REU Project - detail

We studied how to estimate out-of-sample and generalization error for Least Absolute Shrinkage and Selection Operator (LASSO) at each iteration of the Iterative Shrinkage-Thresholding Algorithm (ISTA). Our goal was to derive novel estimators for the aforementioned quantities as well as study their derivative structures. Due to the iterative nature of the problem, there was a natural connection to multitask learning. Thus the approach developed is analogous to an approach used to estimate these quantities in multitask learning. The results were confirmed with initial simulations.