ICML 2011 workshop on unsupervised
and transfer learning
Self-reflective Multi-task Gaussian Process
Kohei
Hayashi, Takashi Takenouchi
Nara Institute of Science and Technology, Japan
Ryota Tomioka, Hisashi Kashima
University of Tokyo, Japan
Multi-task learning
aims at transferring knowledge between similar tasks. The multi-task
Gaussian process framework of Bonilla et al. (2008) models (incomplete)
responses of C data points for R tasks by a Gaussian process; the
covariance function is defined as the product of a covariance function
on input-dependent features and the inter-task covariance matrix
(empirically estimated as a model parameter). We propose a new model in
which we incorporate similarities based on the observed responses,
which allows for the representation of much more complex data
structures. The proposed approach enables us to construct covariance
matrices via kernel functions even when additional information (e.g.,
the inputdependent features) are not given. We also propose an
efficient conjugate-gradient-based
algorithm for prediction, which solves an R ~ 900 and C ~ 1600 problem
in roughly 10 minutes. Finally, we apply our model to the Movielens
100k dataset and show that the proposed method achieves the best
prediction accuracy on the dataset.