| 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 | 102 | 2022 |
| 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 | 61 | 2019 |
| 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 | 37 | 2020 |
| Differentially private simple linear regression D Alabi, A McMillan, J Sarathy, A Smith, S Vadhan arXiv preprint arXiv:2007.05157, 2020 | 35 | 2020 |
| Property testing for differential privacy AC Gilbert, A McMillan 2018 56th Annual Allerton Conference on Communication, Control, and …, 2018 | 27 | 2018 |
| Online learning via the differential privacy lens JD Abernethy, YH Jung, C Lee, A McMillan, A Tewari Advances in Neural Information Processing Systems 32, 2019 | 26* | 2019 |
| Stronger privacy amplification by shuffling for rényi and approximate differential privacy V Feldman, A McMillan, K Talwar Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms …, 2023 | 18 | 2023 |
| Mean estimation with user-level privacy under data heterogeneity R Cummings, V Feldman, A McMillan, K Talwar Advances in Neural Information Processing Systems 35, 29139-29151, 2022 | 15 | 2022 |
| 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 |
| 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 | 8 | 2022 |
| Private federated statistics in an interactive setting A McMillan, O Javidbakht, K Talwar, E Briggs, M Chatzidakis, J Chen, ... arXiv preprint arXiv:2211.10082, 2022 | 4 | 2022 |
| Instance-optimal differentially private estimation A McMillan, A Smith, J Ullman arXiv preprint arXiv:2210.15819, 2022 | 3 | 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 | 3 | 2022 |
| Controlling privacy loss in survey sampling M Bun, J Drechsler, M Gaboardi, A McMillan arXiv preprint arXiv:2007.12674, 2020 | 3 | 2020 |
| 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 | 2 | 2018 |
| Total positivity of a shuffle matrix A McMillan Involve, a Journal of Mathematics 5 (1), 61-65, 2012 | 2 | 2012 |
| Samplable Anonymous Aggregation for Private Federated Data Analysis K Talwar, S Wang, A McMillan, V Jina, V Feldman, B Basile, A Cahill, ... arXiv preprint arXiv:2307.15017, 2023 | 1 | 2023 |
| Differential Privacy, Property Testing, and Perturbations A McMillan | 1 | 2018 |
| Online linear optimization through the differential privacy lens J Abernethy, C Lee, A McMillan, A Tewari arXiv preprint arXiv:1711.10019, 2017 | 1 | 2017 |
| Differentially Private Heavy Hitters using Federated Analytics K Chadha, J Chen, J Duchi, V Feldman, H Hashemi, O Javidbakht, ... Federated Learning and Analytics in Practice: Algorithms, Systems …, 2023 | | 2023 |
