19 citations · 33 across the 8 of their papers we have counts for
9 papers
Newfluence: Boosting Model interpretability and Understanding in High Dimensions
Haolin Zou, Arnab Auddy, Yongchan Kwon +2
The increasing complexity of machine learning (ML) and artificial intelligence (AI) models has created a pressing need for tools that help scientists, engineers, and policymakers i…
Certified Data Removal Under High-dimensional Settings
Haolin Zou, Arnab Auddy, Yongchan Kwon +2
Machine unlearning focuses on the computationally efficient removal of specific training data from trained models, ensuring that the influence of forgotten data is effectively elim…
Theoretical Analysis of Leave-one-out Cross Validation for Non-differentiable Penalties under High-dimensional Settings
Haolin Zou, Arnab Auddy, Kamiar Rahnama Rad +1
Despite a large and significant body of recent work focused on estimating the out-of-sample risk of regularized models in the high dimensional regime, a theoretical understanding o…
Approximate Leave-one-out Cross Validation for Regression with Regularizers (extended version)
Arnab Auddy, Haolin Zou, Kamiar Rahnama Rad +1
The out-of-sample error (OO) is the main quantity of interest in risk estimation and model selection. Leave-one-out cross validation (LO) offers a (nearly) distribution-free yet co…
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
We study the problem of out-of-sample risk estimation in the high dimensional regime where both the sample size and number of features are large, and can be less than…
Consistent Risk Estimation in Moderately High-Dimensional Linear Regression
Ji Xu, Arian Maleki, Kamiar Rahnama Rad +1
Risk estimation is at the core of many learning systems. The importance of this problem has motivated researchers to propose different schemes, such as cross validation, generalize…