018
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’in-between’ uncertainty in bayesian neural networks
Foong, A. Y. K., Li, Y., Hernández-Lobato, J. M., and Turner, R. E · 2019
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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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Variational bayesian unlearning
Nguyen, Q. P., Low, B. K. H., and Jaillet, P · 2020
Cited alongside, same era.
Machine unlearning
Bourtoule, L., Chandrasekaran, V., Choquette-Choo, C. A., Jia, H., Travers, A., Zhang, B., Lie, D., and Papernot, N · 2021
Gupta, V., Jung, C., Neel, S., Roth, A., Sharifi-Malvajerdi, S., and Waites, C · 2021
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Scalable marginal likelihood estimation for model selection in deep learning
Immer, A., Bauer, M., Fortuin, V., Rätsch, G., and Khan, M. E · 2021
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Knowledge-adaptation priors
Khan, M. E. and Swaroop, S · 2021
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Unrolling SGD: understanding factors influencing machine unlearning
Thudi, A., Deza, G., Chandrasekaran, V., and Papernot, N · 2021
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Wide mean-field Bayesian neural networks ignore the data
Coker, B., Bruinsma, W. P., Burt, D. R., Pan, W., and Doshi-Velez, F · 2022
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Cited alongside, same era.
Laplace redux - effortless bayesian deep learning
Daxberger, E., Kristiadi, A., Immer, A., Eschenhagen, R., Bauer, M., and Hennig, P · 2021
Cited alongside, same era.
Liu, Y., Fan, M., Chen, C., Liu, X., Ma, Z., Wang, L., and Ma, J · 2022
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Discovering inductive bias with gibbs priors: A diagnostic tool for approximate bayesian inference
Rendsburg, L., Kristiadi, A., Hennig, P., and von Luxburg, U · 2022
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