受强制性开放获取政策约束的文章 - Jonathan Ullman了解详情
可在其他位置公开访问的文章：39 篇
Distributed Differential Privacy via Shuffling
A Cheu, A Smith, J Ullman, D Zeber, M Zhilyaev
强制性开放获取政策： US National Science Foundation
Exposed! a survey of attacks on private data
C Dwork, A Smith, T Steinke, J Ullman
Annual Review of Statistics and Its Application 4 (1), 61-84, 2017
强制性开放获取政策： US National Science Foundation
Algorithmic stability for adaptive data analysis
R Bassily, K Nissim, A Smith, T Steinke, U Stemmer, J Ullman
Proceedings of the forty-eighth annual ACM symposium on Theory of Computing …, 2016
强制性开放获取政策： US National Science Foundation
Auditing differentially private machine learning: How private is private SGD?
M Jagielski, J Ullman, A Oprea
Advances in Neural Information Processing Systems 33, 22205-22216, 2020
强制性开放获取政策： US National Science Foundation, US Department of Defense
Differentially private fair learning
M Jagielski, M Kearns, J Mao, A Oprea, A Roth, S Sharifi-Malvajerdi, ...
International Conference on Machine Learning, 3000-3008, 2019
强制性开放获取政策： US National Science Foundation, US Department of Defense
Privately learning high-dimensional distributions
G Kamath, J Li, V Singhal, J Ullman
Conference on Learning Theory, 1853-1902, 2019
强制性开放获取政策： US National Science Foundation, US Department of Defense
Coinpress: Practical private mean and covariance estimation
S Biswas, Y Dong, G Kamath, J Ullman
Advances in Neural Information Processing Systems 33, 14475-14485, 2020
强制性开放获取政策： US National Science Foundation, Natural Sciences and Engineering Research …
Answering n^{2+o(1)} counting queries with differential privacy is hard
J Ullman
SIAM Journal on Computing 45 (2), 473-496, 2016
强制性开放获取政策： US National Science Foundation
Private mean estimation of heavy-tailed distributions
G Kamath, V Singhal, J Ullman
Conference on Learning Theory, 2204-2235, 2020
强制性开放获取政策： US National Science Foundation
Privacy odometers and filters: Pay-as-you-go composition
RM Rogers, A Roth, J Ullman, S Vadhan
Advances in Neural Information Processing Systems 29, 2016
强制性开放获取政策： US National Science Foundation
Manipulation attacks in local differential privacy
A Cheu, A Smith, J Ullman
2021 IEEE Symposium on Security and Privacy (SP), 883-900, 2021
强制性开放获取政策： US National Science Foundation
The power of factorization mechanisms in local and central differential privacy
A Edmonds, A Nikolov, J Ullman
Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing …, 2020
强制性开放获取政策： US National Science Foundation, Natural Sciences and Engineering Research …
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
强制性开放获取政策： US National Science Foundation
Watch and learn: Optimizing from revealed preferences feedback
A Roth, J Ullman, ZS Wu
Proceedings of the forty-eighth annual ACM symposium on Theory of Computing …, 2016
强制性开放获取政策： US National Science Foundation
Leveraging public data for practical private query release
T Liu, G Vietri, T Steinke, J Ullman, S Wu
International Conference on Machine Learning, 6968-6977, 2021
强制性开放获取政策： US National Science Foundation
Covariance-aware private mean estimation without private covariance estimation
G Brown, M Gaboardi, A Smith, J Ullman, L Zakynthinou
Advances in neural information processing systems 34, 7950-7964, 2021
强制性开放获取政策： US National Science Foundation
Private query release assisted by public data
R Bassily, A Cheu, S Moran, A Nikolov, J Ullman, S Wu
International Conference on Machine Learning, 695-703, 2020
强制性开放获取政策： US National Science Foundation, Natural Sciences and Engineering Research …
Make up your mind: The price of online queries in differential privacy
M Bun, T Steinke, J Ullman
Proceedings of the twenty-eighth annual ACM-SIAM symposium on discrete …, 2017
强制性开放获取政策： US National Science Foundation, US Department of Defense
Differentially private algorithms for learning mixtures of separated gaussians
G Kamath, O Sheffet, V Singhal, J Ullman
Advances in Neural Information Processing Systems 32, 2019
强制性开放获取政策： US National Science Foundation, Natural Sciences and Engineering Research …
Efficiently estimating erdos-renyi graphs with node differential privacy
J Ullman, A Sealfon
Advances in Neural Information Processing Systems 32, 2019
强制性开放获取政策： US National Science Foundation, US Department of Defense
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