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There has been a growing interest in Machine Unlearning recently, primarily due to legal requirements such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act.
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Schelter, S., Grafberger, S., Dunning, T.: Hedgecut: Maintaining randomised trees for low-latency machine unlearning. In: Proceedings of the 2021 International Conference on Management of Data. pp. 1545–1557 (2021)
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Bourtoule, L., Chandrasekaran, V., Choquette-Choo, C.A., Jia, H., Travers, A., Zhang, B., Lie, D., Papernot, N.: Machine unlearning. In: 2021 IEEE Symposium on Security and Privacy (SP). pp. 141–159. IEEE (2021)
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Golatkar, A., Achille, A., Ravichandran, A., Polito, M., Soatto, S.: Mixed-privacy forgetting in deep networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 792–801 (2021)
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Neel, S., Roth, A., Sharifi-Malvajerdi, S.: Descent-to-delete: Gradient-based methods for machine unlearning. In: Algorithmic Learning Theory. pp. 931–962. PMLR (2021)
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2022
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2022
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2022
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