Uri Stemmer
Geciteerd door
Geciteerd door
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
Practical locally private heavy hitters
R Bassily, K Nissim, U Stemmer, A Guha Thakurta
Advances in Neural Information Processing Systems 30, 2017
Private learning and sanitization: Pure vs. approximate differential privacy
A Beimel, K Nissim, U Stemmer
International Workshop on Approximation Algorithms for Combinatorial …, 2013
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
Heavy hitters and the structure of local privacy
M Bun, J Nelson, U Stemmer
ACM Transactions on Algorithms (TALG) 15 (4), 1-40, 2019
Characterizing the Sample Complexity of Pure Private Learners.
A Beimel, K Nissim, U Stemmer
J. Mach. Learn. Res. 20, 146:1-146:33, 2019
Simultaneous private learning of multiple concepts
M Bun, K Nissim, U Stemmer
Proceedings of the 2016 ACM Conference on Innovations in Theoretical …, 2016
Clustering algorithms for the centralized and local models
K Nissim, U Stemmer
Algorithmic Learning Theory, 619-653, 2018
Adversarially robust streaming algorithms via differential privacy
A Hassidim, H Kaplan, Y Mansour, Y Matias, U Stemmer
Journal of the ACM 69 (6), 1-14, 2022
Locally private k-means clustering
U Stemmer
The Journal of Machine Learning Research 22 (1), 7964-7993, 2021
Privately learning thresholds: Closing the exponential gap
H Kaplan, K Ligett, Y Mansour, M Naor, U Stemmer
Conference on Learning Theory, 2263-2285, 2020
Locating a small cluster privately
K Nissim, U Stemmer, S Vadhan
Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of …, 2016
Differentially private k-means with constant multiplicative error
U Stemmer, H Kaplan
Advances in Neural Information Processing Systems 31, 2018
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
On the generalization properties of differential privacy
K Nissim, U Stemmer
arXiv preprint arXiv:1504.05800, 2015
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
Private center points and learning of halfspaces
A Beimel, S Moran, K Nissim, U Stemmer
Conference on Learning Theory, 269-282, 2019
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
Friendlycore: Practical differentially private aggregation
E Tsfadia, E Cohen, H Kaplan, Y Mansour, U Stemmer
International Conference on Machine Learning, 21828-21863, 2022
A framework for adversarial streaming via differential privacy and difference estimators
I Attias, E Cohen, M Shechner, U Stemmer
arXiv preprint arXiv:2107.14527, 2021
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Artikelen 1–20