Bridging supervised learning and test-based co-optimization
Elena Popovici · Journal of Machine Learning Research · 2017
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 cooptimization. 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.