Galen Reeves

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Citations	1605	989
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Michael GastparProfessor, Ecole Polytechnique Fédérale (EPFL), SwitzerlandVerified email at epfl.ch
Henry PfisterProfessor of Electrical and Computer Engineering, Duke UniversityVerified email at duke.edu
Jie LiuHarbin Institute of Technology (Shenzhen)Verified email at hit.edu.cn
Suman NathPrincipal Researcher, Microsoft ResearchVerified email at microsoft.com
Feng ZhaoMicrosoft ResearchVerified email at alum.mit.edu
Miguel RodriguesProfessor of Information Theory and Processing, University College LondonVerified email at ucl.ac.uk
Vaishakhi MayyaGraduate Student, Duke UniversityVerified email at duke.edu
Alon KipnisSenior Lecturer at Reichman UniversityVerified email at runi.ac.il
Yonina EldarMath and Computer Science, WeizmannVerified email at weizmann.ac.il
David DunsonArts & Sciences Professor of Statistical Science & MathematicsVerified email at duke.edu
Willem van den BoomScientist, Agency for Science Technology and ResearchVerified email at sics.a-star.edu.sg
B O MainsahDuke UniversityVerified email at duke.edu
Ilias ZadikAssistant Professor, Yale University, Department of Statistics and Data ScienceVerified email at yale.edu
Jianbo YangTencentVerified email at duke.edu
Xin YuanWestlake University, Bell Labs, Duke University, Hong Kong Polytechnic UniversityVerified email at westlake.edu.cn
Francesco RennaINESC TEC, Faculdade de Ciências da Universidade do PortoVerified email at fc.up.pt
R. CalderbankBell Labs, AT&T Labs, Princeton University, Duke UniversityVerified email at duke.edu
Guillermo SapiroAugustine Family Professor, Princeton University; Distinguished Engineer, Health AI Lead, Apple.Verified email at princeton.edu
Leslie M CollinsProfessor of Electrical and Computer Engineering, Duke UniversityVerified email at duke.edu
Naveen GoelaStaff Scientist, UC Berkeley, MITVerified email at alum.mit.edu

Galen Reeves

Duke University

Verified email at duke.edu - Homepage

information theory high-dimensional statistics


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
The sampling rate-distortion tradeoff for sparsity pattern recovery in compressed sensing G Reeves, M Gastpar IEEE Transactions on Information Theory 58 (5), 3065-3092, 2012	179*	2012
The replica-symmetric prediction for random linear estimation with Gaussian matrices is exact G Reeves, HD Pfister IEEE Transactions on Information Theory 65 (4), 2252-2283, 2019	168*	2019
The gaussian equivalence of generative models for learning with shallow neural networks S Goldt, B Loureiro, G Reeves, F Krzakala, M Mézard, L Zdeborová Mathematical and Scientific Machine Learning, 426-471, 2022	123	2022
Querying compressed time-series signals J Liu, SK Nath, F Zhao, GA Reeves, SK Gandhi US Patent 8,219,574, 2012	95	2012
Managing massive time series streams with multi-scale compressed trickles G Reeves, J Liu, S Nath, F Zhao Proceedings of the VLDB Endowment 2 (1), 97-108, 2009	91	2009
Approximate sparsity pattern recovery: Information-theoretic lower bounds G Reeves, MC Gastpar IEEE Transactions on Information Theory 59 (6), 3451-3465, 2013	90	2013
Adversarially learned representations for information obfuscation and inference M Bertran, N Martinez, A Papadaki, Q Qiu, M Rodrigues, G Reeves, ... International Conference on Machine Learning, 614-623, 2019	74	2019
Classification and reconstruction of high-dimensional signals from low-dimensional features in the presence of side information F Renna, L Wang, X Yuan, J Yang, G Reeves, R Calderbank, L Carin, ... IEEE Transactions on Information Theory 62 (11), 6459-6492, 2016	51*	2016
The all-or-nothing phenomenon in sparse linear regression G Reeves, J Xu, I Zadik Conference on Learning Theory, 2652-2663, 2019	46	2019
A note on optimal support recovery in compressed sensing G Reeves, M Gastpar 2009 Conference Record of the Forty-Third Asilomar Conference on Signals …, 2009	45	2009
Additivity of information in multilayer networks via additive Gaussian noise transforms G Reeves 2017 55th Annual Allerton Conference on Communication, Control, and …, 2017	40	2017
Information-theoretic limits for the matrix tensor product G Reeves IEEE Journal on Selected Areas in Information Theory 1 (3), 777-798, 2020	38	2020
Reed-Muller codes achieve capacity on BMS channels G Reeves, HD Pfister arXiv preprint arXiv:2110.14631 4 (6), 7, 2021	34	2021
Conditional central limit theorems for Gaussian projections G Reeves 2017 IEEE International Symposium on Information Theory (ISIT), 3045-3049, 2017	32	2017
Mutual information in community detection with covariate information and correlated networks V Mayya, G Reeves 2019 57th annual allerton conference on communication, control, and …, 2019	31	2019
A compressed sensing wire-tap channel G Reeves, N Goela, N Milosavljevic, M Gastpar 2011 IEEE Information Theory Workshop, 548-552, 2011	29	2011
Sparse signal sampling using noisy linear projections G Reeves Univ. of California, Berkeley, Dept. of Elec. Eng. and Comp. Sci., Tech. Rep …, 2008	29	2008
Mutual information as a function of matrix snr for linear gaussian channels G Reeves, HD Pfister, A Dytso 2018 IEEE International Symposium on Information Theory (ISIT), 1754-1758, 2018	27	2018
Compressed sensing under optimal quantization A Kipnis, G Reeves, YC Eldar, AJ Goldsmith 2017 IEEE international symposium on information theory (ISIT), 2148-2152, 2017	27	2017
The gaussian equivalence of generative models for learning with two-layer neural networks S Goldt, G Reeves, M Mézard, F Krzakala, L Zdeborová	24	2020

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