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
Online Learning via the Differential Privacy Lens
JD Abernethy, YH Jung, C Lee, A McMillan, A Tewari
Advances in Neural Information Processing Systems, 8894-8904, 2019
Private identity testing for high-dimensional distributions
CL Canonne, G Kamath, A McMillan, J Ullman, L Zakynthinou
arXiv preprint arXiv:1905.11947, 2019
Property testing for differential privacy
AC Gilbert, A McMillan
2018 56th Annual Allerton Conference on Communication, Control, and …, 2018
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
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
Online linear optimization through the differential privacy lens
J Abernethy, C Lee, A McMillan, A Tewari
arXiv preprint arXiv:1711.10019, 2017
Private Identity Testing for High-Dimensional Distributions
CL Canonne, G Kamath, A McMillan, J Ullman, L Zakynthinou
Advances in Neural Information Processing Systems 33, 2020
Total positivity of a shuffle matrix
A McMillan
Involve, a Journal of Mathematics 5 (1), 61-65, 2012
Controlling Privacy Loss in Survey Sampling
M Bun, J Drechsler, M Gaboardi, A McMillan
arXiv preprint arXiv:2007.12674, 2020
Differentially Private Simple Linear Regression
D Alabi, A McMillan, J Sarathy, A Smith, S Vadhan
arXiv preprint arXiv:2007.05157, 2020
Differential Privacy, Property Testing, and Perturbations
A McMillan
The Structure of Optimal Private Tests of Simple Hypotheses
A McMillan, C Canonne, G Kamath, AS BU, J Ullman
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Artiklar 1–13