Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912858688716800 |
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| author | Hedman, Marcel Ivanova, Desi R. Guan, Cong Rainforth, Tom |
| author_facet | Hedman, Marcel Ivanova, Desi R. Guan, Cong Rainforth, Tom |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_14057 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design Hedman, Marcel Ivanova, Desi R. Guan, Cong Rainforth, Tom Machine Learning 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. |
| title | Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.14057 |