Quaetal11.pdf (196.08 kB)
Multitask learning without label correspondences
conference contribution
posted on 2023-06-08, 16:45 authored by Novi QuadriantoNovi Quadrianto, Alexander Smola, Tiberio Caetano, S V N Vishwanathan, James PettersonWe propose an algorithm to perform multitask learning where each task has potentially distinct label sets and label correspondences are not readily available. This is in contrast with existing methods which either assume that the label sets shared by different tasks are the same or that there exists a label mapping oracle. Our method directly maximizes the mutual information among the labels, and we show that the resulting objective function can be efficiently optimized using existing algorithms. Our proposed approach has a direct application for data integration with different label spaces for the purpose of classification, such as integrating Yahoo! and DMOZ web directories.
History
Publication status
- Published
Journal
Proceedings of the Advances in Neural Information Processing Systems 23; Vancouver, British Columbia, Canada; 6-9 December 2010Publisher
Neural Information Processing Systems FoundationIssue
23Volume
1Page range
1957-1965Pages
2631.0Book title
Proceedings of the 24th annual conference on neural information processing systems 2010Place of publication
Red Hook, NYISBN
9781617823800Series
Advances in neural information processing systemsDepartment affiliated with
- Informatics Publications
Full text available
- Yes
Peer reviewed?
- Yes
Editors
Chris Williams, Aron Culotta, John Shawe-Taylor, John Lafferty, Richard ZemelLegacy Posted Date
2014-02-24Usage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC