2010
NeurIPS
NeurIPS 2010
Multitask Learning without Label Correspondences
Abstract
We 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.
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Interdisciplinary Bridge
- Artificial Intelligence and Mathematics & Optimization
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Trend Setter
- Transfer Learning
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Keyword Pioneer
- mutual information maximization
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Hot Topic Early Bird
- mutual information
Topics
Artificial Intelligence > Learning Paradigms > Transfer Learning
Machine Learning > Core Methods > Classification
Machine Learning > Core Methods > Representation Learning
Machine Learning > Learning Types > Weakly Supervised Learning
Mathematics & Optimization > Mathematics > Information Theory
Machine Learning > Learning Types > Multi-Task Learning
Machine Learning > Learning Types > Transfer Learning
Deep Learning > Learning Types > Classification