[BBV08] Maria-Florina Balcan, Avrim Blum, and Santosh Vempala. A discriminative framework for clustering via similarity functions. In Proceedings of the Fortieth Annual ACM Symposium on Theory of Computing, STOC '08, page 671–680, New York, NY, USA, 2008. Association for Computing Machinery.
$\left[\mathrm{BCM}^{+}20\right]$  Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan R. Ullman, and Steven Wu. Private query release assisted by public data. In Proceedings of the 37th International Conference on Machine Learning (ICML '20), 2020.
[BKS22] Alex Bie, Gautam Kamath, and Vikrant Singhal. Private estimation with public data. In Advances in Neural Information Processing Systems 35 (NeurIPS'22), 2022.
[BKSW19] Mark Bun, Gautam Kamath, Thomas Steinke, and Zhiwei Steven Wu. Private hypothesis selection. In Advances in Neural Information Processing Systems 32, NeurIPS '19, pages 156-167. Curran Associates, Inc., 2019.
[BLR13] Avrim Blum, Katrina Ligett, and Aaron Roth. A learning theory approach to noninteractive database privacy. Journal of the ACM, 60(2):1-25, 2013.
[BMN20] Raef Bassily, Shay Moran, and Anupama Nandi. Learning from mixtures of private and public populations. In Advances in Neural Information Processing Systems 33, NeurIPS '20, pages 2947-2957. Curran Associates, Inc., 2020.
[BNS16] Amos Beimel, Kobbi Nissim, and Uri Stemmer. Private learning and sanitization: Pure vs. approximate differential privacy. Theory of Computing, 12(1):1-61, 2016.
[BTGT18] Raef Bassily, Om Thakkar, and Abhradeep Guha Thakurta. Model-agnostic private learning. In Advances in Neural Information Processing Systems 31, NeurIPS '18, pages 7102–7112. Curran Associates, Inc., 2018.
$\left[\mathrm{CCd}^{+}23\right]$  Hongjie Chen, Vincent Cohen-Addad, Tommaso d'Orsi, Alessandro Epasto, Jacob Imola, David Steurer, and Stefan Tiegel. Private estimation algorithms for stochastic block models and mixture models. arXiv preprint arXiv:2301.04822, 2023.
$\left[\mathrm{CDE}^{+}23\right]$  Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Matthew Jagielski, Yangsibo Huang, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohri-menko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Sergey Yekhanin, Da Yu, Huanyu Zhang, and Wanrong Zhang. Challenges towards the next frontier in privacy. arXiv preprint arXiv:2304.06929, 2023.
$\left[\mathrm{CKM}^{+}21\right]$  Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer, and Eliad Tsfadia. Differentially-private clustering of easy instances. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 2049-2059. PMLR, 18-24 Jul 2021.
[CSV17] Moses Charikar, Jacob Steinhardt, and Gregory Valiant. Learning from untrusted data. In Proceedings of the 49th Annual ACM Symposium on the Theory of Computing, STOC '17, pages 47-60, New York, NY, USA, 2017. ACM.
[DKS18] Ilias Diakonikolas, Daniel M. Kane, and Alistair Stewart. List-decodable robust mean estimation and learning mixtures of spherical Gaussians. In Proceedings of the 50th Annual ACM Symposium on the Theory of Computing, STOC '18, pages 1047-1060, New York, NY, USA, 2018. ACM.
[DL01] Luc Devroye and Gábor Lugosi. Combinatorial Methods in Density Estimation. Springer Series in Statistics. Springer New York, 2001.
[DMNS06] Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. Calibrating noise to sensitivity in private data analysis. In Proceedings of the 3rd Conference on Theory of Cryptography (TCC'06), 2006.