IJCAI 2019 DSO Workshop

Data science and optimisation are closely related. On the one hand, many problems in data science can be solved using optimisers, on the other hand optimisation problems stated through classical models such as those from Mathematical Programming cannot be considered independent of historical data. Examples are ample. Machine learning often relies on optimisation techniques such as Linear or Integer Programming. Reasoning systems have been applied to constrained pattern and sequence mining tasks. A parallel development of metaheuristic approaches has taken place in the domains of Data Mining and Machine Learning. In the last decades, methods aimed at high level combinatorial optimisation have been shown to strongly profit from configuration and tuning tools building on historical data. Algorithm selection has since the seventies of the previous century been considered as a tool to identify the most appropriate algorithm for a given instance. Empirical Model Learning uses Machine Learning models to approximate the behavior of a system, and such empirical models can be embedded into an optimisation model for efficiently finding optimal system configurations.

The workshop co-chairs are:

• Patrick De Causmaecker (KU Leuven, BE)
• Michele Lombardi (University of Bologna, IT)
• Yingqian Zhang (TU Eindhoven, NL)