Semisupervised Multitask Learning With Gaussian Processes

Grigorios Skolidis, Guido Sanguinetti

Research output: Contribution to journalArticlepeer-review


We present a probabilistic framework for transferring learning across tasks and between labeled and unlabeled data. The approach is based on Gaussian process (GP) prediction and incorporates both the geometry of the data and the similarity between tasks within a GP covariance, allowing Bayesian prediction in a natural way. We discuss the transfer of learning in a multitask scenario in the two cases where the underlying geometry is assumed to be the same across tasks and where different tasks are assumed to have independent geometric structures. We demonstrate the method on a number of real datasets, indicating that the semisupervised multitask approach can result in very significant improvements in performance when very few labeled training examples are available.
Original languageEnglish
Pages (from-to)2101-2112
Number of pages12
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number12
Publication statusPublished - 1 Dec 2013


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