  In this paper, we show how mixed-integer conic optimization can be used to
combine feature subset selection with holistic generalized linear models to
fully automate the model selection process. Concretely, we directly optimize
for the Akaike and Bayesian information criteria while imposing constraints
designed to deal with multicollinearity in the feature selection task.
Specifically, we propose a novel pairwise correlation constraint that combines
the sign coherence constraint with ideas from classical statistical models like
Ridge regression and the OSCAR model.
