*Papers Uploaded* -- The presented papers and talks can now be found on the schedule page.
Date: December 17, 2011
Location: Sierra Nevada, Spain
Submission Deadline: October 21st, 2011
Author Notification: November 1st, 2011
A best paper prize (sponsored by Google) will be presented at the workshop!
The main theme of this workshop is the theoretical, algorithmic, and empirical analysis of such cases where there is a mismatch between the training and test distributions. This includes the crucial scenario of domain adaptation where the training examples are drawn from a source domain distinct from the target domain from which the test examples are extracted, or the more general scenario of multiple source adaptation where training instances may have been collected from multiple source domains, all distinct from the target. The topic of our workshop also covers other important problems such that of sample bias correction and has tight connections with other problems such as active learning where the active distribution corresponding to the learner's labeling request differs from the target distribution. Many other intermediate problems and scenarios appear in practice, which will be all covered by this workshop.
These problems are all critical and appear in almost all real-world applications of machine learning. Ignoring them can lead to dramatically poor results. Some straightforward existing solutions based on importance weighting are not always successful. Which algorithms should be used for domain adaptation? Under what theoretical conditions will they be successful? How do these algorithms scale to large domain adaptation problems? These are some of the questions that the workshop aims to address.