The paper introduces ACTION-BED, a Bayesian experimental design framework that replaces traditional uncertainty‑reduction objectives with expected future loss (EFL) on downstream actions. By reformulating EFLs into singly intractable objectives, ACTION-BED enables joint optimisation of design and action policies using stochastic gradients without explicit posterior or marginal likelihood estimation. This approach simplifies learning of design policies and allows easy customization to various downstream tasks and loss functions.
arXiv:2608.16466v2 Announce Type: replace-cross Abstract: Bayesian optimal experimental design (BOED) aims to collect informative data by optimizing an expected utility reflecting the goals of an exp...
TestBED is an open‑source toolkit that modularises Bayesian experimental design (BED) into reusable components such as tasks, objectives, training mechanisms, policy architectures, and evaluation procedures. It offers scalable implementations of core primitives like expected information gain estimators, enabling researchers to reproduce existing BED methods and explore new configurations within a common framework. The authors also use TestBED to run a large‑scale benchmark of contemporary BED approaches, highlighting critical methodological choices such as the role of time embeddings in design policy architectures.
GO-CBED is a goal‑oriented Bayesian framework for sequential causal experimental design that directly maximizes expected information gain on user‑specified causal quantities, rather than inferring the full causal model. It employs a variational lower bound estimator optimized with a transformer‑based policy network and normalizing flow posteriors, enabling real‑time, non‑myopic decision making. Experiments on synthetic structural causal models and semi‑synthetic gene regulatory networks show that GO‑CBED consistently outperforms existing baselines, especially under limited experimental budgets and complex causal mechanisms.
arXiv:2502. 08004v2 Announce Type: replace-cross Abstract: Simulation-based inference (SBI) is a method to perform inference on a variety of complex scientific models with challenging inference (inverse) problems.
The paper introduces Generalised Bayesian Optimal Experimental Design (GBOED), extending generalised Bayesian inference to experimental design. It replaces the likelihood in Bayesian updates with a loss function, deriving a new acquisition function called Gibbs expected information gain. Empirical results show that GBOED improves robustness against outliers and mis-specified outcome noise distributions.
The paper proposes a method to embed sequential Bayesian experimental design (BED) into large language models (LLMs) by training the models to reason about action selection using information-gain rewards. By specifying the experimental generative model separately from the LLM’s beliefs, the authors optimize expected information gain under a chosen model. Experiments on the Twenty Questions task show that the resulting policies outperform both inference- and training-time BED baselines while incurring lower computational costs, and the approach generalizes to unseen categories and transfers positively to the Medi-Q clinical dialogue benchmark.
arXiv:2607. 28894v1 Announce Type: new Abstract: Computational cognitive modeling seeks to infer latent cognitive mechanisms underlying observed behavior.
arXiv:2607. 16927v1 Announce Type: cross Abstract: We introduce Deep Adaptive Bayesian Screening (DABS), a method for performing adaptive factorial screening in high-dimensional discrete design spaces.
arXiv:2607. 08335v1 Announce Type: cross Abstract: Policy-based approaches to Bayesian experimental design (BED) allow the learning of deep policy networks that adaptively make intelligent design decisions based on previously collected data.
arXiv:2512. 22999v2 Announce Type: replace-cross Abstract: We consider problems of parameter estimation where design variables can be actively optimized to maximize information gain.
The paper introduces usage‑aware experimental design, a framework that extends standard Bayesian experimental design to settings where external decision makers act on collected data. It trains adaptive design policies to minimise the experimenter’s expected loss under external rules, which may be non‑Bayesian or black‑box. The authors show that more informative data can be harmful under such rules, so usage risk cannot be expressed as expected uncertainty reduction, thereby generalising standard BED.
This paper proposes the Bayesian Expected Uncertainty Reduction (B-EUR) model, which formalizes the value of trying a candidate design action as its expected reduction of epistemic uncertainty about action--outcome relations. The model addresses one part of the Uncertainty Driven Action (UDA) model's open question concerning how changes in uncertainty perception determine action selection.