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ORCIDVikrant Singhal
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2020 – today
- 2024
[c12]Mark Bun, Gautam Kamath, Argyris Mouzakis, Vikrant Singhal:
Not All Learnable Distribution Classes are Privately Learnable. ALT 2024: 390-401- [c11]Vikrant Singhal:
A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions. ALT 2024: 1030-1054 - [i12]Mark Bun, Gautam Kamath
, Argyris Mouzakis, Vikrant Singhal:
Not All Learnable Distribution Classes are Privately Learnable. CoRR abs/2402.00267 (2024) - [i11]Jack Fitzsimons, James Honaker, Michael Shoemate, Vikrant Singhal:
Private Means and the Curious Incident of the Free Lunch. CoRR abs/2408.10438 (2024) - 2023
- [c10]Shai Ben-David, Alex Bie, Clément L. Canonne, Gautam Kamath, Vikrant Singhal:
Private Distribution Learning with Public Data: The View from Sample Compression. NeurIPS 2023 - [i10]Gautam Kamath, Argyris Mouzakis, Matthew Regehr, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman:
A Bias-Variance-Privacy Trilemma for Statistical Estimation. CoRR abs/2301.13334 (2023) - [i9]Vikrant Singhal:
A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions. CoRR abs/2304.06787 (2023) - [i8]Shai Ben-David, Alex Bie, Clément L. Canonne, Gautam Kamath, Vikrant Singhal:
Private Distribution Learning with Public Data: The View from Sample Compression. CoRR abs/2308.06239 (2023) - 2022
- [c9]Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman:
A Private and Computationally-Efficient Estimator for Unbounded Gaussians. COLT 2022: 544-572 - [c8]Gautam Kamath, Argyris Mouzakis, Vikrant Singhal:
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma. NeurIPS 2022 - [c7]Alex Bie, Gautam Kamath, Vikrant Singhal:
Private Estimation with Public Data. NeurIPS 2022 - [i7]Gautam Kamath, Argyris Mouzakis, Vikrant Singhal:
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma. CoRR abs/2205.08532 (2022) - [i6]Alex Bie, Gautam Kamath, Vikrant Singhal:
Private Estimation with Public Data. CoRR abs/2208.07984 (2022) - 2021
- [c6]Vikrant Singhal, Thomas Steinke:
Privately Learning Subspaces. NeurIPS 2021: 1312-1324 - [i5]Vikrant Singhal, Thomas Steinke:
Privately Learning Subspaces. CoRR abs/2106.00001 (2021) - [i4]Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman:
A Private and Computationally-Efficient Estimator for Unbounded Gaussians. CoRR abs/2111.04609 (2021) - 2020
- [c5]Gautam Kamath, Vikrant Singhal, Jonathan R. Ullman:
Private Mean Estimation of Heavy-Tailed Distributions. COLT 2020: 2204-2235 - [c4]Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman:
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. ITA 2020: 1-62 - [i3]Gautam Kamath, Vikrant Singhal, Jonathan R. Ullman:
Private Mean Estimation of Heavy-Tailed Distributions. CoRR abs/2002.09464 (2020)
2010 – 2019
- 2019
- [c3]Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan R. Ullman:
Privately Learning High-Dimensional Distributions. COLT 2019: 1853-1902 - [c2]Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman:
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. NeurIPS 2019: 168-180 - [i2]Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman:
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. CoRR abs/1909.03951 (2019) - 2018
- [i1]Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan R. Ullman:
Privately Learning High-Dimensional Distributions. CoRR abs/1805.00216 (2018) - 2016
- [c1]Ehsan Emamjomeh-Zadeh, David Kempe, Vikrant Singhal:
Deterministic and probabilistic binary search in graphs. STOC 2016: 519-532
Coauthor Index
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