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Vikrant Singhal
Vikrant Singhal
Research Associate, Harvard University
Geverifieerd e-mailadres voor seas.harvard.edu - Homepage
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Privately learning high-dimensional distributions
G Kamath, J Li, V Singhal, J Ullman
Conference on Learning Theory, 1853-1902, 2019
1182019
Private mean estimation of heavy-tailed distributions
G Kamath, V Singhal, J Ullman
Conference on Learning Theory, 2204-2235, 2020
792020
Deterministic and probabilistic binary search in graphs
E Emamjomeh-Zadeh, D Kempe, V Singhal
Proceedings of the forty-eighth annual ACM symposium on Theory of Computing …, 2016
562016
Differentially private algorithms for learning mixtures of separated gaussians
G Kamath, O Sheffet, V Singhal, J Ullman
Advances in Neural Information Processing Systems 32, 2019
462019
A private and computationally-efficient estimator for unbounded gaussians
G Kamath, A Mouzakis, V Singhal, T Steinke, J Ullman
Conference on Learning Theory, 544-572, 2022
272022
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma
G Kamath, A Mouzakis, V Singhal
Advances in Neural Information Processing Systems 35, 24405-24418, 2022
192022
Private Estimation with Public Data
A Bie, G Kamath, V Singhal
Advances in Neural Information Processing Systems 35, 18653-18666, 2022
182022
Privately learning subspaces
V Singhal, T Steinke
Advances in Neural Information Processing Systems 34, 1312-1324, 2021
152021
A Bias-Variance-Privacy Trilemma for Statistical Estimation
G Kamath, A Mouzakis, M Regehr, V Singhal, T Steinke, J Ullman
arXiv preprint arXiv:2301.13334, 2023
52023
Private Distribution Learning with Public Data: The View from Sample Compression
S Ben-David, A Bie, CL Canonne, G Kamath, V Singhal
arXiv preprint arXiv:2308.06239, 2023
22023
A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions
V Singhal
arXiv preprint arXiv:2304.06787, 2023
12023
Differentially Private Statistical Estimation
V Singhal
Northeastern University, 2021
2021
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Artikelen 1–12