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ACML 2016 : 8th Asian Conference on Machine Learning


Conference Series : Asian Conference on Machine Learning
When Nov 16, 2016 - Nov 18, 2016
Where Hamilton, New Zealand
Submission Deadline Aug 15, 2016
Categories    machine learning   artificial intelligence   data mining

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 for other details.

*Important Dates*
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:

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


Biomedical information
Collaborative filtering
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

We look forward to your submissions, and to welcoming you to Hamilton in November!

Bob Durrant
Kee-Eung Kim

ACML 2016 Program Committee Chairs

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