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Janardhan Kulkarni
Janardhan Kulkarni
Microsoft Research, Redmond
Verified email at cs.washington.edu
Title
Cited by
Cited by
Year
Collecting telemetry data privately
B Ding, J Kulkarni, S Yekhanin
Advances in Neural Information Processing Systems 30, 2017
7252017
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
3402016
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
3102016
{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
2512016
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
1842021
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
742017
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
522014
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
482019
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
412019
Locally private gaussian estimation
M Joseph, J Kulkarni, J Mao, SZ Wu
Advances in Neural Information Processing Systems 32, 2019
402019
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
392021
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
362022
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
362015
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
342022
Differentially private set union
S Gopi, P Gulhane, J Kulkarni, JH Shen, M Shokouhi, S Yekhanin
International Conference on Machine Learning, 3627-3636, 2020
342020
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
342014
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
332021
Hardware protection for differential privacy
JD Benaloh, JD KULKARNI, JS ALLEN, JR Lorch, ME CHASE, ...
US Patent 10,977,384, 2021
292021
Parallel batch-dynamic graphs: Algorithms and lower bounds
L Dhulipala, D Durfee, J Kulkarni, R Peng, S Sawlani, X Sun
Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete …, 2020
282020
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
282020
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