We present a method for learning sparse representations shared across multiple tasks. This method is a generalization of the well known single task 1-norm regularization. It is based on a novel non-convex regularizer which controls the number of learned features common across the tasks. We prove...

In recent years, a number of international organisations, regulators, governments, academics, and as well businesses have worked on developing principles of Artificial Intelligence (AI). Alongside the development of these principles, there is an on-going discussion on how to regulate AI in order...

Firms must often decide how to target households that did not respond to past promotions. For example, when prospecting for new customers, households that purchase are no longer eligible, and so the remaining households are an increasingly pure pool of non-responders. Past response data reflects...

We develop an approach that combines the estimation of monthly firm-level expected returns with an assignment of firms to (possibly) latent groups, both based upon observable characteristics, using machine learning principles with linear models. The best performing methods are flexible two-stage...