Sharp upper bound on error probability of exact sparsity recovery. - Kamiar Rahnama Rad (CISS, 2009)

Sharp Sufficient Conditions on Exact Sparsity Pattern Recovery - Kamiar Rahnama Rad (CoRR, 2009)

Mean-Field Approximations for Coupled Populations of Generalized Linear Model Spiking Neurons with Markov Refractoriness. - Taro Toyoizumi; Kamiar Rahnama Rad; Liam Paninski (Neural Comput., 2009)

Distributed parameter estimation in networks. - Kamiar Rahnama Rad; Alireza Tahbaz-Salehi (CDC, 2010)

A new look at state-space models for neural data. - Liam Paninski; Yashar Ahmadian; Daniel Gil Ferreira; Shinsuke Koyama; Kamiar Rahnama Rad; Michael Vidne; Joshua T. Vogelstein; Wei Wu 006 (J. Comput. Neurosci., 2010)

Information Rates and Optimal Decoding in Large Neural Populations. - Kamiar Rahnama Rad; Liam Paninski (NIPS, 2011)

Nearly Sharp Sufficient Conditions on Exact Sparsity Pattern Recovery. - Kamiar Rahnama Rad (IEEE Trans. Inf. Theory, 2011)

On consensus and exponentially fast social learning. - Pooya Molavi; Kamiar Rahnama Rad; Alireza Tahbaz-Salehi; Ali Jadbabaie (ACC, 2012)

Robust particle filters via sequential pairwise reparameterized Gibbs sampling. - Liam Paninski; Kamiar Rahnama Rad; Michael Vidne (CISS, 2012)

Reaching Consensus With Increasing Information. - Pooya Molavi; Ali Jadbabaie; Kamiar Rahnama Rad; Alireza Tahbaz-Salehi (IEEE J. Sel. Top. Signal Process., 2013)

Consistent Risk Estimation in High-Dimensional Linear Regression. - Ji Xu 003; Arian Maleki; Kamiar Rahnama Rad (CoRR, 2019)

Error bounds in estimating the out-of-sample prediction error using leave-one-out cross validation in high-dimensions. - Kamiar Rahnama Rad; Wenda Zhou; Arian Maleki (AISTATS, 2020)

Consistent Risk Estimation in Moderately High-Dimensional Linear Regression. - Ji Xu 003; Arian Maleki; Kamiar Rahnama Rad; Daniel Hsu 001 (IEEE Trans. Inf. Theory, 2021)

Approximate Leave-one-out Cross Validation for Regression with ℓ1 Regularizers. - Arnab Auddy; Haolin Zou; Kamiar Rahnama Rad; Arian Maleki (AISTATS, 2024)

Approximate Leave-One-Out Cross Validation for Regression With ℓ₁ Regularizers. - Arnab Auddy; Haolin Zou; Kamiar Rahnama Rad; Arian Maleki (IEEE Trans. Inf. Theory, 2024)

Theoretical Analysis of Leave-one-out Cross Validation for Non-differentiable Penalties under High-dimensional Settings. - Haolin Zou; Arnab Auddy; Kamiar Rahnama Rad; Arian Maleki (AISTATS, 2025)

Certified Data Removal Under High-dimensional Settings. - Haolin Zou; Arnab Auddy; Yongchan Kwon; Kamiar Rahnama Rad; Arian Maleki (CoRR, 2025)

Newfluence: Boosting Model interpretability and Understanding in High Dimensions. - Haolin Zou; Arnab Auddy; Yongchan Kwon; Kamiar Rahnama Rad; Arian Maleki (CoRR, 2025)