AI RESEARCH
Goal-driven Bayesian Optimal Experimental Design for Robust Decision-Making Under Model Uncertainty
arXiv CS.LG
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ArXi:2605.26093v1 Announce Type: new Bayesian optimal experimental design (BOED) selects experiments to maximize information gain about model parameters. However, in decision-critical settings, reducing parameter uncertainty does not necessarily improve downstream decisions, as only specific parameter directions relevant to the objective truly matter. We propose GoBOED, a goal-driven BOED framework that directly optimizes experimental designs for a specified decision-making objective.