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