JADAI: Jointly Amortizing Adaptive Design and Bayesian Inference
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911342696333312 |
|---|---|
| author | Bracher, Niels Kühmichel, Lars Ivanova, Desi R. Intes, Xavier Bürkner, Paul-Christian Radev, Stefan T. |
| author_facet | Bracher, Niels Kühmichel, Lars Ivanova, Desi R. Intes, Xavier Bürkner, Paul-Christian Radev, Stefan T. |
| contents | We consider problems of parameter estimation where design variables can be actively optimized to maximize information gain. To this end, we introduce JADAI, a framework that jointly amortizes Bayesian adaptive design and inference by training a policy, a history network, and an inference network end-to-end. The networks minimize a generic loss that aggregates incremental reductions in posterior error along experimental sequences. Inference networks are instantiated with diffusion-based posterior estimators that can approximate high-dimensional and multimodal posteriors at every experimental step. Across standard adaptive design benchmarks, JADAI achieves superior or competitive performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_22999 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | JADAI: Jointly Amortizing Adaptive Design and Bayesian Inference Bracher, Niels Kühmichel, Lars Ivanova, Desi R. Intes, Xavier Bürkner, Paul-Christian Radev, Stefan T. Machine Learning Artificial Intelligence We consider problems of parameter estimation where design variables can be actively optimized to maximize information gain. To this end, we introduce JADAI, a framework that jointly amortizes Bayesian adaptive design and inference by training a policy, a history network, and an inference network end-to-end. The networks minimize a generic loss that aggregates incremental reductions in posterior error along experimental sequences. Inference networks are instantiated with diffusion-based posterior estimators that can approximate high-dimensional and multimodal posteriors at every experimental step. Across standard adaptive design benchmarks, JADAI achieves superior or competitive performance. |
| title | JADAI: Jointly Amortizing Adaptive Design and Bayesian Inference |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2512.22999 |