When is Nontrivial Estimation Possible for Graphons and Stochastic Block Models? - Audra McMillan; Adam D. Smith 001 (CoRR, 2016)

Online Learning via Differential Privacy. - Jacob D. Abernethy; Chansoo Lee; Audra McMillan; Ambuj Tewari (CoRR, 2017)

Property Testing For Differential Privacy. - Anna C. Gilbert; Audra McMillan (Allerton, 2018)

Local differential privacy for physical sensor data and sparse recovery. - Audra McMillan; Anna C. Gilbert (CISS, 2018)

Online Learning via the Differential Privacy Lens. - Jacob D. Abernethy; Young Hun Jung; Chansoo Lee; Audra McMillan; Ambuj Tewari (NeurIPS, 2019)

The structure of optimal private tests for simple hypotheses. - Clément L. Canonne; Gautam Kamath 001; Audra McMillan; Adam D. Smith 001; Jonathan R. Ullman (STOC, 2019)

Private Identity Testing for High-Dimensional Distributions. - Clément L. Canonne; Gautam Kamath 001; Audra McMillan; Jonathan R. Ullman; Lydia Zakynthinou (NeurIPS, 2020)

Controlling Privacy Loss in Survey Sampling (Working Paper). - Mark Bun; Jörg Drechsler; Marco Gaboardi; Audra McMillan (CoRR, 2020)

Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling. - Vitaly Feldman; Audra McMillan; Kunal Talwar (FOCS, 2021)

Non-parametric Differentially Private Confidence Intervals for the Median. - Jörg Drechsler; Ira Globus-Harris; Audra McMillan; Jayshree Sarathy; Adam D. Smith 001 (CoRR, 2021)

Controlling Privacy Loss in Sampling Schemes: An Analysis of Stratified and Cluster Sampling. - Mark Bun; Jörg Drechsler; Marco Gaboardi; Audra McMillan; Jayshree Sarathy (FORC, 2022)

Mean Estimation with User-level Privacy under Data Heterogeneity. - Rachel Cummings; Vitaly Feldman; Audra McMillan; Kunal Talwar (NeurIPS, 2022)

Instance-Optimal Differentially Private Estimation. - Audra McMillan; Adam D. Smith 001; Jonathan R. Ullman (CoRR, 2022)

Private Federated Statistics in an Interactive Setting. - Audra McMillan; Omid Javidbakht; Kunal Talwar; Elliot Briggs; Mike Chatzidakis; Junye Chen; John C. Duchi; Vitaly Feldman; Yusuf Goren; Michael Hesse; Vojta Jina; Anil Katti; et al. (CoRR, 2022)

Differentially Private Simple Linear Regression. - Daniel Alabi; Audra McMillan; Jayshree Sarathy; Adam D. Smith 001; Salil P. Vadhan (Proc. Priv. Enhancing Technol., 2022)

Stronger Privacy Amplification by Shuffling for Renyi and Approximate Differential Privacy. - Vitaly Feldman; Audra McMillan; Kunal Talwar (SODA, 2023)

Samplable Anonymous Aggregation for Private Federated Data Analysis. - Kunal Talwar; Shan Wang; Audra McMillan; Vojta Jina; Vitaly Feldman; Bailey Basile; Áine Cahill; Yi Sheng Chan; Mike Chatzidakis; Junye Chen; Oliver R. A. Chick; Mona Chitnis; et al. (CoRR, 2023)

Samplable Anonymous Aggregation for Private Federated Data Analysis. - Kunal Talwar; Shan Wang; Audra McMillan; Vitaly Feldman; Pansy Bansal; Bailey Basile; Áine Cahill; Yi Sheng Chan; Mike Chatzidakis; Junye Chen; Oliver R. A. Chick; Mona Chitnis; et al. (CCS, 2024)

Instance-Optimal Private Density Estimation in the Wasserstein Distance. - Vitaly Feldman; Audra McMillan; Satchit Sivakumar; Kunal Talwar (NeurIPS, 2024)

Differentially Private Heavy Hitter Detection using Federated Analytics. - Karan N. Chadha; Junye Chen; John C. Duchi; Vitaly Feldman; Hanieh Hashemi; Omid Javidbakht; Audra McMillan; Kunal Talwar (SaTML, 2024)

Private Selection with Heterogeneous Sensitivities. - Daniela Antonova; Allegra Laro; Audra McMillan; Lorenz Wolf (SaTML, 2025)

Local Pan-Privacy for Federated Analytics. - Vitaly Feldman; Audra McMillan; Guy N. Rothblum; Kunal Talwar (CoRR, 2025)