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DSO Workshop at FEDERATED ARTIFICIAL INTELLIGENCE MEETING 2018 ([email protected])
Data science and optimisation are closely related. On the one hand, many problems in data science (data mining, machine learning, statistical methods, but also problems set in constraint programming) 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. A parallel development of metaheuristic approaches has taken place in the domains of data mining and combinatorial optimisation. 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 select the most appropriate algorithm for a given instance. One observation is that the models of combinatorial optimisation are incomplete and need extra information that may be implicit in the available data. Another observation is that data science methods employ algorithms from combinatorial optimisation without profiting from the latest developments. The aim of the current workshop is to bring scientists from the different fields together for a fruitful day of discussions. Feel free to visit Euro working group website.
The interaction of data science (DS) and optimisation (O) is the central theme of the working group (DSO). DSO originates from the observation that, on the one hand, real time optimisation algorithms are tightly linked to the data-context, and on the other hand, many data-analytic algorithms rely on optimisation algorithms, while many modern optimisation algorithms have some form of machine learning embedded. The first observation has led to developments in automated algorithm tuning, configuration and construction to adapt or even create algorithms from a historical body of data. The second observation is cause to development of similar ideas in different contexts but without much interaction. It is the aim of the working group to bridge gaps between the two domains. All contributors will be invited to send their paper to a special issue (to be announced).
The aim of the current workshop is to organise an open discussion and exchange of ideas by researchers from AI and OR domains. Authors are invited to send in a contribution in the form of a position paper. A reviewing panel will select the papers to be presented at the workshop according to their suitability to the aims. Finished work highlighting the opportunities will be welcomed, as will be sound descriptions and elaborations on good ideas.
Papers of size up to 4 pages are welcomed at the submission page.
Please use the IJCAI template.
A post conference publication will be prepared, contributors will be invited.