23rd IEEE ICDM / Multi-Armed Bandits for Knowledge Discovery Workshop (MAB-KD 2023) Hybrid, Dec 4 2023, Shanghai, China
Multi-Armed Bandit (MAB) problem is a class of sequential decision-making problems concerned with choosing one or more actions among several alternatives. MAB problems are paradigms of the fundamental trade-off between exploitation and exploration that can be observed in many knowledge-discovery tasks. MAB algorithms have been used in many applications like e-commerce, online advertisement, news recommendation, and next-query prediction, among many others, in light of knowledge acquisition and information gathering. Due to its simplicity and applicability, there is a surge in the domains where MAB algorithms are considered, including conversational AI, IoT, Transportation systems, and dynamic pricing.
The MAB-KD 2023 Workshop, in conjuction with the 23rd IEEE ICDM 2023 Conference, Dec 4, 2023, Shanghai, China, aims to provide a forum for disseminating late-breaking research ideas, paradigms, and results related to the adoption and current developments of MAB systems in knowledge discovery and data mining in a high variety of application domains, bringing together researchers from academia and industry. The MAB-KD workshop welcomes the submission of late-breaking and preliminary research results, opinions, and position papers.
Call for Paper
Topics of Interest
The MAB-KD Workshop [CFPMAB-KDWorkshop] aims at novel solutions towards sequential prediction problems associated with knowledge acquisition and data management systems bringing together scientists and scholars. Submitted papers will be evaluated based on criteria such as technical originality, creativity, and applicability. The topics are concentrated on (but not limited to) the following themes:
- Foundations and theoretical aspects of MABs
- Learning Paradigms of MABs in Distributed Knowledge Discovery, IoT, conversational AI, Dynamic pricing
- MAB algorithms for problems with structured and dependent arms
- Problem settings and algorithms for arms with switching costs
- MAB-led Data Mining & Information Gathering
- Deep MABs for dynamic optimization
- Application of MABs in e.g., Intelligent Transportation Systems, Dynamic Pricing, Smart Cities, Recommendation Systems.
- All times are at 11:59PM Beijing Time.
- Paper Submission: September 8, 2023
- Author Notification: September 24, 2023
- Camera Ready & Copyright Form: October 1, 2023
- Workshop Day: December 4, 2023
Paper Submission Guidelines & Attendance (Hybrid)
By the unique ICDM tradition, all accepted workshop papers will be published in the dedicated ICDMW proceedings published by the IEEE Computer Society Press. Paper submissions should be limited to max 8 pages plus 2 extra pages (for references, appendix, etc.) and follow the IEEE ICDM format. More detailed information is available in the IEEE ICDM 2023 Submission Guidelines. All submissions will be reviewed by the Program Committee based on technical quality, relevance to scope of the workshop, originality, significance, and clarity. Please submit your papers via the submission link.
Online Attendance Option: In light of potential hesitancy for international traveling, we are considering providing an online attendance option for the MAB-KD ICDM workshop. This would cater to those who may face travel restrictions or have concerns about in-person attendance.
Dr. Zhenhui (Jessie) Li: 'Making Decision for the City: A Real Case in Traffic Optimization'
Bio: Dr. Zhenhui (Jessie) Li is the chief scientist at Yunqi Academy of Engineering, a non-profit organization located in Hangzhou China. Previously, she held a tenured associate professorship at the Pennsylvania State University. Dr. Li has been dedicated to developing computational techniques for cross-disciplinary data-driven research, with a particular focus on applications in the city. Learn more about her on her website (https://jessielzh.com/).
Jordi Mateo Fornés, University of Lleida, Spain
Ibrahim Alghamdi, Al Baha University, SA
Jordi Vilaplana, University of Lleida, Spain
Katie Aleksandrova, Microsoft, UK
Natascha Weber, BMW Research Group, Germany
Kostas Kolomvatsos, University of Thessaly, Greece
Fani Deligianni, University of Glasgow, UK
Ekaterina Gilman, University of Oulu, Finland
Francesca Bugiotti CentraleSupélec, France
Yves Grandvalet, Heudiasyc Lab, CNRS, France
Zoi Kaoudi, IT University of Copenhagen, Denmark
Abdulhakim Qahtan, Utrecht University, Netherlands