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Recently, the enactment of privacy regulations has promoted the rise of the machine unlearning paradigm.
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Making ai forget you: Data deletion in machine learning. In Proceedings of Conference on Neural Information Processing Systems (NeurIPS)
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Lei Geral de Proteção de Dados (LGPD): Guia de implantação
Lara Rocha Garcia, Edson Aguilera-Fernandes, Rafael Augusto Moreno Gonçalves, and Marcos Ribeiro Pereira-Barretto. 2020 · 2020
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Supervised contrastive learning. In Proceedings of Conference on Neural Information Processing Systems (NeurIPS)
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Parallel training of deep networks with local updates
Michael Laskin, Luke Metz, Seth Nabarro, Mark Saroufim, Badreddine Noune, Carlo Luschi, Jascha Sohl-Dickstein, and Pieter Abbeel. 2020 · 2020
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Learning from failure: De-biasing classifier from biased classifier. In Proceedings of Conference on Neural Information Processing Systems (NeurIPS)
Junhyun Nam, Hyuntak Cha, Sungsoo Ahn, Jaeho Lee, and Jinwoo Shin. 2020 · 2020
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What makes for good views for contrastive learning?. In Proceedings of Conference on Neural Information Processing Systems (NeurIPS)
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola. 2020 · 2020
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Towards fairness in visual recognition: Effective strategies for bias mitigation. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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DeltaGrad: Rapid retraining of machine learning models. In Proceedings of International Conference on Machine Learning (ICML)
Yinjun Wu, Edgar Dobriban, and Susan Davidson. 2020 · 2020
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When machine learning meets privacy: A survey and outlook
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Descent-to-delete: Gradient-based methods for machine unlearning. In Proceedings of International Conference on Algorithmic Learning Theory (ALT)
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Fair attribute classification through latent space de-biasing. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Vikram V Ramaswamy, Sunnie SY Kim, and Olga Russakovsky. 2021 · 2021
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Interpreting and Disentangling Feature Components of Various Complexity from DNNs. In Proceedings of International Conference on Machine Learning (ICML)
Jie Ren, Mingjie Li, Zexu Liu, and Quanshi Zhang. 2021 · 2021
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Hedgecut: Maintaining randomised trees for low-latency machine unlearning. In Proceedings of ACM SIGMOD/PODS International Conference on Management of Data
Sebastian Schelter, Stefan Grafberger, and Ted Dunning. 2021 · 2021
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Remember what you want to forget: Algorithms for machine unlearning. In Proceedings of Conference on Neural Information Processing Systems (NeurIPS)
Ayush Sekhari, Jayadev Acharya, Gautam Kamath, and Ananda Theertha Suresh. 2021 · 2021
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Learning with Selective Forgetting. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI)
Takashi Shibata, Go Irie, Daiki Ikami, and Yu Mitsuzumi. 2021 · 2021
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Revisiting Locally Supervised Learning: an Alternative to End-to-end Training. In Proceedings of International Conference on Learning Representations (ICLR)
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Recommendation Unlearning. In Proceedings of ACM Web Conference (WWW)
Chong Chen, Fei Sun, Min Zhang, and Bolin Ding. 2022 · 2022
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The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining. In Proceedings of IEEE International Conference on Computer Communications (INFOCOM)
Yi Liu, Lei Xu, Xingliang Yuan, Cong Wang, and Bo Li. 2022 · 2022
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Anatomizing Bias in Facial Analysis. In Proceedings of AAAI Conference on Artificial Intelligence
Richa Singh, Puspita Majumdar, Surbhi Mittal, and Mayank Vatsa. 2022 · 2022
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Federated Unlearning via Class-Discriminative Pruning. In Proceedings of ACM Web Conference (WWW)
Junxiao Wang, Song Guo, Xin Xie, and Heng Qi. 2022 · 2022
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