Jonathan Ullman 个人学术档案

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	总计	2019 年至今
引用	5298	4005
h 指数	40	36
i10 指数	62	56

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Adam SmithProfessor of Computer Science, Boston University在 bu.edu 的电子邮件经过验证
Thomas SteinkeGoogle DeepMind在 google.com 的电子邮件经过验证
Aaron RothProfessor of Computer Science, University of Pennsylvania在 cis.upenn.edu 的电子邮件经过验证
Salil VadhanVicky Joseph Professor of Computer Science and Applied Mathematics, Harvard University在 g.harvard.edu 的电子邮件经过验证
Gautam KamathAssistant Professor @ University of Waterloo, Faculty Member @ Vector Institute在 uwaterloo.ca 的电子邮件经过验证
Alina OpreaNortheastern University在 neu.edu 的电子邮件经过验证
Albert CheuVisiting Researcher, Google在 google.com 的电子邮件经过验证
Kobbi NissimDept. of Computer Science (Georgetown)在 georgetown.edu 的电子邮件经过验证
Matthew JagielskiGoogle DeepMind在 google.com 的电子邮件经过验证
Cynthia DworkMicrosoft Research在 microsoft.com 的电子邮件经过验证
Vikrant SinghalResearch Associate, Harvard University在 seas.harvard.edu 的电子邮件经过验证
Zhiwei Steven WuCarnegie Mellon University在 andrew.cmu.edu 的电子邮件经过验证
Michael KearnsProfessor of Computer Science, University of Pennsylvania在 cis.upenn.edu 的电子邮件经过验证
Mallesh M. PaiRice University在 rice.edu 的电子邮件经过验证
Lydia ZakynthinouPostdoctoral Researcher, UC Berkeley在 northeastern.edu 的电子邮件经过验证
Raef BassilyAssociate Professor, Computer Science & Engineering, The Ohio State University在 osu.edu 的电子邮件经过验证
Justin ThalerGeorgetown University在 georgetown.edu 的电子邮件经过验证
Anupam GuptaProfessor of Computer Science at New York University在 nyu.edu 的电子邮件经过验证
Uri StemmerTel Aviv University在 tau.ac.il 的电子邮件经过验证
Moritz HardtMax Planck Institute for Intelligent Systems在 is.mpg.de 的电子邮件经过验证

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Jonathan Ullman

Associate Professor of Computer Science, Northeastern University

在 ccs.neu.edu 的电子邮件经过验证 - 首页

Differential Privacy Machine Learning Theory Cryptography


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
Distributed Differential Privacy via Shuffling A Cheu, A Smith, J Ullman, D Zeber, M Zhilyaev	386	2018
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	300	2016
Exposed! a survey of attacks on private data C Dwork, A Smith, T Steinke, J Ullman Annual Review of Statistics and Its Application 4, 61-84, 2017	283	2017
Fingerprinting codes and the price of approximate differential privacy M Bun, J Ullman, S Vadhan SIAM Journal on Computing 47 (5), 1888-1938, 2018	228*	2018
Iterative constructions and private data release A Gupta, A Roth, J Ullman Theory of Cryptography: 9th Theory of Cryptography Conference, TCC 2012 …, 2012	220	2012
Robust mediators in large games M Kearns, MM Pai, R Rogers, A Roth, J Ullman arXiv preprint arXiv:1512.02698, 2015	197*	2015
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	195	2015
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	193	2020
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	166	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	163	2011
Between pure and approximate differential privacy T Steinke, J Ullman arXiv preprint arXiv:1501.06095, 2015	154	2015
Privately learning high-dimensional distributions G Kamath, J Li, V Singhal, J Ullman Conference on Learning Theory, 1853-1902, 2019	145	2019
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	130	2014
PCPs and the hardness of generating private synthetic data J Ullman, S Vadhan Theory of Cryptography Conference, 400-416, 2011	129*	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	128	2010
Interactive fingerprinting codes and the hardness of preventing false discovery T Steinke, J Ullman Conference on learning theory, 1588-1628, 2015	116	2015
Answering n^{2+o(1)} counting queries with differential privacy is hard J Ullman SIAM Journal on Computing 45 (2), 473-496, 2016	111	2016
Faster algorithms for privately releasing marginals J Thaler, J Ullman, S Vadhan International Colloquium on Automata, Languages, and Programming, 810-821, 2012	103	2012
Local differential privacy for evolving data M Joseph, A Roth, J Ullman, B Waggoner Advances in Neural Information Processing Systems 31, 2018	102	2018
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	95	2020

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