Shift-Invariant Grouped Multi-task Learning for Gaussian Processes

Multi-task learning leverages shared information among data sets to improve the learning performance of individual tasks. The paper applies this framework for data where each task is a phase-shifted periodic time series. In particular, we develop a novel Bayesian nonparametric model capturing a mixture of Gaussian processes where each task is a sum of a group-specific function and a component capturing individual variation, in addition to each task being phase shifted. We develop an efficient em algorithm to learn the parameters of the model. As a special case we obtain the Gaussian mixture model and em algorithm for phased-shifted periodic time series. Experiments in regression, classification and class discovery demonstrate the performance of the proposed model using both synthetic data and real-world time series data from astrophysics. Our methods are particularly useful when the time series are sparsely and non-synchronously sampled.