  In noisy and low-data regimes prevalent in real-world applications, a key
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 ML
pipelines. While the former relies on a purely data-driven source of priors,
the latter is guided by prior domain knowledge. In this paper, we formalise a
hybrid paradigm, informed meta-learning, facilitating the incorporation of
priors from unstructured knowledge representations, such as natural language;
thus, unlocking 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 experiments, we demonstrate the potential benefits
of informed meta-learning in improving data efficiency, robustness to
observational noise and task distribution shifts.
