@lacerbi.bsky.social wrote a very nice summary post of our paper here if anyone missed it:

bsky.app/profile/lace...

I can give some more behind-the-scenes information. 🧵

1/ Introducing ACE (Amortized Conditioning Engine)! Our new AISTATS 2025 paper presents a transformer framework that unifies tasks from image completion to BayesOpt & simulator-based inference under *one* probabilistic conditioning approach. It's Bayes all the way down!

Interested in amortization + experimental design + decision making? #NeurIPS2024
Come by to our poster, starting soon (11-2, East Hall)!

NeurIPS link: neurips.cc/virtual/2024...
Paper: openreview.net/forum?id=zBG...

with @huangdaolang.bsky.social Yujia Guo @samikaski.bsky.social

I am at #NeurIPS2024 and looking for postdocs. Feel free to reach out if you want to discuss!

We have also other positions: fcai.fi/winter-calls.... My slightly outdated home page is: kaski-lab.com

1/ Hi all, I am at #NeurIPS2024 and I will be hiring a postdoc in probabilistic machine learning starting asap.

Research interests: amortized, approximate & simulator-based inference, Bayesian optimization, and AI4science.

Get in touch for a chat or come to our posters today 11AM or Friday 11AM!

Great list! Can I join?

8/ Join us at our poster session at #NeurIPS2024. Unfortunately this year I can’t attend in person, but @lacerbi.bsky.social will present our work. We are excited to discuss and explore future directions in Bayesian experimental design and amortization!

7/ Experiments show TNDP significantly outperforms traditional methods across various tasks, including targeted active learning and hyperparameter optimization, retrosynthesis planning.

6/ Our Transformer Neural Decision Process (TNDP) unifies experimental design and decision-making in a single framework, allowing instant design proposals while maintaining high decision quality.

5/ We introduce Decision Utility Gain (DUG) to guide experimental design with a direct focus on optimizing decision-making tasks, moving beyond traditional information-theoretic objectives. DUG measures the improvement in the maximum expected utility from observing a new experimental design.

4/ In our work, we present a new amortized BED framework that optimizes experiments directly for downstream decision-making.

3/ But what if our goal goes beyond parameter inference? In many real-world tasks like medical diagnosis, we care more about making the right decisions than learning model parameters.

2/ Bayesian Experimental Design (BED) is a powerful framework to optimize experiments aimed at reducing uncertainty about unknown system parameters. Recent amortized BED methods use pre-trained neural networks for instant design proposals.

Optimizing decision utility in Bayesian experimental design is key to improving downstream decision-making.

Excited to share our #NeurIPS2024 paper on Amortized Decision-Aware Bayesian Experimental Design: arxiv.org/abs/2411.02064

@lacerbi.bsky.social @samikaski.bsky.social

Details below.

1/ Excuse me, can I interest you in eliciting your beliefs as flexible probability distributions? No worries, we only need pairwise comparisons or rankings, no personal details.

Led by **Petrus Mikkola** and joint with **Arto Klami**, to be presented soon at @neuripsconf.bsky.social #NeurIPS2024