| 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 | 258 | 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 |
| 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 | 79* | 2020 |
| Nearly sharp sufficient conditions on exact sparsity pattern recovery K Rahnama Rad IEEE Transactions on Information Theory 57 (7), 4672-4679, 2011 | 59* | 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 |
| 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 | 45 | 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 | 39* | 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 | 36 | 2014 |
| Consistent risk estimation in moderately high-dimensional linear regression J Xu, A Maleki, KR Rad, D Hsu IEEE Transactions on Information Theory 67 (9), 5997-6030, 2021 | 32* | 2021 |
| 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 | 23 | 2020 |
| Information rates and optimal decoding in large neural populations K Rahnama Rad, L Paninski Advances in neural information processing systems, 846-854, 2011 | 22* | 2011 |
| 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 |
| Approximate Leave-one-out Cross Validation for Regression with Regularizers (extended version) A Auddy, H Zou, KR Rad, A Maleki arXiv preprint arXiv:2310.17629, 2023 | 3 | 2023 |
| 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 |
| Approximate Leave-one-out Cross Validation for Regression with Regularizers A Auddy, H Zou, K Rahnamarad, A Maleki International Conference on Artificial Intelligence and Statistics, 2377-2385, 2024 | 2 | 2024 |
| “No sufro, estoy bien/I am not suffering, so I am doing OK”: A mixed method exploration of individual and network-level factors and Type 2 Diabetes Mellitus (T2DM) among … KR Flórez, NS Hwang, M Hernandez, S Verdaguer-Johe, ... Plos one 19 (1), e0295499, 2024 | 1 | 2024 |
| Theoretical Analysis of Leave-one-out Cross Validation for Non-differentiable Penalties under High-dimensional Settings H Zou, A Auddy, KR Rad, A Maleki arXiv preprint arXiv:2402.08543, 2024 | | 2024 |
Liam PaninskiColumbia UniversityVerified email at stat.columbia.edu
