Search Results for author:
Found 1 papers, 1 papers with code
A conditional one-output likelihood formulation for multitask Gaussian processes
At the same time, by extending this approach with both a hierarchical and an approximate model, the proposed extensions are capable of recovering the multitask covariance and noise matrices after learning only $2T$ parameters, avoiding the validation of any model hyperparameter and reducing the overall complexity of the model as well as the risk of overfitting.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
