Title: Generalized Linear Model Approach to Multi-Task Learning
Advisor: Professor George Michailidis
Committee Members: Associate Professor Kerby Shedden, Associate Professor Ji Zhu
Abstract: Multi-task learning is a machining learning method that improves learning of signal task by using information from other train sets. In this proposal, we explain how multi-task learning works and give an overview of existing multi-task learning methods and some theoretical backgrounds. We propose a new multi-task learning method based on generalized linear models and discuss the possible steps of statistical inference.