Martín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS), pages 308-318, Vienna, Austria, 2016.
Ameer Ahmed Abbasi and Mohamed F. Younis. A survey on clustering algorithms for wireless sensor networks. Comput. Commun., 30(14-15):2826-2841, 2007.
Sara Ahmadian, Ashkan Norouzi-Fard, Ola Svensson, and Justin Ward. Better guarantees for k-means and euclidean k-median by primal-dual algorithms. In Proceedings of the 58th IEEE Annual Symposium on Foundations of Computer Science (FOCS), pages 61-72, Berkeley, CA, 2017.
David Arthur and Sergei Vassilitskii. k-means++: the advantages of careful seeding. In Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 1027-1035, New Orleans, LA, 2007.
Vijay Arya, Naveen Garg, Rohit Khandekar, Adam Meyerson, Kamesh Munagala, and Vinayaka Pandit. Local search heuristics for k-median and facility location problems. SIAM J. Comput., 33 (3):544-562, 2004.
Olivier Bachem, Mario Lucic, S. Hamed Hassani, and Andreas Krause. Approximate k-means++ in sublinear time. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI), pages 1459-1467, Phoenix, AZ, 2016.
Bahman Bahmani, Benjamin Moseley, Andrea Vattani, Ravi Kumar, and Sergei Vassilvitskii. Scalable k-means++. Proc. VLDB Endow., 5(7):622-633, 2012.
Maria-Florina Balcan, Steven Ehrlich, and Yingyu Liang. Distributed k-means and k-median clustering on general communication topologies. In Advances in Neural Information Processing Systems (NIPS), pages 1995-2003, Lake Tahoe, NV, 2013.
Maria-Florina Balcan, Travis Dick, Yingyu Liang, Wenlong Mou, and Hongyang Zhang. Differentially private clustering in high-dimensional euclidean spaces. In Proceedings of the 34th International Conference on Machine Learning (ICML), pages 322-331, Sydney, Australia, 2017.
Arindam Banerjee, Srujana Merugu, Inderjit S. Dhillon, and Joydeep Ghosh. Clustering with bregman divergences. J. Mach. Learn. Res., 6:1705-1749, 2005.
Yair Bartal. Probabilistic approximations of metric spaces and its algorithmic applications. In Proceedings of the 37th Annual Symposium on Foundations of Computer Science (FOCS), pages 184-193, Burlington, VT, 1996.
John E Beasley. OR-Library: distributing test problems by electronic mail. Journal of the Operational Research Society, 41(11):1069-1072, 1990.
Pavel Berkhin. A survey of clustering data mining techniques. In Grouping Multidimensional Data, pages 25-71. Springer, 2006.
Guy E. Blelloch, Yan Gu, and Yihan Sun. Efficient construction of probabilistic tree embeddings. In Proceedings of the 44th International Colloquium on Automata, Languages, and Programming (ICALP), pages 26:1-26:14, Warsaw, Poland, 2017.
Jaroslaw Byrka, Thomas W. Pensyl, Bartosz Rybicki, Aravind Srinivasan, and Khoa Trinh. An improved approximation for  $k$ -median, and positive correlation in budgeted optimization. In Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 737-756, San Diego, CA, 2015.
Kamalika Chaudhuri and Claire Monteleoni. Privacy-preserving logistic regression. In Advances in Neural Information Processing Systems (NIPS), pages 289-296, Vancouver, Canada, 2008.
Kamalika Chaudhuri, Claire Monteleoni, and Anand D. Sarwate. Differentially private empirical risk minimization. J. Mach. Learn. Res., 12:1069-1109, 2011.