Data Science Meets Optimisation

An IJCAI 2020 Workshop (id: W14)

Latest Update

The workshop will take place in the IJCAI20 virtual venue designed by Virtual Chair on the gather.town platform. Please download and read the guide for presenters here.

The workshop is scheduled on the 7th and 8th of January 2021. See "Program" for details.


How to reach the venue?

The workshop is virtual at this link, but only for registered IJCAI participants. You can still register.

To get the Zoom link of the talks, you need to 'walk' to the Red Wing, South 1 room (the left-most room on the left-most wing in the left-most side of the map). Or type the name of one of the organizers, press 'locate' and follow the line on the floor.


We would like the workshop to be a medium for staying up-to-date with Data Science Meets Optimisation developments, and encourage submissions and participation in light-weight formats, see below.

Important Dates

  • NEW October 30 (AOE): deadline for submitting contributions [Late submissions accept till 4 Nov AoE]

  • November 20: notification of acceptance

  • January 7 & 8: workshop

More information to be announced!

About the Workshop

Data science and optimization are closely related. On the one hand, many problems in data science can be solved using optimizers, on the other hand optimization problems stated through classical models such as those from mathematical programming cannot be considered independent of historical data. Examples are ample: Machine Learning (ML) often relies on optimization techniques such as linear or integer programming; 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; methods aimed at high level combinatorial optimization have been shown to strongly profit from configuration, algorithm selection and tuning tools building on historical data; ML models can be embedded in combinatorial optimization problems to address hard-to-model systems, or for validation of the ML model itself; “predict, then optimize” scenarios can be dealt with in an integrated fashion to improve considerably the solution quality.

This workshop continues the DSO@IJCAI2019 workshop at the International Joint Conference on Artificial Intelligence 2019 in Macao. The DSO workshop is closely related to the DSO working group of The Association of European Operational Research Societies (EURO). In addition to DSO@FAIM 2018, previous related activities include: DSO@IJCAI-ECAI 2018 (Stockholm); stream at EURO 2018 (Valencia); stream at IFORS 2017 (Quebec); workshop at CPAIOR 2017 (Padua); workshop at CEC 2017 (San Sebastian); the foundational workshop (Leuven). More information about the previous editions of DSO is available at https://www.euro-online.org/websites/dso/.

Aim AND Topics

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-pages extended abstracts.

Submission should 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 an international journal [TBA].

Submissions through: EasyChair link


Format and Schedule

The workshop will be a virtual workshop, and last either half or a full day ([INSERT HOURS]). It willl 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.

Organization

The workshop co-chairs are:

  • Patrick De Causmaecker (KU Leuven, BE)

  • Tias Guns (Vrije Universiteit Brussel, BE)

  • Michele Lombardi (University of Bologna, IT)

  • Yingqian Zhang (TU Eindhoven, NL)

Technical program committee

To Be Defined

  • June 10 (AOE): deadline for submitting contributions

  • June 15: notification of acceptance

  • July 11: workshop

Note: IJCAI-PRICAI has been postponed to January 2021 due to the COVID-19 pandemic. We are working on new deadlines for the workshop. The new submission and notification deadlines will be announced later.