Audra McMillan

According to our database, Audra McMillan authored at least 20 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Instance-Optimal Private Density Estimation in the Wasserstein Distance.

Vitaly Feldman, Audra McMillan, Satchit Sivakumar, Kunal Talwar

CoRR, 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

Proceedings of the IEEE Conference on Secure and Trustworthy Machine Learning, 2024

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, Suman Ganta, Yusuf Goren, Filip Granqvist, Kristine Guo, Frederic Jacobs, Omid Javidbakht, Albert Liu, Richard Low, Dan Mascenik, Steve Myers, David Park, Wonhee Park, Gianni Parsa, Tommy Pauly, Christian Priebe, Rehan Rishi, Guy N. Rothblum, Congzheng Song, Linmao Song, Karl Tarbe, Sebastian Vogt, Shundong Zhou, Vojta Jina, Michael Scaria, Luke Winstrom

Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, 2024

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, Suman Ganta, Yusuf Goren, Filip Granqvist, Kristine Guo, Frederic Jacobs, Omid Javidbakht, Albert Liu, Richard Low, Dan Mascenik, Steve Myers, David Park, Wonhee Park, Gianni Parsa, Tommy Pauly, Christian Priebe, Rehan Rishi, Guy N. Rothblum, Michael Scaria, Linmao Song, Congzheng Song, Karl Tarbe, Sebastian Vogt, Luke Winstrom, Shundong Zhou

CoRR, 2023

Stronger Privacy Amplification by Shuffling for Renyi and Approximate Differential Privacy.

Vitaly Feldman, Audra McMillan, Kunal Talwar

Proceedings of the 2023 ACM-SIAM Symposium on Discrete Algorithms, 2023

2022

Differentially Private Simple Linear Regression.

Daniel Alabi, Audra McMillan, Jayshree Sarathy, Adam D. Smith, Salil P. Vadhan

Proc. Priv. Enhancing Technol., 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, Albert Liu, Cheney Lyford, Joey Meyer, Alex Palmer, David Park, Wonhee Park, Gianni Parsa, Paul Pelzl, Rehan Rishi, Congzheng Song, Shan Wang, Shundong Zhou

CoRR, 2022

Instance-Optimal Differentially Private Estimation.

Audra McMillan, Adam D. Smith, Jonathan R. Ullman

CoRR, 2022

Mean Estimation with User-level Privacy under Data Heterogeneity.

Rachel Cummings, Vitaly Feldman, Audra McMillan, Kunal Talwar

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Controlling Privacy Loss in Sampling Schemes: An Analysis of Stratified and Cluster Sampling.

Mark Bun, Jörg Drechsler, Marco Gaboardi, Audra McMillan, Jayshree Sarathy

Proceedings of the 3rd Symposium on Foundations of Responsible Computing, 2022

2021

Non-parametric Differentially Private Confidence Intervals for the Median.

Jörg Drechsler, Ira Globus-Harris, Audra McMillan, Jayshree Sarathy, Adam D. Smith

CoRR, 2021

Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling.

Vitaly Feldman, Audra McMillan, Kunal Talwar

Proceedings of the 62nd IEEE Annual Symposium on Foundations of Computer Science, 2021

2020

Controlling Privacy Loss in Survey Sampling (Working Paper).

Mark Bun, Jörg Drechsler, Marco Gaboardi, Audra McMillan

CoRR, 2020

Private Identity Testing for High-Dimensional Distributions.

Clément L. Canonne, Gautam Kamath, Audra McMillan, Jonathan R. Ullman, Lydia Zakynthinou

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

The structure of optimal private tests for simple hypotheses.

Clément L. Canonne, Gautam Kamath, Audra McMillan, Adam D. Smith, Jonathan R. Ullman

Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, 2019

Online Learning via the Differential Privacy Lens.

Jacob D. Abernethy, Young Hun Jung, Chansoo Lee, Audra McMillan, Ambuj Tewari

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Local differential privacy for physical sensor data and sparse recovery.

Audra McMillan, Anna C. Gilbert

Proceedings of the 52nd Annual Conference on Information Sciences and Systems, 2018

Property Testing For Differential Privacy.

Anna C. Gilbert, Audra McMillan

Proceedings of the 56th Annual Allerton Conference on Communication, 2018

2017

Online Learning via Differential Privacy.

Jacob D. Abernethy, Chansoo Lee, Audra McMillan, Ambuj Tewari

CoRR, 2017

2016

When is Nontrivial Estimation Possible for Graphons and Stochastic Block Models?

Audra McMillan, Adam D. Smith

CoRR, 2016