Uri Stemmer
Uri Stemmer
Ben-Gurion University
Verified email at uri.co.il - Homepage
TitleCited byYear
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
148*2016
Practical locally private heavy hitters
R Bassily, K Nissim, U Stemmer, AG Thakurta
Advances in Neural Information Processing Systems, 2288-2296, 2017
672017
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
662015
Private learning and sanitization: Pure vs. approximate differential privacy
A Beimel, K Nissim, U Stemmer
Approximation, Randomization, and Combinatorial Optimization. Algorithms and …, 2013
642013
Heavy hitters and the structure of local privacy
M Bun, J Nelson, U Stemmer
Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of …, 2018
482018
Characterizing the sample complexity of private learners
A Beimel, K Nissim, U Stemmer
Proceedings of the 4th conference on Innovations in Theoretical Computer …, 2013
382013
Simultaneous Private Learning of Multiple Concepts.
M Bun, K Nissim, U Stemmer
ITCS, 369-380, 2016
202016
Learning privately with labeled and unlabeled examples
A Beimel, K Nissim, U Stemmer
Proceedings of the twenty-sixth annual ACM-SIAM symposium on Discrete …, 2015
162015
Clustering algorithms for the centralized and local models
K Nissim, U Stemmer
arXiv preprint arXiv:1707.04766, 2017
132017
Locating a small cluster privately
K Nissim, U Stemmer, S Vadhan
Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of …, 2016
122016
Concentration Bounds for High Sensitivity Functions Through Differential Privacy
K Nissim, U Stemmer
Journal of Privacy and Confidentiality 9 (1), 2019
7*2019
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
52018
Differentially private k-means with constant multiplicative error
U Stemmer, H Kaplan
Advances in Neural Information Processing Systems, 5431-5441, 2018
32018
Private Center Points and Learning of Halfspaces
A Beimel, S Moran, K Nissim, U Stemmer
arXiv preprint arXiv:1902.10731, 2019
22019
Privately Learning Thresholds: Closing the Exponential Gap
H Kaplan, K Ligett, Y Mansour, M Naor, U Stemmer
arXiv preprint arXiv:1911.10137, 2019
2019
Locally private k-means clustering
U Stemmer
arXiv preprint arXiv:1907.02513, 2019
2019
Differentially Private Learning of Geometric Concepts
H Kaplan, Y Mansour, Y Matias, U Stemmer
arXiv preprint arXiv:1902.05017, 2019
2019
Characterizing the Sample Complexity of Pure Private Learners
A Beimel, K Nissim, U Stemmer
Journal of Machine Learning Research 20 (146), 1-33, 2019
2019
Locally private determination of heavy hitters
YN Kobliner, U Stemmer, RBY Bassily, AG Thakurta
US Patent App. 15/986,734, 2018
2018
Individuals and Privacy in the Eye of Data Analysis
U Stemmer
Ben-Gurion University of the Negev, 2016
2016
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Articles 1–20