| Capacity-achieving sparse superposition codes via approximate message passing decoding C Rush, A Greig, R Venkataramanan IEEE Transactions on Information Theory 63 (3), 1476-1500, 2017 | 141* | 2017 |
| Estimation of low-rank matrices via approximate message passing A Montanari, R Venkataramanan Annals of Statistics 49 (1), 321-345, 2021 | 119 | 2021 |
| Finite Sample Analysis of Approximate Message Passing Algorithms C Rush, R Venkataramanan IEEE Transactions on Information Theory 64 (11), 7264 - 7286, 2018 | 101 | 2018 |
| A unifying tutorial on approximate message passing OY Feng, R Venkataramanan, C Rush, RJ Samworth Foundations and Trends® in Machine Learning 15 (4), 335-536, 2022 | 99 | 2022 |
| Source coding with feed-forward: Rate-distortion theorems and error exponents for a general source R Venkataramanan, S Sandeep Pradhan IEEE Transactions on Information Theory 53 (6), 2154-2179, 2007 | 93* | 2007 |
| Low-Complexity Interactive Algorithms for Synchronization from Deletions, Insertions, and Substitutions R Venkataramanan, VN Swamy, K Ramchandran IEEE Transactions on Information Theory 61 (10), 5670-5689, 2015 | 59* | 2015 |
| Sparse regression codes R Venkataramanan, S Tatikonda, A Barron Foundations and Trends in Communications and Information Theory 15 (1-2), 1-195, 2019 | 47 | 2019 |
| Achievable Rates for Channels with Deletions and Insertions R Venkataramanan, S Tatikonda, K Ramchandran IEEE Transactions on Information Theory 59 (11), 6990-7013, 2013 | 47 | 2013 |
| Lossy compression via sparse linear regression: Computationally efficient encoding and decoding R Venkataramanan, T Sarkar, S Tatikonda IEEE Transactions on Information Theory 60 (6), 3265-3278, 2014 | 46 | 2014 |
| Approximate message passing with spectral initialization for generalized linear models M Mondelli, R Venkataramanan International Conference on Artificial Intelligence and Statistics, 397-405, 2021 | 44 | 2021 |
| An Achievable Rate Region for the Broadcast Channel with Feedback R Venkataramanan, SS Pradhan IEEE Transactions on Information Theory 59 (10), 6175-6191, 2013 | 44 | 2013 |
| Techniques for improving the finite length performance of sparse superposition codes A Greig, R Venkataramanan IEEE Transactions on Communications 66 (3), 905-917, 2017 | 41 | 2017 |
| Estimation in rotationally invariant generalized linear models via approximate message passing R Venkataramanan, K Kögler, M Mondelli International Conference on Machine Learning, 22120-22144, 2022 | 38 | 2022 |
| Capacity-achieving spatially coupled sparse superposition codes with AMP decoding C Rush, K Hsieh, R Venkataramanan IEEE Transactions on Information Theory 67 (7), 4446-4484, 2021 | 36* | 2021 |
| A New Achievable Rate Region for the Multiple-Access Channel With Noiseless Feedback R Venkataramanan, SS Pradhan IEEE Transactions on Information Theory 57 (12), 8038-8054, 2011 | 35* | 2011 |
| Coding for deletion channels with multiple traces M Abroshan, R Venkataramanan, L Dolecek, AG i Fabregas 2019 IEEE International Symposium on Information Theory (ISIT), 1372-1376, 2019 | 34 | 2019 |
| The error probability of sparse superposition codes with approximate message passing decoding C Rush, R Venkataramanan IEEE Transactions on Information Theory 65 (5), 3278-3303, 2018 | 32* | 2018 |
| PCA initialization for approximate message passing in rotationally invariant models M Mondelli, R Venkataramanan Advances in Neural Information Processing Systems 34, 29616-29629, 2021 | 31 | 2021 |
| Lossy compression via sparse linear regression: Performance under minimum-distance encoding R Venkataramanan, A Joseph, S Tatikonda IEEE Transactions on Information Theory 60 (6), 3254-3264, 2014 | 27* | 2014 |
| Optimal combination of linear and spectral estimators for generalized linear models M Mondelli, C Thrampoulidis, R Venkataramanan Foundations of Computational Mathematics 22 (5), 1513-1566, 2022 | 23 | 2022 |
Cynthia RushColumbia UniversityVerified email at columbia.edu
