WHL 2015 - 2015 Workshop on Heterogeneous Learning

SDM 2015 Workshop on Heterogeneous Learning
Vancouver, British Columbia, Canada
http://www.cs.cmu.edu/~jingruih/workshop-index.htm...
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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 and domain heterogeneity (as in
multi-task learning, domain adaptation), 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:
01/12/2015: Paper Submission
01/30/2015: Author Notification
02/09/2015: 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). Authors are required to
submit their papers electronically in PDF format to
sdm14hl-AT-gmail.com by 11:59pm EST, January 12, 2015.
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ORGANIZERS
Jieping Ye (Arizona State University)
Yuhong Guo (Temple University)
Jingrui He (Arizona State University)