Jonathan Ullman

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Adam SmithProfessor of Computer Science, Boston UniversityZweryfikowany adres z bu.edu
Thomas SteinkeGoogle DeepMindZweryfikowany adres z google.com
Aaron RothProfessor of Computer Science, University of PennsylvaniaZweryfikowany adres z cis.upenn.edu
Salil VadhanVicky Joseph Professor of Computer Science and Applied Mathematics, Harvard UniversityZweryfikowany adres z g.harvard.edu
Gautam KamathAssistant Professor @ University of Waterloo, Faculty Member @ Vector InstituteZweryfikowany adres z uwaterloo.ca
Alina OpreaNortheastern UniversityZweryfikowany adres z neu.edu
Albert CheuResearch Scientist, GoogleZweryfikowany adres z google.com
Matthew JagielskiGoogle DeepMindZweryfikowany adres z google.com
Kobbi NissimDept. of Computer Science (Georgetown)Zweryfikowany adres z georgetown.edu
Vikrant SinghalResearch Associate, Harvard UniversityZweryfikowany adres z seas.harvard.edu
Cynthia DworkMicrosoft ResearchZweryfikowany adres z microsoft.com
Zhiwei Steven WuCarnegie Mellon UniversityZweryfikowany adres z andrew.cmu.edu
Michael KearnsProfessor of Computer Science, University of PennsylvaniaZweryfikowany adres z cis.upenn.edu
Mallesh M. PaiRice UniversityZweryfikowany adres z rice.edu
Lydia ZakynthinouPostdoctoral Researcher, UC BerkeleyZweryfikowany adres z northeastern.edu
Raef BassilyAssociate Professor, Computer Science & Engineering, The Ohio State UniversityZweryfikowany adres z osu.edu
Justin ThalerGeorgetown UniversityZweryfikowany adres z georgetown.edu
Anupam GuptaProfessor of Computer Science at New York UniversityZweryfikowany adres z nyu.edu
Mark BunAssistant Professor, Boston UniversityZweryfikowany adres z bu.edu
Uri StemmerTel Aviv UniversityZweryfikowany adres z tau.ac.il

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Jonathan Ullman

Associate Professor of Computer Science, Northeastern University

Zweryfikowany adres z ccs.neu.edu - Strona główna

Differential Privacy Machine Learning Theory Cryptography


Tytuł Sortuj wg cytatów Sortuj wg roku Sortuj wg tytułu	Cytowane przez Cytowane przez	Rok
Distributed Differential Privacy via Shuffling A Cheu, A Smith, J Ullman, D Zeber, M Zhilyaev	442	2018
Exposed! a survey of attacks on private data C Dwork, A Smith, T Steinke, J Ullman Annual Review of Statistics and Its Application 4 (1), 61-84, 2017	334	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	320	2016
Fingerprinting codes and the price of approximate differential privacy M Bun, J Ullman, S Vadhan SIAM Journal on Computing 47 (5), 1888-1938, 2018	258*	2018
Auditing differentially private machine learning: How private is private sgd? M Jagielski, J Ullman, A Oprea Advances in Neural Information Processing Systems 33, 22205-22216, 2020	251	2020
Iterative constructions and private data release A Gupta, A Roth, J Ullman Theory of Cryptography: 9th Theory of Cryptography Conference, TCC 2012 …, 2012	232	2012
Robust traceability from trace amounts C Dwork, A Smith, T Steinke, J Ullman, S Vadhan 2015 IEEE 56th Annual Symposium on Foundations of Computer Science, 650-669, 2015	226	2015
Robust mediators in large games M Kearns, MM Pai, R Rogers, A Roth, J Ullman arXiv preprint arXiv:1512.02698, 2015	208*	2015
Differentially private fair learning M Jagielski, M Kearns, J Mao, A Oprea, A Roth, S Sharifi-Malvajerdi, ... International Conference on Machine Learning, 3000-3008, 2019	196	2019
Between pure and approximate differential privacy T Steinke, J Ullman arXiv preprint arXiv:1501.06095, 2015	180	2015
Privately learning high-dimensional distributions G Kamath, J Li, V Singhal, J Ullman Conference on Learning Theory, 1853-1902, 2019	176	2019
Privately releasing conjunctions and the statistical query barrier A Gupta, M Hardt, A Roth, J Ullman Proceedings of the forty-third annual ACM symposium on Theory of computing …, 2011	164	2011
Preventing false discovery in interactive data analysis is hard M Hardt, J Ullman Foundations of Computer Science (FOCS), 2014 IEEE 55th Annual Symposium on …, 2014	142	2014
PCPs and the hardness of generating private synthetic data J Ullman, S Vadhan Theory of Cryptography Conference, 400-416, 2011	136*	2011
The price of privately releasing contingency tables and the spectra of random matrices with correlated rows SP Kasiviswanathan, M Rudelson, A Smith, J Ullman Proceedings of the forty-second ACM symposium on Theory of computing, 775-784, 2010	132	2010
Interactive fingerprinting codes and the hardness of preventing false discovery T Steinke, J Ullman Conference on learning theory, 1588-1628, 2015	130	2015
Coinpress: Practical private mean and covariance estimation S Biswas, Y Dong, G Kamath, J Ullman Advances in Neural Information Processing Systems 33, 14475-14485, 2020	127	2020
Private mean estimation of heavy-tailed distributions G Kamath, V Singhal, J Ullman Conference on Learning Theory, 2204-2235, 2020	118	2020
Answering n^{2+o(1)} counting queries with differential privacy is hard J Ullman SIAM Journal on Computing 45 (2), 473-496, 2016	118	2016
Privacy odometers and filters: Pay-as-you-go composition RM Rogers, A Roth, J Ullman, S Vadhan Advances in Neural Information Processing Systems 29, 2016	114	2016

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