ICML 2025poster6 citations
Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design
Marcel Hedman, Desi R. Ivanova, Cong Guan, Tom Rainforth
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.
Bayesian experimental designBayesian optimal designBayesian adaptive designadaptive design optimizationinformation maximization
BibTeX
@inproceedings{
hedman2025stepdad,
title={Step-{DAD}: Semi-Amortized Policy-Based Bayesian Experimental Design},
author={Marcel Hedman and Desi R. Ivanova and Cong Guan and Tom Rainforth},
booktitle={Forty-second International Conference on Machine Learning},
year={2025},
url={https://openreview.net/forum?id=JRg8P2bX8P}
}