[2] Rajendra Bhatia and Chandler Davis. A cauchy-schwarz inequality for operators with applications. Linear Algebra and its Applications, 223:119-129, 1995.
[3] Vladimir I Bogachev and Aleksandr V Kolesnikov. The monge-kantorovich problem: achievements, connections, and perspectives. Russian Mathematical Surveys, 67(5):785, 2012.
[4] Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot. Machine unlearning. In Proceedings in the 42nd IEEE Symposium on Security and Privacy (SP), 2021.
[5] Leo Breiman. Bagging predictors. Machine learning, 24:123-140, 1996.
[6] Yinzhi Cao and Junfeng Yang. Towards making systems forget with machine unlearning. In Proceedings in the 36th IEEE Symposium on Security and Privacy (SP), pages 463-480, 2015.
[7] Chong Chen, Fei Sun, Min Zhang, and Bolin Ding. Recommendation unlearning. In Proceedings of the ACM Web Conference 2022 (WWW), pages 2768-2777, 2022.
[8] Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, and Yang Zhang. Graph unlearning. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (CCS), pages 499-513, 2022.
[9] Marco Cuturi. Sinkhorn distances: Lightspeed computation of optimal transport. Advances in Neural Information Processing Systems (NeurIPS), 26, 2013.
[10] Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, et al. Pot: Python optimal transport. The Journal of Machine Learning Research, 22(1):3571-3578, 2021.
[11] Aditya Golatkar, Alessandro Achille, and Stefano Soatto. Eternal sunshine of the spotless net: Selective forgetting in deep networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 9304–9312, 2020.
[12] Laura Graves, Vineel Nagisetty, and Vijay Ganesh. Amnesiac machine learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 11516-11524, 2021.
[13] Chuan Guo, Tom Goldstein, Awni Hannun, and Laurens Van Der Maaten. Certified data removal from machine learning models. In Proceedings of the 37th International Conference on Machine Learning (ICML), pages 3832-3842, 2020.
[14] F Maxwell Harper and Joseph A Konstan. The movielens datasets: History and context. Acm Transactions on Interactive Intelligent Systems (TIIS), 5(4):1-19, 2015.
[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pages 770-778, 2016.
[16] Ruining He and Julian McAuley. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In proceedings of the 25th International Conference on World Wide Web (WWW), pages 507-517, 2016.
[17] Xiangnan He, Tao Chen, Min-Yen Kan, and Xiao Chen. Trirank: Review-aware explainable recommendation by modeling aspects. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (CIKM), pages 1661-1670, 2015.
[18] Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, pages 639-648, 2020.
[19] Yifan Hu, Yehuda Koren, and Chris Volinsky. Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE international conference on data mining, pages 263-272. IEEE, 2008.
[20] Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton. Adaptive mixtures of local experts. Neural computation, 3(1):79-87, 1991.
[21] Pang Wei Koh and Percy Liang. Understanding black-box predictions via influence functions. In International Conference on Machine Learning (ICML), pages 1885-1894, 2017.