The 8th Asian Conference on Machine Learning (ACML 2016) 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.
Submission
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.
Conference track
For the conference track please submit your manuscript via CMT at:
https://cmt.research.microsoft.com/ACML2016
Submissions to the conference track will reopen on Monday July 4th.
Please be aware that ACML runs two submission rounds to the conference track: The first round can result in an Accept, Reject or Conditional Acceptance (i.e. subject to changes made by the second round) decision. There is no author rebuttal phase in this round. The second round has the more usual Accept or Reject outcomes and does include an author rebuttal phase.
Papers will be published in a dedicated volume of the JMLR Workshop & Conference Proceedings. Manuscripts must be written in English, be a maximum of 16 pages (including references, appendices etc.) and follow the JMLR W&CP style. If required supplementary material may be submitted as a separate file, but reviewers are not obligated to consider this, and your manuscript should therefore stand on its own merits without any supplementary material.
You can download a suitable Latex template and style file from here.
Submissions must have all detail identifying the author(s) removed from the original manuscript.
Submissions that are overlength, not in the correct format, or contain clear identifying detail - for example the authors' names below the title - will be rejected without review.
Journal track
For the journal track please submit via Springer's Editorial Manager system at:
https://www.editorialmanager.com/mach
Log in as Author, click on Submit New Manuscript, and select S.I.: ACML 2016 from the Choose Article Type drop-down list. Papers will be published in a special issue of Machine Learning. Manuscripts must be written in English; please follow the instructions for authors at:
http://www.springer.com/computer/ai/journal/10994
There is no specific page limit for journal submissions, however papers accepted to the journal track must still be presented at the conference in order to be published, therefore it must be possible to describe at least the major parts of your submission in a talk of around 20 minutes duration.
Important Dates
| Workshop Proposal Deadline | May, 23 2016 | |
| Tutorial Proposal Deadline | June, 30 2016 | |
| Journal Submission Deadlines | - 1st round | March, 21 2016 |
| - 2nd round | April, 4 2016 | |
| - 3rd round | April, 18 2016 | |
| - Final round | May, 2 2016 | |
| Early Submission Deadline | May, 9 2016 | |
| Submissions to conference track reopen | July, 4 2016 | |
| Early Notification Date | July, 11-12 2016 | |
| Final Submission Deadline | August, 15 2016 | |
| Final Notification Date | October, 4 2016 | |
| Conference Dates | Nov, 16-18 2016 | |
Deadlines are 23:59 Pacific Standard Time (PST)
Topics
Topics of interest include but are not limited to:
- Learning problems
- Active learning
- Bayesian machine learning
- Deep learning, latent variable models
- Dimensionality reduction
- Feature selection
- Graphical models
- Learning for big data
- Learning in graphs
- Multiple instance learning
- Multi-objective learning
- Multi-task learning
- Semi-supervised learning
- Sparse learning
- Structured output learning
- Supervised learning
- Online learning
- Transfer learning
- Unsupervised learning
- Analysis of learning systems
- Computational learning theory
- Experimental evaluation
- Knowledge refinement
- Reproducible research
- Statistical learning theory
- Applications
- Bioinformatics
- Biomedical information
- Collaborative filtering
- Healthcare
- Computer vision
- Human activity recognition
- Information retrieval
- Natural language processing
- Social networks
- Web search
- Learning in knowledge-intensive systems
- Knowledge refinement and theory revision
- Multi-strategy learning
- Other systems