Posted by: Patrick De Causmaecker



September 12-13, 2016. Kortrijk, Belgium


Benelearn is the annual machine learning conference of Belgium and The Netherlands. It serves as a forum for researchers to exchange ideas, present recent work, and foster collaboration in the broad field of Machine Learning and its applications. The 25th edition will be co-organised by  KU Leuven, UGent and the Flemish Supercomputer Centre at the Katholieke Universiteit Leuven, Campus Kortrijk (KULAK).


In contrast to previous editions, the anniversary edition of Benelearn will be organised on two days, where the first day will feature a workshop on Big Data, including keynote speakers, a tutorial session, corporate presentations, and ample opportunity for networking.





The following keynote speakers have already agreed to speak at the conference: 


Jeff Ullman (Stanford University)

Christian Blum (University of the Basque Country)

Hugo Ceulemans (Janssen R&D)

Greg Tsoumakas (Aristotle University of Thessaloniki)

Emilia Barakova (Technische Universiteit Eindhoven)





The conference solicits abstract contributions (2 pages max.) of original work or work that was recently accepted or published in a peer-reviewed machine-learning journal or at a high level international conference. In the latter case, the publication reference should be clearly mentioned, and the abstracts should be checked mainly for relevance, rather than receive a full review.


The program committee will decide which contributions are selected for an oral presentation, and which ones are presented during a poster session (with spotlight presentations). Submissions related to Big Data analysis will preferably be presented on the thematic day. 


All accepted contributions will be published on the Benelearn website, but no copyright will be claimed. For submission instructions, please refer to





Although we particularly encourage submissions related to learning from Big Data, submissions from all topics of interest within machine learning are welcome (non-exhaustive list):


Kernel Methods

Bayesian Learning

Case-based Learning

Causal Learning

Ensemble Methods

Computational Learning Theory

Data Mining

Evolutionary Computation

Inductive Logic Programming

Knowledge Discovery in Databases

Online Learning

Learning in Multi-Agent Systems

Neural Networks

Deep Learning

Reinforcement Learning

Robot Learning

Feature Selection and Dimensionality Reduction

Scientific Discovery

Transfer Learning

Statistical Learning

Ranking / Preference Learning / Information Retrieval

Computational models of Human Learning

Structured Output Learning

Learning for Language and Speech

Media Mining and Text Analytics

Learning and Ubiquitous Computing

Applications of Machine Learning





Submission deadline: June 1, 2016.

Notification of acceptance: July 1, 2016.

Conference: September 12-13, 2016.