Uri Stemmer
Titolo
Citata da
Citata da
Anno
Algorithmic stability for adaptive data analysis
R Bassily, K Nissim, A Smith, T Steinke, U Stemmer, J Ullman
SIAM Journal on Computing, STOC16-377-STOC16-405, 2021
1902021
Practical locally private heavy hitters
R Bassily, K Nissim, U Stemmer, A Thakurta
arXiv preprint arXiv:1707.04982, 2017
1412017
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
1182015
Private learning and sanitization: Pure vs. approximate differential privacy
A Beimel, K Nissim, U Stemmer
Approximation, Randomization, and Combinatorial Optimization. Algorithms and …, 2013
1162013
Heavy hitters and the structure of local privacy
M Bun, J Nelson, U Stemmer
ACM Transactions on Algorithms (TALG) 15 (4), 1-40, 2019
1012019
Characterizing the Sample Complexity of Pure Private Learners.
A Beimel, K Nissim, U Stemmer
Journal of Machine Learning Research 20 (146), 1-33, 2019
65*2019
Clustering algorithms for the centralized and local models
K Nissim, U Stemmer
Algorithmic Learning Theory, 619-653, 2018
412018
Simultaneous Private Learning of Multiple Concepts.
M Bun, K Nissim, U Stemmer
J. Mach. Learn. Res. 20, 94:1-94:34, 2019
392019
On the generalization properties of differential privacy
K Nissim, U Stemmer
arXiv preprint arXiv:1504.05800, 2015
392015
Locating a small cluster privately
K Nissim, U Stemmer, S Vadhan
Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of …, 2016
252016
Learning privately with labeled and unlabeled examples
A Beimel, K Nissim, U Stemmer
Algorithmica 83 (1), 177-215, 2021
222021
Differentially private k-means with constant multiplicative error
U Stemmer, H Kaplan
NeurIPS, 2018
17*2018
Locally Private k-Means Clustering
U Stemmer
Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete …, 2020
152020
Privately learning thresholds: Closing the exponential gap
H Kaplan, K Ligett, Y Mansour, M Naor, U Stemmer
Conference on Learning Theory, 2263-2285, 2020
142020
Private center points and learning of halfspaces
A Beimel, S Moran, K Nissim, U Stemmer
Conference on Learning Theory, 269-282, 2019
132019
Adversarially robust streaming algorithms via differential privacy
A Hassidim, H Kaplan, Y Mansour, Y Matias, U Stemmer
arXiv preprint arXiv:2004.05975, 2020
92020
Concentration Bounds for High Sensitivity Functions Through Differential Privacy
K Nissim, U Stemmer
Journal of Privacy and Confidentiality 9 (1), 2019
92019
The limits of post-selection generalization
K Nissim, A Smith, T Steinke, U Stemmer, J Ullman
arXiv preprint arXiv:1806.06100, 2018
92018
Closure properties for private classification and online prediction
N Alon, A Beimel, S Moran, U Stemmer
Conference on Learning Theory, 119-152, 2020
52020
Locally private determination of heavy hitters
YN Kobliner, U Stemmer, RBY Bassily, AG Thakurta
US Patent 11,023,594, 2021
42021
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
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