IJCAI 2020 DSO Workshop

The workshop co-chairs are:

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

Data science and optimization are closely related. On the one hand, many problems in data science can be solved using optimizers, on the other hand optimization problems stated through classical models such as those from mathematical programming cannot be considered independent of historical data. Examples are ample: Machine Learning (ML) often relies on optimization 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; methods aimed at high level combinatorial optimization have been shown to strongly profit from configuration, algorithm selection and tuning tools building on historical data; ML models can be embedded in combinatorial optimization problems to address hard-to-model systems, or for validation of the ML model itself; “predict, then optimize” scenarios can be dealt with in an integrated fashion to improve considerably the solution quality.