Protection against reconstruction and its applications in private federated learning A Bhowmick, J Duchi, J Freudiger, G Kapoor, R Rogers
arXiv preprint arXiv:1812.00984, 2018
448 2018 Differentially private chi-squared hypothesis testing: Goodness of fit and independence testing M Gaboardi, H Lim, R Rogers, S Vadhan
International conference on machine learning, 2111-2120, 2016
163 2016 Learning with Privacy at Scale DP Team
Apple Machine Learning Journal 1 (8), 2017
126 2017 Lower bounds for locally private estimation via communication complexity J Duchi, R Rogers
Conference on Learning Theory, 1161-1191, 2019
118 2019 Privacy odometers and filters: Pay-as-you-go composition RM Rogers, A Roth, J Ullman, S Vadhan
Advances in Neural Information Processing Systems, 1921-1929, 2016
109 2016 Practical differentially private top-k selection with pay-what-you-get composition D Durfee, RM Rogers
Advances in Neural Information Processing Systems 32, 2019
105 2019 Psi M Gaboardi, J Honaker, G King, J Murtagh, K Nissim, J Ullman, S Vadhan, ...
arXiv preprint arXiv:1609.04340, 2016
99 2016 LinkedIn's Audience Engagements API: A privacy preserving data analytics system at scale R Rogers, S Subramaniam, S Peng, D Durfee, S Lee, SK Kancha, ...
arXiv preprint arXiv:2002.05839, 2020
94 2020 Max-information, differential privacy, and post-selection hypothesis testing R Rogers, A Roth, A Smith, O Thakkar
2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS …, 2016
94 2016 Privatized machine learning using generative adversarial networks A Bhowmick, AH Vyrros, RM Rogers
US Patent App. 15/892,246, 2019
80 2019 Local private hypothesis testing: Chi-square tests M Gaboardi, R Rogers
International Conference on Machine Learning, 1626-1635, 2018
74 2018 Optimal differential privacy composition for exponential mechanisms J Dong, D Durfee, R Rogers
International Conference on Machine Learning, 2597-2606, 2020
67 2020 Locally Private Mean Estimation: M Gaboardi, R Rogers, O Sheffet
The 22nd international conference on artificial intelligence and statistics …, 2019
64 2019 Asymptotically truthful equilibrium selection in large congestion games RM Rogers, A Roth
Proceedings of the fifteenth ACM conference on Economics and computation …, 2014
50 2014 Fully-adaptive composition in differential privacy J Whitehouse, A Ramdas, R Rogers, S Wu
International Conference on Machine Learning, 36990-37007, 2023
42 2023 Differentially private histograms under continual observation: Streaming selection into the unknown AR Cardoso, R Rogers
International Conference on Artificial Intelligence and Statistics, 2397-2419, 2022
39 2022 Do prices coordinate markets? J Hsu, J Morgenstern, R Rogers, A Roth, R Vohra
Proceedings of the forty-eighth annual ACM symposium on Theory of Computing …, 2016
39 2016 Bounding, concentrating, and truncating: Unifying privacy loss composition for data analytics M Cesar, R Rogers
Algorithmic Learning Theory, 421-457, 2021
38 2021 Challenges towards the next frontier in privacy R Cummings, D Desfontaines, D Evans, R Geambasu, M Jagielski, ...
arXiv preprint arXiv:2304.06929 1, 2023
37 2023 A new class of private chi-square hypothesis tests R Rogers, D Kifer
Artificial Intelligence and Statistics, 991-1000, 2017
37 2017 
Aaron RothProfessor of Computer Science, University of Pennsylvaniacis.upenn.edu üzerinde doğrulanmış e-posta adresine sahip
