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 | 176* | 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 | 157* | 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 | 103 | 2022 |
Querying compressed time-series signals J Liu, SK Nath, F Zhao, GA Reeves, SK Gandhi US Patent 8,219,574, 2012 | 93 | 2012 |
Approximate sparsity pattern recovery: Information-theoretic lower bounds G Reeves, MC Gastpar IEEE Transactions on Information Theory 59 (6), 3451-3465, 2013 | 89 | 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 | 89 | 2009 |
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 | 69 | 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 | 50* | 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 | 45 | 2009 |
The all-or-nothing phenomenon in sparse linear regression G Reeves, J Xu, I Zadik Conference on Learning Theory, 2652-2663, 2019 | 42 | 2019 |
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 |
Reed-Muller codes achieve capacity on BMS channels G Reeves, HD Pfister arXiv preprint arXiv:2110.14631 4 (6), 7, 2021 | 34 | 2021 |
Information-theoretic limits for the matrix tensor product G Reeves IEEE Journal on Selected Areas in Information Theory 1 (3), 777-798, 2020 | 32 | 2020 |
Conditional central limit theorems for Gaussian projections G Reeves 2017 IEEE International Symposium on Information Theory (ISIT), 3045-3049, 2017 | 30 | 2017 |
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 |
Compressed sensing under optimal quantization A Kipnis, G Reeves, YC Eldar, AJ Goldsmith 2017 IEEE international symposium on information theory (ISIT), 2148-2152, 2017 | 28 | 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 | 27 | 2019 |
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 | 24 | 2018 |
The gaussian equivalence of generative models for learning with two-layer neural networks S Goldt, G Reeves, M Mézard, F Krzakala, L Zdeborová | 23 | 2020 |