Action-BED recasts BED as expected future loss on actions, producing singly intractable objectives jointly optimized for design and action policies via stochastic gradients without explicit posterior estimation.
Ruppert (1988), Efficient estimations from a slowly convergent Robbins-Monro pro- cess, Technical report, Cornell University
4 Pith papers cite this work. Polarity classification is still indexing.
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Joint-DP co-optimizes sensing geometry with Bellman-optimal adaptive policies via differentiable dynamic programming and relaxations, scaling to photonic designs exceeding 10^5 pixels.
vsOED uses a variational one-point reward and RL policy optimization to provide a lower bound on expected information gain for sequential experimental design, supporting nuisance parameters, implicit likelihoods, and multiple design goals.
A systematic survey of optimal experimental design covering criteria formulations, estimation and optimization methods, and emerging sequential design policies.
citing papers explorer
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Action-BED: Task-Driven Bayesian Experimental Design with Singly Intractable Objectives
Action-BED recasts BED as expected future loss on actions, producing singly intractable objectives jointly optimized for design and action policies via stochastic gradients without explicit posterior estimation.
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Adaptive Sensing beyond Non-Adaptive Information Limits: End-to-End Co-Design of Geometry, Policy, and Inference
Joint-DP co-optimizes sensing geometry with Bellman-optimal adaptive policies via differentiable dynamic programming and relaxations, scaling to photonic designs exceeding 10^5 pixels.
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Variational Sequential Optimal Experimental Design using Reinforcement Learning
vsOED uses a variational one-point reward and RL policy optimization to provide a lower bound on expected information gain for sequential experimental design, supporting nuisance parameters, implicit likelihoods, and multiple design goals.
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Optimal experimental design: Formulations and computations
A systematic survey of optimal experimental design covering criteria formulations, estimation and optimization methods, and emerging sequential design policies.