Tommy Rochussen

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.

Very pleased to share that our new paper “Sparse Gaussian Neural Processes” has been accepted under the proceedings track at AABI 2025! 🎉 (1/n)

I've seen things you people wouldn't believe.

Attacks from reviewers on fire off the shoulders of #OpenReview.

I watched logic fallacies glitter in the dark near @iclr-conf.bsky.social

All those moments will be lost in time, like tears in the next resubmission. 

Time to die.

#ML #Ai #PhDlife

🙋‍♂️

Thanks for putting this together - keen to be added!