Informed Meta-Learning
<|reference_start|>Informed Meta-Learning: 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.<|reference_end|>
@article{kobalczyk2024informed,
    title={Informed Meta-Learning},
    author={Katarzyna Kobalczyk, Mihaela van der Schaar},
    journal={arXiv preprint arXiv:2402.16105},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16105},
    primaryClass={cs.LG}
}