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
261 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
81 * 2020 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
60 * 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
55 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
40 * 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
35 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
25 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 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 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
2 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 University在 stat.columbia.edu 的電子郵件地址已通過驗證
