| k-means++: The advantages of careful seeding D Arthur, S Vassilvitskii Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete …, 2006 | 12199 | 2006 |
| Scalable k-means++ B Bahmani, B Moseley, A Vattani, R Kumar, S Vassilvitskii arXiv preprint arXiv:1203.6402, 2012 | 1003 | 2012 |
| A model of computation for MapReduce H Karloff, S Suri, S Vassilvitskii Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete …, 2010 | 776 | 2010 |
| How slow is the k-means method? D Arthur, S Vassilvitskii Proceedings of the twenty-second annual symposium on Computational geometry …, 2006 | 641 | 2006 |
| Fair clustering through fairlets F Chierichetti, R Kumar, S Lattanzi, S Vassilvitskii Advances in neural information processing systems 30, 2017 | 566 | 2017 |
| Counting triangles and the curse of the last reducer S Suri, S Vassilvitskii Proceedings of the 20th international conference on World wide web, 607-614, 2011 | 548 | 2011 |
| Competitive caching with machine learned advice T Lykouris, S Vassilvitskii Journal of the ACM (JACM) 68 (4), 1-25, 2021 | 457 | 2021 |
| Generalized distances between rankings R Kumar, S Vassilvitskii Proceedings of the 19th international conference on World wide web, 571-580, 2010 | 406 | 2010 |
| Filtering: a method for solving graph problems in mapreduce S Lattanzi, B Moseley, S Suri, S Vassilvitskii Proceedings of the twenty-third annual ACM symposium on Parallelism in …, 2011 | 327 | 2011 |
| Densest subgraph in streaming and mapreduce B Bahmani, R Kumar, S Vassilvitskii arXiv preprint arXiv:1201.6567, 2012 | 326* | 2012 |
| Fast greedy algorithms in mapreduce and streaming R Kumar, B Moseley, S Vassilvitskii, A Vattani ACM Transactions on Parallel Computing (TOPC) 2 (3), 1-22, 2015 | 281 | 2015 |
| Algorithms with predictions M Mitzenmacher, S Vassilvitskii Communications of the ACM 65 (7), 33-35, 2022 | 244 | 2022 |
| Online scheduling via learned weights S Lattanzi, T Lavastida, B Moseley, S Vassilvitskii Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete …, 2020 | 220 | 2020 |
| How to dp-fy ml: A practical guide to machine learning with differential privacy N Ponomareva, H Hazimeh, A Kurakin, Z Xu, C Denison, HB McMahan, ... Journal of Artificial Intelligence Research 77, 1113-1201, 2023 | 180 | 2023 |
| Bidding for representative allocations for display advertising A Ghosh, P McAfee, K Papineni, S Vassilvitskii International workshop on internet and network economics, 208-219, 2009 | 174 | 2009 |
| A complete, local and parallel reconfiguration algorithm for cube style modular robots S Vassilvitskii, M Yim, J Suh Proceedings 2002 IEEE International Conference on Robotics and Automation …, 2002 | 173 | 2002 |
| Connected components in mapreduce and beyond R Kiveris, S Lattanzi, V Mirrokni, V Rastogi, S Vassilvitskii Proceedings of the ACM Symposium on Cloud Computing, 1-13, 2014 | 153* | 2014 |
| Local search methods for k-means with outliers S Gupta, R Kumar, K Lu, B Moseley, S Vassilvitskii Proceedings of the VLDB Endowment 10 (7), 757-768, 2017 | 144 | 2017 |
| The dynamics of repeat consumption A Anderson, R Kumar, A Tomkins, S Vassilvitskii Proceedings of the 23rd international conference on World wide web, 419-430, 2014 | 139 | 2014 |
| WORST-CASE AND SMOOTHED ANALYSIS OF THE ICP ALGORITHM, WITH AN APPLICATION TO THE κ-MEANS METHOD D ARTHUR, S VASSILVITSKII SIAM journal on computing 39 (2), 766-782, 2010 | 129* | 2010 |
Ravi KumarGoogleVerified email at google.com
