Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
Nonparametric identification of population models via Gaussian processes
Automatica (Journal of IFAC)
Vision system for image recognition based on three-dimensional vector patterns
Machine Graphics & Vision International Journal
Prediction error identification of linear systems: A nonparametric Gaussian regression approach
Automatica (Journal of IFAC)
Sparse gaussian processes for multi-task learning
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
A multi-task learning approach for the extraction of single-trial evoked potentials
Computer Methods and Programs in Biomedicine
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Population models are widely applied in biomedical data analysis since they characterize both the average and individual responses of a population of subjects. In the absence of a reliable mechanistic model, one can resort to the Bayesian nonparametric approach that models the individual curves as Gaussian processes. This paper develops an efficient computational scheme for estimating the average and individual curves from large data sets collected in standardized experiments, i.e. with a fixed sampling schedule. It is shown that the overall scheme exhibits a ''client-server'' architecture. The server is in charge of handling and processing the collective data base of past experiments. The clients ask the server for the information needed to reconstruct the individual curve in a single new experiment. This architecture allows the clients to take advantage of the overall data set without violating possible privacy and confidentiality constraints and with negligible computational effort.