[9] Vladimir Braverman, Petros Drineas, Cameron Musco, Christopher Musco, Jalaj Upadhyay, David P. Woodruff, and Samson Zhou. Near optimal linear algebra in the online and sliding window models. In 61st IEEE Annual Symposium on Foundations of Computer Science, FOCS, pages 517-528, 2020. 2, 4, 21
[10] Vladimir Braverman, Dan Feldman, Harry Lang, Adiel Statman, and Samson Zhou. Efficient coreset constructions via sensitivity sampling. In *Asian Conference on Machine Learning*, ACML, pages 948–963, 2021. 2, 4
[11] Vladimir Braverman, Gereon Frahling, Harry Lang, Christian Sohler, and Lin F. Yang. Clustering high dimensional dynamic data streams. In Proceedings of the 34th International Conference on Machine Learning, ICML, pages 576-585, 2017. 2, 8
[12] Vladimir Braverman, Ran Gelles, and Rafail Ostrovsky. How to catch  $l_{2}$ -heavy-hitters on sliding windows. Theor. Comput. Sci., 554:82-94, 2014. 2
[13] Vladimir Braverman, Elena Grigorescu, Harry Lang, David P. Woodruff, and Samson Zhou. Nearly optimal distinct elements and heavy hitters on sliding windows. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, APPROX/RANDOM, pages 7:1-7:22, 2018. 2
[14] Vladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal, and Samson Zhou. Adversarial robustness of streaming algorithms through importance sampling. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems, NeurIPS, pages 3544-3557, 2021. 2
[15] Vladimir Braverman, Harry Lang, Keith Levin, and Morteza Monemizadeh. Clustering on sliding windows in polylogarithmic space. In 35th IARCS Annual Conference on Foundation of Software Technology and Theoretical Computer Science, FSTTCS, pages 350-364, 2015. 2, 8
[16] Vladimir Braverman, Harry Lang, Keith Levin, and Morteza Monemizadeh. Clustering problems on sliding windows. In Proceedings of the Twenty-Seventh Annual ACM-SIAM Symposium on Discrete Algorithms, SODA, pages 1374-1390, 2016. 2, 3, 4, 9
[17] Vladimir Braverman, Adam Meyerson, Rafail Ostrovsky, Alan Roytman, Michael Shindler, and Brian Tagiku. Streaming k-means on well-clusterable data. In Proceedings of the Twenty-Second Annual ACM-SIAM Symposium on Discrete Algorithms, SODA, pages 26–40, 2011. 2
[18] Vladimir Braverman and Rafail Ostrovsky. Smooth histograms for sliding windows. In 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS), Proceedings, pages 283-293, 2007. 2, 4
[19] Vladimir Braverman, Rafail Ostrovsky, and Carlo Zaniolo. Optimal sampling from sliding windows. J. Comput. Syst. Sci., 78(1):260-272, 2012. 2
[20] Vladimir Braverman, Viska Wei, and Samson Zhou. Symmetric norm estimation and regression on sliding windows. In Computing and Combinatorics - 27th International Conference, COCOON, Proceedings, pages 528-539, 2021. 2
[21] Moses Charikar, Liadan O'Callaghan, and Rina Panigrahy. Better streaming algorithms for clustering problems. In Proceedings of the 35th Annual ACM Symposium on Theory of Computing, pages 30-39, 2003. 2
[22] Jiecao Chen, Huy L. Nguyen, and Qin Zhang. Submodular maximization over sliding windows. CoRR, abs/1611.00129, 2016. 2
[23] Ke Chen. On coresets for k-median and k-means clustering in metric and euclidean spaces and their applications. SIAM J. Comput., 39(3):923-947, 2009. 2
[24] Vincent Cohen-Addad, 2022. Private communication. 17
[25] Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, and Chris Schwiegelshohn. Towards optimal lower bounds for k-median and k-means coresets. In STOC '22: 54th Annual ACM SIGACT Symposium on Theory of Computing, pages 1038–1051, 2022. 2, 3, 4, 5, 22