Call for Participation - BNAIC/BENELEARN 2025

[Benoît Frenay]

Dear Colleagues,

 

The deadline for the early bird registration (= 7/11) is approaching fast for the 34th Belgian Dutch Conference on Machine Learning (BNAIC/BENELEARN 2025).

 

Please see all details here below!

 

See you soon in Namur,

 

Katrien Beuls, Benoît Frénay and the organising team

 

 

CALL FOR PARTICIPATION

37th Benelux Conference on Artificial Intelligence and 34th Belgian Dutch Conference on Machine Learning (BNAIC/BENELEARN 2025)

19-21 November, 2025
Namur, Belgium

https://bnaic2025.unamur.be

BNAIC/BENELEARN 2025 will be held at the University of Namur under the auspices of the Belgian-Dutch Association for Artificial Intelligence (BNVKI) and the Dutch Research School for Information and Knowledge Systems (SIKS). The conference aims at presenting an overview of state-of-the-art research in artificial intelligence and machine learning in Belgium, The Netherlands, and Luxembourg.

BNAIC/BENELEARN 2025 will host a special session where researchers from universities and industrial partners come together to exchange ideas. Other highlights include keynote talks by Anna Rogers (IT University of Copenhagen) and Decebal Mocanu (University of Luxembourg), a special European AI session including researchers from the Italian Association for Artificial Intelligence (AIxIA), and ample opportunity to expand your network and explore the beautiful city of Namur!


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SUBMISSION INFORMATION

Researchers are invited to submit unpublished original research on all aspects of Artificial Intelligence and Machine Learning. Additionally, high-quality research results already published at international AI/ML conferences or journals are also welcome as extended abstracts. Five types of submissions are invited:


Type A: Regular papers

Papers presenting original work that advances Artificial Intelligence and Machine Learning. Position and review papers are also welcomed. These contributions should address a well-developed body of research, an important new area, or a promising new topic, and provide a big picture view. Type A papers can be long (10-15 pages, including references) or short (6-10 pages, including references). Contributions will be reviewed on the basis of their overall quality and relevance.


Type B: Encore abstracts

Abstracts of work published (or accepted) in an international conference or journal relating to AI/ML and closely related fields. These should have been accepted on or after 1st September 2024. Authors are invited to submit the authors’ version of their officially published paper together with an abstract of at most 2 pages (excluding references). Authors are encouraged to include further results obtained after the publication in their abstract and presentation. Submissions will be judged based on their relevance to the conference. Authors may submit at most one type B paper of which they are the corresponding author.


Type C: Posters and demonstrations

Posters and demonstration abstracts. Proposals should be submitted as a 2-page (excluding references) abstract. Demonstrations should also submit a short video illustrating the working of the system (not exceeding 15 minutes). Any system requirements should also be mentioned in the submission. Posters and demonstrations will be evaluated based on their originality and innovative character, the technology deployed, the purpose of the systems in interaction with users and/or other systems, and their economic and/or societal potential.


Type D: Thesis abstracts

Abstracts of graduation reports. Bachelor and Master students are invited to submit a 2-page abstract (excluding references) of their completed AI/ML-related thesis. Supervisors should be listed. The thesis should have been accepted after 1st September 2024 Submissions will be judged based on their originality and relevance for the conference.

Type E: Late-breaking abstracts

Original and ongoing AI/ML-related work can be submitted as a late-breaking abstract of 2-pages (excluding references). These late-breaking abstracts will not be selected for oral presentations.



PRESENTATION
Type A, B, and D papers can be accepted for either oral or poster presentation. Type E submissions will not be selected for oral presentations.


PRIZES
Just like in past years, there will be prizes for the best paper (type A), best demonstration (type C), and best thesis (type D). 


PREPROCEEDINGS & POSTPROCEEDINGS
Accepted contributions within all five categories will be included in the online conference proceedings. All contributions should be written in English, using the Springer CCIS/LNCS format (see https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines) and submitted electronically via OpenReview: https://openreview.net/group?id=BNAIC-BeNeLearn/2025/Conference

Submission implies willingness of at least one author to register for BNAIC/BENELEARN 2025 and present the accepted paper. For each paper, a separate author registration is required.

A selection of Type A long papers will be invited to submit to the post-proceedings published in Springer’s CCIS series (https://link.springer.com/conference/bnaic).


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IMPORTANT DATES 

Paper submission deadline: September 8, 2025
Author notification: October 6, 2025
Late-breaking poster submission deadline: October 13, 2025
Camera-ready submission deadline: October 27, 2025

Early bird registration = 7/11

All deadlines are at 23:59, AoE time zone: https://time.is/Anywhere_on_Earth
Conference: November 19-21, 2025

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TOPICS OF INTEREST

We invite contributions on any topic in the broad area of Artificial Intelligence and Machine Learning. In addition to fundamental work we encourage cross-domain and interdisciplinary work, as well as application of AI or ML-based techniques. A non-exhaustive list of topics includes:

Automated Machine Learning and meta-learning
Bayesian Learning
Case-based Learning
Causal Learning
Clustering
Computational Creativity
Computational Learning Theory
Computational Models of Human Learning
Data Mining
Data Visualisation
Deep Learning
Ensemble Methods
Evaluation Frameworks
Evolutionary Computation
Feature Selection and Dimensionality Reduction
Inductive Logic Programming
Interactive AI Methods and Applications
Kernel Methods
Knowledge Discovery in Databases
Learning and Ubiquitous Computing
Learning in Multi-Agent Systems
Learning from Big Data
Learning from User Interactionsµ
Learning for Language and Speech
Media Mining and Text Analytics
ML and Information Theory
ML Applications in Industry
ML for Scientific Discovery
ML in Non-stationary Environments
ML with Expert-in-the-loop
Natural Language Processing / Natural Language Understanding
Neural Networks
Online Learning
Pattern Mining
Predictive Modeling
Ranking / Preference Learning / Information Retrieval
Reinforcement Learning
Representation Learning
Robot Learning
Social Networks
Statistical Learning
Structured Output Learning
Transfer and Adversarial Learning

BNAIC/BeNeLearn - Home

 

 

 

Benoît Frénay
Professor
Faculty of Computer Science
Human-Center Machine Learning (HuMaLearn)
UNamur / NaDI / PReCISE

 

https://directory.unamur.be/staff/bfrenay
https://humalearn.info.unamur.be

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