Gaussian Process Models for Multi-Task Learning


Multi-task learning refers to a framework in machine learning where multiple tasks sharing a common domain are learned simultaneously. In this talk, I explained how to incorporate this framework within Bayesian modelling using Gaussian process models. The talk describes the general structure of a multi-output GP, and explores some of the most common kernel structures that correspond to such models. The sildes are available here.