Uri Stemmer
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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
183*2016
Practical locally private heavy hitters
R Bassily, K Nissim, U Stemmer, AG Thakurta
Advances in Neural Information Processing Systems, 2288-2296, 2017
1092017
Differentially private release and learning of threshold functions
M Bun, K Nissim, U Stemmer, S Vadhan
2015 IEEE 56th Annual Symposium on Foundations of Computer Science, 634-649, 2015
1022015
Private learning and sanitization: Pure vs. approximate differential privacy
A Beimel, K Nissim, U Stemmer
Approximation, Randomization, and Combinatorial Optimization. Algorithms and …, 2013
932013
Heavy hitters and the structure of local privacy
M Bun, J Nelson, U Stemmer
ACM Transactions on Algorithms (TALG) 15 (4), 1-40, 2019
762019
Characterizing the Sample Complexity of Pure Private Learners.
A Beimel, K Nissim, U Stemmer
Journal of Machine Learning Research 20 (146), 1-33, 2019
55*2019
Clustering algorithms for the centralized and local models
K Nissim, U Stemmer
Algorithmic Learning Theory, 619-653, 2018
312018
Simultaneous Private Learning of Multiple Concepts.
M Bun, K Nissim, U Stemmer
ITCS, 369-380, 2016
292016
Locating a small cluster privately
K Nissim, U Stemmer, S Vadhan
Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of …, 2016
222016
Learning privately with labeled and unlabeled examples
A Beimel, K Nissim, U Stemmer
Algorithmica, 1-39, 2020
212020
Differentially private k-means with constant multiplicative error
U Stemmer, H Kaplan
Advances in Neural Information Processing Systems, 5431-5441, 2018
12*2018
Locally Private k-Means Clustering
U Stemmer
Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete …, 2020
112020
Privately learning thresholds: Closing the exponential gap
H Kaplan, K Ligett, Y Mansour, M Naor, U Stemmer
Conference on Learning Theory, 2263-2285, 2020
92020
The limits of post-selection generalization
J Ullman, A Smith, K Nissim, U Stemmer, T Steinke
Advances in Neural Information Processing Systems, 6400-6409, 2018
82018
Concentration Bounds for High Sensitivity Functions Through Differential Privacy
K Nissim, U Stemmer
Journal of Privacy and Confidentiality 9 (1), 2019
72019
Private center points and learning of halfspaces
A Beimel, S Moran, K Nissim, U Stemmer
arXiv preprint arXiv:1902.10731, 2019
62019
Differentially private learning of geometric concepts
H Kaplan, Y Mansour, Y Matias, U Stemmer
arXiv preprint arXiv:1902.05017, 2019
32019
How to Find a Point in the Convex Hull Privately
H Kaplan, M Sharir, U Stemmer
arXiv preprint arXiv:2003.13192, 2020
22020
Closure Properties for Private Classification and Online Prediction
N Alon, A Beimel, S Moran, U Stemmer
arXiv preprint arXiv:2003.04509, 2020
22020
Adversarially Robust Streaming Algorithms via Differential Privacy
A Hassidim, H Kaplan, Y Mansour, Y Matias, U Stemmer
arXiv preprint arXiv:2004.05975, 2020
12020
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Artiklar 1–20