Abstract

As the sheer volume of available benchmark datasets increases, the problem of joint learning of classi?ers and knowledge-transfer between classi?ers, becomes more and more relevant. We present a hierarchical approach which exploits information sharing among di?erent classi?cation tasks, in multitask and multi-class settings. It engages a top-down iterative method, which begins by posing an optimization problem with an incentive for large scale sharing among all classes. This incentive to share is gradually decreased, until there is no sharing and all tasks are considered separately. The method therefore exploits di?erent levels of sharing within a given group of related tasks, without having to make hard decisions about the grouping of tasks. In order to deal with large scale problems, with many tasks and many classes, we extend our batch approach to an online setting and provide regret analysis of the algorithm. We tested our approach extensively on synthetic and real datasets, showing signi?cant improvement over baseline and state-of-the-art methods.