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ACML 2020 : The 12th Asian Conference on Machine Learning


Conference Series : Asian Conference on Machine Learning
When Nov 18, 2020 - Nov 22, 2020
Where Bangkok, Thailand
Submission Deadline Jun 15, 2020
Notification Due Aug 12, 2020
Final Version Due Aug 31, 2020
Categories    general machine learning   learning in knowledge   applications   deep learning

Call For Papers

ACML 2020 Call for Paper (Bangkok, Thailand):
Deadline: June 15, 2020,
Conference date: November 18 - 22, 2020

If you are interested (FYI)

Call for Paper
The 12th Asian Conference on Machine Learning
ACML 2020
November 18 - 22, 2020
Bangkok, Thailand

Important Dates:
Submission: June 15, 2020
Author Rebuttal: July 20-August 3, 2020:
Notification: August 12, 2020
Camera-Ready: August 31, 2020

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AI Week 2020
November 18 - 22, 2020
Bangkok, Thailand

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 instances 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
AI Association of Thailand
SIIT, Thammasat University

General Co-chairs
Thanaruk Theeramunkong, Sirindhorn International Institute of Technology
Wray Buntine, Monash University

Program Co-chairs
Sinno Jialin Pan, Nanyang Technological University
Masashi Sugiyama, RIKEN/The University of Tokyo

Journal Track Co-chairs
Kee-Eung Kim, KAIST
Vineeth N Balasubramanian, IIT Hyderabad

Local Arrangement Co-chairs
Boonserm Kijsirikul, Chulalongkorn University
Thepchai Supnithi, NECTEC

Sponsorship Co-chairs
Sarana Nutanong, VISTEC
Ekapol Chuangsuwanich, Chulalongkorn University

Tutorial Co-chairs
Sanparith Marukatat, NECTEC
Ivor Tsang, University of Technology, Sydney

Workshop Co-chairs
Prachya Boonkwan, NECTEC
Taiji Suzuki, University of Tokyo

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