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*** Apologies for multiple copies ***<o:p class=""></o:p></div>
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Call for Papers<o:p class=""></o:p></div>
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<u class="">Data Science Meets Optimisation (DSO) Workshop at IJCAI-19 (the 28th International Joint Conference on Artificial Intelligence)<o:p class=""></o:p></u></div>
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August 10-16, 2019, Macao, China<o:p class=""></o:p></div>
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<a href="https://sites.google.com/view/ijcai2019dso/" style="color: rgb(149, 79, 114);" class="">https://sites.google.com/view/ijcai2019dso/</a><o:p class=""></o:p></div>
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<b class="">Extended submission deadline: May 27, 2019<o:p class=""></o:p></b></div>
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<b class="">(New!!!!) Special issue in the Annals of Mathematics and Artificial Intelligence<o:p class=""></o:p></b></div>
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<b class="">Keynote speaker: Prof. dr. Holger Hoos (Leiden University, NL)<o:p class=""></o:p></b></div>
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*Important dates*<o:p class=""></o:p></div>
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Submission deadline (extended): May 27, 2019<o:p class=""></o:p></div>
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Notification of acceptance: June 15, 2019<o:p class=""></o:p></div>
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*Scope*<o:p class=""></o:p></div>
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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. Algorithms may be complete, approximative or heuristic and may be applied in on-line or off line settings. 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 select the most appropriate algorithm for a given
instance. Empirical model learning uses machine learning models to approximate the behaviour of a system, and such empirical models can be embedded into an optimisation model for efficiently finding an optimal system configuration.<o:p class=""></o:p></div>
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The aim of the workshop is to organize an open discussion and exchange of ideas by researchers from Data Science and Operations Research domains in order to identify how techniques from these two fields can benefit each other. The program committee invites
submissions that include but are not limited to the following topics:<o:p class=""></o:p></div>
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- Applying data science and machine learning methods to solve combinatorial optimisation problems, such as algorithm selection based on historical data, speeding up (or driving) the search process using machine learning, and handling uncertainties
of prediction models for decision-making.<o:p class=""></o:p></div>
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- Using optimisation algorithms in developing machine learning models: formulating the problem of learning predictive models as MIP, constraint programming (CP), or satisfiability (SAT). Tuning machine learning models using search algorithms and
meta-heuristics. Learning in the presence of constraints.<o:p class=""></o:p></div>
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- Embedding methods: combining machine learning with combinatorial optimization, model transformations and solver selection, reasoning over Machine Learning models.<o:p class=""></o:p></div>
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- 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.<o:p class=""></o:p></div>
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- Computing explanations for ML model via techniques developed for optimization or constraint reasoning systems<o:p class=""></o:p></div>
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- Applications of integration of techniques of data science and optimization.<o:p class=""></o:p></div>
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*Submission*<o:p class=""></o:p></div>
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We invite the following submissions (all in the IJCAI proceedings format, see: <a href="https://www.ijcai.org/authors_kit" style="color: rgb(149, 79, 114);" class="">https://www.ijcai.org/authors_kit</a> ):<o:p class=""></o:p></div>
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- Submission of original work up to 8 pages in length. <o:p class=""></o:p></div>
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- Submission of work in progress (with preliminary results) and position papers, up to 6 pages in length. <o:p class=""></o:p></div>
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- Published journal/conference papers in the form of a 2-pages abstract.<o:p class=""></o:p></div>
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The program committee will select the papers to be presented at the workshop according to their suitability to the aims.<o:p class=""></o:p></div>
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Contributors of the workshop will be invited to submit full versions of their papers for inclusion in a special volume of the Annals of Mathematics and Artificial Intelligence, published by Springer.<o:p class=""></o:p></div>
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Those invited submissions will be subject to refereeing at the usual standards of the journal, and authors will receive more details with the acceptance notice.<o:p class=""></o:p></div>
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Submissions through: <a href="https://easychair.org/conferences/?conf=ijcai2019dso" style="color: rgb(149, 79, 114);" class="">https://easychair.org/conferences/?conf=ijcai2019dso</a><o:p class=""></o:p></div>
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<span lang="NL" class="">*Workshop organizers*<o:p class=""></o:p></span></div>
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Patrick De Causmaecker (KU Leuven, BE), <a href="mailto:patrick.decausmaecker@kuleuven.be" style="color: rgb(149, 79, 114);" class="">patrick.decausmaecker@kuleuven.be</a><o:p class=""></o:p></div>
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Michele Lombardi (University of Bologna, IT), <a href="mailto:michele.lombardi2@unibo.it" style="color: rgb(149, 79, 114);" class="">michele.lombardi2@unibo.it</a><o:p class=""></o:p></div>
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<span lang="NL" class="">Yingqian Zhang (TU Eindhoven, NL), <a href="mailto:yqzhang@tue.nl" style="color: rgb(149, 79, 114);" class="">yqzhang@tue.nl</a></span></div>
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