Interactive Multi-task Relationship Learning

Abstract

Multi-task learning (MTL) is a learning paradigm that provides a principled way to improve the generalization performance of a set of related machine learning tasks by transferring knowledge among the tasks. The past decade has witnessed many successful applications of MTL in different domains. In the center of MTL algorithms is how the relatedness of tasks are modeled and encoded in learning formulations to facilitate knowledge transfer. Among the MTL algorithms, the multi-task relationship learning (MTRL) attracted much attention in the community because it learns task relationship from data to guide knowledge transfer, instead of imposing a prior task relatedness assumption. However, this method heavily depends on the quality of training data. When there is insufficient training data or the data is too noisy, the algorithm could learn an inaccurate task relationship that misleads the learning towards suboptimal models. To address the aforementioned challenge, in this paper we propose a novel interactive multi-task relationship learning (iMTRL) framework that efficiently solicits partial order knowledge of task relationship from human experts, effectively incorporates the knowledge in a proposed knowledge-aware MTRL formulation. We propose an efficient optimization algorithm for kMTRL and comprehensively study query strategies that identify the critical pairs that are most influential to the learning. We present extensive empirical studies on both synthetic and real datasets to demonstrate the effectiveness of proposed framework.