November 19th, 2013 
Daniela Lopez de Luise SDM 2014 Workshop on Heterogeneous Learning
Philadelphia, Pennsylvania, USA
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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/
(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:
12/31/2013: Paper Submission
01/10/2014: Author Notification
01/20/2014: Camera Ready Paper Due
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