Arxiv link: arxiv.org/pdf/2504.01650

It’s nice to be able to get the ball rolling on my PhD with this paper, and a nice achievement to have published my first non-workshop paper. A big thanks to @vincefort.bsky.social for his supervision on this project!

1.) you want/need GP levels of interpretability
2.) you don’t have that many training tasks, so need SOTA data efficiency (at the meta-level)
3.) you have accurate domain knowledge (in GP-prior form)
4.) each task has too many observations for exact GP inference

If you need probabilistic predictions across multiple related tasks/datasets, you should use this model if any combination of the following hold:

We introduce the ability to meta-learn sparse variational Gaussian process inference, resulting in a new type of neural process that is amenable to prior elicitation.

🙋‍♂️

Thanks for putting this together - keen to be added!