Convex Techniques for Model Selection
This work addresses the time-consuming manual tuning of regularization parameters for practitioners, though it appears incremental as it extends existing convex techniques to a specific application.
The paper tackles the problem of automating regularization parameter selection in model selection by developing a robust convex algorithm, which is implemented and tested for K-fold cross-validation on ridge regression and compared with standard methods.
We develop a robust convex algorithm to select the regularization parameter in model selection. In practice this would be automated in order to save practitioners time from having to tune it manually. In particular, we implement and test the convex method for $K$-fold cross validation on ridge regression, although the same concept extends to more complex models. We then compare its performance with standard methods.