In noisy and low-data regimes prevalent in real-world applications, an
outstanding challenge of machine learning lies in effectively incorporating
inductive biases that promote data efficiency and robustness. Meta-learning and
informed ML stand out as two approaches for incorporating prior knowledge into
the ML pipeline. While the former relies on a purely data-driven source of
priors, the latter is guided by a formal representation of expert knowledge.
This paper introduces a novel hybrid paradigm, informed meta-learning, seeking
complementarity in cross-task knowledge sharing of humans and machines. We
establish the foundational components of informed meta-learning and present a
concrete instantiation of this framework--the Informed Neural Process. Through
a series of illustrative and larger-scale experiments, we demonstrate the
potential benefits of informed meta-learning in improving data efficiency and
robustness to observational noise, task distribution shifts, and heterogeneity.

在实际应用中，机器学习在嘈杂和低数据的环境下面临一个重要挑战，即如何有效地整合具有数据效率和鲁棒性的归纳偏好。本论文通过介绍一种新的混合范式，即信息元学习，旨在实现人类和机器之间跨任务知识共享的互补性，为信息元学习的基本组成部分和具体实例 - Informed Neural Process 进行了阐述，通过一系列示例和大规模实验，展示了信息元学习在提高数据效率和对观测噪声、任务分布偏移和异质性的鲁棒性方面的潜在益处。