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WOR and $p$'s: Sketches for $\ell_p$-Sampling Without Replacement

Aug 15, 2020
Edith Cohen, Rasmus Pagh, David P. Woodruff

* 21 pages. Added references in related work in the introduction 

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Advances and Open Problems in Federated Learning

Dec 10, 2019
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao


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Private Heavy Hitters and Range Queries in the Shuffled Model

Aug 29, 2019
Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh, Ameya Velingker

* 30 pages 

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Scalable and Differentially Private Distributed Aggregation in the Shuffled Model

Jun 19, 2019
Badih Ghazi, Rasmus Pagh, Ameya Velingker

* 17 pages, 1 figure, 1 table, 2 algorithms 

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Fair Near Neighbor Search: Independent Range Sampling in High Dimensions

Jun 05, 2019
Martin Aumüller, Rasmus Pagh, Francesco Silvestri


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Scalable Alignment Kernels via Space-Efficient Feature Maps

Mar 28, 2018
Yasuo Tabei, Yoshihiro Yamanishi, Rasmus Pagh

* Under reviewing paper 

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On the Complexity of Inner Product Similarity Join

Apr 07, 2016
Thomas D. Ahle, Rasmus Pagh, Ilya Razenshteyn, Francesco Silvestri

* in Proc. 35th ACM Symposium on Principles of Database Systems, 2016 

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Finding Associations and Computing Similarity via Biased Pair Sampling

Feb 17, 2010
Andrea Campagna, Rasmus Pagh

* This is an extended version of a paper that appeared at the IEEE International Conference on Data Mining, 2009. The conference version is (c) 2009 IEEE 

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Generic Global Constraints based on MDDs

Feb 28, 2007
Peter Tiedemann, Henrik Reif Andersen, Rasmus Pagh

* Preliminary 15 pages version of the tech-report cs.AI/0611141 

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A Generic Global Constraint based on MDDs

Nov 28, 2006
Peter Tiedemann, Henrik Reif Andersen, Rasmus Pagh

* Tech report, 31 pages, 3 figures 

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