DA2PL: From Multiple Criteria Decision Aid to Preference Learning Centre d'innovation Compiègne, France, November 17-18, 2022 |
Conference website | https://da2pl.pre.utc.fr/ |
Submission link | https://easychair.org/conferences/?conf=da2pl |
Submission deadline | September 1, 2022 |
DA2PL 2020 (From Multiple Criteria Decision Aid to Preference Learning) aims to bring together researchers from decision analysis and machine learning. It provides a forum for discussing recent advances and identifying new research challenges in the intersection of both fields, thereby supporting a cross-fertilisation of these disciplines.
Following the five previous editions of this workshop, which took place in Mons in 2012, Paris in 2014, Paderborn in 2016, Poznan in 2018, and Trento (virtually) in 2020, DA2PL 2022 will be held at the Université de Technologie de Compiègne, France, the 17 and 18 november.
Submission Guidelines
DA2PL accepts two kinds of submissions:
- Long papers that will undergo a full review process, should be at most 8 pages long in a 2‑columns format and submitted before the paper submission deadline.
- Extended abstracts that should be at most 2 pages long, will undergo a light review process and are intended to present preliminary works that would not justify a full paper submission. Abstracts will be reviewed on the fly and can be submitted up to the deadline for giving Camera-ready version of papers.
List of Topics
DA2PL 2020 solicits contributions to the usage of theoretically supported preference models and formalisms in preference learning as well as communications devoted to innovative preference learning methods in decision analysis and multicriteria decision aiding. Specific topics of interest include, but are not limited to:
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quantitative and qualitative approaches to modelling preferences, user feedback and training data
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preference representation in terms of graphical models, logical formalisms, and soft constraints
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dealing with incomplete and uncertain preferences
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preference aggregation and disaggregation
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learning utility functions using regression-based approaches
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preference elicitation and active learning
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preference learning in combinatorial domains
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learning relational preference models and related regression problems
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classification problems, such as ordinal and hierarchical classification
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inducing monotonic decision models for preference representation
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comparison of different preference learning paradigms (e.g., monolithic vs. decomposition)
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ranking problems, such as object ranking, instance ranking and label ranking
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complementarity of preference models and hybrid methods
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explanation of recommendations
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applications of preference learning, such as web search, information retrieval, electronic commerce, games, personalization, recommender systems
Committees
Chairs
- Khaled Belahcene
- Sébastien Destercke
Program Committee
- To be filled
Organizing committee
- Loic Adam
- Khaled Belahcene
- Sébastien Destercke
- Sylvain Lagrue
- Mylene Masson
- Soundouss Messoudi
- Benjamin Quost
Contact
All questions about submissions should be emailed to sebastien.destercke@hds.utc.fr or khaled.belahcene@hds.utc.fr