Singhal, Vikrant

10 publications

ALT 2024 A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions Vikrant Singhal

ALT 2024 Not All Learnable Distribution Classes Are Privately Learnable Mark Bun, Gautam Kamath, Argyris Mouzakis, Vikrant Singhal

NeurIPS 2023 Private Distribution Learning with Public Data: The View from Sample Compression Shai Ben-David, Alex Bie, Clément L Canonne, Gautam Kamath, Vikrant Singhal

COLT 2022 A Private and Computationally-Efficient Estimator for Unbounded Gaussians Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan Ullman

NeurIPS 2022 New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma Gautam Kamath, Argyris Mouzakis, Vikrant Singhal

NeurIPS 2022 Private Estimation with Public Data Alex Bie, Gautam Kamath, Vikrant Singhal

NeurIPS 2021 Privately Learning Subspaces Vikrant Singhal, Thomas Steinke

COLT 2020 Private Mean Estimation of Heavy-Tailed Distributions Gautam Kamath, Vikrant Singhal, Jonathan Ullman

NeurIPS 2019 Differentially Private Algorithms for Learning Mixtures of Separated Gaussians Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan Ullman

COLT 2019 Privately Learning High-Dimensional Distributions Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan Ullman