SDM 2014 Workshop on Heterogeneous Learning
Key Dates:
01/10/2014: Paper Submission
01/31/2014: Author Notification
02/10/2014: Camera Ready Paper Due
From the post:
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;
Apologies but I saw this announcement too late for you to have a realistic opportunity to submit a paper. 🙁
Very unfortunate because the focus of the workshop is right up the topic map alley.
The main conference, which focuses on data mining, is April 24-26, 2014 in Philadelphia, Pennsylvania, USA.
I am very much looking forward to reading the papers from this workshop! (And looking for notice of next year’s workshop much earlier!)