Bridging Supervised Learning and Test-Based Co-optimization

This paper takes a close look at the important commonalities and subtle differences between the well-established field of supervised learning and the much younger one of co-optimization. It explains the relationships between the problems, algorithms and views on cost and performance of the two fields, all throughout providing a two-way dictionary for the respective terminologies used to describe these concepts. The intent is to facilitate advancement of both fields through transfer and cross-pollination of ideas, techniques and results. As a proof of concept, a theoretical study is presented on the connection between existence / lack of free lunch in the two fields, showcasing a few ideas for improving computational complexity of certain supervised learning approaches. [abs] [ pdf ][ bib ] [ appendix ] © JMLR 2017. (edit, beta)