The fifth DSO Workshop at IJCAI 2022

The workshop co-chairs are:

• Tias Guns (KU Leuven, BE) <tias.guns@kuleuven.be>
• Michele Lombardi (University of Bologna, IT) <michele.lombardi2@unibo.it>
• Neil Yorke-Smith (TU Delft, NL) <n.yorke-smith@tudelft.nl>
• Yingqian Zhang (TU Eindhoven, NL) <yqzhang@tue.nl>

The aim of the workshop is to organize an open discussion and exchange of ideas by researchers from data science, constraint optimization and operations research in order to identify how techniques from these fields can benefit each other. The workshop invites submissions that include but are not limited to the following topics:

• Applying data science and machine learning methods to solve combinatorial optimization problems, such as algorithm selection based on historical data, speeding up or driving the search process using machine learning including (deep) reinforcement learning, neural combinatorial optimization, and handling uncertainties of prediction models for decision-making.

• Using optimization algorithms for the development of machine learning models: such as formulating the problem of learning predictive models as MIP, constraint programming or boolean satisfiability (SAT). Tuning machine learning models using search algorithms and meta-heuristics. Learning constraint models from empirical data.

• Embedding/encoding methods: combining machine learning with combinatorial optimization, model transformations and solver selection, reasoning over machine learning models. Introducing constraints in (hybrid) machine learning models as well as ‘predict and optimize’ frameworks.

• Formal analysis of machine learning models via optimization or constraint satisfaction techniques: safety checking and verification via SMT or MIP, generation of adversarial examples via similar combinatorial techniques.

• Computing explanations for ML model via techniques developed for optimization or constraint reasoning systems.

• Applications of integrations of techniques of data science and optimization.