{"ID":2824671,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.22999","arxiv_id":"2512.22999","title":"JADAI: Jointly Amortizing Adaptive Design and Bayesian Inference","abstract":"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.","short_abstract":"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 network...","url_abs":"https://arxiv.org/abs/2512.22999","url_pdf":"https://arxiv.org/pdf/2512.22999v1","authors":"[\"Niels Bracher\",\"Lars Kühmichel\",\"Desi R. Ivanova\",\"Xavier Intes\",\"Paul-Christian Bürkner\",\"Stefan T. Radev\"]","published":"2025-12-28T16:54:43Z","proceeding":"stat.ML","tasks":"[\"stat.ML\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Diffusion Model\"]","has_code":false}