Rachel Cummings

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	Wszystkie	Od 2020
Cytowania	9274	8701
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i10-indeks	38	36

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Katrina LigettHebrew UniversityZweryfikowany adres z mail.huji.ac.il
Wanrong ZhangHarvard UniversityZweryfikowany adres z fas.harvard.edu
Aaron RothProfessor of Computer Science, University of PennsylvaniaZweryfikowany adres z cis.upenn.edu
Gabriel KaptchukAssistant Professor, University of MarylandZweryfikowany adres z umd.edu
Elissa M. RedmilesClare Luce Boothe Assistant Professor, Georgetown UniversityZweryfikowany adres z georgetown.edu
Zhiwei Steven WuCarnegie Mellon UniversityZweryfikowany adres z andrew.cmu.edu
Wei Chen （陈卫）Microsoft ResearchZweryfikowany adres z microsoft.com
Wei WeiResearch Scientist at GoogleZweryfikowany adres z cs.cmu.edu
Zhenming LiuCollege of William and MaryZweryfikowany adres z cs.wm.edu
Yifei YuanupennZweryfikowany adres z cis.upenn.edu
Yajun WangMicrosoft CorporationZweryfikowany adres z microsoft.com
David Rincón RiveraDept. of Network Engineering, Universitat Politècnica de Catalunya (UPC - BarcelonaTech)Zweryfikowany adres z upc.edu
Jamie MorgensternUniversity of WashingtonZweryfikowany adres z cs.washington.edu
Dhamma KimparaPhD Student, University of Colorado BoulderZweryfikowany adres z colorado.edu
Sara KrehbielAssistant Professor of Mathematics and Computer Science, Santa Clara UniversityZweryfikowany adres z scu.edu
Uthaipon TantipongpipatTwitterZweryfikowany adres z twitter.com
T. Tony KeChinese University of Hong KongZweryfikowany adres z cuhk.edu.hk
Xiaorui SunUniversity of Illinois at ChicagoZweryfikowany adres z uic.edu
Chris WaitesStanford UniversityZweryfikowany adres z stanford.edu
David DotyAssociate Professor of Computer Science, University of California, DavisZweryfikowany adres z ucdavis.edu

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Rachel Cummings

Columbia University

Zweryfikowany adres z columbia.edu - Strona główna

Data privacy Machine learning Algorithmic economics Privacy Policy


Tytuł Sortuj wg cytatów Sortuj wg roku Sortuj wg tytułu	Cytowane przez Cytowane przez	Rok
Advances and open problems in federated learning P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ...	7000	2021
Influence Maximization in Social Networks When Negative Opinions May Emerge and Propagate. W Chen, A Collins, R Cummings, T Ke, Z Liu, D Rincon, X Sun, Y Wang, ... SIAM International Conference on Data Mining (SDM), 379-390, 2011	468	2011
Mariana Raykova, Dawn Song, Weikang Song, Sebastian U P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth …, 2021	227	2021
On the compatibility of privacy and fairness R Cummings, V Gupta, D Kimpara, J Morgenstern Adjunct publication of the 27th conference on user modeling, adaptation and …, 2019	200	2019
" I need a better description": An Investigation Into User Expectations For Differential Privacy R Cummings, G Kaptchuk, EM Redmiles Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications …, 2021	110	2021
Truthful linear regression R Cummings, S Ioannidis, K Ligett Conference on Learning Theory (COLT), 448-483, 2015	80	2015
Adaptive learning with robust generalization guarantees R Cummings, K Ligett, K Nissim, A Roth, ZS Wu Conference on Learning Theory (COLT), 772-814, 2016	70	2016
Advances and open problems in federated learning. arXiv 2019 P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... arXiv preprint arXiv:1912.04977, 2019	68	2019
Accuracy for sale: Aggregating data with a variance constraint R Cummings, K Ligett, A Roth, ZS Wu, J Ziani Conference on Innovations in Theoretical Computer Science (ITCS), 317-324, 2015	61	2015
Speed faults in computation by chemical reaction networks HL Chen, R Cummings, D Doty, D Soloveichik International Symposium on Distributed Computing (DISC), 16-30, 2014	60	2014
Differentially private synthetic mixed-type data generation for unsupervised learning UT Tantipongpipat, C Waites, D Boob, AA Siva, R Cummings Intelligent Decision Technologies 15 (4), 779-807, 2021	57	2021
Privacy and truthful equilibrium selection for aggregative games R Cummings, M Kearns, A Roth, ZS Wu Conference Web and Internet Economics (WINE), 286-299, 2015	57	2015
Differential privacy for growing databases R Cummings, S Krehbiel, KA Lai, U Tantipongpipat Advances in Neural Information Processing Systems 31, 2018	53	2018
The role of differential privacy in gdpr compliance R Cummings, D Desai FAT’18: Proceedings of the Conference on Fairness, Accountability, and …, 2018	51	2018
Probability 1 computation with chemical reaction networks R Cummings, D Doty, D Soloveichik International Conference on DNA Computing and Molecular Programming (DNA), 37-52, 2014	46	2014
Attribute privacy: Framework and mechanisms W Zhang, O Ohrimenko, R Cummings Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022	45	2022
Differentially private change-point detection R Cummings, S Krehbiel, Y Mei, R Tuo, W Zhang Advances in neural information processing systems 31, 2018	44	2018
What are the chances? explaining the epsilon parameter in differential privacy P Nanayakkara, MA Smart, R Cummings, G Kaptchuk, EM Redmiles 32nd USENIX Security Symposium (USENIX Security 23), 1613-1630, 2023	42	2023
Challenges towards the next frontier in privacy R Cummings, D Desfontaines, D Evans, R Geambasu, M Jagielski, ... arXiv preprint arXiv:2304.06929 1, 2023	38	2023
& Zhao, S.(2021). Advances and open problems in federated learning P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... Foundations and Trends® in Machine Learning 14 (1-2), 1-210, 1912	37	1912

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