A Bayesian approach to (online) transfer learning: Theory and algorithms

Abstract

Transfer learning is a machine learning paradigm where knowledge from one problem is utilized to solve a new but related problem. While conceivable that knowledge from one task could help solve a related task, if not executed properly, transfer learning algorithms can impair the learning performance instead of improving it – commonly known as negative transfer. In this paper, we use a parametric statistical model to study transfer learning from a Bayesian perspective. Specifically, we study three variants of transfer learning problems, instantaneous, online, and time-variant transfer learning. We define an appropriate objective function for each problem and provide either exact expressions o..