I have cleaned up the notebooks for my course on Optimal Transport for Machine Learners and added links to the slides and lecture notes. github.com/gpeyre/ot4ml

🔥 Got a great work on causal representation learning, abstraction, high-dimensional discovery, or other hot topics in causality? 🇧🇷 Don’t miss your chance to present in Rio at the CAR Workshop at #UAI2025! ⏰ Deadline is in 1 week – May 26! 🌐 sites.google.com/view/car-25/

If it were an episode of Friends, it would be: The one where Causal Abstractions (finally) meet Causal Representation Learning! Speakers and CFP are out! See you in Rio🌴 #UAI #causality #workshop

Hot off the press!💥 arxiv.org/abs/2502.00407

New preprint out with @drtnowotny.bsky.social and @neworderofjamie.bsky.social arxiv.org/abs/2501.07331

That might be the most useful @neuripsconf.bsky.social post I've seen btw

Interested in estimating posterior predictives in Bayesian inference? Really want to know if your approximate inference "is working"? Come to our poster at the NeurIPS BDU workshop on Saturday - see TL;DR below.

State of AI #NeurIPS

See you at #NeurIPS2024! We’ll present two papers: 1) “Interventionally Consistent Surrogates for Complex Simulation Models”. Here, we propose a method for learning causal abstractions (“interventionally consistent surrogates”) of complex simulation models! openreview.net/pdf?id=UtTjg... (1/3)

Next week will be ✈️to @neuripsconf.bsky.social together with @joelnmdyer.bsky.social and Nick Bishop to present our work on "Interventionally Consistent Surrogates for Complex Simulation Models". Details 👇