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Machine Unlearning removes specific knowledge about training data samples from an already trained model.
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (2017)
2017
Earlier work this paper cites.
Bojchevski, A., Günnemann, S.: Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking. In: International Conference on Learning Representations (2018)
2018
Earlier work this paper cites.
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (Jun 2019). https://doi.org/10.18653/v1/N19-1423, https://aclanthology.org/N19-1423
2019
Earlier work this paper cites.
Ginart, A., Guan, M., Valiant, G., Zou, J.Y.: Making ai forget you: Data deletion in machine learning. In: Advances in Neural Information Processing Systems (2019)
2019
Earlier work this paper cites.
Sousa, D., Lamurias, A., Couto, F.M.: A silver standard corpus of human phenotype-gene relations. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). pp. 1487–1492. Association for Computational Linguistics, Minneapolis, Minnesota (Jun 2019). https://doi.org/10.18653/v1/N19-1152, https://aclanthology.org/N19-1152
2019
Earlier work this paper cites.
Suhr, A., Zhou, S., Zhang, A., Zhang, I., Bai, H., Artzi, Y.: A corpus for reasoning about natural language grounded in photographs. In: Korhonen, A., Traum, D., Màrquez, L. (eds.) Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. pp. 6418–6428. Association for Computational Linguistics, Florence, Italy (Jul 2019). https://doi.org/10.18653/v1/P19-1644, https://aclanthology.org/P19-1644
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
Golatkar, A., Achille, A., Soatto, S.: Eternal sunshine of the spotless net: Selective forgetting in deep networks. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020)
2020
Earlier work this paper cites.
Guo, C., Goldstein, T., Hannun, A., Van Der Maaten, L.: Certified data removal from machine learning models. In: Proceedings of the 37th International Conference on Machine Learning. Proceedings of Machine Learning Research, PMLR (2020), https://proceedings.mlr.press/v119/guo20c.html
2020
Earlier work this paper cites.
Nguyen, Q.P., Low, B.K.H., Jaillet, P.: Variational bayesian unlearning. In: Advances in Neural Information Processing Systems (2020)
2020
Earlier work this paper cites.
Wu, Y., Dobriban, E., Davidson, S.: DeltaGrad: Rapid retraining of machine learning models. In: Proceedings of the International Conference on Machine Learning (2020)
2020
Earlier work this paper cites.
Wu, Y., Dobriban, E., Davidson, S.B.: Deltagrad: Rapid retraining of machine learning models. In: International Conference on Machine Learning (2020), https://api.semanticscholar.org/CorpusID:220128049
2020
Earlier work this paper cites.
Bourtoule, L., Chandrasekaran, V., Choquette-Choo, C.A., Jia, H., Travers, A., Zhang, B., Lie, D., Papernot, N.: Machine unlearning. In: IEEE Symposium on Security and Privacy (SP) (2021)
2021
Earlier work this paper cites.
Brophy, J., Lowd, D.: Machine unlearning for random forests. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 139, pp. 1092–1104. PMLR (18–24 Jul 2021), https://proceedings.mlr.press/v139/brophy21a.html
2021
Earlier work this paper cites.
Chen, M., Zhang, Z., Wang, T., Backes, M., Humbert, M., Zhang, Y.: Graph unlearning. Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (2021), https://api.semanticscholar.org/CorpusID:232404451
2021
Earlier work this paper cites.
Gupta, V., Jung, C., Neel, S., Roth, A., Sharifi-Malvajerdi, S., Waites, C.: Adaptive machine unlearning. In: Advances in Neural Information Processing Systems (2021)
2021
Earlier work this paper cites.
Izzo, Z., Smart, M.A., Chaudhuri, K., Zou, J.: Approximate data deletion from machine learning models. In: Proceedings of The International Conference on Artificial Intelligence and Statistics (2021)
2021
Earlier work this paper cites.
Kim, W., Son, B., Kim, I.: Vilt: Vision-and-language transformer without convolution or region supervision. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 139, pp. 5583–5594. PMLR (18–24 Jul 2021), https://proceedings.mlr.press/v139/kim21k.html
2021
Earlier work this paper cites.
Li, J., Selvaraju, R., Gotmare, A., Joty, S., Xiong, C., Hoi, S.C.H.: Align before fuse: Vision and language representation learning with momentum distillation. In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems. vol. 34, pp. 9694–9705. Curran Associates, Inc. (2021), https://proceedings.neurips.cc/paper_files/paper/2021/file/505259756244493872b7709a8a01b536-Paper.pdf
2021
Earlier work this paper cites.
Neel, S., Roth, A., Sharifi-Malvajerdi, S.: Descent-to-delete: Gradient-based methods for machine unlearning. In: Proceedings of the International Conference on Algorithmic Learning Theory (2021)
2021
Earlier work this paper cites.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision (2021)
2021
Earlier work this paper cites.
Schelter, S., Grafberger, S., Dunning, T.: Hedgecut: Maintaining randomised trees for low-latency machine unlearning. In: Proceedings of the International Conference on Management of Data (2021)
2021
Earlier work this paper cites.
Ullah, E., Mai, T., Rao, A., Rossi, R.A., Arora, R.: Machine unlearning via algorithmic stability. In: Belkin, M., Kpotufe, S. (eds.) Proceedings of Thirty Fourth Conference on Learning Theory. Proceedings of Machine Learning Research, vol. 134, pp. 4126–4142. PMLR (15–19 Aug 2021), https://proceedings.mlr.press/v134/ullah21a.html
2021
Earlier work this paper cites.
Chen, C., Sun, F., Zhang, M., Ding, B.: Recommendation unlearning. In: Proceedings of the ACM Web Conference 2022 (2022)
2022
Earlier work this paper cites.
Chen, M., Zhang, Z., Wang, T., Backes, M., Humbert, M., Zhang, Y.: Graph unlearning. In: Proceedings of the ACM SIGSAC Conference on Computer and Communications Security (2022)
2022
Earlier work this paper cites.
Chien, E., Pan, C., Milenkovic, O.: Certified graph unlearning. In: NeurIPS 2022 Workshop: New Frontiers in Graph Learning (2022)
2022
Earlier work this paper cites.
Chundawat, V.S., Tarun, A.K., Mandal, M., Kankanhalli, M.: Zero-shot machine unlearning. arXiv (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Goel, S., Prabhu, A., Sanyal, A., Lim, S.N., Torr, P., Kumaraguru, P.: Towards adversarial evaluations for inexact machine unlearning. arXiv (2022)
2022
Cited alongside, same era.
Li, J., Li, D., Xiong, C., Hoi, S.: BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In: Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., Sabato, S. (eds.) Proceedings of the 39th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 162, pp. 12888–12900. PMLR (17–23 Jul 2022), https://proceedings.mlr.press/v162/li22n.html
2022
Jang, J., Yoon, D., Yang, S., Cha, S., Lee, M., Logeswaran, L., Seo, M.: Knowledge unlearning for mitigating privacy risks in language models. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 14389–14408. Association for Computational Linguistics, Toronto, Canada (Jul 2023). https://doi.org/10.18653/v1/2023.acl-long.805, https://aclanthology.org/2023.acl-long.805
2023
Closest in time.
Jia, J., Liu, J., Ram, P., Yao, Y., Liu, G., Liu, Y., Sharma, P., Liu, S.: Model sparsity can simplify machine unlearning. In: Thirty-seventh Conference on Neural Information Processing Systems (2023), https://openreview.net/forum?id=0jZH883i34
2023
Closest in time.
Kassem, A., Mahmoud, O., Saad, S.: Preserving privacy through dememorization: An unlearning technique for mitigating memorization risks in language models. In: Bouamor, H., Pino, J., Bali, K. (eds.) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. pp. 4360–4379. Association for Computational Linguistics, Singapore (Dec 2023). https://doi.org/10.18653/v1/2023.emnlp-main.265, https://aclanthology.org/2023.emnlp-main.265
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Li, Y., Zheng, X., Chen, C., Liu, J.: Making recommender systems forget: Learning and unlearning for erasable recommendation (2022)
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Liu, B., Liu, Q., Stone, P.: Continual learning and private unlearning. In: Chandar, S., Pascanu, R., Precup, D. (eds.) Proceedings of The 1st Conference on Lifelong Learning Agents. Proceedings of Machine Learning Research, vol. 199, pp. 243–254. PMLR (22–24 Aug 2022), https://proceedings.mlr.press/v199/liu22a.html
2022
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Liu, Y., Xu, L., Yuan, X., Wang, C., Li, B.: The right to be forgotten in federated learning: An efficient realization with rapid retraining. In: IEEE Conference on Computer Communications (2022)
2022
Cited alongside, same era.
Lu, X., Welleck, S., Hessel, J., Jiang, L., Qin, L., West, P., Ammanabrolu, P., Choi, Y.: QUARK: Controllable text generation with reinforced unlearning. In: Oh, A.H., Agarwal, A., Belgrave, D., Cho, K. (eds.) Advances in Neural Information Processing Systems (2022), https://openreview.net/forum?id=5HaIds3ux5O
2022
Cited alongside, same era.
Ma, Z., Liu, Y., Liu, X., Liu, J., Ma, J., Ren, K.: Learn to forget: Machine unlearning via neuron masking. IEEE Transactions on Dependable and Secure Computing 20
2022
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Mehta, R.R., Pal, S., Singh, V., Ravi, S.: Deep unlearning via randomized conditionally independent hessians. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 10412–10421 (2022), https://api.semanticscholar.org/CorpusID:248227997
2022
Cited alongside, same era.
Setlur, A., Eysenbach, B., Smith, V., Levine, S.: Adversarial unlearning: Reducing confidence along adversarial directions. In: Oh, A.H., Agarwal, A., Belgrave, D., Cho, K. (eds.) Advances in Neural Information Processing Systems (2022), https://openreview.net/forum?id=cJ006qBE8Uv
2022
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Sommer, D.M., SOng, L., Wagh, S., Mittal, P.: Athena: Probabilistic verification of machine unlearning. In: Proceedings on Privacy Enhancing Technologies Symposium (PETS) (2022)
2022
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2023
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Kurmanji, M., Triantafillou, P., Hayes, J., Triantafillou, E.: Towards unbounded machine unlearning. In: Oh, A., Neumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (eds.) Advances in Neural Information Processing Systems. vol. 36, pp. 1957–1987. Curran Associates, Inc. (2023), https://proceedings.neurips.cc/paper_files/paper/2023/file/062d711fb777322e2152435459e6e9d9-Paper-Conference.pdf
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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