Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design
Abstract
We develop a semi-amortized, policy-based, approach to Bayesian experimental design (BED) called Stepwise Deep Adaptive Design (Step-DAD). Like existing, fully amortized, policy-based BED approaches, Step-DAD trains a design policy upfront before the experiment. However, rather than keeping this policy fixed, Step-DAD periodically updates it as data is gathered, refining it to the particular experimental instance. This test-time adaptation improves both the flexibility and the robustness of the design strategy compared with existing approaches. Empirically, Step-DAD consistently demonstrates superior decision-making and robustness compared with current state-of-the-art BED methods.
Cite
Text
Hedman et al. "Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design." Proceedings of the 42nd International Conference on Machine Learning, 2025.Markdown
[Hedman et al. "Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design." Proceedings of the 42nd International Conference on Machine Learning, 2025.](https://mlanthology.org/icml/2025/hedman2025icml-stepdad/)BibTeX
@inproceedings{hedman2025icml-stepdad,
title = {{Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design}},
author = {Hedman, Marcel and Ivanova, Desi R. and Guan, Cong and Rainforth, Tom},
booktitle = {Proceedings of the 42nd International Conference on Machine Learning},
year = {2025},
pages = {22904-22923},
volume = {267},
url = {https://mlanthology.org/icml/2025/hedman2025icml-stepdad/}
}