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Machine Unlearning is an emerging field that addresses data privacy issues by enabling the removal of private or irrelevant data from the Machine Learning process.
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2019
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2020
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2020
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2020
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2022
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2022
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K. Vasilevski, “Meta-learning for clinical and imaging data fusion for improved deep learning inference,” 2023
2023
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2023
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2023
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Z. Guan, Y. Li, Z. Pan, Y. Liu, and Z. Xue, “Rfdg: Reinforcement federated domain generalization,”
2023
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H. Miao, X. Zhong, J. Liu, Y. Zhao, X. Zhao, W. Qian, K. Zheng, and C. S. Jensen, “Task assignment with efficient federated preference learning in spatial crowdsourcing,”
2023
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P. D. (NYPD), “Nypd complaint data current (year to date): Nyc open data,”
2023
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X. Zhu, G. Li, and W. Hu, “Heterogeneous federated knowledge graph embedding learning and unlearning,” in
2023
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V. S. Chundawat, A. K. Tarun, M. Mandal, and M. Kankanhalli, “Zero-shot machine unlearning,”
2023
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2023
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