• Gautam Kamath, Argyris Mouzakis, Matthew Regehr, Vikrant Singhal, Thomas Steinke, and Jonathan Ullman.
  A Bias-Variance-Privacy Trilemma for Statistical Estimation.
  [arXiv]

• Vikrant Singhal.
  A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions.
  ALT 2024.
  [arXiv]

• Mark Bun, Gautam Kamath, Argyris Mouzakis, and Vikrant Singhal.
  Not All Learnable Distribution Classes are Privately Learnable.
  ALT 2024.
  [arXiv]

• Shai Ben-David, Alex Bie, Clément Canonne, Gautam Kamath, and Vikrant Singhal.
  Private Distribution Learning with Public Data: The View from Sample Compression.
  NeurIPS 2023 (Spotlight Presentation); TPDP 2023.
  [arXiv]

• Gautam Kamath, Argyris Mouzakis, and Vikrant Singhal.
  New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma.
  NeurIPS 2022; TPDP 2022.
  [arXiv]

• Alex Bie, Gautam Kamath, and Vikrant Singhal.
  Private Estimation with Public Data.
  NeurIPS 2022; TPDP 2022.
  [arXiv]

• Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, and Jonathan Ullman.
  A Private and Computationally-Efficient Estimator for Unbounded Gaussians.
  COLT 2022; TPDP 2022.
  [arXiv]

• Vikrant Singhal and Thomas Steinke.
  Privately Learning Subspaces.
  NeurIPS 2021; TPDP 2021.
  [arXiv]

• Gautam Kamath, Vikrant Singhal, and Jonathan Ullman.
  Private Mean Estimation of Heavy-Tailed Distributions.
  COLT 2020; TPDP 2020.
  [arXiv]

• Gautam Kamath, Or Sheffet, Vikrant Singhal, and Jonathan Ullman.
  Differentially Private Algorithms for Learning Mixtures of Separated Gaussians.
  NeurIPS 2019.
  [arXiv] [poster]

• Gautam Kamath, Jerry Li, Vikrant Singhal, and Jonathan Ullman.
  Privately Learning High-Dimensional Distributions.
  COLT 2019; TPDP 2018.
  [arXiv]

• Ehsan Emamjomeh-Zadeh, David Kempe, and Vikrant Singhal.
  Deterministic and Probabilistic Binary Search in Graphs.
  STOC 2016.
  [arXiv]

Differentially Private Statistical Estimation