[BBV08] M.-F. Balcan, A. Blum, and S. Vempala. “A discriminative framework for clustering via similarity functions”. In: STOC. 2008, pp. 671–680.
[BDHKKK20] A. Bakshi, I. Diakonikolas, S. B. Hopkins, D. M. Kane, S. Karmalkar, and P. K. Kothari. "Outlier-Robust Clustering of Gaussians and Other Non-Spherical Mixtures". In: Proc. 61st IEEE Symposium on Foundations of Computer Science (FOCS). 2020.
[BDJKKV22] A. Bakshi, I. Diakonikolas, H. Jia, D. M. Kane, P. K. Kothari, and S. S. Vempala. "Robustly Learning Mixtures of k Arbitrary Gaussians". In: Proc. 54th Annual ACM Symposium on Theory of Computing (STOC). 2022.
[Ber18] D. S. Bernstein. Scalar, Vector, and Matrix Mathematics: Theory, Facts, and Formulas. Revised and expanded edition. Princeton: Princeton University Press, 2018. ISBN: 978-0-691-15120-5 978-0-691-17653-6.
[BK21] A. Bakshi and P. Kothari. "List-Decodable Subspace Recovery: Dimension Independent Error in Polynomial Time". In: Proc. 32nd Annual Symposium on Discrete Algorithms (SODA). 2021.
[BS10] M. Belkin and K. Sinha. “Polynomial Learning of Distribution Families”. In: FOCS. 2010, pp. 103–112.
[CMY20] Y. Cherapanamjeri, S. Mohanty, and M. Yau. "List decodable mean estimation in nearly linear time". In: Proc. 61st IEEE Symposium on Foundations of Computer Science (FOCS). 2020.
[CSV17] M. Charikar, J. Steinhardt, and G. Valiant. "Learning from untrusted data". In: Proceedings of STOC 2017. 2017, pp. 47-60.
[Das99] S. Dasgupta. "Learning mixtures of Gaussians". In: Proceedings of the 40th Annual Symposium on Foundations of Computer Science. 1999, pp. 634-644.
[DHKK20] I. Diakonikolas, S. B. Hopkins, D. Kane, and S. Karmalkar. "Robustly Learning any Clusterable Mixture of Gaussians". In: CoRR abs/2005.06417 (2020).
[DJKS22] A. Das, A. Jain, W. Kong, and R. Sen. "Efficient List-Decodable Regression using Batches". In: CoRR abs/2211.12743 (2022).
[DK23] I. Diakonikolas and D. M. Kane. Algorithmic High-Dimensional Robust Statistics. Cambridge University Press, 2023.
[DKK20a] I. Diakonikolas, D. M. Kane, and D. Kongsgaard. "List-decodable mean estimation via iterative multi-filtering". In: Advances in Neural Information Processing Systems 33 (NeurIPS). 2020.
[DKK20b] Ilias Diakonikolas, Daniel Kane, and Daniel Kongsgaard. "List-decodable mean estimation via iterative multi-filtering". In: Advances in Neural Information Processing Systems 33 (2020), pp. 9312-9323.
[DKKLMS16] I. Diakonikolas, G. Kamath, D. M. Kane, J. Li, A. Moitra, and A. Stewart. "Robust Estimators in High Dimensions without the Computational Intractability". In: Proc. 57th IEEE Symposium on Foundations of Computer Science (FOCS). 2016, pp. 655-664.
[DKKLT21] I. Diakonikolas, D. M. Kane, D. Kongsgaard, J. Li, and K. Tian. "List-decodable mean estimation in nearly-pca time". In: Advances in Neural Information Processing Systems 34 (NeurIPS). Vol. 34. 2021.
[DKKLT22] I. Diakonikolas, D. M. Kane, D. Kongsgaard, J. Li, and K. Tian. "Clustering Mixture Models in Almost-Linear Time via List-Decodable Mean Estimation". In: Proc. 54th Annual ACM Symposium on Theory of Computing (STOC). 2022.
[DKKPP22] I. Diakonikolas, D. M. Kane, S. Karmalkar, A. Pensia, and T. Pittas. "List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering". In: Advances in Neural Information Processing Systems 35 (NeurIPS). 2022.
[DKPPS21] I. Diakonikolas, D. M. Kane, A. Pensia, T. Pittas, and A. Stewart. "Statistical query lower bounds for list-decodable linear regression". In: Advances in Neural Information Processing Systems 34 (NeurIPS). 2021.
[DKS17] I. Diakonikolas, D. M. Kane, and A. Stewart. "Statistical Query Lower Bounds for Robust Estimation of High-Dimensional Gaussians and Gaussian Mixtures". In: Proc. 58th IEEE Symposium on Foundations of Computer Science (FOCS). 2017, pp. 73-84.