Conference Information
ACML 2020: Asian Conference on Machine Learning
Submission Date:
2020-06-29 Extended
Notification Date:
Conference Date:
Bangkok, Thailand
CCF: c   CORE: b   Viewed: 34844   Tracked: 187   Attend: 37

Conference Location
Call For Papers
The 12th Asian Conference on Machine Learning, Bangkok, Thailand (ACML 2020) aims to provide a leading international forum for researchers in machine learning and related fields to share their new ideas, progress and achievements. Submissions from regions other than the Asia-Pacific are also highly encouraged. It is planned to take place during November 18-20, 2020 in Bangkok, Thailand, and is co-located with ICONIP2020. 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 paradigms in machine learning.

ACML has taken place annually since 2009 in locations throughout the Asia-Pacific region. The series of the conferences were held in Nagoya, Japan (2019), Beijing, China (2018), Seoul, Korea (2017), Hamilton, New Zealand (2016), Hong Kong, China (2015), Nha Trang, Vietnam (2014), Canberra, Australia (2013), Singapore (2012), Taoyuan, Taiwan (2011), Tokyo, Japan (2010), and Nanjing, China (2009). As usual, the committee plans to execute two publication tracks this year: Authors may submit either to the conference track, for which the proceedings will be published as a volume of Proceedings of Machine Learning Research (PMLR) series, or to the journal track for which accepted papers will appear in a special issue of the Springer journal Machine Learning.


General machine learning methodologies

Active learning ⬩ Bayesian machine learning ⬩ Dimensionality reduction ⬩ Feature selection ⬩ Graphical models ⬩ Latent variable models ⬩ Learning for big data ⬩ Learning in graphs ⬩ Multi-objective learning ⬩ Multiple instance learning ⬩ Multi-task learning ⬩ Online learning ⬩ Optimization ⬩ Reinforcement Learning ⬩ Semi-supervised learning ⬩ Sparse learning ⬩ Structured output learning ⬩ Supervised learning ⬩ Transfer learning ⬩ Unsupervised learning ⬩ Other machine learning methodologies

Learning in knowledge-intensive systems

Knowledge refinement and theory revision ⬩ Multi-strategy learning ⬩ Other learning systems


Bioinformatics ⬩ Biomedical informatics ⬩ Collaborative filtering ⬩ Computer vision ⬩Healthcare ⬩ Human activity recognition ⬩ Information retrieval ⬩ Natural language processing ⬩ Social networks ⬩ Web search ⬩ Other applications

Deep learning

Deep learning theory ⬩ Generative model ⬩ Reinforcement learning ⬩ Supervised learning ⬩ Other topics in deep learning


Computational learning theory ⬩ Optimization ⬩ Reproducible research ⬩ Statistical learning theory ⬩ Other theories
Last updated by Dou Sun in 2020-06-21
Acceptance Ratio
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Related Journals
CCFFull NameImpact FactorPublisherISSN
aJournal of Machine Learning Research Microtome Publishing1532-4435
International Journal of Artificial Intelligence & Machine Learning AR Publication0000-0000
Foundations and Trends in Machine LearningNow Publishers Inc.1935-8237
International Journal of Modern Physics CWorld Scientific0129-1831
cMachine TranslationSpringer0922-6567
Mechanism and Machine Theory3.535Elsevier0094-114X
Computer Assisted Language Learning2.018Taylor & Francis0958-8221
Visualization in EngineeringSpringer2213-7459
Journal of Function Spaces0.451Hindawi2314-8896
Full NameImpact FactorPublisher
Journal of Machine Learning Research Microtome Publishing
International Journal of Artificial Intelligence & Machine Learning AR Publication
Foundations and Trends in Machine LearningNow Publishers Inc.
International Journal of Modern Physics CWorld Scientific
Machine TranslationSpringer
Mechanism and Machine Theory3.535Elsevier
Computer Assisted Language Learning2.018Taylor & Francis
Visualization in EngineeringSpringer
Journal of Function Spaces0.451Hindawi