Today, @yuqichengzhu.bsky.social opened the oral session with a beautiful talk on a Neurosymbolic extension of Retrieval Augmented Generation (RAG) with an argumentation framework

🤓 In our paper, we tackle this issue by addressing a crucial question:
"How many entities do we need to guarantee coverage of the true answer at a pre-defined confidence level (e.g. 90%)?"
(The more entities we need, the more uncertain the KGE model is about its predictions.)

🤔 When can we trust the predictions of Knowledge Graph Embedding (KGE) methods?
😲 Do the plausibility scores they return provide this uncertainty information?
Unfortunately, no—these scores are not calibrated and lack a probabilistic interpretation.

tractability has a precise definition taken from computer science!

So in probabilistic inference, it means solving exactly in polytime a certain query class of interest. In this case compute all possible probabilities for joint assignments.

Shameless plug from a tutorial here 👇👇👇

You can create your own rule-based feed with @skyfeed.app, or run a completely self-hosted feed server if you want to go fully custom.

For instance, @serge.belongie.com and I just set up an ML Internship feed that collects posts by a keywords-regex and hashtag-MLinternship

bsky.app/profile/did:...

Congratulations Michael!