Kamiar Rahnama Rad — accepted AI-conference papers

2025

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 2025poster

Despite a large and significant body of recent work focusing on the hyperparameter tuning of regularized models in the high dimensional regime, a theoretical understanding of this problem for non-differentiable penalties such as generalized LASSO and nuclear norm is missing. In this paper we resolve…

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2020

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 2020poster

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. Extensive empirical evidence confirms the accuracy of leave-one-out cross validation (LO) for out-of-sample risk e…

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