ACML 2016 : 8th Asian Conference on Machine Learning
Conference Series : Asian Conference on Machine Learning
Call For Papers
*Call For Papers -- ACML 2016*
The 8th Asian Conference on Machine Learning (ACML2016) will be held at the University of Waikato, Hamilton, New Zealand on November 16-18, 2016. The conference aim is to provide a leading international forum for researchers in machine learning and related fields to share their new ideas, progresses and achievements. Submissions from regions other than the Asia-Pacific are highly encouraged.
The conference calls for high-quality, original research papers in the theory and practice of machine learning. The conference also solicits proposals focusing on frontier research, new ideas and new paradigms in machine learning.
This year we are running *two publication tracks*: Authors may submit either to the *conference track*, for which the proceedings will be published as a volume of Journal of Machine Learning Research (JMLR): Workshop and Conference Proceedings series, or to the *journal track* for which accepted papers will appear in a special issue of the Springer journal Machine Learning.
Please note that submission arrangements for the two tracks are different - there are different deadlines and conference track papers are submitted via CMT and journal track papers are submitted via Springer's Editorial Manager system -- submission deadlines follow below, but please see the conference website http://acml-conf.org/2016/authors/call-for-papers/ for other details.
Journal Track Submission Deadlines:
March, 21 2016
April, 4 2016
April, 18 2016
May, 2 2016
Conference Track Submission Deadlines:
Early Submission Deadline May 9, 2016
Final Submission Deadline August, 15 2016
Deadlines are all 23:59 Pacific Standard Time (PST)
Topics of interest include but are not limited to:
Bayesian machine learning
Deep learning, latent variable models
Learning for big data
Learning in graphs
Multiple instance learning
Structured output learning
Analysis of learning systems
Computational learning theory
Statistical learning theory
Human activity recognition
Natural language processing
Learning in knowledge-intensive systems
Knowledge refinement and theory revision
We look forward to your submissions, and to welcoming you to Hamilton in November!
ACML 2016 Program Committee Chairs