A Private and Computationally-Efficient Estimator for Unbounded Gaussians. - Gautam Kamath 001; Argyris Mouzakis; Vikrant Singhal; Thomas Steinke 002; Jonathan R. Ullman (COLT, 2022)

New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma. - Gautam Kamath 001; Argyris Mouzakis; Vikrant Singhal (NeurIPS, 2022)

A Bias-Variance-Privacy Trilemma for Statistical Estimation. - Gautam Kamath 001; Argyris Mouzakis; Matthew Regehr; Vikrant Singhal; Thomas Steinke 002; Jonathan R. Ullman (CoRR, 2023)

Not All Learnable Distribution Classes are Privately Learnable. - Mark Bun; Gautam Kamath 001; Argyris Mouzakis; Vikrant Singhal (ALT, 2024)

Optimal Differentially Private Sampling of Unbounded Gaussians. - Valentio Iverson; Gautam Kamath 001; Argyris Mouzakis (COLT, 2025)

Private Mean Estimation with Person-Level Differential Privacy. - Sushant Agarwal; Gautam Kamath 001; Mahbod Majid; Argyris Mouzakis; Rose Silver; Jonathan R. Ullman (SODA, 2025)