=============== CALL FOR PAPERS =============== 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 26th edition is hosted by the Data Mining group of Eindhoven University of Technology (TU/e) under the auspices of the Dutch Research School for Information and Knowledge Systems (SIKS) and Data Science Center Eindhoven (DSC/e). Benelearn 2017 is organised as a two-day event on June 9 and 10, 2017, at the TU/e campus. Due to Eindhoven's favorable logistics for international collaboration (being located inside the Netherlands, but only 20km from the Belgian and 60km from the German border; also having its own airport), for the 26th edition of Benelearn we also explicitly invite submissions from nearby universities that are not technically within the Benelux area. Don't let national borders stop you from submitting! ------------------ MANAGEMENT SUMMARY ------------------ Paper submission deadline is March 27. We invite new, full research papers of 5-10 pages. We also welcome extended abstracts of 2 pages (excl. references), covering either work in progress or already published papers. All ML/DM/AI research is welcome. We have special tracks on Deep Learning, Complex Networks, and Industry. The official language (for all presentations and communications) of the conference will be English. Detailed information can be found at: http://wwwis.win.tue.nl/~benelearn2017/ For the special tracks, see: Deep Learning - http://wwwis.win.tue.nl/~benelearn2017/deeplearning.html Complex Networks - http://wwwis.win.tue.nl/~benelearn2017/complexnetworks.html Industry - http://wwwis.win.tue.nl/~benelearn2017/industry.html -------------------------- CONFIRMED INVITED SPEAKERS -------------------------- Benelearn 2017 will feature keynote talks by Prof. Dr. Peter Grunwald, head of the information-theoretic learning group at the Centrum voor Wiskunde en Informatica (CWI), the Dutch national research institute for mathematics and computer science, Amsterdam and part-time full professor at Leiden University, and Prof. Dr. Max Welling, research chair in Machine Learning at the University of Amsterdam, with secondary appointments at the University of California Irvine and at the Canadian Institute for Advanced Research (CIFAR) ---------------------------------------- CONFERENCE FORMAT AND TOPICS OF INTEREST ---------------------------------------- The program will feature original, unpublished work, early work in progress as well as recently published work. This way we provide an attractive program and make it easy to attend the conference by both senior and junior researchers. Besides regular sessions consisting of presentations of selected peer-reviewed papers, the programme will feature invited talks, a poster session and a panel discussion in the closing session. We will lead an open discussion, in a workshop spirit, aimed to foresee the future of Machine Learning and data mining in general and to identify immediate opportunities for collaboration for researchers in Belgium and the Netherlands. A non-exhaustive list of topics includes: Neural Networks Reinforcement learning Representation learning Statistical Learning Bayesian Learning Causal Learning Structured Output Learning Online Learning ML in non-stationary environments Transfer Learning Learning in Multi-Agent Systems Robot Learning Computational Learning Theory ML and information theory ML with expert-in-the-loop Visual analytics and ML Computational models of Human Learning Evaluation frameworks ML for scientific discovery Social network (see also Special track on Complex Networks) Deep learning (see also Special track on Deep Learning) Data Mining Predictive modeling Ensemble Methods Kernel Methods Case-based Learning Evolutionary Computation Inductive Logic Programming Knowledge Discovery in Databases Pattern mining Clustering Feature Selection and Dimensionality Reduction Ranking / Preference Learning / Information Retrieval Learning for Language and Speech Media Mining and Text Analytics Learning and Ubiquitous Computing Learning from Big Data ML applications in industry (see also Industry Track) --------------- IMPORTANT DATES --------------- March 27, 2017 Submission due (for both full and short papers) April 25, 2017 Notification of acceptance May 15, 2017 Final papers due June 9-10, 2017 Conference -------------- SPECIAL TRACKS -------------- As part of the main program, we organize three special tracks: * Special track on Deep Learning http://wwwis.win.tue.nl/~benelearn2017/deeplearning.html * Special track on Complex Networks http://wwwis.win.tue.nl/~benelearn2017/complexnetworks.html * Industry Track http://wwwis.win.tue.nl/~benelearn2017/industry.html -------------------- SUBMISSION PROCEDURE -------------------- The conference solicits regular contributions (5-10 pages) of original work and extended abstracts (2 pages excl. references) of original work or work that was recently accepted or published in a peer-reviewed machine-learning, data mining or other relevant journal or high level international conference. We explicitly invite different kinds of contributions, including technical contributions, visionary papers, case studies, demos, benchmark real and synthetic datasets. 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). Preference of authors will be taken into account as well. If your submission falls under one of the special tracks, we encourage you to submit it there. All submissions must be in English and must be submitted as PDF files. All accepted contributions will be published in the conference proceedings and publicly available on the Benelearn website, but no copyright will be claimed. At least one author of each accepted submission will be expected to attend and present their findings at the conference.