Paper Submission Return to Top

12/31/2013:     Paper Submission

01/10/2014:     Author Notification

01/20/2014:     Camera Ready Paper Due

Paper Submission Instructions

Papers submitted to this workshop should be limited to 6 pages formatted using the SIAM SODA macro (http://www.siam.org/proceedings/macros.php). Authors are required to submit their papers electronically in PDF format to sdm14hl@gmail.com by 11:59pm EST, December 31, 2013.

Overview by Organizers

The main objective of this workshop is to bring the attention of researchers to real problems with multiple types of heterogeneities, ranging from online social media analysis, traffic prediction, to the manufacturing process, brain image analysis, etc. Some commonly found heterogeneities include task heterogeneity (as in multi-task learning), view heterogeneity (as in multi-view learning), instance heterogeneity (as in multi-instance learning), label heterogeneity (as in multi-label learning), oracle heterogeneity (as in crowdsourcing), etc. In the past years, researchers have proposed various techniques for modeling a single type of heterogeneity as well as multiple types of heterogeneities.

This workshop focuses on novel methodologies, applications and theories for effectively leveraging these heterogeneities. Here we are facing multiple challenges. To name a few: (1) how can we effectively exploit the label/example structure to improve the classification performance; (2) how can we handle the class imbalance problem when facing one or more types of heterogeneities; (3) how can we improve the effectiveness and efficiency of existing learning techniques for large-scale problems, especially when both the data dimensionality and the number of labels/examples are large; (4) how can we jointly model multiple types of heterogeneities to maximally improve the classification performance; (5) how do the underlying assumptions associated with multiple types of heterogeneities affect the learning methods.

We encourage submissions on a variety of topics, including but not limited to:

(1) Novel approaches for modeling a single type of heterogeneity, e.g., task/view/instance/label/oracle heterogeneities.

(2) Novel approaches for simultaneously modeling multiple types of heterogeneities, e.g., multi-task multi-view learning to leverage both the task and view heterogeneities.

(3) Novel applications with a single or multiple types of heterogeneities.

(4) Systematic analysis regarding the relationship between the assumptions underlying each type of heterogeneity and the performance of the predictor;

For this workshop, the potential participants and target audience would be faculty, students and researchers in related areas, e.g., multi-task learning, multi-view learning, multi-instance learning, multi-label learning, etc. We also encourage people with application background to actively participate in this workshop.

We believe that advancements on these topics will benefit a variety of application domains.