The fifth Data Science meets Optimisation (DSO) Workshop at IJCAI'22
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Tias Guns
May 20, 2022, 6:27:06 AM5/20/22
to const...@googlegroups.com
Visiting IJCAI in Vienna before CP, or looking for an additional reason to go to IJCAI?
Consider submitting and participating at the "Data Science meets Optimisation" workshop!
Both already published work and work in progress welcome.
Deadline is next week, Friday 30 May, see below.
Kind regards,
Tias
-------- Forwarded Message --------
[apologies for double posting]
The fifth Data Science meets Optimisation (DSO) Workshop at IJCAI-22
(https://sites.google.com/view/ijcai2022dso/)
Submissions through: https://easychair.org/conferences/?conf=dsoijcai2022
====================================================================
Important dates
---------------
- (Extended!) May 27 (AOE): deadline for submitting contributions
- (Updated!) May 30: notification of acceptance
- July 24: workshop
Workshop organizers
-------------------
- Tias Guns (Vrije Universiteit Brussel, BE) <tias...@vub.be>
- Michele Lombardi (University of Bologna, IT) <michele....@unibo.it>
- Neil Yorke-Smith (TU Delft, NL) <n.york...@tudelft.nl>
- Yingqian Zhang (TU Eindhoven, NL) <yqz...@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 workshop 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 (deep) reinforcement learning, neural combinatorial optimization, and handling uncertainties of prediction models for decision-making.
- Using optimization algorithms for the development of machine learning models: such as formulating the problem of learning predictive models as MIP, constraint programming or boolean 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' frameworks.
- 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 integrations 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 programme 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 a journal.
Submissions through: https://easychair.org/conferences/?conf=dsoijcai2022
Format and schedule
-------------------
The workshop is planned to be a physical event, and last 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.
Tias
-------- Forwarded Message --------
| Subject: | CfP: The fifth Data Science meets Optimisation (DSO) Workshop at IJCAI'22 |
|---|---|
| Date: | Wed, 18 May 2022 22:11:15 +0200 |
| From: | Neil Yorke-Smith <n.york...@tudelft.nl> |
| Organization: | Delft University of Technology |
| To: | TAILOR-...@googlegroups.com |
[apologies for double posting]
The fifth Data Science meets Optimisation (DSO) Workshop at IJCAI-22
(https://sites.google.com/view/ijcai2022dso/)
Submissions through: https://easychair.org/conferences/?conf=dsoijcai2022
====================================================================
Important dates
---------------
- (Extended!) May 27 (AOE): deadline for submitting contributions
- (Updated!) May 30: notification of acceptance
- July 24: workshop
Workshop organizers
-------------------
- Tias Guns (Vrije Universiteit Brussel, BE) <tias...@vub.be>
- Michele Lombardi (University of Bologna, IT) <michele....@unibo.it>
- Neil Yorke-Smith (TU Delft, NL) <n.york...@tudelft.nl>
- Yingqian Zhang (TU Eindhoven, NL) <yqz...@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 workshop 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 (deep) reinforcement learning, neural combinatorial optimization, and handling uncertainties of prediction models for decision-making.
- Using optimization algorithms for the development of machine learning models: such as formulating the problem of learning predictive models as MIP, constraint programming or boolean 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' frameworks.
- 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 integrations 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 programme 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 a journal.
Submissions through: https://easychair.org/conferences/?conf=dsoijcai2022
Format and schedule
-------------------
The workshop is planned to be a physical event, and last 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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