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.

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.