https://da2pl.ulb.be/
DA2PL 2026 (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, Compiègne in 2022, DA2PL 2026 will be held at the Université libre de Bruxelles, Belgium, 16, 17 April 2026.
As usual DA2PL will accept 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. Abstracts will be reviewed on the fly and can be submitted up to the deadline for giving Camera-ready version of papers.
Submission are open!
While writing your paper or abstract, you should follow the template given here.
Your paper should be submitted on the easychair platform, to which you can connect by clicking here.
The important dates are:
- Dec, 2025: Submission opens
- End of Jan, 2026: Registration opens
- Feb 20, 2026: Paper submission deadline
- March 15, 2026: Authors notification of acceptance
- March 31, 2026: Camera-ready version of the paper or the abstract
- April 16-17, 2026: Conference
Topics of interest include, but are not limited to:
- Quantitative and qualitative approaches to modelling preferences, user feedback and training data;
- Preference representation in terms of graphical models, logical formalisms, and soft constraints;
- Dealing with incomplete and uncertain preferences;
- Preference aggregation and disaggregation;
- Learning utility functions using regression-based approaches;
- Preference elicitation and active learning;
- Preference learning in combinatorial domains;
- Learning relational preference models and related regression problems;
- Classification problems, such as ordinal and hierarchical classification;
- Inducing monotonic decision models for preference representation;
- Comparison of different preference learning paradigms (e.g.,monolithic vs. decomposition);
- Ranking problems, such as object ranking, instance ranking and label ranking;
- Complementarity of preference models and hybrid methods;
- Explanation of recommendations;
- Applications of preference learning, such as web search, information retrieval, electronic commerce, games, personalization, recommender systems, …
Posted on 2026-01-23 by Sarah Fores