Workshop 1 (W1): The First International Workshop on Machine Learning for Artificial Intelligence Platforms (MLAIP)

Date & Time: Wednesday, 15 Nov 2017 at 08:30 am – 3:30 pm

Recently, several successful AI systems such as Amazon Alexa, Google Assistant, and NAVER X LINE Clova are developed based on AI-assistant platforms. These AI platforms contain several common technologies including speech recognition/synthesis, natural language understanding, image recognition, and dialog recommendation.

Building a successful MLAIP is a challenging mission because it requires a novel combination of heterogeneous machine learning models in a unified framework with efficient data processing. The goals of this workshop is to investigate and advance important topics in Machine Learning for AI Platforms (MLAIPs) further. In addition, we expect to provide the collaboration opportunities to researchers on ML theory on diverse application domains as well as industrial engineers.

Important dates

  • Extended abstracts deadline: 30 Sep 2017
  • Author notification: 10 Oct 2017
  • Workshop day: 15 Nov 2017

Keynote Speaker

  • Masashi Sugiyama (University of Tokyo)

Invited Speakers

  • Kyomin Jung (Seoul National University)
  • Huamin Qu (Hong Kong University of Science and Technology)
  • Il Chul Moon (KAIST)
  • Lucy Eunjeong Park (Papago, NAVER Corp.)
  • Nako Sung (Clova AI Research, NAVER Corp.)

Paper Submission

We request 2-page extended abstracts with free-style to be submitted by 30th Sep, 2017. Accepted abstracts will be presented as posters. Works that had previously been published elsewhere can also be submitted as there will be no proceeding publication. Topics of interest include but not limited to:

  • Novel machine learning models and algorithms for AI platforms
  • MLAIP for heterogeneous, multi-modal deep learning
  • Speech recognition and synthesis with MLAIP
  • Natural language processing applications with MLAIP
  • Computer vision applications with MLAIP
  • Recommendation systems with MLAIP
  • MLAIP for novel integration of multiple application domains
  • Deep learning for large-scale data

Papers should be submitted directly to the following e-mail:

Organizing Committee

  • Byoung-Tak Zhang (Seoul National University)
  • Sungroh Yoon (Seoul National University)
  • Dit-Yan Yeung (Hong Kong University of Science and Technology)
  • Sung Kim (Hong Kong University of Science and Technology)
  • Jaesik Choi (Ulsan National Institute of Science and Technology)
  • Jung-Woo Ha (Clova, NAVER Corp.)

Workshop homepage:

Workshop 2 (W2): The 2nd Asian Workshop on Reinforcement Learning (AWRL’17)

Date & Time: Wednesday, 15 Nov 2017 at 08:30 am – 3:30 pm

Keynote Speaker

Invited Speakers


The Asian Workshop on Reinforcement Learning (AWRL) focuses on both theoretical foundations, models, algorithms, and practical applications. In the last few years, we have seen a growing interest in RL of researchers from different research areas and industries. We invite reinforcement learning researchers and practitioners to participate in this world-class gathering. We intend to make this an exciting event for researchers and practitioners in RL worldwide, not only for the presentation of top quality papers, but also as a forum for the discussion of open problems, future research directions and application domains of RL.

AWRL 2017 (in conjunction with ACML 2017) will consist of keynote talks (TBA), contributed paper presentations, and discussion sessions spread over a one-day period.


The workshop will cover a range of sub-topics in RL, from theoretical aspects to empirical evaluations, including but not limited to:

  • Exploration/Exploitation
  • Function approximation in RL
  • Deep RL
  • Policy search methods
  • Batch RL
  • Kernel methods for RL
  • Evolutionary RL
  • Partially observable RL
  • Bayesian RL
  • Multi-agent RL
  • RL in non-stationary domains
  • Life-long RL
  • Non-standard Criteria in RL, e.g.:
    • Risk-sensitive RL
    • Multi-objective RL
    • Preference-based RL
  • Transfer Learning in RL
  • Knowledge Representation in RL
  • Hierarchical RL
  • Interactive RL
  • RL in psychology and neuroscience
  • Applications of RL, e.g.:
    • Recommender systems
    • Robotics
    • Video games
    • Finance

Paper Submission

Workshop submissions and camera ready versions will be handled by EasyChair. Click here for submission.

Papers should be formatted according to the ACML formatting instructions for the Conference Track. Submissions need not be anonymous.

AWRL is a non-archival venue and there will be no published proceedings. However, the papers will be posted on the workshop website. Therefore it will be possible to submit to other conferences and journals both in parallel to and after AWRL 2017. Besides, we also welcome submissions to AWRL that are under review at other conferences and workshops. For this reason, please feel free to submit either anonymized or non-anonymized versions of your work. We have enabled anonymous reviewing so EasyChair will not reveal the author’s names unless you chose to do so in your PDF. At least one author from each accepted paper must register for the workshop. Please see the ACML 2017 Website for information about accommodation and registration.

Important Dates

  • Submission deadline: 10 Aug. --> 27 Aug. 2017
  • Notification of acceptance: 10 Sep. 2017
  • Online camera-ready: 10 Oct. 2017
  • Workshop date: 15 Nov. 2017

Organizing Committee

Workshop 3 (W3): 2017 Annual Korea AI Society Meeting

Date & Time: Wednesday, 15 Nov 2017 at 10:00 am – 3:30 pm

Korea AI society is an organization with five special interest groups: SIG CVPR Pattern Recognition, SIG Machine Learning, SIG Artificial Intelligence, SIG Bio and Health, and SIG Brain Computer Interface. This is an annual joint meeting of five SIGs, introducing recent research achievements in each SIG. This meeting is also an opportunity for meeting researchers in working in different research areas for potential collaboration.

Keynote Speaker


Organizing Committee

  • Sun Kim (Chair, Seoul National University)
  • Hyeran Byun (Yonsei University)
  • Heejoon Chae (Sookmyung Women’s University)
  • Seungjin Choi (Pohang University of Science and Technology)
  • Geun-Sik Jo (Inha University)
  • Sung-Bae Jo (Yonsei University)
  • Daijin Kim (Pohang University of Science and Technology)
  • Hyunjung Shin (Ajou University)
  • Sungroh Yoon (Seoul National University)

Korea AI Society Meeting homepage: