<div dir="ltr"><p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif">*** Apologies for multiple copies ***</p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif"><span style="color:black"><br>
Call for Papers</span></p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif"><u>Data Science Meets Optimisation (DSO) Workshop at
IJCAI-19 (the 28th International Joint Conference on Artificial Intelligence)</u></p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif">August 10-16, 2019, Macao, China</p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif"><a href="https://sites.google.com/view/ijcai2019dso/" style="color:rgb(5,99,193)">https://sites.google.com/view/ijcai2019dso/</a></p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif"><b>Extended submission deadline: May 27, 2019</b></p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif"><b>(New!!!!) Special issue in the Annals of Mathematics and
Artificial Intelligence</b></p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif"><b>Keynote speaker: Prof. dr. Holger Hoos (Leiden
University, NL)</b></p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif"> </p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif">*Important dates*</p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif">Submission deadline (extended): May 27, 2019</p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif">Notification of acceptance: June 15, 2019</p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif"> </p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif">*Scope*</p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif">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.</p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif"> </p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif">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:</p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif">-
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.</p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif">-
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.</p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif">-
Embedding methods: combining machine learning with combinatorial optimization,
model transformations and solver selection, reasoning over Machine Learning
models.</p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif">-
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.</p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif">-
Computing explanations for ML model via techniques developed for optimization
or constraint reasoning systems</p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif">-
Applications of integration of techniques of data science and optimization.</p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif"> </p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif"> </p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif">*Submission*</p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif">We invite the following submissions (all in the IJCAI
proceedings format, see: <a href="https://www.ijcai.org/authors_kit" style="color:rgb(5,99,193)">https://www.ijcai.org/authors_kit</a>
):</p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif">- Submission of original work up to 8 pages in
length. </p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif">- Submission of work in progress (with preliminary results)
and position papers, up to 6 pages in length. </p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif">- Published journal/conference papers in the form of a
2-pages abstract.</p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif">The program committee will select the papers to be presented
at the workshop according to their suitability to the aims. </p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif">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. </p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif">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.</p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif"> </p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif">Submissions through: <a href="https://easychair.org/conferences/?conf=ijcai2019dso" style="color:rgb(5,99,193)">https://easychair.org/conferences/?conf=ijcai2019dso</a></p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif"> </p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif"> </p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif"><span lang="NL">*Workshop
organizers*</span></p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif">Patrick De Causmaecker (KU Leuven, BE), <a href="mailto:patrick.decausmaecker@kuleuven.be" style="color:rgb(5,99,193)">patrick.decausmaecker@kuleuven.be</a></p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif">Michele Lombardi (University of Bologna, IT), <a href="mailto:michele.lombardi2@unibo.it" style="color:rgb(5,99,193)">michele.lombardi2@unibo.it</a></p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif"><span lang="NL">Yingqian Zhang
(TU Eindhoven, NL), <a href="mailto:yqzhang@tue.nl" style="color:rgb(5,99,193)">yqzhang@tue.nl</a></span></p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif"><span lang="NL"> </span></p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif"><span lang="NL"> </span></p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:11pt;font-family:Calibri,sans-serif"><span lang="NL"> </span></p></div>