Conference on Theoretical Foundations of Machine Learning
(TFML 2017) will take place in Kraków, Poland, on February 13-17, 2017.
We invite submissions of papers addressing theoretical aspects of machine learning and related topics. We strongly support a broad definition of learning theory, including, but not limited to:
- Theoretical foundations of
- supervised learning
- unsupervised learning
- semi-supervised learning
- deep learning
- active learning
- concept drift
- natural language processing
- drug design
- activity prediction
- representation learning
- generalization bounds
- optimization methods
- computational complexity of learning
- information theoretic learning
Conference is organized by Department of Machine Learning, Institute of Computer Science and Computational Mathematics, Faculty of Mathematics and Computer Science, Jagiellonian University.
- paper submission -
30 Nov 2016
- decision notification -
5 Jan 2017
- camera ready version -
11 Jan 2017
- conference -
13-17 Feb 2017
Registration fee of 600 PLN (150 euro) covers:
- presentation slot
- publication of one paper
- coffee breaks
- gala dinner
There is possibility of discounts for phd students.
Original, unpublished research papers can be submitted through easychair system. They will be peer reviewed and after acceptance and presentation during the TFML conference published in Schedae Informaticae Journal.
Papers should be prepared in English, according to the Schedae Informaticae template (LaTeX template
). Page limit (including references, and other additional materials) is 8 pages
(soft limit) and a hard limit of 10 pages. Papers longer than 10 pages will be rejected without reviewing.
EasyChair submission system is now open
submit your paper
All papers will be peer-reviewed. All accepted papers will be published in Schedae Informaticae
. At least one author of each publication has to attend the conference and present it during oral/poster session.
1For Polish scientists, this journal is located on the B list of the Polish Ministry of Science and Higher Education, with 11pts.