Meta-learning is a powerful approach that exploits historical data to quickly solve new tasks from the same distribution. In the low-data regime, methods based on the closed-form posterior of Gaussian processes (GP) together with Bayesian optimization have achieved high performance. However, these methods are either computationally expensive or introduce assumptions that hinder a principled propagation of uncertainty between task models. This may disrupt the balance between exploration and exploitation during optimization. In this paper, we develop ScaML-GP, a modular GP model for meta-learning that is scalable in the number of tasks. Our core contribution is a carefully designed multi-task kernel that enables hierarchical training and task scalability. Conditioning ScaML-GP on the meta-data exposes its modular nature yielding a test-task prior that combines the posteriors of meta-task GPs. In synthetic and real-world meta-learning experiments, we demonstrate that ScaML-GP can learn efficiently both with few and many meta-tasks.

翻译：元学习是一种利用历史数据快速解决同一分布中新任务的有效方法。在低数据场景下，基于高斯过程（GP）闭式后验与贝叶斯优化的方法已取得高性能表现。然而，这些方法要么计算开销高昂，要么引入阻碍任务模型间不确定性原则性传播的假设，这可能在优化过程中破坏探索与利用之间的平衡。本文提出ScaML-GP——一种面向元学习的模块化GP模型，其可随任务数量进行扩展。我们的核心贡献在于精心设计的、支持层次化训练与任务可扩展性的多任务核函数。通过基于元数据对ScaML-GP进行条件化处理，可显现其模块化特性，从而生成融合元任务GP后验的测试任务先验。在合成数据与真实世界的元学习实验中，我们证明ScaML-GP能够在少量及大量元任务场景下均实现高效学习。