(apologies for double posting) Data Science meets Optimisation (DSO) Workshop at IJCAI-21 ==========================================================
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.decausmaec...@kuleuven.be> - Tias Guns (Vrije Universiteit Brussel, BE) <tias.g...@vub.be> - Michele Lombardi (University of Bologna, IT) <michele.lombar...@unibo.it> - Yingqian Zhang (TU Eindhoven, NL) <yqzh...@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.
_______________________________________________ uai mailing list uai@engr.orst.edu https://it.engineering.oregonstate.edu/mailman/listinfo/uai
