RT @[email protected]

Since it's impossible to search for past tweets and it's that time of the year again - here is our brief "How to ML Paper" guide again: https://docs.google.com/document/d/16R1E2ExKUCP5SlXWHr-KzbVDx9DBUclra-EbU8IB-iE/edit#heading=h.16t67gkeu9dx

Good luck with #ICML2023 and remember there is always another deadline!

🐦🔗: https://twitter.com/j_foerst/status/1617670799552569345

I'm happy to share that our paper "Bayesian Structure Scores for Probabilistic Circuits" has been accepted at #aistats23! This is joint work with Yang Yang (MSc @mastodon.world, incoming PhD @leuvenai and Gennaro Gala.

The main contribution of the paper is to propose a new (actually old) way to learn the structure of #ProbabilisticCircuits. We take lessons from classical structure learning in Bayesian networks which optimizes some structure score, where a principled choice is a Bayesian score.

This term, my group is teaching a Master-level lecture course on *Numerics of Machine Learning*. Naturally, from the perspective of #probabilisticnumerics. Featuring established results and new insights, the course is taught primarily by the PhD students - the people closest to the cutting edge.

We are making the course public as material for interested students and lecturers.

Today we're releasing Lectures 1 (Intro) and 2-4, which cover linear algebra. Here are links, and what to expect: