提出新算法与复杂度度量，统一长期与多任务学习的表示学习。
Bridging Lifelong and Multi-Task Representation Learning via Algorithm and Complexity Measure
- 用多任务经验风险最小化做子程序，实现在线持续学习。
- 引入任务去相关维数，给出样本复杂度理论保证。
- 适用于分类与回归，对噪声鲁棒，适合长期学习场景。
在长期学习中，学习者需面对一系列具有共享结构的任务，并利用该结构加速学习过程。本文研究数据共享公共表示的学习场景。与多任务学习或元学习不同，长期学习要求学习者在不断获取部分信息的同时，充分利用已有知识进行在线更新。本文提出一种通用的长期表示学习框架，设计了一种基于多任务经验风险最小化的简单算法，并引入新的复杂度度量——任务去相关维数，建立了相应的样本复杂度上界。该结果适用于广泛的一般函数类学习问题。具体实例包括在噪声环境下进行分类与回归任务，验证了方法的有效性与理论稳健性。
原文摘要 · Abstract (English)
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.
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