Follow
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
7352017
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
3382016
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
3132016
{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
2112021
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
732017
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
512014
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
462019
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
432019
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
422022
Locally private gaussian estimation
M Joseph, J Kulkarni, J Mao, SZ Wu
Advances in Neural Information Processing Systems 32, 2019
422019
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
412022
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
392021
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
382021
Differentially private set union
S Gopi, P Gulhane, J Kulkarni, JH Shen, M Shokouhi, S Yekhanin
International Conference on Machine Learning, 3627-3636, 2020
372020
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
372015
Hardware protection for differential privacy
JD Benaloh, JD KULKARNI, JS ALLEN, JR Lorch, ME CHASE, ...
US Patent 10,977,384, 2021
342021
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
332014
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
292020
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
282022
The system can't perform the operation now. Try again later.
Articles 1–20