On The Role of (Machine) Learning in (Mathematical) Optimization

摘要

In this talk, I try to explain my point of view as a Mathematical Optimizer - especially concerned with discrete (integer) decisions - on Big Data. I advocate a tight integration of Machine Learning and Mathematical Optimization (among others) to deal with the challenges of decision-making in Data Science. For such an integration I concentrate on three questions: (1) what can optimization do for machine learning? (2) what can machine learning do for optimization? (3) which new applications can be solved by the combination of machine learning and optimization? Finally, 1 will discuss in details two areas in which machine learning techniques have been (successfully) applied in the area of mixed-integer programming.