Call For Papers

The 7th Asian Conference on Machine Learning (ACML2015) will be held in Hong Kong on November 20-22, 2015. The conference aims 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 paradigms in machine learning.

The proceedings will be published as a volume of Journal of Machine Learning Research (JMLR): Workshop and Conference Proceedings series. Selected papers from ACML’2014 will be invited to submit extended journal versions to an ACML special issue/section of the Machine Learning Journal.

For questions and suggestions on paper submission, please write to acml2015hk@gmail.com

Important Dates

Workshop Proposal Deadline:

May 11, 2015 at
23:59pm Pacific Time
Tutorial Proposal Deadline:

May 11, 2015 at
23:59pm Pacific Time
Early Submission Deadline:

May 11, 2015 at
23:59pm Pacific Time
Early Notification Date:June 22, 2015
Final Submission Deadline:

August 3, 2015 August 11, 2015 at
23:59pm Pacific Time
Final Notification Date:September 21, 2015
Camera Ready Deadline:October 5, 2015
Conference Dates:November 20-22, 2015

Topics of Interest

Topics of interest include but are not limited to:

  1. 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
  1. Analysis of learning systems
  • Computational learning theory
  • Experimental evaluation
  • Knowledge refinement
  • Reproducible research
  • Statistical learning theory
  1. Applications
  • Bioinformatics
  • Biomedical information
  • Collaborative filtering
  • Healthcare
  • Computer vision
  • Human activity recognition
  • Information retrieval
  • Natural language processing
  • Social networks
  • Web search
  1. Learning in knowledge-intensive systems
  • Knowledge refinement and theory revision
  • Multi-strategy learning
  • Other systems

Organizing Committee

General Co-Chairs

  • Irwin King, The Chinese University of Hong Kong, Hong Kong
  • Hang Li, Huawei Noah’s Ark Lab, Hong Kong

Program Co-Chairs

Local Arrangements Co-Chairs

  • Raymond Wong, Hong Kong University of Science and Technology, Hong Kong
  • Haiqin Yang, The Chinese University of Hong Kong, Hong Kong

Workshop Co-Chairs

  • Zhengdong Lu, Huawei Noah’s Ark Lab, Hong Kong
  • Zenglin Xu, University of Electronic Science and Technology of China, China

Tutorial Co-Chairs

Publication Co-Chairs

  • Kaizhu Huang, Xi’an Jiaotong-Liverpool University, China
  • Paul Pang, Unitec Institut of Technology, New Zealand

Steering Committee

Senior Program Committee

  • Wray Buntine, Monash University, Australia
  • Kevin Duh, NAIST, Japan
  • Stephen Gould, NICTA/Australian National University, Australia
  • Steven Hoi, NTU Singapore, Singapore
  • Hisashi Kashima, Kyoto University, Japan
  • Kristian Kersting, Technical University of Dortmund, Germany
  • Kee-Eung Kim, KAIST, South Korea
  • James Kwok, Hong Kong University of Science and Technology, Hong Kong
  • Hoai An Le Thi, University of Metz, France
  • Yuh-Jye Lee, NTUST Taiwan, Taiwan
  • Ping Li, Rutgers University, USA
  • Hsuan-Tien Lin, National Taiwan University, Taiwan
  • Shoude Lin, National Taiwan University, Taiwan
  • Zhengdong Lu, Huawei Technologies, Hong Kong
  • Michael Lyu, Chinese University of Hong Kong, Hong Kong
  • Alice Oh, KAIST, South Korea
  • Bernhard Pfahringer, University of Waikato, New Zealand
  • Dinh Phung, Deakin University, Australia
  • Tao Qin, Microsoft Research Asia, China
  • Volker Roth, University of Basel, Switzerland
  • Shirish Shevade, Indian Institute of Science, India
  • Masashi Sugiyama, Tokyo Institute of Technology, Japan
  • Dat Tran, University of Canberra, Australia
  • Truyen Tran, Deakin University, Australia
  • Liwei Wang, Peking University, China
  • Jun Xu, Chinese Academy of Sciences, China
  • Jieping Ye, Arizona State University, USA
  • Hwanjo Yu, POSTECH, South Korea
  • Junping Zhang, Fudan University, China
  • Min-Ling Zhang, Southeast University, China