Workshop 1: ACML 2018 Workshop on Multi-output Learning (ACML-Mol’18)

Date & Time

Wednesday, 14 Nov 2018

Motivation and Objectives

Multi-output learning aims to predict multiple outputs for an input, where the output values are characterized by diverse data types, such as binary, nominal, ordinal and real-valued variables. Such learning tasks arise in a variety of real-world applications, ranging from document classification, computer emulation, sensor network analysis, concept-based information retrieval, human action/causal induction, to video analysis, image annotation/retrieval, gene function prediction and brain science. Due to its popularity in applications, multi-output learning has also been widely explored in machine learning community, such as multi-label/multi-class classification, multi-target regression, hierarchical classification with class taxonomies, label sequence learning, sequence alignment learning, and supervised grammar learning, and so on.

The theoretical properties of existing approaches for multi-output data are still not well understood. This triggers practitioners to develop novel methodologies and theories to deeply understand multi-output learning tasks. Moreover, the emerging trends of ultrahigh input and output dimensionality, and the complexly structured objects, lead to formidable challenges for multi-output learning. Therefore, it is imperative to propose practical mechanisms and efficient optimization algorithms for large-scale applications. Deep learning has gained much popularity in today’s research, and has been developed in recent years to deal with multi-label and multi-class classification problems. However, it remains non-trivial for practitioners to design novel deep neural networks that are appropriate for more comprehensive multi-output learning domains.

Topics of Interest

Interested topics include, but are not limited to:

  • Novel deep learning methods for multi-output learning tasks
  • Novel modellings for multi-output learning from new perspectives
  • Statistical theory analysis for multiple output learning
  • Large-scale optimization algorithms for multiple output learning
  • Sparse representation learning for large-scale multiple output learning
  • Active learning for multi-output data
  • Online learning for multi-output data
  • Metric learning for multi-output data
  • Multi-output learning with noisy data
  • Multi-output learning with imbalanced data

Submissions Guidelines

Workshop submissions and camera ready versions will be handled by Microsoft CMT. Click https://cmt3.research.microsoft.com/ACMLMoL2018 for submission.

Papers should be formatted according to the ACML formatting instructions for the Conference Track. The submissions with 2 pages will be considered for the poster, while submissions with at least 6 pages will be considered for the oral presentation. The selective oral papers will be invited for IEEE TNNLS Special Issue on “Structured Multi-output Learning: Modelling, Algorithm, Theory and Applications” (https://cis.ieee.org/images/files/Documents/Transactions/TNNLS/TNNLS_SMLMATA-CFP.pdf)

ACML-MoL is a non-archival venue and there will be no published proceedings. The papers will be posted on the workshop website. It will be possible to submit to other conferences and journals both in parallel to and after ACML-MoL’18. Besides, we also welcome submissions to ACML-MoL that are under review at other conferences and workshops.

At least one author from each accepted paper must register for the workshop. Please see the ACML 2018 Website for information about accommodation and registration.

Important Dates

Submission: 20 Sep, 2018
Notification: 01 Oct, 2018
Workshop: 14 Nov, 2018

Organizers

  • Weiwei Liu, University of New South Wales, Australia
  • Xiaobo Shen, Nanyang Technological University, Singapore
  • Yew-Soon Ong, Nanyang Technological University, Singapore
  • Ivor W. Tsang, University of Technology Sydney, Australia
  • Chen Gong, Nanjing University of Science and Technology, China

Workshop Homepage

https://acml-mol.github.io/

Workshop 2: The 3rd Asian Workshop on Reinforcement Learning (AWRL’18)

Date & Time

Wednesday, 14 Nov 2018

Background and Motivation

The Asian Workshop on Reinforcement Learning (AWRL) focuses on both theoretical foundations, models, algorithms, and practical applications. We intend to make this an exciting event for researchers and practitioners in RL worldwide as a forum for the discussion of open problems, future research directions and application domains of RL.

AWRL 2018 will consist of keynote talks, invited paper presentations, and discussion sessions spread over a one-day period.

Organizers

  • Paul Weng, University of Michigan-Shanghai Jiao Tong University Joint Institute, China
  • Yang Yu, Nanjing University, China
  • Zongzhang Zhang, Soochow University, China
  • Li Zhao, Microsoft Research Asia, China

Workshop Homepage

http://awrl.cc/2018.html

Workshop 3: ACML 2018 Workshop on Machine Learning in China (MLChina’18)

Date & Time

Wednesday, 14 Nov 2018

Background and Motivation

During the past decade, machine learning researches in China have been growing in a blooming way. This is witnessed by the increasing number of works appeared in major machine learning related conferences and journals, and also numerous successful applications of machine learning techniques in major Chinese high-tech companies such as Huawei, Tencent, Baidu, Alibaba, etc. There are also many domestic machine learning conferences regularly held in China which attract a significant number of participants, such as the biennial Chinese Conference on Machine Learning, the annual Chinese Workshop on Machine Learning and Applications, the annual Chinese Vision and Learning Seminar, etc.

To take full advantage of the opportunity that ACML is to be held in Beijing, a dedicated full-day workshop for machine learning researchers and practitioners in China is organized. This workshop would be a great chance for sharing ideas and expertise among interested participants, encouraging students and junior researchers to get suggestions and advices from senior experts, and also fostering connections and possible collaborations between Chinese and International machine learning communities.

Organizers

Workshop Homepage

https://mlchina18.github.io/

Workshop 4: ACML 2018 Workshop on Machine Learning in Education

Date & Time

Wednesday, 14 Nov 2018

Background and Motivation

As AlphaGo defeated the world's best Go player in 2016, AI is brought into the classroom to individualize learning in the form of adaptive learning. It analyzes the students and note their weaknesses and strengths, then changes the course around so that students can polish up areas which they may be struggling with. It also responds to the students' needs and personalize the course to best fit their talents. We take this chance to discuss the most recent development of machine learning technology used in education and to provide a forum for communication of researchers active in machine learning used in education.

Topics of Interest

Interested topics include, but are not limited to:

  • Personalized learning paths
  • Content Analytics
  • Scoring
  • Automating repetitive tasks
  • Learning analytics

Organizers

Workshop Homepage

https://jim-zhenxue.github.io/ACML-2018/