[AAAK21] Ishaq Aden-Ali, Hassan Ashtiani, and Gautam Kamath. On the sample complexity of privately learning unbounded high-dimensional Gaussians. In Proceedings of the 32nd International Conference on Algorithmic Learning Theory (ALT'21), 2021.
$\left[\mathrm{AABD}^{+}20\right]$  Sushant Agarwal, Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, and Ruth Urner. On learnability with computable learners. In Proceedings of the 31st International Conference on Algorithmic Learning Theory (ALT'20), 2020.
[AAL21] Ishaq Aden-Ali, Hassan Ashtiani, and Christopher Liaw. Privately learning mixtures of axis-aligned Gaussians. In Advances in Neural Information Processing Systems 34 (NeurIPS'21), 2021.
[AAL23a] Mohammad Afzali, Hassan Ashtiani, and Christopher Liaw. Mixtures of gaussians are privately learnable with a polynomial number of samples. CoRR, abs/2309.03847, 2023.
[AAL23b] Jamil Arbas, Hassan Ashtiani, and Christopher Liaw. Polynomial time and private learning of unbounded Gaussian mixture models. In Proceedings of the 40th International Conference on Machine Learning (ICML'23), 2023.
$\left[\mathrm{ABDH}^{+}20\right]$  Hassan Ashtiani, Shai Ben-David, Nicholas J. A. Harvey, Christopher Liaw, Abbas Mehrabian, and Yaniv Plan. Near-optimal sample complexity bounds for robust learning of Gaussian mixtures via compression schemes. J. ACM, 67(6), 2020.
[ABM19] Noga Alon, Raef Bassily, and Shay Moran. Limits of private learning with access to public data. In Advances in Neural Information Processing Systems 32 (NeurIPS'19), 2019.
[Abo18] John M. Abowd. The U.S. Census Bureau adopts differential privacy. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018.
[ACG+16] Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. Deep learning with differential privacy. In CCS'16: 2016 ACM SIGSAC Conference on Computer and Communications Security, 2016.
[ADK20] Brendan Avent, Yatharth Dubey, and Aleksandra Korolova. The power of the hybrid model for mean estimation. Proc. Priv. Enhancing Technol., 2020(4):48-68, 2020.
[AGM+22] Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, and Abhradeep Thakurta. Public data-assisted mirror descent for private model training. In Proceedings of the 39th International Conference on Machine Learning (ICML'22), 2022.
$\left[\mathrm{AKZ}^{+}17\right]$  Brendan Avent, Aleksandra Korolova, David Zeber, Torgeir Hovden, and Benjamin Livshits. BLENDER: Enabling local search with a hybrid differential privacy model. In 26th USENIX Security Symposium (USENIX Security'17), 2017.
[AL22] Hassan Ashtiani and Christopher Liaw. Private and polynomial time algorithms for learning Gaussians and beyond. In Proceedings of the 35th Annual Conference on Learning Theory (COLT'22), 2022.
[AM18] Hassan Ashtiani and Abbas Mehrabian. Some techniques in density estimation. arXiv preprint arXiv:1801.04003, 2018.
[App17] Differential Privacy Team, Apple. Learning with privacy at scale, 2017.
[Ass83] Patrick Assouad. Densité et dimension. Annales de l'Institut Fourier, 33(3):233-282, 1983.
[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.