Asian Workshop on Reinforcement Learning (AWRL 2016)

The first Asian Workshop on Reinforcement Learning (AWRL 2016) focuses on both theoretical models and algorithms of reinforcement learning (RL) and its practical applications. In the last few years, we have seen the 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 2016 will consist of keynote talks (TBA), contributed paper presentations, discussion sessions spread over a one-day period.

Reinforcement learning (RL) is an active field of research that deals with the problem of (single or multiple agents') sequential decision-making in unknown possibly partially observable domains, whose (potentially non-stationary) dynamics may be deterministic, stochastic or adversarial. RL's objective is to develop agents' capability of learning optimal policies in unknown environments (possibly in face of other coexisting agents) by trial-and-error and with limited supervision. Recent developments in exploration-exploitation, online learning, planning, and representation learning are making RL more and more appealing to real-world applications, with promising results in challenging domains such as recommendation systems, computer games, or robotics systems. We would like to create a forum to discuss interesting results both theoretically and empirically related with RL. The ultimate goal of this workshop is to bring together diverse viewpoints in the RL area in an attempt to consolidate the common ground, identify new research directions, and promote the rapid advance of RL research community.

First New Zealand Text Mining Workshop

Motivation and Objectives

In recent times, there has been an astronomical surge in demand for data scientists with Harvard Business Review naming Data Scientist as The Sexiest Job of the 21st Century.

The workshop will aim to foster collaboration among Data Science academics and practitioners focussing on text data. We have reached a point in Data Science where there is an increasing demand to integrate information from text data into models. This workshop calls for recent advances made both in the area of theoretical Text Processing dealing with lower level algorithms as well applications in Text Mining. The workshop aims to foster collaboration between academic researchers and practitioners so that the two groups could be able to integrate new advances in approaches into real world innovations being worked on by the practitioners.

Topics of Interest

Papers are solicited from the following list of topics, however papers dealing with any aspect of Natural Language Processing, Text Processing and Text Mining are welcome.

  • Topic detection/Modelling
  • Sentiment detection
  • Language modelling
  • Social Media text processing
  • Information Extraction
  • Summarization
  • Knowledge based Predictive models
  • Knowledge Representation
  • Linked Data Development/Applications
  • Parsing, NER, POS tagging
  • Pragmatics Discourse Semantics
  • Lexicon Development
  • Natural Language Generation

Submission Guidelines

Paper submission and reviewing for this workshop will be electronic via EasyChair. The papers should be written in English, following the Springer LNCS format, and be submitted in PDF on or before Sept. 30, 2016.

The following types of contributions are welcome. The recommended page length is given in brackets. There is no strict page limit but the length of a paper should be commensurate with its contribution.

  • Full research papers (8-12 pages);
  • Short research papers (4-6 pages);
  • System papers (4-6 pages).

Accepted papers will be published as a volume in the CEUR Workshop Proceedings series.

ACML Workshop on Learning on Big Data

Call for Papers

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.


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


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