Uri Stemmer

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Kobbi NissimDept. of Computer Science (Georgetown)Verified email at georgetown.edu
Haim KaplanSchool of Computer Science, Tel Aviv UniversityVerified email at post.tau.ac.il
Yishay MansourTel Aviv UniversityVerified email at tauex.tau.ac.il
Amos BeimelBen Gurion UniversityVerified email at bgu.ac.il
Edith CohenGoogle, Tel Aviv UniversityVerified email at alumni.stanford.edu
Raef BassilyAssociate Professor, Computer Science & Engineering, The Ohio State UniversityVerified email at osu.edu
Jelani NelsonProfessor, EECS, University of California, BerkeleyVerified email at berkeley.edu
Mark BunAssistant Professor, Boston UniversityVerified email at bu.edu
Shay Moran(Math, CS, & DDS, Technion) & (Google Research)Verified email at google.com
Abhradeep Guha ThakurtaGoogle Research - Brain TeamVerified email at google.com
Yossi MatiasGoogleVerified email at tau.ac.il
Tamás SarlósGoogleVerified email at google.com
Eliad TsfadiaGeorgetown UniversityVerified email at georgetown.edu
Jonathan UllmanAssociate Professor of Computer Science, Northeastern UniversityVerified email at ccs.neu.edu
Adam SmithProfessor of Computer Science, Boston UniversityVerified email at bu.edu
Thomas SteinkeGoogle DeepMindVerified email at google.com
Salil VadhanVicky Joseph Professor of Computer Science and Applied Mathematics, Harvard UniversityVerified email at g.harvard.edu
Moshe ShechnerPhD student, Computer Science, Tel Aviv UniversityVerified email at tauex.tau.ac.il
avinatan hassidimVerified email at biu.ac.il
Xin LyuGraduate Student, University of California, BerkeleyVerified email at berkeley.edu

Uri Stemmer

Tel Aviv University

Verified email at tau.ac.il - Homepage

Data privacy Learning theory Theoretical computer science


Title Sort by citations Sort by year Sort by title	Cited by 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	299	2016
Practical locally private heavy hitters R Bassily, K Nissim, U Stemmer, A Guha Thakurta Advances in Neural Information Processing Systems 30, 2017	293	2017
Private learning and sanitization: Pure vs. approximate differential privacy A Beimel, K Nissim, U Stemmer International Workshop on Approximation Algorithms for Combinatorial …, 2013	223	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	214	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	178	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	110*	2019
Simultaneous private learning of multiple concepts M Bun, K Nissim, U Stemmer Journal of Machine Learning Research 20 (94), 1-34, 2019	95	2019
Clustering algorithms for the centralized and local models K Nissim, U Stemmer Algorithmic Learning Theory, 619-653, 2018	82	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	73	2022
Locally private k-means clustering U Stemmer Journal of Machine Learning Research 22 (176), 1-30, 2021	69	2021
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	64	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	62	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	58	2016
Differentially private k-means with constant multiplicative error U Stemmer, H Kaplan Advances in Neural Information Processing Systems 31, 2018	52	2018
Friendlycore: Practical differentially private aggregation E Tsfadia, E Cohen, H Kaplan, Y Mansour, U Stemmer International Conference on Machine Learning, 21828-21863, 2022	44	2022
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	42*	2021
Dynamic algorithms against an adaptive adversary: generic constructions and lower bounds A Beimel, H Kaplan, Y Mansour, K Nissim, T Saranurak, U Stemmer Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing …, 2022	37	2022
On the generalization properties of differential privacy K Nissim, U Stemmer arXiv preprint arXiv:1504.05800, 2015	37	2015
Private center points and learning of halfspaces A Beimel, S Moran, K Nissim, U Stemmer Conference on Learning Theory, 269-282, 2019	34	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	34*	2014

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