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
Jelani NelsonProfessor, EECS, University of California, BerkeleyVerified email at berkeley.edu
Raef BassilyAssociate Professor, Computer Science & Engineering, The Ohio State UniversityVerified email at osu.edu
Mark BunAssistant Professor, Boston UniversityVerified email at bu.edu
Shay Moran(Math, CS, & DDS, Technion) & (Google Research)Verified email at google.com
Tamás SarlósGoogleVerified email at google.com
Abhradeep Guha ThakurtaGoogle Research - Brain TeamVerified email at google.com
Yossi MatiasGoogleVerified email at tau.ac.il
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
Moshe ShechnerPhD student, Computer Science, Tel Aviv UniversityVerified email at tauex.tau.ac.il
Salil VadhanVicky Joseph Professor of Computer Science and Applied Mathematics, Harvard UniversityVerified email at g.harvard.edu
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


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Practical locally private heavy hitters R Bassily, K Nissim, U Stemmer, A Guha Thakurta Advances in Neural Information Processing Systems 30, 2017	311	2017
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	309	2016
Private learning and sanitization: Pure vs. approximate differential privacy A Beimel, K Nissim, U Stemmer International Workshop on Approximation Algorithms for Combinatorial …, 2013	231	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	224	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	184	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	114*	2019
Simultaneous private learning of multiple concepts M Bun, K Nissim, U Stemmer Journal of Machine Learning Research 20 (94), 1-34, 2019	103	2019
Clustering algorithms for the centralized and local models K Nissim, U Stemmer Algorithmic Learning Theory, 619-653, 2018	86	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	77	2022
Locally private k-means clustering U Stemmer Journal of Machine Learning Research 22 (176), 1-30, 2021	74	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	73	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	66	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	64	2016
Differentially private k-means with constant multiplicative error U Stemmer, H Kaplan Advances in Neural Information Processing Systems 31, 2018	59	2018
Friendlycore: Practical differentially private aggregation E Tsfadia, E Cohen, H Kaplan, Y Mansour, U Stemmer International Conference on Machine Learning, 21828-21863, 2022	54	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	43*	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	41	2022
On the generalization properties of differential privacy K Nissim, U Stemmer arXiv preprint arXiv:1504.05800, 2015	39	2015
Private center points and learning of halfspaces A Beimel, S Moran, K Nissim, U Stemmer Conference on Learning Theory, 269-282, 2019	38	2019
Differentially private multi-armed bandits in the shuffle model J Tenenbaum, H Kaplan, Y Mansour, U Stemmer Advances in Neural Information Processing Systems 34, 24956-24967, 2021	36	2021

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