BENELEARN 2017 INDUSTRY TRACK

The Industry Track is a special track within the Benelearn conference focussing on topics related to applications of machine learning, data mining and data science. A long history of machine learning research exists, but recently there has been yet another sudden surge in interest in machine learning applications within industry, the public sector, arts and the general public, as well as in societal aspects such as privacy, existential risk and using machine learning for the social good.

Topics of interest

We aim to attract researchers as well as practitioners, end users or policy makers with an interest in machine learning applications. Contributions could be case application studies, demonstrations, but also papers that more in general discuss topics that are relevant in the context of widespread application of machine learning, such as methodology, standards and platforms, ethical and societal aspects.

A non-exhaustive list of topics includes:

• Industry applications, for example in banking, healthcare, insurance, telecommunications, manufacturing, ecommerce, transportation and law
• Artistic, creative research and creative coding applications
• Public sector applications, such as government, non profit, NGO, citizens applications
• Scientific research applications, on scientific domains outside machine learning
• Applications in marketing, sales, credit risk, supply chain, human resources, customer service, CRM, customer experience, quality management and production
• Machine learning and data mining for the social good
• Machine learning in games and gaming
• Machine learning for chat bots, conversational systems and other forms of human computer interaction
• Machine learning for business experimentation, optimization and testing
• Learning from images, video, audio, texts, networks, streams and other media
• Automation of end to end data mining; machine learning methodology, processes and pipelines; metalearning
• Application topics beyond the core modeling step such as data preparation, model evaluation and diagnosis, deployment and monitoring
• Industry and web scale machine learning
• Machine learning and big data
• Open and open-source machine learning
• New machine learning standards, platforms, toolkits and data resources
• Driving user adoption and democratization of machine learning and data mining
• Machine learning in education; Teaching machine learning
• Ethical, political, privacy or existential issues and risks related to machine learning
• Any of the topics mentioned in the general Benelearn CFP, but discussed or researched from an application point of view

IMPORTANT DATES

Programme Committee

• Arne Koopman, ASML
• Lukas Vermeer, Booking.com
• Jakub Zavrel, TextKernel
• Mathias Verbeke, Sirris
• Murat Eken, Microsoft
• Hugo Koopmans, DIKW Consulting
• Wannes Meert, KU Leuven
• Kurt Driessens, Maastricht University
• Johan Suykens, KU Leuven
• Willem Waegeman, Ghent University
• Menno Israel, Erasmus Medisch Centrum
• Arno Knobbe, Leiden University
• Dejan Radosavljevik, T Mobile
• Ivar Siccama, Pegasystems
• Michiel van Wezel, Dudok Wonen
• Cor Veenman, Netherlands Forensic Institute and Leiden University
• Jef Wijsen, University of Mons
• Hendrik Blockeel, KU Leuven
• Jan Van Haaren, KU Leuven

SUBMISSION PROCEDURE

The conference solicits regular contributions (5-10 pages) of original work and extended abstracts (max 2 pages excl references) of either 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. In the latter case, the publication reference should be clearly mentioned, and the abstracts will be checked mainly for relevance, rather than receive a full review.

Papers will be reviewed for relevance to the scope of the Industry Track, clarity, novelty, soundness, significance and impact. Note that technical or algorithmical depth and novelty is not strictly a requirement for the industry track given the goals and scope above, and we specifically are also open to non-technical papers, or application related papers that are not from industry, but from the arts, public sector, science or even people who just use machine learning for fun.

The program committee and track chairs will decide which contributions are selected for an oral presentation, and which ones are presented during a poster session (with spotlight presentations). If your submission falls under the general research track, or one of the special tracks on deep learning or complex networks, we encourage you to submit it there.

All submissions must be in English and must be submitted as PDF files through the EasyChair submission page.

Submissions should be formatted using the Benelearn 2017 LaTeX template. For detailed formatting instructions, please refer to the template files.

All accepted contributions will be published in the conference proceedings and be made publicly available on the Benelearn website, but copyright remains with the authors.

At least one author of each accepted submission will be expected to attend and present their findings at the conference.

TRACK CHAIRS