1/ Wouldn't it be nice if you could perform Bayesian inference *efficiently* but also *reliably*? Amortized inference offers the former, while MCMC is often presented as the "gold standard" for accuracy and reliability. Enter the Amortized Bayesian Workflow...

1/ Another @iclr-conf.bsky.social paper thread! Do you want to make SOTA probabilistic predictions using transformers & your dataset is a *set* (not a sequence or time series), so you care about permutation invariance... but also efficiency? Keep reading, we have exactly what you need. 👇

1/ One of the issues of fully amortized inference / pretrained simulator-based inference is that you are stuck with the "prior" training distribution. What if you change your mind after training? In PriorGuide, one of our papers at ICLR this week, we allow the prior to be changed at runtime!

A bit of a delayed celebration, but happy that our three submitted papers were accepted at @iclr-conf.bsky.social 2026! This was a... complicated year for ICLR, but hopefully now we can focus on the science.

1/ Excited to share our new work published in Transactions on Machine Learning Research (TMLR), Stacking Variational Bayesian Monte Carlo (S-VBMC)!

If you are at ELLIS UnConference on Dec 2 (pre @euripsconf.bsky.social ), join us at our Amortized ProbML workshop! Come for our great speakers: @paulbuerkner.com Yingzhen Li @davidruegamer.bsky.social @liza-semenova.bsky.social & poster session; stay for our hot takes on amortized probML!