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Error bounds for Regularized Trigonometric Regression in the Multi-task setting
Multi-task learning is the problem of jointly learning multiple functions
that are “related” to each other. By leveraging this similarity, estimation performance can be
improved on each (possibly unseen) task, and one can make an efficient use of the available
data. The project aims at deriving
uncertainty bounds around the multi-task-system estimates. Specifically, the candidate will
work with the regularized trigonometric regression inspired by the so-called sparse-spectrum
Gaussian process regression, investigate the issue of bias learning (i.e., finding the features
that encode similarity among tasks) and derive error bounds for it, possibly setting the analysis
in the statistical learning framework.
MultiTaskBounds.pdf
