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Multi-task Learning for HIV Therapy Screening

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44132

Bickel,  Steffen
Machine Learning, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44156

Bogojeska,  Jasmina
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44907

Lengauer,  Thomas
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45377

Scheffer,  Tobias
Machine Learning, MPI for Informatics, Max Planck Society;

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Zitation

Bickel, S., Bogojeska, J., Lengauer, T., & Scheffer, T. (2008). Multi-task Learning for HIV Therapy Screening. In A. McCallum, & S. Roweis (Eds.), ICML'08 (pp. 56-63). New York, NY: ACM. doi:10.1145/1390156.1390164.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-1C5A-5
Zusammenfassung
We address the problem of learning classifers for a large number of tasks. We derive a solution that produces resampling weights which match the pool of all examples to the target distribution of any given task. Our work is motivated by the problem of predicting the outcome of a therapy attempt for a patient who carries an HIV virus with a set of observed genetic properties. Such predictions need to be made for hundreds of possible combinations of drugs, some of which use similar biochemical mechanisms. Multi-task learning enables us to make predictions even for drug combinations with few or no training examples and substantially improves the overall prediction accuracy.