JMLR 2015 - JMLR Special Topic on Multi-Task Learning, Domain Adaptation and Transfer Learning

JMLR Special Topic on Multi-Task Learning, Domain Adaptation and Transfer Learning
Guest Editors: Urun Dogan, Marius Kloft, Francesco Orabona, Tatiana Tommasi
In the last years there has been an increase of activity in the areas of domain adaptation, transfer and multi-task learning. All born as solutions to better exploit the available data at training time and often moved by the need to deal with a reduced amount of information, these three topics grew fast in several directions and have multiple applications. Today new open research questions present the challenge. On one side, the literature is missing a joint theoretical framework over all of them, replaced instead by many theoretical formulations model regimes that are rarely used in practice (e.g. adaptive methods that store all the source samples). On the other, in the “big data” era, existing methods should be extended to manage large amount of data that do not lack anymore in size but may lack in quality or may continuously change over time.
This special topic is intended to to gather contributions that indicate new directions, innovative views and to serve as an outlet for recent advances in learning in such environments. We welcome both theoretical advances in this field as well as detailed reports on applications.
Topics of interest include:
Learning the task similarities/dissimilarities from large amount of data
Regularization strategies in multi-task learning
Domain adaptation and dataset bias on large data collections
Deep Learning on domain adaptation, transfer and multi-task applications
Incremental, online and active transfer for open-ended learning
Innovative adaptive procedures with applications e.g. in computer vision or computational biology
Domain adaptation theory
Large number of tasks, small number of examples setting in multi-task learning
Reinforcement learning and adaption
Applications of multi-task learning to natural language processing tasks such as machine translation and syntactic or semantic parsing.
Important Dates:
Submission: 15 April 2015
Decision: 15 August 2015
Final Version Due: 1 October 2015
Submission Procedure:
Authors are kindly invited to follow the standard JMLR format and submission procedure. The number of pages is limited to 30. Please include a note stating that your submission is for the special topic on Multi-Task Learning, Domain Adaptation and Transfer Learning.
For further details or enquiries, please contact the guest editors: mtldatl-AT-gmail.com