Call for Papers

Annual Workshop on Learning on Big Data will be held in conjunction with the Asian Conference on Machine Learning (ACML) on November 16, 2016 in the University of Waikato, Hamilton, New Zealand. 

Scope

With the advance of data storage and Internet technology, data becomes more massive, noisier and more complex, which also brings good opportunities and challenges for machine learning. Learning technologies on Big Data have attracted many attentions. They have been successfully applied to many machine learning applications, including text mining, natural language processing, image categorization, video analysis, recommendation systems, sensor-based prediction problems, software engineering and so forth. 

The aim of this workshop is to document recent process of Big Data technologies (e.g. Big Data Infrastructure, Distributed optimization, Stochastic optimization, MapReduce and Cloud Computing, etc.) in different real-world applications, to understand how computational bottlenecks trade-off with statistical efficiency for Big Data analysis tools, and also to stimulate discussion about potential challenges that may open new directions of learning on Big Data. We appreciate not only the manuscripts that dedicate to handle learning on Big Data, but also those which aim to discuss the approaches and/or theories for handling the new Big Data issues when exploiting massive data of different formats or structures.


Topics

Manuscripts are solicited to address a wide range of topics of Big Data methodologies, as well as topics related to Big Data applications. The list of topics below are for the reference of authors, however, the submissions are not restricted to the topics listed below:
  • Theoretical Foundations of Big Data Analytics
  • Systematic Frameworks of Big Data Processing
  • Big Data Collection and Preprocessing Technologies
  • Big Data Storage and Management Models
  • Indexing techniques for Big Data
  • Big Data Problems and Large-scale Optimization
  • MapReduce and Parallel Computing for Big Data
  • Distributed Machine Learning
  • Stochastic Optimization
  • Transfer learning for Big Data: the source/target data is in large scale.
  • Learning with Noisy Big Data
  • Learning on Streaming Data
  • Learning with multiple source domains
  • Learning with Big Dimensionality
  • Learning with non-i.i.d and/or Heterogeneous Data
  • Big Data Knowledge Discovery
  • Big Data Applications
  • Big Data Visual Analytics
  • Security and Privacy in Big Data
  • Theoretical analysis on the learning algorithms of above problems

Submission

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

WLBD is a non-archival venue and there will be no published proceedings. However, the papers will be posted on this website. Therefore it will be possible to submit to other conferences and journals both in parallel to and after WLBD 2016. Besides, we also welcome submissions to WLBD 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 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 2016 Website for information about accommodation and registration.

Important Dates

Submission deadline: Sep. 26, 2016 (extended to Oct. 6, 2016)
Notification of acceptance: Oct. 14, 2016
Camera ready deadline: Oct. 24, 2016
Workshop date: Nov. 16, 2016
ACML dates: Nov. 16-18, 2016

Organizers

Ivor Wai-Hung Tsang, QCIS, University of Technology Sydney
Ling Chen, QCIS, University of Technology Sydney
Ying Zhang, QCISUniversity of Technology Sydney
Joey Tianyi Zhou, IHPC, A*Star, Singapore