DSAA2017 Special Session “Non-IID Learning”

Overview

Learning from big data is increasingly becoming a major challenge and opportunity for big business and innovative learning theories and tools. Some of the most critical challenges of learning from big data are the uncovering of the explicit and implicit coupling relationships embedded in mixed heterogeneous data from single/multiple sources. The coupling and heterogeneity of the non-IID aspects form the essence of big data and most real-world applications, namely the data is non-IID.

Most of classic theoretical systems and tools in machine learning , statistics, data mining, database, and knowledge management assume the independence and identical distribution of underlying objects, features and values. Such theories and tools may lead to misleading or incorrect understanding of real-life data complexities. Non-IID learning in big data is a foundational theoretical problem in machine learning, AI and data science, which considers the complex couplings and heterogeneity between entities, properties, interactions and contexts.

This special session aims to gather researchers in the area of machine learning to discuss recent findings and new challenges around the concept of non-IID.

Topics of interest

Topics of interest include all aspects of learning from implicitly and/or explicitly non-IID data including, but not limited to:

Statistical foundation for non-IID learning

Mathematical foundation for non-IID learning

Probabilistic methods for non-IID learning

Statistical machine learning for non-IID learning

Non-IID learning theory and foundation

Non-IID data characterization

Non-IID data transformation

Non-IID data representation and encoding

Non-IID learning models and algorithms

Non-IID single-source analytics

Non-IID multi-source analytics

Non-IID clustering

Non-IID classification

Non-IID recommender systems

Non-IID text mining and document analysis

Non-IID image and video analytics

Organizers

Special Session Chairs:

Longbing Cao, Professor, Advanced Analytics Institute, University of Technology Sydney, Australia.

Yang Gao, Professor, Department of Computer Science and Technology, Nanjing University, China.

Philip S Yu, Professor, Department of Computer Science, University of Illinois at Chicago, USA.

Organization Chairs: 

Yinghuan Shi, Department of Computer Science and Technology, Nanjing University, China.

Guansong Pang, Advanced Analytics Institute, University of Technology Sydney, Australia.

Chengzhang Zhu, Advanced Analytics Institute, University of Technology Sydney, Australia.

Important Dates

Paper Submission: May 25, 2017

Notification of acceptance: July 25, 2017

Camera-Ready: Aug 15, 2017

Advanced Registration: Aug. 31, 2017

Publications

All accepted papers, including main tracks and special sessions, will be published by IEEE and will be submitted for inclusion in the IEEE Xplore Digital Library. The conference proceedings will be submitted for EI indexing through INSPEC by IEEE. Top quality papers accepted and presented at the conference will be selected for extension and invited to the special issues of International Journal of Data Science and Analytics (JDSA, Springer) or other SCI-indexed journals.

Submissions

The paper length allowed is a maximum of ten (10) pages, in 2-column U.S. letter style using IEEE Conference template (see the IEEE Proceedings Author Guidelines: http://www.ieee.org/conferences_events/conferences/publishing/templates.html).

All submissions will be blind reviewed by the Special Session Program Committee on the basis of technical quality, relevance to conference topics of interest, originality, significance, and clarity. Author names and affiliations must not appear in the submissions, and bibliographic references must be adjusted to preserve author anonymity.