Call for Papers: SDM 2014 Workshop on Heterogeneous Learning

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*                             CALL FOR PAPERS*
*       SDM 2014 Workshop on Heterogeneous Learning*
*                    Philadelphia, Pennsylvania, USA*
*    http://www.cs.cmu.edu/~jingruih/workshop-index.html
<http://www.cs.cmu.edu/~jingruih/workshop-index.html>*


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*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.*

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*IMPORTANT DATES:*

*01/10/2014:     Paper Submission*
*01/31/2014:     Author Notification*
*02/10/2014:     Camera Ready Paper Due*

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*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
<http://www.siam.org/proceedings/macros.php>). Authors are required to *
*submit their papers electronically in PDF format to *
*sdm14hl@gmail.com <sdm14hl@gmail.com> by 11:59pm EST, January 10, 2014.*

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*INVITED SPEAKERS*

*Dale Schuurmans (University of Alberta)*
*Qiang Yang (Hong Kong University of Science and Technology)*
*Jun (Luke) Huan (University of Kansas)*

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*ORGANIZERS*

*Jieping Ye (Arizona State University)*
*Yuhong Guo (Temple University)*
*Jingrui He (Stevens Institute of Technology)*