[AFJ+18] Jayadev Acharya, Moein Falahatgar, Ashkan Jafarpour, Alon Orlitsky, and Ananda Theertha Suresh. Maximum selection and sorting with adversarial comparators. Journal of Machine Learning Research, 19(1):2427-2457, 2018.
$\left[\mathrm{AKT}^{+}23\right]$  Daniel Alabi, Pravesh K Kothari, Pranay Tankala, Prayaag Venkat, and Fred Zhang. Privately estimating a Gaussian: Efficient, robust and optimal. In Proceedings of the 55th Annual ACM Symposium on the Theory of Computing, STOC '23, New York, NY, USA, 2023. ACM.
[AM20] Marco Avella-Medina. The role of robust statistics in private data analysis. Chance, 33(4):37-42, 2020.
[ASZ21] Jayadev Acharya, Ziteng Sun, and Huanyu Zhang. Differentially private assouad, fano, and le cam. In Proceedings of the 32nd International Conference on Algorithmic Learning Theory, ALT '21, pages 48-78. JMLR, Inc., 2021.
$\left[\mathrm{BBK}^{+}22\right]$  Olivier Bousquet, Mark Braverman, Gillat Kol, Klim Efremenko, and Shay Moran. Statistically near-optimal hypothesis selection. In Proceedings of the 62nd Annual IEEE Symposium on Foundations of Computer Science, FOCS '21, pages 909-919. IEEE Computer Society, 2022.
[BDJ+22] Ainesh Bakshi, Ilias Diakonikolas, He Jia, Daniel M Kane, Pravesh K Kothari, and Santosh S Vempala. Robustly learning mixtures of k arbitrary Gaussians. In Proceedings of the 54th Annual ACM Symposium on the Theory of Computing, STOC '22, pages 1234-1247, New York, NY, USA, 2022. ACM.
[BEHW89] Anselm Blumer, Andrzej Ehrenfeucht, David Haussler, and Manfred K. Warmuth. Learnability and the Vapnik-Chervonenkis dimension. Journal of the ACM, 36(4):929-965, 1989.
$\left[\mathrm{BGS}^{+}21\right]$  Gavin Brown, Marco Gaboardi, Adam Smith, Jonathan Ullman, and Lydia Zakynthiou. Covariance-aware private mean estimation without private covariance estimation. In Advances in Neural Information Processing Systems 34, NeurIPS '21. Curran Associates, Inc., 2021.
$\left[\mathrm{BHM}^{+}17\right]$  Shai Ben-David, Pavel Hrubes, Shay Moran, Amir Shpilka, and Amir Yehudayoff. A learning problem that is independent of the set theory ZFC axioms. CoRR, abs/1711.05195, 2017.
[BKM19] Olivier Bousquet, Daniel M. Kane, and Shay Moran. The optimal approximation factor in density estimation. In Proceedings of the 32nd Annual Conference on Learning Theory, COLT '19, pages 318-341, 2019.
[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.
[BLMT22] Guy Blanc, Jane Lange, Ali Malik, and Li-Yang Tan. On the power of adaptivity in statistical adversaries. In Proceedings of the 35th Annual Conference on Learning Theory, COLT '22, pages 5030-5061, 2022.
[BNL12] Battista Biggio, Blaine Nelson, and Pavel Laskov. Poisoning attacks against support vector machines. In Proceedings of the 29th International Conference on Machine Learning, ICML '12, pages 1467-1474. JMLR, Inc., 2012.
[BNS16] Mark Bun, Kobbi Nissim, and Uri Stemmer. Simultaneous private learning of multiple concepts. In Proceedings of the 7th Conference on Innovations in Theoretical Computer Science, ITCS '16, pages 369-380, New York, NY, USA, 2016. ACM.
[BPS09] Shai Ben-David, David Pál, and Shai Shalev-Shwartz. Agnostic online learning. In COLT 2009 - The 22nd Conference on Learning Theory, Montreal, Quebec, Canada, June 18-21, 2009, 2009.