1 code implementation • 3 Mar 2020 • Kamiar Rahnama Rad, Wenda Zhou, Arian Maleki
We study the problem of out-of-sample risk estimation in the high dimensional regime where both the sample size $n$ and number of features $p$ are large, and $n/p$ can be less than one.
no code implementations • 5 Feb 2019 • Ji Xu, Arian Maleki, Kamiar Rahnama Rad, Daniel Hsu
This paper studies the problem of risk estimation under the moderately high-dimensional asymptotic setting $n, p \rightarrow \infty$ and $n/p \rightarrow \delta>1$ ($\delta$ is a fixed number), and proves the consistency of three risk estimates that have been successful in numerical studies, i. e., leave-one-out cross validation (LOOCV), approximate leave-one-out (ALO), and approximate message passing (AMP)-based techniques.
2 code implementations • 30 Jan 2018 • Kamiar Rahnama Rad, Arian Maleki
Motivated by the low bias of the leave-one-out cross validation (LO) method, we propose a computationally efficient closed-form approximate leave-one-out formula (ALO) for a large class of regularized estimators.
no code implementations • 24 Jun 2016 • Kamiar Rahnama Rad, Timothy A. Machado, Liam Paninski
On the other hand, sharing information between adjacent neurons can errantly degrade estimates of tuning functions across space if there are sharp discontinuities in tuning between nearby neurons.