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Uri Stemmer
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Cited by
Year
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
2362016
Practical locally private heavy hitters
R Bassily, K Nissim, U Stemmer, A Guha Thakurta
Advances in Neural Information Processing Systems 30, 2017
2152017
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
1672015
Private learning and sanitization: Pure vs. approximate differential privacy
A Beimel, K Nissim, U Stemmer
Approximation, Randomization, and Combinatorial Optimization. Algorithms and …, 2013
1632013
Heavy hitters and the structure of local privacy
M Bun, J Nelson, U Stemmer
ACM Transactions on Algorithms (TALG) 15 (4), 1-40, 2019
1392019
Characterizing the Sample Complexity of Pure Private Learners.
A Beimel, K Nissim, U Stemmer
J. Mach. Learn. Res. 20, 146:1-146:33, 2019
85*2019
Simultaneous private learning of multiple concepts
M Bun, K Nissim, U Stemmer
Proceedings of the 2016 ACM Conference on Innovations in Theoretical …, 2016
712016
Clustering algorithms for the centralized and local models
K Nissim, U Stemmer
Algorithmic Learning Theory, 619-653, 2018
602018
Locating a small cluster privately
K Nissim, U Stemmer, S Vadhan
Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of …, 2016
422016
Locally private k-means clustering
U Stemmer
The Journal of Machine Learning Research 22 (1), 7964-7993, 2021
392021
Privately learning thresholds: Closing the exponential gap
H Kaplan, K Ligett, Y Mansour, M Naor, U Stemmer
Conference on Learning Theory, 2263-2285, 2020
392020
Adversarially robust streaming algorithms via differential privacy
A Hasidim, H Kaplan, Y Mansour, Y Matias, U Stemmer
Advances in Neural Information Processing Systems 33, 147-158, 2020
392020
On the generalization properties of differential privacy
K Nissim, U Stemmer
arXiv preprint arXiv:1504.05800, 2015
372015
Differentially private k-means with constant multiplicative error
U Stemmer, H Kaplan
Advances in Neural Information Processing Systems 31, 2018
282018
Learning privately with labeled and unlabeled examples
A Beimel, K Nissim, U Stemmer
Proceedings of the twenty-sixth annual ACM-SIAM symposium on Discrete …, 2014
27*2014
Separating adaptive streaming from oblivious streaming using the bounded storage model
H Kaplan, Y Mansour, K Nissim, U Stemmer
Annual International Cryptology Conference, 94-121, 2021
25*2021
Private center points and learning of halfspaces
A Beimel, S Moran, K Nissim, U Stemmer
Conference on Learning Theory, 269-282, 2019
212019
Learning and evaluating a differentially private pre-trained language model
S Hoory, A Feder, A Tendler, S Erell, A Peled-Cohen, I Laish, H Nakhost, ...
Findings of the Association for Computational Linguistics: EMNLP 2021, 1178-1189, 2021
182021
The limits of post-selection generalization
J Ullman, A Smith, K Nissim, U Stemmer, T Steinke
Advances in Neural Information Processing Systems 31, 2018
172018
Differentially private k-means with constant multiplicative error
H Kaplan, U Stemmer
arXiv preprint arXiv:1804.08001, 2018
152018
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