Vikrant Singhal

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 23

Top Topics

Pac Learnability

Statistical Estimation

Differentially Private

Top Venues

Publications

Mark Bun, Gautam Kamath, Argyris Mouzakis, Vikrant Singhal
Not All Learnable Distribution Classes are Privately Learnable. ALT (2024)
Mark Bun, Gautam Kamath, Argyris Mouzakis, Vikrant Singhal
Not All Learnable Distribution Classes are Privately Learnable. CoRR (2024)
Vikrant Singhal
A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions. ALT (2024)
Shai Ben-David, Alex Bie, Clément L. Canonne, Gautam Kamath, Vikrant Singhal
Private Distribution Learning with Public Data: The View from Sample Compression. CoRR (2023)
Vikrant Singhal
A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions. CoRR (2023)
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)
Gautam Kamath, Argyris Mouzakis, Matthew Regehr, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman
A Bias-Variance-Privacy Trilemma for Statistical Estimation. CoRR (2023)
Alex Bie, Gautam Kamath, Vikrant Singhal
Private Estimation with Public Data. CoRR (2022)
Gautam Kamath, Argyris Mouzakis, Vikrant Singhal
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma. NeurIPS (2022)
Alex Bie, Gautam Kamath, Vikrant Singhal
Private Estimation with Public Data. NeurIPS (2022)
Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman
A Private and Computationally-Efficient Estimator for Unbounded Gaussians. COLT (2022)
Gautam Kamath, Argyris Mouzakis, Vikrant Singhal
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma. CoRR (2022)
Vikrant Singhal, Thomas Steinke
Privately Learning Subspaces. NeurIPS (2021)
Vikrant Singhal, Thomas Steinke
Privately Learning Subspaces. CoRR (2021)
Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman
A Private and Computationally-Efficient Estimator for Unbounded Gaussians. CoRR (2021)
Gautam Kamath, Vikrant Singhal, Jonathan R. Ullman
Private Mean Estimation of Heavy-Tailed Distributions. CoRR (2020)
Gautam Kamath, Vikrant Singhal, Jonathan R. Ullman
Private Mean Estimation of Heavy-Tailed Distributions. COLT (2020)
Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. ITA (2020)
Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. NeurIPS (2019)
Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. CoRR (2019)
Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan R. Ullman
Privately Learning High-Dimensional Distributions. COLT (2019)
Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan R. Ullman
Privately Learning High-Dimensional Distributions. CoRR (2018)
Ehsan Emamjomeh-Zadeh, David Kempe, Vikrant Singhal
Deterministic and probabilistic binary search in graphs. STOC (2016)