We introduce a new framework for studying meta-learning methods using PAC-Bayesian theory. Its main advantage over previous work is that it allows for more flexibility in how the transfer of knowledge between tasks is realized. For previous approaches, this could only happen indirectly, by means of learning prior distributions over models. In contrast, the new generalization bounds that we prove express the process of meta-learning much more directly as learning the learning algorithm that should be used for future tasks. The flexibility of our framework makes it suitable to analyze a wide range of meta-learning mechanisms and even design new mechanisms. Other than our theoretical contributions we also show empirically that our framework improves the prediction quality in practical meta-learning mechanisms.

翻译：我们提出了一种基于PAC-贝叶斯理论研究元学习方法的新框架。与先前工作相比，该框架的主要优势在于允许更加灵活地实现任务间知识迁移。在以往方法中，知识迁移只能通过先验模型分布的学习间接实现。相比之下，我们证明的新泛化上界更直接地表达了元学习过程——即学习未来任务应使用的学习算法本身。该框架的灵活性使其适用于分析各类元学习机制，甚至设计新型机制。除理论贡献外，实验表明该框架能够提升实际元学习机制中的预测质量。