Factorial multi-task learning : a Bayesian nonparametric approach
Gupta, Sunil Kumar, Phung, Dinh and Venkatesh, Svetha 2013, Factorial multi-task learning : a Bayesian nonparametric approach, in ICML 2013 : Proceedings of the Machine Learning 2013 International Conference, International Machine Learning Society (IMLS), [Atlanta, Ga.], pp. 1694-1702.
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Title
Factorial multi-task learning : a Bayesian nonparametric approach
Multi-task learning is a paradigm shown to improve the performance of related tasks through their joint learning. However, for real-world data, it is usually difficult to assess the task relatedness and joint learning with unrelated tasks may lead to serious performance degradations. To this end, we propose a framework that groups the tasks based on their relatedness in a subspace and allows a varying degree of relatedness among tasks by sharing the subspace bases across the groups. This provides the flexibility of no sharing when two sets of tasks are unrelated and partial/total sharing when the tasks are related. Importantly, the number of task-groups and the subspace dimensionality are automatically inferred from the data. To realize our framework, we introduce a novel Bayesian nonparametric prior that extends the traditional hierarchical beta process prior using a Dirichlet process to permit potentially infinite number of child beta processes. We apply our model for multi-task regression and classification applications. Experimental results using several synthetic and real datasets show the superiority of our model to other recent multi-task learning methods. Copyright 2013 by the author(s).
Language
eng
Field of Research
080109 Pattern Recognition and Data Mining
Socio Economic Objective
970108 Expanding Knowledge in the Information and Computing Sciences
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