[agents] Cfp: Data Science Meets Optimisation (DSO) Workshop at IJCAI-19, Macao, China

[Yingqian Zhang]

Call for Papers



Data Science Meets Optimisation (DSO) Workshop at IJCAI-19

August 10-16, 2019, Macao, China

https://sites.google.com/view/ijcai2019dso/



*Scope*



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.



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:

-              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.

-              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.

-              Embedding methods: combining machine learning with
combinatorial optimization, model transformations and solver selection,
reasoning over Machine Learning models.

-              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 integration of techniques of data science
and optimization.





*Submission*



We invite the following submissions (all in the IJCAI proceedings format,
see: https://www.ijcai.org/authors_kit ):



- Submission of original work up to 8 pages in length.

- Submission of work in progress (with preliminary results) and position
papers, up to 6 pages in length.

- Published journal/conference papers in the form of a 2-pages abstract.

The program committee will select the papers to be presented at the
workshop according to their suitability to the aims. Contributors will be
invited to submit extended articles to a post-conference special issue.



Submissions through: https://easychair.org/conferences/?conf=ijcai2019dso



*Important dates*



Apr 20, 2019: deadline for submitting contributions

May 17, 2019: notification of acceptance



*Workshop organizers*



Patrick De Causmaecker (KU Leuven, BE), patrick.decausmaecker at kuleuven.be

Michele Lombardi (University of Bologna, IT),  michele.lombardi2 at unibo.it

Yingqian Zhang (TU Eindhoven, NL), yqzhang at tue.nl
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