| A new look at state-space models for neural data L Paninski, Y Ahmadian, DG Ferreira, S Koyama, K Rahnama Rad, ... Journal of computational neuroscience 29 (1-2), 107-126, 2010 | 242 | 2010 |
| Distributed parameter estimation in networks K Rahnama Rad, A Tahbaz-Salehi 49th IEEE Conference on Decision and Control (CDC), 5050-5055, 2010 | 81 | 2010 |
| Nearly sharp sufficient conditions on exact sparsity pattern recovery K Rahnama Rad IEEE Transactions on Information Theory 57 (7), 4672-4679, 2011 | 54* | 2011 |
| Mean-field approximations for coupled populations of generalized linear model spiking neurons with Markov refractoriness T Toyoizumi, K Rahnama Rad, L Paninski Neural computation 21 (5), 1203-1243, 2009 | 54 | 2009 |
| A scalable estimate of the out-of-sample prediction error via approximate leave-one-out cross-validation KR Rad, A Maleki Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2020 | 53* | 2020 |
| Efficient, adaptive estimation of two-dimensional firing rate surfaces via Gaussian process methods K Rahnama Rad, L Paninski Network: Computation in Neural Systems 21 (3-4), 142-168, 2010 | 42 | 2010 |
| Reaching consensus with increasing information P Molavi, A Jadbabaie, K Rahnama Rad, A Tahbaz-Salehi IEEE Journal of Selected Topics in Signal Processing 7 (2), 358-369, 2013 | 34* | 2013 |
| Fast Kalman filtering and forward–backward smoothing via a low-rank perturbative approach EA Pnevmatikakis, K Rahnama Rad, J Huggins, L Paninski Journal of Computational and Graphical Statistics 23 (2), 316-339, 2014 | 33 | 2014 |
| Information rates and optimal decoding in large neural populations K Rahnama Rad, L Paninski Advances in neural information processing systems, 846-854, 2011 | 24* | 2011 |
| Consistent risk estimation in high-dimensional linear regression J Xu, A Maleki, K Rahnama Rad, D Hsu arXiv preprint arXiv:1902.01753, 2019 | 21* | 2019 |
| Error bounds in estimating the out-of-sample prediction error using leave-one-out cross validation in high-dimensions KR Rad, W Zhou, A Maleki International Conference on Artificial Intelligence and Statistics, 4067-4077, 2020 | 14 | 2020 |
| Sparse superposition codes for Gaussian vector quantization I Kontoyiannis, K Rahnama Rad, S Gitzenis 2010 IEEE Information Theory Workshop on Information Theory (ITW 2010, Cairo …, 2010 | 12 | 2010 |
| Robust and scalable Bayesian analysis of spatial neural tuning function data K Rahnama Rad, TA Machado, L Paninski The Annals of Applied Statistics 11 (2), 598-637, 2017 | 5 | 2017 |
| Robust particle filters via sequential pairwise reparameterized Gibbs sampling L Paninski, K Rahnama Rad, M Vidne 2012 46th Annual Conference on Information Sciences and Systems (CISS), 1-6, 2012 | 5 | 2012 |
| Iterative detection for V-BLAST MIMO communication systems based on expectation maximisation algorithm K Rahnama Rad, M Nasiri-Kenari Electronics Letters 40 (11), 684-685, 2004 | 3 | 2004 |
| Fast Kalman Filtering and Forward-Backward Smoothing via a Low-Rank Perturbative Approach (vol 23, pg 316, 209) EA Pnevmatikakis, K Rahnama Rad, J Huggins, L Paninski JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS 28 (4), 1017-1017, 2019 | | 2019 |
Liam PaninskiColumbia UniversityVerified email at stat.columbia.edu
