| Private prediction for large-scale synthetic text generation K Amin, A Bie, W Kong, A Kurakin, N Ponomareva, U Syed, A Terzis, ... arXiv preprint arXiv:2407.12108, 2024 | | 2024 |
| Warm-starting Push-Relabel S Davies, S Vassilvitskii, Y Wang arXiv preprint arXiv:2405.18568, 2024 | | 2024 |
| Winner-Pays-Bid Auctions Minimize Variance P McAfee, RP Leme, B Sivan, S Vassilvitskii arXiv preprint arXiv:2403.04856, 2024 | | 2024 |
| Scaling laws for downstream task performance of large language models B Isik, N Ponomareva, H Hazimeh, D Paparas, S Vassilvitskii, S Koyejo arXiv preprint arXiv:2402.04177, 2024 | 9 | 2024 |
| Controlling Tail Risk in Online Ski-Rental M Dinitz, S Im, T Lavastida, B Moseley, S Vassilvitskii Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms …, 2024 | | 2024 |
| Systems and Methods for Anonymizing Large Scale Datasets A Epasto, H Esfandiari, VS Mirrokni, AM Medina, U Syed, S Vassilvitskii US Patent App. 18/345,657, 2023 | | 2023 |
| Massively parallel computation: Algorithms and applications S Im, R Kumar, S Lattanzi, B Moseley, S Vassilvitskii Foundations and Trends® in Optimization 5 (4), 340-417, 2023 | 5 | 2023 |
| Dagstuhl Reports, Vol. 13, Issue 2 ISSN 2192-5283 N Megow, BJ Moseley, D Shmoys, O Svensson, S Vassilvitskii, J Schlöter, ... | | 2023 |
| Improved Differentially Private Densest Subgraph: Local and Purely Additive M Dinitz, S Kale, S Lattanzi, S Vassilvitskii arXiv preprint arXiv:2308.10316, 2023 | 2 | 2023 |
| Systems and methods for anonymizing large scale datasets A Epasto, H Esfandiari, VS Mirrokni, AM Medina, U Syed, S Vassilvitskii US Patent 11,727,147, 2023 | 1 | 2023 |
| 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 | 103 | 2023 |
| Speeding up bellman ford via minimum violation permutations S Lattanzi, O Svensson, S Vassilvitskii International Conference on Machine Learning, 18584-18598, 2023 | 4 | 2023 |
| Label differential privacy and private training data release RI Busa-Fekete, AM Medina, U Syed, S Vassilvitskii International Conference on Machine Learning, 3233-3251, 2023 | 6 | 2023 |
| Learning-augmented private algorithms for multiple quantile release M Khodak, K Amin, T Dick, S Vassilvitskii International Conference on Machine Learning, 16344-16376, 2023 | 2 | 2023 |
| Predictive flows for faster ford-fulkerson S Davies, B Moseley, S Vassilvitskii, Y Wang International Conference on Machine Learning, 7231-7248, 2023 | 9 | 2023 |
| Measuring re-identification risk CJ Carey, T Dick, A Epasto, A Javanmard, J Karlin, S Kumar, ... Proceedings of the ACM on Management of Data 1 (2), 1-26, 2023 | 10 | 2023 |
| Advancing differential privacy: Where we are now and future directions for real-world deployment R Cummings, D Desfontaines, D Evans, R Geambasu, Y Huang, ... arXiv preprint arXiv:2304.06929, 2023 | 5 | 2023 |
| Differentially private continual releases of streaming frequency moment estimations A Epasto, J Mao, AM Medina, V Mirrokni, S Vassilvitskii, P Zhong arXiv preprint arXiv:2301.05605, 2023 | 23 | 2023 |
| Scheduling (Dagstuhl Seminar 23061) N Megow, BJ Moseley, D Shmoys, O Svensson, S Vassilvitskii, J Schlöter Dagstuhl Reports 13 (2), 2023 | | 2023 |
| Challenges towards the next frontier in privacy R Cummings, D Desfontaines, D Evans, R Geambasu, M Jagielski, ... arXiv preprint arXiv:2304.06929 1, 2023 | 30 | 2023 |
Ravi KumarGoogleVerified email at google.com