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In case you missed it, we extended the submission deadline for AABI 2025! You’ve got until Feb 14th to send us your workshop and proceedings track submissions! Looking forward to seeing you all in Singapore 🇸🇬

The history of diffusion models is a striking example of how fundamental research—like non-equilibrium statistical physics—can ultimately shape technology and society in unimaginable ways.

Excited to start my sabbatical at ETH Zurich! The CS department treated me to a beautiful apartment. In exchange, I brought the California sun with me. 😎

Introducing the first #NeuralCompression benchmark for astrophysics data! #ICLR2025 🚀🌌

Thrilled to share that my student Justus Will and former student Yibo Yang had their work selected as an ICLR 2025 Oral (top 2%)! Presenting the first runtime-efficient progressive coding method using diffusion models. 👇

I am so excited to share our latest research paper "Transferring climate change physical knowledge" published in PNASNews! 🧵 1/4 pnas.org/doi/10.1073/pn…

Make sure to get your tickets to AABI if you are in Singapore on April 29 (just after #ICLR2025) and interested in probabilistic modeling, inference, and decision-making! Tickets (free but limited!): lu.ma/5syzr79m More info: approximateinference.org #ICLR2025 #ML

Just gave a talk on Scientific Inference with Diffusion Models at ETH AI Center, sharing our recent work—from test-time control and distributional matching to uncertainty calibration. Great crowd, thoughtful questions, nice view. Thanks, Julia Vogt, for hosting!

AABI 2025 has just been concluded. We had a great time talking to each other and listening to great talks about probabilistic ML and its applications in Singapore 🇸🇬 Looking forward to AABI 2026! Stay tuned!

Proud to share TranscriptFormer - a generative model for single-cell transcriptomics, trained on 112 million cells across 12 species spanning 1.5 billion years of evolution. TranscriptFormer is distinct in being both generative and multi-species. Built entirely on raw gene

Congratulations to Metod Jazbec and the team on winning the ICLR QUESTION workshop's Best Paper Award! 🎓

#AISTATS2025 is off to a strong start! First keynote: Chris Holmes rethinks Bayesian inference through the lens of predictive distributions—introducing tools like martingale posteriors. 🌴🌴🤖🎓

Aapo Hyvärinen speaking at AISTATS Conference. What are your assumptions, sir?

Congrats!!

The #AISTATS 2025 Test of Time Award goes to ... 🥁 ... Chen-Yu Lee, Saining Xie, Patrick Gallagher, Zhengyou Zhang, Zhuowen Tu, for "Deeply Supervised Nets"! Congratulations!

And last but not least... the Best Student Paper Award at #AISTATS 2025 goes to Daniel Marks and Dario Paccagnan for "Pick-to-Learn and Self-Certified Gaussian Process Approximations". Congratulations!

#AISTATS2025 day 3 keynote by Akshay Krishnamurthy about how to do theory research on inference time compute 👍 AISTATS Conference

Back from AISTATS Conference in Thailand to my Zurich sabbatical—sipping coffee in the same spots Einstein once did. What a journey! Huge thanks to my entire AISTATS team: reviewers, ACs, senior ACs, and Chairs. It’s been amazing to work with you!

TL;DR: Guidance = variational optimal control. I'm excited to share the outcomes of this collaboration with Theofanis Karaletsos at the Mark Zuckerberg Initiative. All credit to my amazing students Farrin Marouf Sofian and Kushagra Pandey!

Thanks for the great collaboration!