[DMANET] Data Science meets Optimization w.shop @IJCAI: extended deadline + TSP competition

Data Science meets Optimisation (DSO) Workshop at IJCAI-21
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UPDATES
-------

- Extended submission deadline till May 24!
- Co-located with the IJCAI competition "AI for TSP (https://www.tspcompetition.com)

Other details:
- The workshop will be an online event
- August, 2021, Montreal, Canada
- https://sites.google.com/view/ijcai2021dso

Important dates
---------------

- May 24 (AOE): deadline for submitting contributions (extended)
- June 10: notification of acceptance

The workshop date will be in the August 21-26, 2021 window.
Workshop organizers
-------------------

- Patrick De Causmaecker (KU Leuven, BE) <patrick.decausmaecker@kuleuven.be>
- Tias Guns (Vrije Universiteit Brussel, BE) <tias.guns@vub.be>
- Michele Lombardi (University of Bologna, IT) <michele.lombardi2@unibo.it>
- Yingqian Zhang (TU Eindhoven, NL) <yqzhang@tue.nl>

Scope
-----

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 program committee 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
reinforcement learning, and handling uncertainties of prediction models for
decision-making or neural combinatorial optimization.

- Using optimization algorithms for the development of 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 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'.

- 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
----------

Authors are invited to send a contribution in the in the IJCAI proceedings
format, in the form of:

- Submission of original work up to 6 pages in length (+ references).
- Submission of work in progress with preliminary results, and position papers,
up to 4 pages in length (+ references).

- Published journal/conference papers in the form of a 2-page extended abstracts.
Submission should be prepared following the IJCAI formatting instructions
at: https://www.ijcai.org/authors_kit.

The review process is single-blind. The program committee will select the
papers to be presented at the workshop according to their suitability to the
aims. Selected contributors will be invited to submit extended articles to a
special issue of the journal Annals of Mathematics and Artificial Intelligence.

Submissions through: https://easychair.org/conferences/?conf=dsoijcai2021
Format and schedule
-------------------

The workshop will have a virtual format, and last either half or a full day. It
will include both contributed and invited talks by experts in the field. The
detailed schedule will be made available after the list of accepted papers is
finalized.


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