Galen Reeves

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Michael GastparProfessor, Ecole Polytechnique Fédérale (EPFL), SwitzerlandVerified email at epfl.ch
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
Henry PfisterProfessor of Electrical and Computer Engineering, Duke UniversityVerified email at duke.edu
Vaishakhi MayyaGraduate Student, Duke UniversityVerified email at duke.edu
Miguel RodriguesProfessor of Information Theory and Processing, University College LondonVerified email at ucl.ac.uk
R. CalderbankBell Labs, AT&T Labs, Princeton University, Duke UniversityVerified email at duke.edu
Alon KipnisPostdoctoral Scholar, Stanford UniversityVerified email at stanford.edu
David DunsonArts & Sciences Professor of Statistical Science & MathematicsVerified email at duke.edu
Willem van den BoomResearch Fellow, Yale-NUS CollegeVerified email at yale-nus.edu.sg
B O MainsahDuke UniversityVerified email at duke.edu
Jianbo YangTencentVerified email at duke.edu
Xin YuanBell Labs, Duke University, Hong Kong Polytechnic UniversityVerified email at bell-labs.com
Francesco RennaInstituto de Telecomunicações, University of PortoVerified email at dcc.fc.up.pt
Yonina EldarMath and Computer Science, WeizmannVerified email at weizmann.ac.il
Ilias ZadikCDS Moore-Sloan Fellow, New York UniversityVerified email at nyu.edu
Leslie M CollinsProfessor of Electrical and Computer Engineering, Duke UniversityVerified email at duke.edu
Naveen GoelaU.C. Berkeley, MITVerified email at alum.mit.edu
Guillermo SapiroJames B. Duke Professor at Duke University and Senior Research Scientist, Health AI, at Apple, Inc.Verified email at duke.edu

Galen Reeves

Duke University

Verified email at duke.edu - Homepage

information theory high-dimensional statistics


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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	162*	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	106*	2019
Approximate sparsity pattern recovery: Information-theoretic lower bounds G Reeves, MC Gastpar IEEE Transactions on Information Theory 59 (6), 3451-3465, 2013	83	2013
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	78	2009
Querying compressed time-series signals J Liu, SK Nath, F Zhao, GA Reeves, SK Gandhi US Patent 8,219,574, 2012	72	2012
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	46*	2016
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	42	2009
Sparse signal sampling using noisy linear projections G Reeves CALIFORNIA UNIV BERKELEY DEPT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE, 2008	31	2008
A compressed sensing wire-tap channel G Reeves, N Goela, N Milosavljevic, M Gastpar 2011 IEEE Information Theory Workshop, 548-552, 2011	29	2011
Additivity of information in multilayer networks via additive Gaussian noise transforms G Reeves 2017 55th Annual Allerton Conference on Communication, Control, and …, 2017	25	2017
Compressed sensing under optimal quantization A Kipnis, G Reeves, YC Eldar, AJ Goldsmith 2017 IEEE international symposium on information theory (ISIT), 2148-2152, 2017	24	2017
The all-or-nothing phenomenon in sparse linear regression G Reeves, J Xu, I Zadik Conference on Learning Theory, 2652-2663, 2019	20	2019
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	18	2019
Single letter formulas for quantized compressed sensing with Gaussian codebooks A Kipnis, G Reeves, YC Eldar 2018 IEEE international symposium on information theory (ISIT), 71-75, 2018	15	2018
The minimax noise sensitivity in compressed sensing G Reeves, D Donoho 2013 IEEE International Symposium on Information Theory, 116-120, 2013	15	2013
The Gaussian equivalence of generative models for learning with two-layer neural networks S Goldt, G Reeves, M Mézard, F Krzakala, L Zdeborová arXiv preprint arXiv:2006.14709, 2020	14	2020
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	13	2018
Conditional central limit theorems for Gaussian projections G Reeves 2017 IEEE International Symposium on Information Theory (ISIT), 3045-3049, 2017	12	2017
Compressed sensing phase transitions: Rigorous bounds versus replica predictions G Reeves, M Gastpar 2012 46th Annual Conference on Information Sciences and Systems (CISS), 1-6, 2012	12	2012
Sparsity pattern recovery in compressed sensing G Reeves UC Berkeley, 2011	11	2011

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