| Collecting telemetry data privately B Ding, J Kulkarni, S Yekhanin Advances in Neural Information Processing Systems 30, 2017 | 735 | 2017 |
| Projector: Agile reconfigurable data center interconnect M Ghobadi, R Mahajan, A Phanishayee, N Devanur, J Kulkarni, ... Proceedings of the 2016 ACM SIGCOMM Conference, 216-229, 2016 | 338 | 2016 |
| Morpheus: Towards automated {SLOs} for enterprise clusters SA Jyothi, C Curino, I Menache, SM Narayanamurthy, A Tumanov, ... 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2016 | 313 | 2016 |
| {GRAPHENE}: Packing and {Dependency-Aware} Scheduling for {Data-Parallel} Clusters R Grandl, S Kandula, S Rao, A Akella, J Kulkarni 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2016 | 251 | 2016 |
| Differentially private fine-tuning of language models D Yu, S Naik, A Backurs, S Gopi, HA Inan, G Kamath, J Kulkarni, YT Lee, ... arXiv preprint arXiv:2110.06500, 2021 | 211 | 2021 |
| Competitive algorithms from competitive equilibria: Non-clairvoyant scheduling under polyhedral constraints S Im, J Kulkarni, K Munagala Journal of the ACM (JACM) 65 (1), 1-33, 2017 | 73 | 2017 |
| Selfishmigrate: A scalable algorithm for non-clairvoyantly scheduling heterogeneous processors S Im, J Kulkarni, K Munagala, K Pruhs 2014 IEEE 55th Annual Symposium on Foundations of Computer Science, 531-540, 2014 | 51 | 2014 |
| Deterministically Maintaining a (2 + ∊)-Approximate Minimum Vertex Cover in O(1/∊2) Amortized Update Time S Bhattacharya, J Kulkarni Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete …, 2019 | 46 | 2019 |
| An algorithmic framework for differentially private data analysis on trusted processors J Allen, B Ding, J Kulkarni, H Nori, O Ohrimenko, S Yekhanin Advances in Neural Information Processing Systems 32, 2019 | 43 | 2019 |
| Looking beyond {GPUs} for {DNN} scheduling on {Multi-Tenant} clusters J Mohan, A Phanishayee, J Kulkarni, V Chidambaram 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2022 | 42 | 2022 |
| Locally private gaussian estimation M Joseph, J Kulkarni, J Mao, SZ Wu Advances in Neural Information Processing Systems 32, 2019 | 42 | 2019 |
| When does differentially private learning not suffer in high dimensions? X Li, D Liu, TB Hashimoto, HA Inan, J Kulkarni, YT Lee, A Guha Thakurta Advances in Neural Information Processing Systems 35, 28616-28630, 2022 | 41 | 2022 |
| Accuracy, interpretability, and differential privacy via explainable boosting H Nori, R Caruana, Z Bu, JH Shen, J Kulkarni International conference on machine learning, 8227-8237, 2021 | 39 | 2021 |
| Fast and memory efficient differentially private-sgd via jl projections Z Bu, S Gopi, J Kulkarni, YT Lee, H Shen, U Tantipongpipat Advances in Neural Information Processing Systems 34, 19680-19691, 2021 | 38 | 2021 |
| Differentially private set union S Gopi, P Gulhane, J Kulkarni, JH Shen, M Shokouhi, S Yekhanin International Conference on Machine Learning, 3627-3636, 2020 | 37 | 2020 |
| Tight bounds for online vector scheduling S Im, N Kell, J Kulkarni, D Panigrahi 2015 IEEE 56th Annual Symposium on Foundations of Computer Science, 525-544, 2015 | 37 | 2015 |
| Hardware protection for differential privacy JD Benaloh, JD KULKARNI, JS ALLEN, JR Lorch, ME CHASE, ... US Patent 10,977,384, 2021 | 34 | 2021 |
| Robust price of anarchy bounds via LP and fenchel duality J Kulkarni, V Mirrokni Proceedings of the twenty-sixth annual ACM-SIAM symposium on Discrete …, 2014 | 34 | 2014 |
| Differentially private release of synthetic graphs M Eliáš, M Kapralov, J Kulkarni, YT Lee Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete …, 2020 | 29 | 2020 |
| Exploring the limits of differentially private deep learning with group-wise clipping J He, X Li, D Yu, H Zhang, J Kulkarni, YT Lee, A Backurs, N Yu, J Bian arXiv preprint arXiv:2212.01539, 2022 | 28 | 2022 |
Sergey YekhaninMicrosoftVerified email at microsoft.com
