[BS19] Mark Bun and Thomas Steinke. Average-case averages: Private algorithms for smooth sensitivity and mean estimation. In Advances in Neural Information Processing Systems 32, NeurIPS '19, pages 181-191. Curran Associates, Inc., 2019.
[CDSS13] Siu On Chan, Ilias Diakonikolas, Rocco A. Servedio, and Xiaorui Sun. Learning mixtures of structured distributions over discrete domains. In Proceedings of the 24th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA '13, pages 1380-1394, Philadelphia, PA, USA, 2013. SIAM.
[CDSS14a] Siu On Chan, Ilias Diakonikolas, Rocco A. Servedio, and Xiaorui Sun. Efficient density estimation via piecewise polynomial approximation. In Proceedings of the 46th Annual ACM Symposium on the Theory of Computing, STOC '14, pages 604-613, New York, NY, USA, 2014. ACM.
[CDSS14b] Siu On Chan, Ilias Diakonikolas, Rocco A. Servedio, and Xiaorui Sun. Near-optimal density estimation in near-linear time using variable-width histograms. In Advances in Neural Information Processing Systems 27, NIPS '14, pages 1844-1852. Curran Associates, Inc., 2014.
[CWZ21] T Tony Cai, Yichen Wang, and Linjun Zhang. The cost of privacy: Optimal rates of convergence for parameter estimation with differential privacy. The Annals of Statistics, 49(5):2825-2850, 2021.
[DDS12] Constantinos Daskalakis, Ilias Diakonikolas, and Rocco A. Servedio. Learning Poisson binomial distributions. In Proceedings of the 44th Annual ACM Symposium on the Theory of Computing, STOC '12, pages 709-728, New York, NY, USA, 2012. ACM.
[Dia16] Ilias Diakonikolas. Learning structured distributions. In Peter Buhlmann, Petros Drineas, Michael J. Kane, and Mark J. van der Laan, editors, Handbook of Big Data, pages 267-283. Chapman and Hall/CRC, 2016.
[DK14] Constantininos Daskalakis and Gautam Kamath. Faster and sample near-optimal algorithms for proper learning mixtures of Gaussians. In Proceedings of the 27th Annual Conference on Learning Theory, COLT '14, pages 1183-1213, 2014.
[DK22] Ilias Diakonikolas and Daniel Kane. Algorithmic High-Dimensional Robust Statistics. Cambridge University Press, 2022.
$\left[\mathrm{DKK}^{+}16\right]$  Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, and Alistair Stewart. Robust estimators in high dimensions without the computational intractability. In Proceedings of the 57th Annual IEEE Symposium on Foundations of Computer Science, FOCS '16, pages 655-664, Washington, DC, USA, 2016. IEEE Computer Society.
$\left[\mathrm{DKK}^{+}17\right]$  Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, and Alistair Stewart. Being robust (in high dimensions) can be practical. In Proceedings of the 34th International Conference on Machine Learning, ICML '17, pages 999-1008. JMLR, Inc., 2017.
$\left[\mathrm{DKK}^{+}18\right]$  Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, and Alistair Stewart. Robustly learning a Gaussian: Getting optimal error, efficiently. In Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA '18, Philadelphia, PA, USA, 2018. SIAM.
$\left[\mathrm{DKK}^{+}19\right]$  Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Jacob Steinhardt, and Alistair Stewart. Sever: A robust meta-algorithm for stochastic optimization. In Proceedings of the 36th International Conference on Machine Learning, ICML '19, pages 1596-1606. JMLR, Inc., 2019.
[DKS17] Ilias Diakonikolas, Daniel M. Kane, and Alistair Stewart. Statistical query lower bounds for robust estimation of high-dimensional Gaussians and Gaussian mixtures. In Proceedings of the 58th Annual IEEE Symposium on Foundations of Computer Science, FOCS '17, pages 73-84, Washington, DC, USA, 2017. IEEE Computer Society.