Bridging Lifelong and Multi-Task Representation Learning: An Algorithm and a Complexity Measure

Zhi Wang, Chicheng Zhang, Ramya Korlakai Vinayak

Keywords: multi-task learning, representation learning, lifelong learning, online learning

Abstract: In lifelong learning, a learner faces a sequence of tasks with shared structure and aims to identify and leverage it to accelerate learning. We study the setting where such structure is captured by a common representation of data. Unlike multi-task learning or learning-to-learn, where tasks are available upfront to learn the representation, lifelong learning requires the learner to make use of its existing knowledge while continually gathering partial information in an *online* fashion. In this paper, we consider a generalized framework of lifelong representation learning. We propose a simple algorithm that uses multi-task empirical risk minimization as a subroutine and establish a sample complexity bound based on a new notion we introduce—the *task-eluder dimension*. Our result applies to a wide range of learning problems involving general function classes. As concrete examples, we instantiate our result on classification and regression tasks under noise.

Submission Number: 161