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Audra McMillan
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The structure of optimal private tests for simple hypotheses
CL Canonne, G Kamath, A McMillan, A Smith, J Ullman
Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing …, 2019
462019
Hiding among the clones: A simple and nearly optimal analysis of privacy amplification by shuffling
V Feldman, A McMillan, K Talwar
2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS …, 2022
432022
Private identity testing for high-dimensional distributions
CL Canonne, G Kamath, A McMillan, J Ullman, L Zakynthinou
Advances in Neural Information Processing Systems 33, 10099-10111, 2020
262020
Online learning via differential privacy
JD Abernethy, C Lee, A McMillan, A Tewari
20*2017
Property testing for differential privacy
AC Gilbert, A McMillan
2018 56th Annual Allerton Conference on Communication, Control, and …, 2018
172018
Differentially private simple linear regression
D Alabi, A McMillan, J Sarathy, A Smith, S Vadhan
arXiv preprint arXiv:2007.05157, 2020
132020
Local differential privacy for physical sensor data and sparse recovery
A McMillan, AC Gilbert
2018 52nd Annual Conference on Information Sciences and Systems (CISS), 1-6, 2018
10*2018
Controlling privacy loss in survey sampling
M Bun, J Drechsler, M Gaboardi, A McMillan
arXiv preprint arXiv:2007.12674, 2020
32020
Nonparametric differentially private confidence intervals for the median
J Drechsler, I Globus-Harris, A McMillan, J Sarathy, A Smith
Journal of Survey Statistics and Methodology 10 (3), 804-829, 2022
22022
When is non-trivial estimation possible for graphons and stochastic block models?
A McMillan, A Smith
Information and Inference: A Journal of the IMA 7 (2), 169-181, 2018
22018
Total positivity of a shuffle matrix
A McMillan
Involve, a Journal of Mathematics 5 (1), 61-65, 2012
22012
Mean Estimation with User-level Privacy under Data Heterogeneity
R Cummings, V Feldman, A McMillan, K Talwar
NeurIPS 2021 Workshop Privacy in Machine Learning, 2021
12021
Differential Privacy, Property Testing, and Perturbations
A McMillan
12018
Online linear optimization through the differential privacy lens
J Abernethy, C Lee, A McMillan, A Tewari
arXiv preprint arXiv:1711.10019, 2017
12017
Stronger Privacy Amplification by Shuffling for R\'enyi and Approximate Differential Privacy
V Feldman, A McMillan, K Talwar
arXiv preprint arXiv:2208.04591, 2022
2022
Controlling Privacy Loss in Sampling Schemes: An Analysis of Stratified and Cluster Sampling
M Bun, J Drechsler, M Gaboardi, A McMillan, J Sarathy
3rd Symposium on Foundations of Responsible Computing (FORC 2022), 2022
2022
Non-parametric differentially private confidence intervals for the median
A Smith, J Drechsler, I Globus-Harris, A McMillan, J Sarathy
https://arxiv. org/abs/2106.10333, 2021
2021
The Structure of Optimal Private Tests of Simple Hypotheses
A McMillan, C Canonne, G Kamath, A Smith, J Ullman
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Articles 1–18